When businesses are able to have access to vital data about their operations which are Customer Analytics, they are able to make better plans. It is important to know the factors that are responsible for your sales and the behavior of your customers.

If you are an online entrepreneur, you can use customer analytics to impact your business profit. If you know the pages on your site that are bringing most of your sales, you can make the necessary adjustment to improve the conversion rate of the other pages. Here are the talking points.

• What is customer analytics
• Types of customer data to analyze
• How to get Customer Analytics
• Benefits of using customer analytics
• How Does Business Analytics Improve Profit?
• How Does Business Analytics Contribute to Business Value?
• In What Way Can Customers Analysis Help Your Business Grow?
• How Important is Customers Analysis to Any Organization?
• Does Business Analytics Require Coding?
• What Are The Benefits of Customer Analytics?
• What is The Most Important Part of Your Business Plan?
• How do You Use Customer Analytics?
• Why is CRM Needed?
• What Are The Four Customer Analysis Principles?
• What is The Future of Business Analytics in Business Management?
• Which Companies Use Business Analytics?
• When Can You Apply Business Analytics?
• Does Business Analytics Need Math?
• Is Python Required For Business Analytics?
• Why do You Think Customer Analytics is Important For KPN?
• What Are The Business Benefits of Analyzing Customer Purchase Data?
• How Customer Analytics May be Used to Support a Particular Business Strategy?
• What is a Customer Analytics Record?
• Advantages of Planning a Customer Analytics Initiative
• How do You Analyze Purchase Data?
• How do You Analyze Customer Service?
• What Are The Types of Customer Analytics?
• How do You Analyze Clients?
• What Are The Activities Involved in The Customer Analytics Process?
• Customer Analysis in Business Plan
• What is The Importance of Customer Analysis?
• Advantage of Customer Analytics
• Importance of Customer Analytics
• Customer Analysis Example
• Why is Customer Profitability Analysis Helpful?
• How do You Analyze Customer Profitabilty?
• How do You Know if a Customer is Profitable?
• What Makes a Customer Profitable to a Company?
• How do Customers Become Profitable?
• Is it a Good Strategy to Focus Most Marketing Efforts on The Most Profitable Customers?
• What Other Considerations Should a Company Look at When Measuring Customer Profitability?
• What is The Meaning of Customer Profitability Analysis?
• What is CRM Process?
• Why is Customer Profitability Analysis an Important Topic For Manager?
• How Can ABC be Used to Track Customer Profitability?
• When Can Determining Customer Profitability Activity Based Costing be Used to Analyze?

What is customer analytics

Customer analytics is the process of using data that has been collected about your customers over a period of time to make vital business decisions. This process plays a very important role in business operations because it makes it possible to predict the behavior of customers.

Read Also: 10 Strategies to Improve Your Customer Service Experience

Getting this type of information might not be possible without the resources of the internet. Previously, it was not easy for businesses to accurately differentiate the variables that are impacting their sales. This was because gathering data offline cannot be done as effectively as on the internet.

The internet has changed a lot of things and it is now possible to differentiate the important variables in any set of data. For instance, you can easily know the activities that resulted in a 20% increase in your sales in a particular month.

The internet facilitates the collection of different types of information about customer behavior. This data can then be analyzed and separated according to their nature and the effect that they have on the business. This is why it is possible to use customer analytics to impact your business profit.

Types of customer data to analyze

Transactional data

It’s easy to explain transactional data through retail, where each purchase deepens a company’s understanding of its customers’ journeys. For example, the analytical solution we implemented for an FMCG company gathers data from retailers and allows identifying sales trends, find out which SKUs and stores showed the best performance, estimate growth potential as well as optimize sales and marketing activities.

Data about service/product use

Manufacturers can examine the data about product use to create a better customer experience and innovate. For instance, a couple of ScienceSoft’s clients from the connected car domain gather vast information about car location, a driver’s behavior, the level of fuel and fluids, the condition of the brakes and any faults detected by the on-board vehicle control units. This info can be used by car manufacturers to improve car design and understand usage patterns.

Web behavior data

A company can analyze every move that their website visitors make: where they come from, which pages they open, how deep visitors’ engagement is, etc. With this data at hand, the company can create relevant content to increase conversion rates.

E-commerce retailers apply this logic to track customer behavior, identify customer preferences and make product recommendations with the help of predictive analytics.

Data from customer-created texts

Customers take the opportunity to share their personal impressions about a product or a service in the form of an online review or a social media post.

Companies can study this content to get a clue about what their customers think about their brand, product or service by identifying trends, recognizing a positive or negative emotional tone of each piece of text, revealing complaints and problems to solve. For example, Samsung uses social media analytics to attract customers from their competitors.

How to get Customer Analytics

There are four options of how a business can get customer analytics:

1. Buy a ready-to-use customer analytics tool.
2. Implement a customer analytics solution with the efforts of an in-house team.
3. Involve a consulting and implementation vendor.
4. Outsource customer data analytics.

Before choosing any of these options, a business needs to know in detail the advantages and disadvantages of each. We invite you to check the comparison table below and weigh the alternatives.

Benefits of using customer analytics

Improved Advertizing

Every advertisement is A/B and even C split-tested. All landing pages, pop-ups, and even product images are assessed for their effectiveness with tweaks being made to ensure maximum results.
Even the positioning of products on the website is measured to identify the best location to help drive engagement and sales.

Advertising can be expensive, and it’s important to know how to get the best return on investment.

Better Service Level Performance

When it comes to delivering flowers, From You Flowers uses a network of florists to fulfill orders, as well as their own distribution centers. Analytics has allowed them to predict their ability to meet customer demands, which are often for same-day delivery, by understanding the impact of traffic patterns and average delivery times for each supplier in major cities.

This allows them to make and meet commitments, or pass on business where they know that delivery is not possible, or to propose a next day delivery.

Customer Profitability

Tracking customer profitability measures leads to being 18.8 times more likely to report as outperforming competitors on profitability per customer. Income metrics should include total sales to the customer, average sale size, the time between purchases, and what specific goods or services the customer purchases.

Expense metrics should include both direct and indirect expenses. Direct expenses include things such as the cost of goods sold, shipping costs and losses due to returns. Indirect expenses include hidden costs such as the time sales reps have to spend with each customer or the need to divert resources to fulfill unusual requests.

Customer Loyalty and Retention

Customer loyalty and retention is divided into two parts — identification and tracking.

First, you must be able to identify individual customers. This is easy when you have service accounts or collect shipping information for online orders, but it becomes more difficult in a retail setting. One of the easiest and most common ways to track retail customers is through loyalty or discount cards.

Once you know your customer, tracking retention is a matter of determining the average amount of time between a customer’s first and last order and how many customers don’t place a second order. To improve your retention rates and times, you can mine the data to look for trends that separate customers retained for a long versus a short period.

Loyalty can be tracked through the average time between orders and what specific items the customer purchases. This can help you determine whether a customer is loyal to your brand, price shops between competitors or finds you inconvenient to deal with due to distance or long shipping times.

The more you understand your customers, the better you’ll be able to predict and meet their needs, and increased sales will naturally follow.

Changing your marketing strategy

Customer analysis will enable you to know the marketing methods that are producing the best results. If a particular campaign led to more sales within certain periods, you can put more focus on that campaign. You can also know the necessary changes that you have to make to improve other marketing campaigns.

Predicting customer behavior

This is an area that is very important to the success of every business. The profitability of the company will increase greatly if the behavior of the customer can be predicted with a high level of accuracy. This can be done through the use of customer analytics.

The data that is gathered over a period of time can be used to create the profiles of the types of demographic groups that are most likely to be interested in a particular product or service.

How Does Business Analytics Improve Profit?

A 2013 Bain report found that companies using analytical techniques are actually:

• Twice as likely to be in the top quartile of financial performance, and
• Five times as likely to make decisions faster than their peers.

These analytical techniques aren’t complicated ones, but rather a systematic study of existing operational and financial data. These are studies that businesses rarely have time to execute themselves, but when they are conducted, either in-house or with external help, the improvement to the business can be substantial.

Analytical projects can help identify savings opportunities or opportunities to increase profitability. In a world where the intelligent use of data is a competitive advantage, those that aren’t equipped to analyze their data should establish a path to do so.

With over 25 years in data analysis, Numerical Insights has helped businesses evaluate their financial and operational data. These analyses are then used to make business decisions that:

• Increase profitability,
• Improve inventory management, and
• Improve internal processes such as order management.

Tracey Smith has worked in engineering, supply chain and on the analytical side of HR. Having experience in these areas provides Ms. Smith with the ability to envision how to use data to improve an owner’s business in multiple ways. Ms. Smith helps businesses:

• Understand where the money went when the business is struggling to turn a profit,
• Know which products are contributing to the bottom line and which ones yield losses,
• Identify the impact of current inventory management practices, and
• Better manage cash flow through the understanding of how decisions impact cash.

How Does Business Analytics Contribute to Business Value?

Companies have widely embraced the use of analytics to streamline operations and improve processes. But implementing analytics data that informs intelligent and effective business decisions is not as easy as a snap of the fingers.

In a survey conducted by Bloomberg Businessweek Research Services, nearly 97% of respondents reported their companies have adopted analytics. The three most sought-after goals were the ability to reduce costs, increase profitability and improve risk management. However, many organizations struggle with making sure the data is accurate and consistent.

Analytics data is everywhere and sorting through it to find what is useful and pertinent to your business is a necessary skill to be effective in the current marketplace. These days, analytics is being used for everything from predicting Supreme Court case outcomes to enhancing marketing campaigns and sales analysis.

The challenge is to understand how analytics can help your business and begin to address any issues you believe are most important to short- and long-term success.

Analyzing data more often than not increases efficiency, but also helps identify new business opportunities that may have been otherwise overlooked, such as untapped customer segments. In doing so, the potential for growth and profitability becomes endless and more intelligence-based.

Many professionals can discern short-term trends but are less proficient at predicting obstacles that plague their business down the road. Computer models based on data analytics help companies see shifts in what customers buy and give a clear picture of what products should be highlighted or updated.

Whether it’s a production concern, a customer service issue or a deficiency among your employees, analytics can help to highlight key areas of concern when it comes to your venture’s ability to make a profit.

Data analytics can also be utilized as a human resources tool. Applications of AI and machine learning are transforming the hiring process in many organizations, while applications of data analytics in people management are informing decisions on promotions, performance evaluations, employee engagement, and professional development.

An analysis by McKinsey & Company showed that using data to make better marketing decisions can increase marketing productivity by 15-20%. A good example of this is retail giant Target’s “pregnancy prediction score.”

Target assigns a score based on a customer’s purchases that indicate the possibility of a pregnancy; the retailer uses purchase data to determine the types of coupons and special discounts Target would send to a customer’s email address.

There is a ton of information companies can use for predictive analytics that help streamline a customer’s experience with a brand. Finding the right tools to examine your customer’s buying and Internet browsing habits, and implementing them to provide reliable and actionable intelligence can activate buyer instincts and embed your brand into customers’ minds.

hrough data analysis, business operators can get a clearer view of what they are doing efficiently and inefficiently within their organizations. When a problem is identified, professionals with an analytics background are capable of answering crucial questions such as:

• What was the cause of the problem? (Reports)
• Why did it happen? (Diagnosis)
• What will happen in the future? (Predictions)
• What is the best way forward? (Recommendations)

Data mining and analysis will help you answer these questions and have confidence that you’re moving forward with the best approach. Data is now capable of improving any business process, whether it’s streamlining the communication in your supply chain or improving the quality and relevance of your offerings.

In What Way Can Customers Analysis Help Your Business Grow?

Customer analytics can help you improve your business in the following areas:

1. Marketing Efficiency

Focusing on the individual customer takes your marketing analysis beyond just knowing your spend and the eyeballs you received in return. Knowing which marketing channels bring the highest value customers in terms of order size, retention rate and profitability allows you to either cut marketing costs or expand your reach more efficiently.

2. Customer Retention

Customer acquisition is expensive, so it’s important to understand what causes customers to leave. Customer analysis can help you identify common denominators among lost customers and give you an early warning that existing customers may be in danger of leaving if you don’t take corrective action.

3. Increased Sales

Understanding customer purchasing decisions is the key to increasing sales. Use customer analysis to identify factors that have both a positive and negative impact on sales. This could include shipping times, how customer service interactions are handled, whether you have a minimum order or bundled discount, or the customer’s location or income.

4. Improved Profit Margins

Not all customers are equal. Some customers are more profitable than others, and some may even cost you money. Factors that affect customer profitability include order size, cost of handling the order, time spent servicing the account and returns.

Amazon has gained notoriety for issuing lifetime bans to customers who cost the company money by returning too many items. Customer service representatives at banks have varying discretion to waive fees and grant other policy exceptions depending on how profitable a customer is.

If you don’t want to take direct action against unprofitable customers, you can learn what attracts these customers versus more profitable ones so you can shift your focus to attracting higher-value customers.

How Important is Customers Analysis to Any Organization?

A recent article in Forbes stated that 81% of enterprises rely on analytics to improve their understanding of customers. Where will you start?

From marketing to delivery, customer analysis and customer analytics reveal the most necessary information for any business plan. Let your competition worry about keeping up: learn who your target market is and how you can stay ahead. As a CMO or marketing director, an understanding of customer analysis is a must. But what is customer analysis?

Customer insight is paramount to the success of any business, and customer analysis and analytics can help CMOs determine key performance indicators. As Forbes recently noted, 81% of businesses rely on customer analytics. How can you avoid being left behind?

Customer analysis is vital to any effective business strategy. If a business doesn’t know who its customers are or what its customers want, it can’t meet customers’ needs. A customer analysis will do three main things:

• Identify the target customer
• Understand the needs of the customer
• Show how the company’s product or service meets the customers’ needs or wants

Does Business Analytics Require Coding?

As business analytics is more statistics orientated, your role should not involve much coding. However, there are a variety of other tools you may find yourself using commonly in your work, which include:

• Excel – for spreadsheets and calculation
• Tableau – for data visualisation
• SQL – for managing data and programming
• Python – a programming language

Data science, however, does involve much more coding and you will need a good knowledge of computer science to excel in this career. Some programming languages you may use in your role include:

• R
• Python
• C, C++, or C#

Some tools you may use as a data scientist include:

• Keras – a Python interface
• PyTorch – a machine learning library

What Are The Benefits of Customer Analytics?

How can businesses find out what their customers want and how to serve them better without asking them directly? That is where the field of customer analysis comes in, and if you are not analyzing your customers, you could be missing out on a golden opportunity. Here are some of the business benefits of knowing the customer and analyzing their behavior.

Lower Customer Acquisition Costs
Customer acquisition costs can be a profit killer for businesses, especially small firms and new startups. Finding new customers requires a combination of costly strategies, from advertising and targeted marketing to underground campaigns aimed at building name recognition and brand awareness.

Without quality customer analysis, much of those customer acquisition efforts could be wasted. If you are looking for customers in the wrong places, you are unlikely to be successful. Quality customer analysis can help you fine tune your efforts, so you can find those hidden buyers and turn them into loyal ambassadors for your brand.

Better Customer Retention
Studies have shown that retaining an existing customer costs far less than attracting a new one. By helping businesses know their customers better, the right analysis can improve retention, boosting profits by enhancing efficiencies and avoiding unnecessary costs.

If you want your business to be successful, focusing on customer retention is always a good place to start. Your existing customers are your biggest and most valuable assets, and anything you can do to keep them will be a positive for your business and your brand.

More Streamlined (and Effective) Customer Service
Customer service can be a challenge for businesses, but the right analysis can make the job easier. Knowing who your customers are can make meeting their needs simpler, streamlining your customer service operations, enhancing efficiency and improving effectiveness.

On-going customer analysis can also uncover deficiencies in your existing customer service operations, so you can beef up your offerings and build a better brand.

Customer analysis might reveal, for instance, that a large percentage of customers are coming from a different time zone, and the business in question could respond with extended customer service hours or even a second shift.

Increased Sales and Improved Profits
No matter what you have to sell, it is your customers who buy it. Without those customers, sales will quickly fall to zero and profits will plunge just as quickly. The profitability of your business and your ability to grow your sales is directly tied to the quality of your customer base.

By analyzing your existing customers and finding out what they have in common, you can better market your products, so profits keep growing and sales growth can continue.

Customer analysis can do more than drive sales of existing products. By uncovering customer needs, the right analysis can help you develop new products and services; ones your customers may not even know they need. The new product lines you develop in this manner could drive sales and profits even more, helping you build an even better business.

What is The Most Important Part of Your Business Plan?

First, here is what a standard business plan should cover:

• The company (its legal formation, history, and ownership)
• What it sells (the product or service)
• The market (including size of market, growth, and trends)
• The plan (sales forecast, sales and marketing strategy, milestones, assumptions, and tasks)
• The management team (organizational structure and managers’ backgrounds)
• Financial analysis (cash flow, profitability, balance, and returns)

The most important part of the plan is where it says specifically what is going to happen. The core of a business plan is the collection of detailed dates, deadlines, responsibilities, and commitments. We call it these the milestones, and we’ve also seen it called MAT, for milestones, assumptions, and tasks.

Ironically, this kind of detail is frequently left out of business plans that are full of big ideas and strategy. What you want from a plan is results, and the way to get results is to build specifics you can track.

The cash flow statement is the second-most important item. Plan cash flow by month for the first 12 months of your plan. “Cash” in this context means money in the bank, not coins and bills; it is critical to business.

There are two good reasons for stressing cash flow. First, businesses live or die with cash — not profits. Second, cash makes much more sense in a plan, laid out month by month, than in your head. Putting it down on paper will help you understand your cash flow projections and any problems will become immediately apparent.

Complete financials include, at the very least, projections for profit, cash, and balance sheet, which should be in monthly detail for the first 12 months in a plan and then annually for the following two to four years.

How do You Use Customer Analytics?

To implement customer analytics and derive useful insights, companies must do two things:

1. Capture, store, and organize their data.
2. Analyze and make decisions with that data.

To capture and make use of data, companies must collect lots of it. They may run surveys, conduct user research, purchase third-party data, and, if they offer a technology solution, passively collect usage data through their site or app.

In the case of websites, most customer analytics platforms passively collect all visitor data. With apps, companies may need to define activities or “events” where data is collected, such as login, logout, and user actions.

For storing data, it is immensely useful to have a central repository that unites all of your data sources into one single view of your customer. This is a key feature of most customer analytics platforms.

Companies that do not properly maintain their data will have trouble getting straight answers from their customer analytics. If data is stale, unstructured, or incomplete, it may lead to misleading results. This often happens when there has been a lack of process, multiple owners over the years, or when companies have custom-built their own customer analytics platforms.

To analyze and interpret results, it can help to have a dedicated product manager, UX/UI designer, or data scientist. They’ll be able to frame customer analytics inquiries in terms of core business objectives and steer the company towards better results.

Companies that invest in customer analytics will gain a tremendous advantage over their competition because they can more easily understand and cater to their customers.

Why is CRM Needed?

Implementing a customer relationship management (CRM) solution might involve considerable time and expense. However, there are many potential benefits.

A major benefit can be the development of better relations with your existing customers, which can lead to:

• increased sales through better timing due to anticipating needs based on historic trends
• identifying needs more effectively by understanding specific customer requirements
• cross-selling of other products by highlighting and suggesting alternatives or enhancements
• identifying which of your customers are profitable and which are not

This can lead to better marketing of your products or services by focusing on:

• effective targeted marketing communications aimed specifically at customer needs
• a more personal approach and the development of new or improved products and services in order to win more business in the future

Ultimately this could lead to:

• enhanced customer satisfaction and retention, ensuring that your good reputation in the marketplace continues to grow
• increased value from your existing customers and reduced cost associated with supporting and servicing them, increasing your overall efficiency and reducing total cost of sales
• improved profitability by focusing on the most profitable customers and dealing with the unprofitable in more cost effective ways

Once your business starts to look after its existing customers effectively, efforts can be concentrated on finding new customers and expanding your market. The more you know about your customers, the easier it is to identify new prospects and increase your customer base.

Even with years of accumulated knowledge, there’s always room for improvement. Customer needs change over time, and technology can make it easier to find out more about customers and ensure that everyone in an organization can exploit this information.

What Are The Four Customer Analysis Principles?

Consumer analysis is the process where information about the consumer is found out from market research like the needs of the consumer, the target market and the relevant demographics so that this information can be used in market segmentation for further steps of market research. It is very useful in predicting consumer behaviour.

Objectives of consumer analysis are to find out information about:

• Profile of the consumers: This includes demographic, economic, social, geographical characteristics of the consumer and any other special interests of the consumer that are relevant. It also includes the buying process of the consumer i.e. factors like the decision making unit, time and frequency of purchase, how the consumer makes the purchase and the method of payment. The former is called demographic analysis and the latter is called behavioural analysis.

• Benefit gained by the consumers: These include functional benefits, psychological benefits, high and low involvement benefits depending on the products, and user & purchaser benefit depending on whether it is a B2B or B2C customer.

• Market customer: A market is the group of customers who gain the same benefit from a product. Market can be undifferentiated or differentiated. In case of differentiated markets, market segmentation can be based on geographic, demographic or psychological segmentation. Whichever the market is, it has to be homogeneous, consistent, executable and profitable.

Steps in consumer analysis:

Step1: Overview of the industry

Step2: Identifying and describing demographics of the customers

Step3: Project future changes

Step4: Determine and describe consumer buying behaviour

Step5: Competitive analysis

Step6: Use information about industry, customer and competitors determined above to identify gaps in the market

Wheel of consumer analysis: It is a model describing the key factors in understanding consumer behaviour and hence developing a marketing strategy.

Example: Nike shoes have a wide range of products designed for different segments of its consumers like shoes for sportspersons, basic sports shoes for gym, walking or running, tougher shoes for football players, etc.

What is The Future of Business Analytics in Business Management?

we look at five business analytics predictions for 2025 and beyond. These predictions will help you prepare for the future of business analytics and stay agile.

1. Cloud adoption to drive the BI market, fueled by SMB demand

Most software applications are moving to the cloud, and BI tools are no exception. According to a market research report, increasing demand for cloud-based solutions among small and midsize businesses (SMBs) is driving the BI market growth. SMBs prefer cloud-based tools because of the pay-per-use or pay-as-you-go option, which allows them to scale their data management and data analysis efforts as needed.

Cloud-based tools account for a large share of the total analytics software market. The cloud-based analytics market is expected to be valued at $65.4 billion by 2025, up from $23.2 billion in 2020. Low costs, hassle-free implementation, and ease of access are the key factors driving the growth of this market segment.

With a cloud-based BI tool, you have all the elements needed for data analysis, including data sources, data models, computing power, data storage, and analytic models, available in the cloud and at a lower cost compared to traditional on-premise BI platforms.

Even small businesses that were previously hesitant to implement advanced BI solutions because of high costs and complex IT requirements, can sign up for these cost-effective, self-service cloud-based BI tools.

2. Data governance to take center stage for improving the quality of data analysis and security

Data governance lies at the heart of business analytics. It refers to a set of formal practices and processes that ensure your business data is of uniform quality, is available to users when needed, and is protected and not tampered with.

The data governance market is expected to reach $5.7 billion by 2025, up from $2.1 billion in 2020. Rising regulatory and compliance requirements, higher collaboration among business teams, and an increase in big data (unstructured data) are some factors driving the market growth.

By 2025, the total global data storage is forecasted to reach 200 zettabytes. With data volumes growing at such staggering rates, it becomes important for business owners like you to ensure proper data governance. Without efficient data governance mechanisms, your BI efforts are highly unlikely to bear fruit.

3. Self-service BI tools to produce more business insights than data scientists

Data analytics is no longer a prerogative of IT teams or data scientists. Business leaders and executives can now use self-service BI tools to generate the insight they need from various types of data, such as sales and inventory data. Self-service solutions help save time as well as build a data-driven culture in your organization.

The self-service BI market is expected to be valued at $14.19 billion by 2026, up from $4.73 in 2018. Ease of use, increasing data volumes, and the need for data insights at every business level are the factors fueling the demand for self-service BI tools.

Self-service BI software can be used in all aspects of data analysis, including querying, visualization, and reporting. The availability of many free self-service BI tools has made every employee a data scientist, producing much more actionable insights than dedicated business analytics professionals or IT teams.

4. Voice assistants to help churn out insights

These days, voice assistants are everywhere—be it Siri, Alexa, Cortana, or Google Assistant. Voice interactions have become a critical source of data for marketers and businesses, as the majority of online searches are done via voice commands.

The global speech analytics market is expected to reach $3.8 billion by 2025 from $1.5 billion in 2020. Increasing use of voice assistants to complete tasks such as web browsing, shopping, and customer service is driving the market growth.

Speech inputs from voice assistants combined with other datasets such as buyer profiles can provide valuable insights into your customers’ state of mind, choices, and preferences. You can use this information to tailor your product and marketing strategies to fulfill customers’ unrealized needs. Speech analytics tools also help you predict customers’ intent and service reps’ performance.

5. Artificial intelligence to support critical decision-making

AI-equipped business intelligence tools can automatically analyze data from multiple sources to identify hidden trends. They can also automate the data cleaning and report generation processes.

Aided by natural language processing (NLP), AI-powered BI systems make your data easy to understand, reduce the time needed to process large volumes of unstructured data, and generate more accurate business insights.

According to Gartner, by the end of 2024, 75% of enterprises will shift from testing AI to fully implementing and using it for data analysis, resulting in a five-time increase in data and analytics infrastructure. The rise of the internet of things (IoT) and other edge devices is also increasing the demand for AI-powered analytics tools for faster automatic computations.

AI-powered systems for data analytics will help you self-solve the most complex problems instead of hiring the services of data science experts. You’ll be able to generate more accurate forecasts, so your business can better prepare to meet market changes.

Which Companies Use Business Analytics?

Real-time analytics plays a crucial role in the acceptance rates and life-cycle development of Big Data. As it dramatically transforms the ways systems use data to envisage outcomes and suggest alternatives, companies are widely turning to this to drive innovation.

Here are 5 companies using Real-Time Analytics to enhance business efficiency.

Amazon

E-commerce giant Amazon is one of the companies enabling data-driven culture within the organization. The company gleans over 2,000 historical and real-time data points on every order and leverages machine learning algorithms to find transactions with an elevated likelihood of being fraudulent.

By doing so, the company’s system stops millions of dollars worth of fraudulent transactions each year. Amazon uses Big Data to automatically customize the browsing experience for its customers based on their past purchases and optimize sales.

Penn Medicine

Penn Medicine, a multi-hospital health system based in Philadelphia, Pennsylvania, developed a dashboard that leverages its electronic health record (EHR) vendor’s real-time data streams. This intended to alert respiratory and nursing staff when interventions are needed and patients may be ready to be weaned from ventilators.

Leveraging real-time data streams, the data science team at the health system is devoted to improving patient outcomes through analytics. Penn Medicine’s ABC, an application dubbed as Awakening and Breathing Coordination, had minimized the time ICU patients spent on a mechanical ventilator by more than 24 hours.

Nissan Motor

Automaker giant Nissan uses Google analytics e-commerce tracking to amass detailed information about product preferences such as car category, model, and color. By assessing this information, the company’s Global Marketing Strategy division understands which vehicles are in demand, thus they can make decisions tailored for each local market.

The auto company has a host of localized websites aimed at assisting consumers to determine which Nissan product is best for them. Nissan has also deployed the Hortonworks Data Platform (HDP) to power its data lake.

The company developed its data lake infrastructure using Apache Hadoop powered HDP to gather all data from across the business, including driving data and quality data.

Shell

Shell, a Netherlands-based oil and gas company, developed an analytics platform based on software from several vendors to run predictive models to foresee when its different oil drilling machine parts might fail.

The company used Databricks that captures streaming data through Apache Spark, to better plan when to purchase machine parts, how long to keep them, and where to place inventory items.

Hosted in Microsoft Azure’s cloud, the tool helped Shell by reducing inventory analysis from more than 48 hours to less than 45 minutes, cutting off millions of dollars a year of moving and reallocating inventory.

Land O’ Lakes

Land O’ Lakes, a Minnesota, US-based food company, is relentlessly looking to optimize its pricing, better target sales, and predict future demand. To accomplish this, the company turned to data analytics and AI and brought Data to Value program.

This leverages data analytics tools and different data sources to gain insights into the company’s profitability, sales call success factors, and commodity markets. The program helped Land O’ Lakes to improve its success rate by delivering the right product at the right price at the right time. The company gained US$14.9 billion in revenue in 2018.

When Can You Apply Business Analytics?

Typically, commerical organizations use business analytics in order to:

• Analyze data from multiple sources
• Use advanced analytics and statistics to find hidden patterns in large datasets
• Deseminate information to relevant stakeholders through interactive dashboards and reports
• Monitor KPIs and react to changing trends in real-time
• Justify and revise decisions based on up-to-date information

If your business is looking to achieve one or more of these goals, business analytics is the way to go. The level of investment in tools, technology, and manpower should vary according to your needs – in some cases increasing your proficiency in Excel might suffice, while in others you might want to look at specialized solutions from business intelligence software vendors.

Does Business Analytics Need Math?

You don’t need to be even from Maths background to become a business analyst.

As name suggest, business analysis can be a person who can analyze the business outcome. You need to be a goal oriented person, problem solver, leader and positive thinker.

Technical knowledge and maths background will surely help but it is not an essential requirement. Try to learn the basics of your domain, product or service, see what problem your client is facing and how efficiently this can be solved.

Observe, document and help implement and valdiate.

That’s all a business analyst does. Over a long period, you would just excel in these skills to apply in any sector.

For a fresher most of the process starts with quantitative aptitude test. It’s nt that difficult. 2–3 months of rigourous practice from a good book like arun sharma is enough.

Just learn some first calculation techniques. You should be very comfortable with addition, subtraction, multiplication n percentage. You should also know how to approximate while calculating. This will help u in case solving n guess estimates rounds.

Is Python Required For Business Analytics?

Business analysts are required to know skills around building and designing models based on the problem statement, requiring them to know how to do that.

Their job role is typically to understand and describe the problem, collaborate with architects and other analysts to define a solution for the problem, organizing requirements for the solution so that the development team can leverage them to build solutions, and ensuring that the solution is built meets the business. In the meantime, it totally depends on the job role if they should know Python or not.

Why do You Think Customer Analytics is Important For KPN?

There’s an impeccable and straightforward logic behind investing in customer analytics – the better you understand and know your customers (their buying habits, their preferred choices, and the offers that they respond to), the more accurately will you be able to draw predictions regarding their future buying behavior patterns.

According to a McKinsey & Company survey, businesses that heavily invest in customer analytics are more likely to outperform their competitors, be it on the grounds of sales, or revenue, or ROI.  

Every single interaction with your customers is sure to leave a trail of data (information) which when combined helps paint a clearer picture of what your customers expect from you.  

If utilized wisely, customer analytics can be one of the greatest strengths of your business – it will allow you to transform data (social media posts, comments, and mentions; customer interactions with your channels and media pages; customer behavior to your products/services, etc.) into resourceful insights that can scale up your profits considerably.
Customer analytics can help you –

• Reduce attrition rates significantly be accurately forecasting about the time periods when customers are most likely to leave, thereby allowing you to chalk out proactive plans and campaigns to retain them.
• Boost the response rates, customer loyalty, and your ROI by allowing you to target the right audience with attractive and befitting offers.
• Reduce campaign costs by streamlining campaigns to target only the customer base that is most likely to respond.
• Optimize the overall customer experience by creating personalized selling and marketing strategies for the different customer segments.
• Identify the current trends in Big Data to boost sales.

What Are The Business Benefits of Analyzing Customer Purchase Data?

There’s a whole host of elements that warrant attention, but here we’re going to look at purchase data (everything you glean from the orders your customers have placed). Analysis of this data is absolutely essential to your operation — here’s why:

It will help you adapt to consumer habit changes

The consumer market moves forward, sometimes slowly, but never falling entirely static. New products are released, existing products pick up steam due to promotion or word-of-mouth recommendation, and previous hit items fall out of favour or are discontinued.

The more rapidly you can adapt to the changes (and even predict what’s just over the horizon), the more strongly you can take advantage of them.

These changes, of course, don’t happen out of nowhere. A product won’t generally go from popular to written-off overnight — instead, it will gradually lose its popularity before eventually reaching a tipping point at which it might as well no longer be sold.

By analysing the purchase data of your customers, you can discern which products are rising or falling in the ranks, and draw inferences about what product types are gaining or losing value.

You might see that sales of skateboarding accessories have risen steadily throughout the last year, for instance, in which case you’d have good reason to think that it might be a niche worth investigating — and if you check Google Trends and discover that interest is rising across the board (for whatever reason), you could make a concerted effort to target skateboarders.

Patterns support enhanced marketing tactics

Marketing is a core concern for retailers, particularly in the online realm where you can’t rely on people merely happening upon your store and wanting to shop there. The more efficient you can make your marketing, the better its ROI will become — you’ll sell more, and spend less to win those sales.

And since digital marketing is inherently suited to complex targeting (relying on smart segmentation of customer sets), customer trends are hugely valuable.

For example, if your purchase data shows that shoppers from a particular region or country have spent 35% more on food products, you can dig deeper into that stat to figure out some tweaks to your marketing.

Could you market other food products to those people? Is there a specific reason why that area is so concerned with food? There could be a new hit cookery show, or a big trend towards healthy eating.

If you identified the latter as the culprit, you could take advantage by marketing other health products to people in that area. The more you know about customer habits, the more sophisticated you can get with persuading them to spend even more with you.

The data is relatively straightforward to collect

Some types of data (e.g. customer feedback) are somewhat laborious to collect because you need to incentivize them somehow and then actively chase them — and even then, you might have a dataset that’s both limited (most people prefer not to comment) and biased (the people most likely to provide feedback are those either very happy or very angry with your service).

Purchase data, though, requires next to no effort to collect. No matter what platform you use, you’ll end up with basic purchase records (they’re necessary for customer accounts and support requests). What’s more, if you use something with rich native data, then you’ll have tagged funnel stages at your disposal — possibly without even realizing it.

Will any e-commerce solution give you a flawlessly-parsed analytics dashboard from the outset? No, of course not — that’s why you’re best served to find someone to help you get everything usefully tagged and categorized. But all the data is there and ready to use, not costing you anything or causing you any strife. Why wouldn’t you make the most of it?

Loyal customers deserve additional effort

Customer loyalty is of paramount importance to sellers of all kinds, but particularly e-commerce sellers. It’s tough to stand out through pricing or product selection in the digital marketplace, and winning someone’s loyalty will give you the edge regardless, so you need to prioritize it. But how do you know which customers to focus on? Simple — your purchase data will tell you.

By checking your customer histories, you can pick out those who have placed the most orders and/or spent the most money with you, then make an effort to serve the needs of those customers. You can reach out to them for direct feedback, ask them what improvements you could make, and even offer them incentives to spend more and/or bring you, referral customers.

When you don’t pay much attention to what your customers buy or how they shop, you can’t go the extra mile to cater to those with the most value. Keep loyal customers happy, and you’ll reduce your churn rate, convert on a more frequent basis, and achieve remarkable stability.

How Customer Analytics May be Used to Support a Particular Business Strategy?

For all types of customer analytics, you may need to use techniques like data collection and segmentation, modeling, data visualization, and more.

Given the many different examples of analytics, you can see that it’s unlikely you’ll capture every single important metric from one department, one method or one software. In fact, you’ll need buy-in from multiple teams and data from a range of disparate sources to complete the full picture – although you can achieve a unified customer view using certain software platforms.

But, the complexity of customer analytics shouldn’t be an argument against using them – just opt for a structured approach and learn exactly what each type of analytics entails. This way you’ll be able to reap all the benefits we mentioned above.

1. Customer journey analytics

This type of customer analytics focuses on understanding the customer’s interactions with your brand – from their initial research on your product or service to the actual purchase and beyond. Mapping the customer journey is an important first step in collecting the right data.

Then, customer journey analytics may involve a mix of data points from different interactions. For example, organic and non-organic traffic to your product pages contains insight about the initial stages of the customer journey: research and information gathering. While shopping cart abandonment rate can tell you how many customers leave their shopping cart before completing a purchase.

Think about what metrics would help you evaluate the particular customer journey steps you’ve identified as important to your business.

2. Customer experience analytics

Customer experience analytics shed light on how your customers feel when they interact with your brand. An important aspect to these metrics is to do with customer support (e.g. time to resolution) and customer onboarding (e.g. user adoption and time to value).

If you’re using a platform to track support tickets or email and live chat, you’ll probably have easy access to the customer support metrics.

Another part of customer experience is CSAT scores. In other words, how satisfied your customers are with your services (think diagnostic analytics). CSAT surveys are easily administered via email or via software like Delighted and ChurnZero after important events (such as training or purchasing). CSAT surveys help you, in part, evaluate the quality of your customer onboarding, too. 

You may also find analytics like the customer effort score (how easy it is for customers to interact with your business) useful to gain insight into customer experience.

It’s important to pay attention to qualitative data as well. For example, if a customer sends you a complaint email, reply appropriately and then note down the complaint (even an excel would do). If you continue to get similar complaints, count them and report on them to discuss actions.

3. Customer engagement analytics

Using analytics to improve customer engagement is very good practice. That’s because, if you want to keep someone’s attention, you need to know what they’re interested in – and data is the most effective way to find that out.

Customer engagement analytics may be divided into two categories: analytics for engagement with your product/service and analytics for engagement with your brand (e.g. web analytics).

Customer success teams may track user engagement with your product (e.g. usage metrics), but engagement marketing is also common: investigating and influencing the relationship of your brand with interested customers.

For example, you could segment your website visitors, and see how they interact with your content and calls-to-action, and their navigation paths, and then target them with personalized ads or email content based on that. Email marketing metrics, like click rates and click through rates, as well as social media engagement, can give you insight into how to increase customer engagement, too. 

Bear in mind that customer engagement is relevant to every stage of the customer journey/lifecycle, so it’s natural the two types of customer analytics will overlap.

4. Customer loyalty and retention analytics

This type of analytics measures how loyal your customers are. How many of your buyers are repeat customers? What percentage of your customers churn? These metrics tell you whether your customers like you more than other similar businesses.

Perhaps the most popular way to measure loyalty is the NPS (Net Promoter Score) survey. It’s the tried and tested “would you recommend us to a friend” question, with the answers categorizing respondents into groups of either passives, promoters, or detractors.

Customer churn and customer retention are both commonly tracked metrics. These are two of the metrics that could point to big problems down the line if the numbers don’t stack up.

Remember, customer retention costs 7x less than customer acquisition, so keeping hold of your customers goes a long way towards ensuring business growth. Combine them with customer experience metrics to find ways to create proactive customer retention programs.

5. Customer lifetime analytics

In a broad sense, customer lifetime overlaps with customer journey and customer experience. But, an important additional metric in this type of analytics is the Customer Lifetime Value (CLTV). This metric shows you how much revenue you can expect from a single customer throughout the entire business relationship. 

The way to calculate this metric may be different depending on your business – sometimes, bringing in a consultant may work better in identifying the right formula for your company. But, a simple way to calculate this metric is by multiplying your average retention rate by the average number of purchases, and then multiplying the product by the average deal total. 

CLTV = (Avg retention rate * Avg # of purchases) * Avg deal total

You can also calculate CLTV by multiplying the average customer value by the average customer lifespan, where:

• Average customer value = Avg purchase value x Avg purchase frequency rate
• Average customer lifespan = # of years customers purchase / # of unique customers = 1 / % churn rate

Example: If your customers spend on average $350 per purchase and buy six times a year on average, the avg customer value is $350*6 = $2100. Then, if your churn rate is 10%, the avg customer lifespan is 1/10% = 10 (years). So, your CLTV = $2100*10 = $21000.

Of course, there are more complex (and therefore more accurate) ways to calculate this metric. Also, segmenting this metric based on type of customer helps you see which ones are more valuable, and should thus be the audience of your more expensive marketing campaigns.

CLTV deserves its own category because you can use it in different ways to inform your decisions. For example, if you see it decline, that signals an issue with repeat customers. If it’s lower than what you spend on acquisition and marketing campaigns, then you’re probably spending too much without getting enough back.

So, track your CLTV and focus on acquiring more high-value and repeat customers.

6. Voice of customer analytics

The voice of the customer is a self-explanatory idea: it’s what your customer says that’s relevant to your business. With these analytics, you capture customer opinions, preferences, and expectations.

Voice of the customer analytics also refers to CSAT and NPS surveys, social media posts and interactions, and really anything that lets you listen to your customers’ thoughts. Take a structured approach to survey customers by following best practices like asking the right questions, digging into demographics, and choosing the right medium.

What is a Customer Analytics Record?

Customer records are retained data a business holds on any existing or prospective customers, this data is used to build a profile for each customer (this is critical to marketing and customer service functions).

These records enable businesses to build models that enable them to personalize a web-based marketing or sales experience to individual customers (with the aim of creating the best chance of conversion and revenue maximisation per customer).

These models are most commonly referred to as customer analytics record and are usually constructed to help build a full analytics model (also known as CAR).

What makes up a typical customer analytics record?

These models are typically built using records with information ranging from behavioristic, geographic, psychographic to demographics.

• Demographic data and geographic records for existing customers are easy to obtain, and they range from sex, income, and age to climate, country or the city you reside in.
• Psychographic data is not as easy to come by. It is data about your hobbies, opinions, personality or interests. This kind of information is usually sourced from social media platforms.
• Behaviouristic data, on the other hand, is what feeds digital marketers clues about your purchase, surfing and communication data, as well as your payment history.

Overall customer analytics models enable businesses to make sense of raw data, join up the dots and better understand who their customers are, what they want and deploy digital marketing strategies that will maximise the sales potential of any particular customer (hence the frequent cases of the Baader-Meinhof phenomenon that customers encounter online).

Such records are life-saving for companies with millions of customers whom all require a listening ear and a personalized customer experience to maximize conversion, upselling and overall revenue generation. Customer analytics is employed in such a case to analyze a customer analytics record and to unearth critical insights that drive patterns and trends.

Advantages of Planning a Customer Analytics Initiative

Access to the right data: It’s hard to increase the frequency of customer purchases, conversions, or attitudes if you don’t know what customers are purchasing, when they are purchasing it, or what they are thinking. Be sure you know that the data you need exists, or that you’ll be able to collect and analyze it.

Customer level data: To do the most with customer analytics, you’ll want to gather data for each customer, not aggregated data at product or company levels. Because customer analytics is about understanding the customer from past data to predict future data, you need to identify transactions, revenue, and survey data for each customer. You can then roll this lower level customer data up to product or company level summaries as needed.

Analytics that focus on the customer: The “right” analytics depend on the method. But one thing that all good customer analytics have in common is that they are meaningful to the customer. Just like airlines should care more about on-time arrivals than on-time departures, your analytics should be felt by customers at all phases of their journey.

If you want to improve the customer support experience, customer satisfaction with the call outcome is a better metric than the number of calls answered in an hour. The latter is an example of company centric and the former is customer centric.

Getting buy-in: Planning, collecting, and analyzing data is only good if something is going to be done about the insights. All too often, organizations spend a lot on research and customer measurement projects but the results stop at the executive presentation meeting.

Unfortunately, insights aren’t acted upon because the people who can change the product, price, or experience aren’t involved with the data collection and planning. They are naturally resistant to outsiders telling them what to do. This can happen with both internally and externally collected data.

Get buy-in from the people you need to implement your insights and minimize the “not invented here” attitude. Customer analytics should be shared with not only executives, but also with product development, sales, and support staffers. As part of the planning and getting buy-in, be sure the analytics will cross customer touchpoints and be accessible across the organization.

How do You Analyze Purchase Data?

Identify and consolidate your spend data

Your data may be stored in a variety of places. In many cases, each department – or even each project within each department – has a separate budget and accounting system.

Each of these separate sources may have its own internal process: accounts payable, general ledger, eProcurement systems or other financial software. The first step is to identify all invoices and payments from all sources, and consolidate everything into one central database.

Clean the data

Information coming from different sources will need to be standardized. Fields may need to be added, for example, to identify where the order originated or define the purpose of the order, and purchases from international sources may require currency conversion.

Once the data is standardized, duplicate entries should be compared to ensure double payments were not issued, and then eliminated.

Identify the scope of your spend analysis process

Determine the minimal time period – quarterly, yearly, or multiple years – that will return an accurate picture of your current spend and still allow you to identify all recurring expenses. Companies with decentralized budgeting systems may also want to limit the scope of each spend analysis process by department, division, or project.

Create a supplier list

Different departments may have similar requirements and use different suppliers. Identify the suppliers on your list with a tag or group designation in order to pull out and analyze prices, turnaround times, and other considerations. Identify the best suppliers and tag them preferred.

Categorize expenses

By breaking your spending into general categories, you’ll be able to see exactly where your money goes. Be as specific as your situation demands, whether your company orders building supplies, outsources digital services, or sends engineers to assess damage in remote locations.

Make a category for every major expense: Personnel, travel, outsourced programming, legal, manufacturing supplies, office supplies, etc.

Analyze your data

With company spends data consolidated, take a close look at how your money is spent and make appropriate changes and informed decisions about future spending. Keep your data up-to-date in order to remain on top.

How do You Analyze Customer Service?

to understand what’s really happening with customers, it’s crucial to dig deep into the data.

Here are nine examples of analysis techniques to find essential insights.

1. Detect New Themes Using Machine Learning

One of the benefits of analyzing unstructured text is the chance to discover completely new or surprising topics in the feedback.

Machine learning techniques can identify these new patterns automatically, shining a spotlight onto new areas of interest as they occur in real time.

Social media data is a great example because themes of feedback occur constantly.

For example, a car manufacturer sees new complaints about bumpers—which were fine at the factory. They trace them back to how bumpers are stacked for shipping and remedy the situation quickly.

2. Monitor Trends and Spikes in Themes

It’s important to view overall volumes of feedback, but also to see how they shift over time.

Trending data allows analysts to see movement in theme volumes by days, weeks, years, or even in a small 24-hour period.

Spikes in data also provide an opportunity to quickly react to customer trends or monitor ongoing issues to gauge their importance.

If a campaign is launched that prompts negative reactions and these outweigh the positives, it can be stopped quickly.

3. Find the Root Cause

It can be difficult to find the root cause of a spike or trend when customer volume, sentiment, or satisfaction scores start moving.

This difficulty is multiplied as more data sources are thrown into the mix, and when several layers of structured attributes may be contributing to the change.

Using statistical analysis techniques, multiple attributes, themes, and natural language understanding (NLU) attributes (for example topics, sentiment, emotions and effort) can be analyzed at once.

Then the significance of these factors can be measured, unlocking the most important issues.

This type of analysis could show, for example, that the root cause of low sentiment for a car dealership is that its new, purpose-built servicing centre has no wi-fi for while-you-wait-customers and no public transport links nearby so it can be accessed easily for car pick-up.

4. Segment to Identify Key Differences

Segmenting data makes it easy to find drivers of customer satisfaction among different groups.

Consider: what themes do high-level revenue customers like more compared to lower-revenue customers? And which of these themes do high-value customers mention most often?

Analysing different segments of data using the same models enables points of differentiation to be located.

5. Use KPI Metrics in Conjunction With Unstructured Themes

Use of KPI metrics such as review rating, Customer Satisfaction scores (CSat), or Net Promoter Score (NPS) is common.

However, additional insights can be found by mapping these metrics against unstructured feedback to reveal the ‘why’ that is driving those scores.

A leading managed cloud provider, for example, runs two survey questions, one NPS and one open-ended on which they run theme detection. This allows the company to determine the key drivers of the NPS score, in other words the ‘why?’, far more effectively than if they had asked fifteen questions.

As a general principle, just remember that shorter surveys will invariably elicit higher response rates.

6. Apply Sentiment Analysis to Find Areas of Customer Unhappiness

Sentiment analysis is an incredibly useful tool in analysing all sources of unstructured data. It examines data for positive or negative context, providing an additional dimension of analysis beyond volume or KPI metrics.

Sentiment analysis also illustrates how different themes affect customer experience, either positively or negatively.

Additionally, sentiment can act as a singular metric of performance across all sets of data.

7. Pair Sentiment Analysis With CSat Metrics

Looking at KPI metrics in conjunction with themes shows the high- and low-performing touchpoints along the customer journey; however, it does not always show how to improve those metrics.

It might be that long call waiting times produce low sentiment—but they are also associated with average CSat scores. This indicates that although people are unhappy with call waiting, it doesn’t significantly impact their overall customer experience.

Equally, low sentiment regarding website navigation is also associated with low CSat, so improving the website is likely to generate improvements to the overall CSat score.

8. Create Predictive Models

Predictive modelling techniques can help guide business decisions and predict improvements in KPI metrics or in monetary metrics like revenue.

When done correctly and when using a combination of structured and unstructured data inputs, predictive analysis can be a confidence booster for customer experience investments.

A global drinks manufacturer, for example, could track social mentions of different sweeteners to predict reactions to new products and understand brand impact.

9. Combine All Techniques to Create a Data-Driven Culture

Data analysis can be incredibly powerful, but its value will only be fully realized if action is taken on the insights that are uncovered.

Dashboards, alerts, and case management functionality can be used to push data and insights through the organization.

These tools allow information to be delivered to the right person at the right time to take action, opening communication across departments and at all levels of leadership.

What Are The Types of Customer Analytics?

To succeed and grow, a business needs to be able to acquire, retain, satisfy and engage their customers effectively. Customer analytics are vital for assessing how well your business does this. Let’s look at some of the key customer analytics in use today.

Customer satisfaction analysis

Customers who are happy with your product or service are much more likely to buy from you again. Customer satisfaction analysis is the process of assessing whether your customers are getting what they want and expect from your business, product or service – in short, whether they are satisfied or unsatisfied.

The most common way to assess customer satisfaction is with a combination of quantitative and qualitative surveys.

Customer lifetime value analytics

If you are able to attribute a lifetime value to each customer, you can immediately see which ones are the most valuable and therefore most important to you. Customer lifetime value analytics is the process of analyzing how valuable the customer is to the business over the entire lifetime of the relationship.

Instead of looking at transaction profitability, you look at how long a customer is likely to stay a customer, how often they are likely to buy during that period and therefore how valuable they are across that timeframe. This allows you

Tip: The biggest challenge with lifetime value is finding the right formula for your business. A KPI expert can help with this.

Customer segmentation analytics

Seeking to sell all things to all customers via the ‘shotgun’ approach doesn’t work. Customer segmentation analytics is the process of finding sub-groups or segments within the overall market.

Being able to assess your customers and split them up into various segments that might buy more of one product than another or buy more often allows you to tailor your marketing and communication efforts. The internet is a vast source of useful customer data, helping companies identify clear segments – data mining and text analysis are useful tools for this.

Tip: It is possible to take segmentation too far and seek to split your customer base down into increasingly smaller subgroups. Instead, stick to some core groups who appear to behave and buy in similar patterns.

Sales channel analytics

Unless you know how your sales are made and what channels are most profitable then you may be wasting time and money on sales channels that don’t work. Sales channel analytics looks at all the various ways that you distribute your products to your market to see which channels are the most effective, allowing you to make the best use of your resources.

For this analysis, you need to identify all the sales channels that you currently use or could use, then attribute each sale to a channel and subtract the relevant cost of sales for each channel.

Tip: Keep in mind that you don’t always know if the customer was exposed to a different sales channel before purchasing. In other words, a customer may have seen your product in a shop but preferred to buy online.

Web analytics

Online sales in just about every industry are increasing. Web analytics is the process of analysing online behaviour so as to optimise website use and increase engagement and sales. There are two types of web analytics: off-site and on-site.

Off-site web analytics is useful for assessing the market and opportunity whereas on-site is useful for measuring commercial results. There are many web analytics tools and service providers available, such as Google Analytics.

Social media analytics

If you don’t know what people are saying about your company or products, you can’t resolve any issues that arise. Social media analytics is the process of gathering and analyzing data from social media to see what people are saying about your product, service, brand or company.

In social media analytics, text data from social media posts and blogs is gathered and mined for commercially relevant insights using text analytics and sentiment analysis.

Tip: The real power of social media analytics is its real-time, immediate nature. If you can spot unhappy customers quickly then you have an opportunity to turn that situation around and create a loyal customer.

Customer engagement analytics

Businesses are notoriously bad at customer engagement, yet it has a direct impact on a company’s bottom line. Customer engagement analytics is a rapidly evolving field where businesses are trying to map the entire customer interactive journey on- and off-line.

Essentially it is the process of assessing how well (or otherwise) you engage your customers with your products, services or brand through these various interactions. Ways of measuring customer engagement include surveys and social media analytics.

Tip: You can’t please all of the people all of the time but customer engagement analytics can help to identify what aspects of your product or service customers value so you can constantly improve your offering.

Customer churn analytics

Keeping your existing customers is always much easier and cheaper than trying to find new customers. Customer churn analytics is the process of assessing how many customers you are losing over the course of a year.

It also allows you to predict customer churn in the future and take evasive action before you lose those customers. Customer churn can be assessed using KPIs such as customer retention rate and customer turnover rate. 

Tip: Pay particular attention to how you count customers and set that as a company wide benchmark for the future. If you don’t then different departments may count customers differently which can pollute the data.

Customer acquisition analytics

If you don’t have enough customers your business will fail, and the same applies if you spend too much money acquiring those customers. Customer acquisition analytics seeks to establish how effective you are at acquiring new customers, including how effective you are at pinching customers from your competitors.

There are a number of metrics that can help to establish customer acquisition, such as the cost per lead and customer conversion rate KPIs.   

Tip: When calculating cost per lead and cost per qualified lead, calculate them separately for each marketing initiative or campaign you execute. This will give you a much clearer picture of what is working and what is not.

How do You Analyze Clients?

There are a number of ways to conduct a customer analysis — everything from automated reporting tools and Google Analytics to good old-fashioned spreadsheets. Your needs will depend on your business’s preferences, but the key is to get enough data to analyze every aspect of your customers’ interactions with your business. Here are the first basic steps you should take.

1. Segment your customers

No two customers are alike, and they all interact with your brand in unique ways. The first step is to get a breakdown of your customers, which will allow you to target them with the most appropriate content and offers.  

To categorize your customer base and develop buyer personas, use a wide variety of characteristics, such as demographic traits, online shopping habits, and other engagement tendencies like customer lifetime value.

An e-commerce-focused analytics tool can help by automating this process – Glew provides 25+ pre-built customer segments, as well as the ability to easily filter data to create your own customer segments, including:

• X months since last purchase
• Recent purchasers
• Active, at-risk and lost customers
• Paying customers
• Recently refunded customers
• High AOV
• Low AOV
• Full price customers
• Value shoppers
• Repeat customers
• First purchase customers
• VIP customers
• Big spenders
• Big ticket shoppers
• Small ticket shoppers
• Refunders
• Most active customers
• Never purchased
• Abandoned carts

2. Identify their needs

Now that you have buyer personas, it’s time to figure out why they chose your business and the pain point you’re solving for them. Did they purchase out of convenience? How much were they willing to spend? Did they consciously seek your brand out? Are they likely to purchase from you again – and when?

Perform this exercise for each of the buyer personas you identified in step 1. Once you think about the context of your buyers’ needs, you’ll be in a better position to gear your outputs towards meeting those needs — which is the next step.

3. Determine how your brand meets those needs

The initial research is done: you have your buyer personas, and you know what their main goals are. The next step is to determine how your business specifically can solve your customers’ problems. The goal is to make their purchasing experience as seamless and easy as possible, so now is the time to focus your efforts on the data-driven insights you’ve gained.

Think about what experience each buyer persona might prefer – what products and price points they might be most attracted to, what marketing channels and strategies will work best on them, and how you can create a purchase and post-purchase experience that promotes repeat visits and customer loyalty.

4. Apply your analysis

Finally, capitalize on all of this data you’ve collected by optimizing the way you connect with your customers and prospective customers. Each persona will respond differently to different channels and types of content, so use it to personalize the buying experience.

With the insights you’ve gained from conducting your customer analysis, you’re in a better position to optimize your marketing campaigns, driving key metrics like total sales, average order value, lifetime value and repeat purchase rate.  

Focus your efforts by thinking about what your goals are here – are you trying to acquire more new customers in a certain customer demographic, or retain more of your existing high-value customers? Are you trying to boost lifetime value over time or get each of your customers to spend more with you on a single purchase?

What Are The Activities Involved in The Customer Analytics Process?

Setting up a robust customer analytics framework certainly requires a strong technology stack, but there’s more to it. Here are 3 key processes for planning customer analytics.

1. Know the customers you wish to analyze

At the very onset of establishing a customer analytics plan, keep the end goal and the preceding customer journeys in mind. Customer journey mapping is the process of drawing a comprehensive, diagrammatic map of all possible stages and points of interaction between a customer and the company, starting from brand discovery to post-sale servicing and repeat purchases.

Here are some questions that may help you map customer journeys:

• Who are our customers – their age, demographic, general location, purchasing capability etc.
• What do customers prefer to buy from us vs competitors
• What is their preferred mode of purchase?
• What is their preferred mode of communication?
• What are their preferred touch points at different stages of the buyers’ journey

You could ask more such questions, depending on the objectives you want to achieve from your customer analytics framework.

The journey mapping exercise gives you insights about the best touchpoints to collect the right data from, opportunities in the journey to collect relevant data, gaps in the journey where you may be missing opportunities to collect crucial input data, and it also helps you connect the dots between various touch points across the journey to draw better insights from the data.

2. Capture, organize, then analyze data

Once you have identified the data you want to collect and the sources from where to collect them, the next step is to actually collate as much data as is relevant to your goals.

Gather data from various sources or customer touchpoints such as your website, in-store visits and purchases, email clicks, website browsing, activities on your app, blog communities and social media interactions, CRM system and other internal and external systems. You may also run surveys, conduct user research or purchase third-party data to feed into your customer analytics framework.

3. Define outcomes

To establish a strong customer analytics practice, it is critical to define the outcomes you seek from the data. Based on the outcomes you are seeking, you will define the analytics that needs to be performed.

For example, as a result of the analytics, do you hope to get clarity on what has happened (descriptive), why it happened (diagnostic), answers to specific questions and possible responses (prescriptive), or what may happen in the future (predictive)?

Customer Analysis in Business Plan

The Customer Analysis section is a key component of your business plan and assesses the customer segments your company serves.

In it, your company must:

1. Identify its target customers

2. Convey the needs of these customers

3. Show how its products and services satisfy these needs

The first step of the Customer Analysis is to define exactly which customers the company is serving. This requires specificity. It is not adequate to say the company is targeting small businesses, for example, because there are several million of these types of customers.

Rather, an expert business plan writer must identify precisely the customers it is serving, such as small businesses with 10 to 50 employees based in large metropolitan cities on the West Coast.

Once the plan has clearly identified and defined the company’s target customers, it is necessary to explain the demographics of these customers. Questions to be answered include:

1. How many potential customers fit the given definition and is this customer base growing or decreasing?

2. What is the average revenues/income of these customers?

3. Where are these customers geographically based?

After explaining customer demographics, the business plan must detail the needs of these customers. Conveying customer needs could take the form of past actions (X% have purchased a similar product in the past), future projections (when interviewed, X% said that they would purchase product/service Y) and/or implications (because X% use a product/service which our product/service enhances/replaces, then X% need our product/service).

The business plan must also detail the drivers of customer decision-making. Sample questions to answer include:

1. Do customers find price to be more important than the quality of the product or service?

2. Are customers looking for the highest level of reliability, or will they have their own support and just seek a basic level of service?

There is one last critical step in the Customer Analysis — showing an understanding of the actual decision-making process. Examples of questions to be answered here include:

1. Will the customer consult others in their organization/family before making a decision?

2. Will the customer seek multiple bids?

3. Will the product/service require significant operational changes (e.g., will the customer have to invest time to learn new technologies and will the product/service cause other members within the organization to lose their jobs? etc.)

It is essential to truly understand customers to develop a successful business and marketing strategy. That’s why including a customer analysis in your business plan is so crucial.

Likewise, sophisticated investors require comprehensive profiles of a company’s target customers. By spending the time to research and analyze your target customers, you will develop both enhance your business strategy and funding success.

What is The Importance of Customer Analysis?

Customer analytics allows a company to discover both the shortcomings and the opportunities based on the customers’ behavioral history.

Nowadays, customers have more access to information, anytime and anywhere on when to shop, where to buy, what to buy, how to pay, etc. This makes it essential to know the customers’ preferences and how they might behave while interacting with your company.

The clearer your understanding of customer’s buying habits or lifestyle preferences are, the more precisely you can predict how they might behave in certain scenarios in the future. This will empower you to be able to devise your response accordingly.

It assures customer satisfaction since they deal with the things they need at the time they do which ensures better customer satisfaction and in turn more loyal customers for your organization.

Advantage of Customer Analytics

The primary benefit of customer analytics is that better decisions are made with data. These decisions lead to a number of tangible benefits, such as the following:

• Streamlined campaigns: You can target your marketing efforts, thus reduce costs.
• Competitive pricing: You can price your products according to demand and by what customers expect.
• Customization: Customers can select from a combination of features or service that meets their needs.
• Reduced waste: Manage your inventory better by anticipating customer demands.
• Faster delivery: Knowing what products will sell when and where allows manufacturing efforts to anticipate demand and prevent a loss of sales.
• Higher profitability: More competitive prices, reduced costs, and higher sales are results of targeted marketing efforts.
• Loyal customers: Delivering the right features at the right price increases customer satisfaction and leads to loyal customers, which are essential for long-term growth

Importance of Customer Analytics

Marketers have long been aware of the importance of consumer orientation, for achieving marketing objectives largely hinges on knowing, serving and influencing consumers.

As such companies have always taken action to understand their consumer base by interviewing, fielding questionnaires and analyzing secondary materials, at the end of the day, the information companies gain through research helps them develop their products and services.

In the past, consumers received most of their information about products and services from print media and unidirectional television advertising, and their shopping primarily took place in brick-and-mortar stores.

While this still applies to a large segment of purchases today, e-commerce and interactive media are bringing about great changes in the way consumers shop. For instance, price-sensitive people can easily compare the offers from different e-commerce businesses through several clicks; word-of-mouth recommendations are not limited to friends in real life; peer pressure on social media websites can also have a powerful effect on purchases.

Apart from information technology, the thinking, feelings and actions of individual consumers, targeted groups, and society at large are changing due to many physical and social-psychological factors in the environment.

Climate issues and concerns for health are leading a going-green lifestyle; an increasing mix of Generation X, Y, and Z results in a diverse range of values pursued in consumption; the economic and political situations also influence consumers’ preferences during a specific period of time.

Customers are now armed with more information, given more choices than ever, and becoming more demanding and less loyal.

Customer Analysis Example

Customer analysis explained as the route to knowing your customers, is one of the most important functions of marketing. Through understanding the customer one can begin to offer services to their needs. Customer analysis, marketing tools included, is a study centered around the buyer.

For example, Shay is a marketing analyst with a major marketing firm. She enjoys going to work because she can use both the creative and analytical sides of her personality. She does well at this job.

Recently, she was tasked with customer analysis for one of the firms clients. Shay is to take the research and make strategic assessments from the information. She is excited to begin this project.

Review the Market Factors

She first starts by reviewing the market factors: total market size, location, interest in the product, and more. Through a series of focus groups, surveys, and similar studies, Shay begins to understand how to coerce the customer to interact with the company.

Analyze Customer Habits

Next, she analyzes the habits which the customer displays during the purchase. Shay notes, from the research, that customers are introduced to the company through cheaper products. Eventually, they earn trust to purchase more expensive products. She notes this as she moves forward.

She finally looks at the way a customer acts after buying. The key factor she notices here is word-of-mouth: customers always tell their friends about the good experiences they had from the product. She recognizes the importance of this and plans for maximization of it’s benefit.

Assemble the Report

Shay finally assembles the report. She makes key discoveries in this document. Shay walks away from it knowing what is important: that the market is nationwide, it is relatively unbothered by price increases, is familiar with technology, is more interested in purchasing quality than saving money, and much more. She begins to strategize from this base.

Shay believes that by changing gears the company could further maximize profits. In her report she recommends several things: creation of an online store to offer another channel for sales, adding an additional product line that uses premium pricing, and creating a customer rewards program where previous purchasers who cause friends to buy can reap the benefits of their actions.

She prepares for the meeting to present her new customer analysis business plan. She knows she will have to overcome some resistance but looks forward to the meeting. Shay can bring value through her actions for the company.

Why is Customer Profitability Analysis Helpful?

Customer profitability is far more than just the calculated lifetime value of a customer, and more than the gross or net margin generated from a transaction. A proper customer profitability analysis involves every touch point a customer has with your company, including customer service contacts, returns, custom fulfillment costs, and more.

Measuring customer profitability is crucially important for continued business success because it helps determine whether certain customers are costing you money rather than making you money. Once you have a framework in place to measure this, it becomes easy to analyze customer profitability as frequently as makes sense for your business.

You may find that a customer group you thought was the most important is actually of lower value to your company than others. These findings can then help shape and shift your business strategy to keep your initiatives and goals aligned.

How do You Analyze Customer Profitabilty?

Analyzing customer profitability begins by identifying the various costs incurred specifically in relation to servicing a specific customer or segment of customers.

For example, a solar panel company serves two types of customers: Individuals and Small Medium Enterprises (SMEs). For the attainment, servicing, and retention of its customers, the company is required to provide consulting and service visits, as well as process sale orders. Individuals require only one site visit before placing an order.

SMEs require more frequent visits, as they are based in multiple locations and are provided with after-sale service as part of the bulk purchase. The customers’ behavior and profitability are given by the following table:

Application of Customer Profitability Analysis

From the given example, the customer profitability of the Individual segment exceeds the SME segment. This insight then supports the company in its strategic decisions. It can shift its focus towards attracting and retaining more customers from the more profitable Individual segment. Alternatively, it can look for cost reduction approaches for its SME segment.

Potentially, it can work to redesign its purchasing process in order to reduce the frequency of visits or orders. Otherwise, it can look to charge its customers for additional service visits to shift the weight of the cost from the company to the customer.

How do You Know if a Customer is Profitable?

Before you measure customer profitability, you need to confirm how your company calculates revenue and expenses. Remember, Profit = Revenue – Expenses. Some companies recognize revenue when it is received (cash basis accounting). But we recommend that organizations use accrual basis accounting – or recognize revenue when it is earned.

If you are bigger than a hot dog stand, then you should be using accrual accounting. In regards to expenses, it’s also important to allocate as many expenses through the customer as possible. Think about capital, debt, operational costs, etc.

There are various KPI’s that can help you understand how your customer profitability is doing at the moment. Here are examples of a few:

A measurement of the average revenue generated by each user or subscriber of a given service. Use the following formula to calculate the average revenue per user (ARPU):

 Total Revenue / Total # of Subscribers

A projection of the entire net profit generated from a customer over their entire relationship with the company. Use the following formula to calculate the customer lifetime value (CLV):

Annual profit per customer X Average number of years that they remain a customer – the initial cost of customer acquisition

If your customer isn’t valuable or is costing you too much, then reassess your pricing.

What Makes a Customer Profitable to a Company?

Customer profitability analyses are detailed, labor-intensive, and difficult to implement. However, there are some metrics that can give you a rough, baseline picture of customer profitability.

Though they don’t capture the intricacies of a full customer profitability analysis, these equations can offer you some perspective in terms of how much you’re making off different types of customers.

Customer Lifetime Value (CLV)

The first formula is Customer Lifetime Value (CLV). It gives you a picture of net profit gained from a customer over the time they’ve done business with your company.

The formula doesn’t consider any costs behind the initial cost of acquisition, and it also doesn’t give you much insight into why they’ve stuck with you or what you stand to gain going forward. Regardless, it’s still a valuable metric to keep track of when you want a rough projection of where a customer relationship could head.

Average Revenue per User (ARPU)

Another useful metric within the context of projecting customer profitability is Average Revenue per User (ARPU). It can tell you how much revenue you’re generating from subscribers of a specific service or customers who fit a specific mold.

Like CLV, it isn’t necessarily predictive of your customers’ behavior. It also only lets you know which customers are generating the most revenue — not the most profit. Still, it provides a solid starting point for understanding your customer profitability.

Though these formulas can give you a rough overview of your customer profitability, the best way to truly understand the value of your customers is through a customer profitability analysis.

How do Customers Become Profitable?

To calculate CPA, you need the annual profit per customer, and the total duration a customer stays with your business.

Annual profit = (Total revenue generated by the customer in a year) – (Total expenses incurred to serve the customer in a year)

The total revenue can be generated by the following sources that you need to include:

• Recurring revenue
• Upgrades to the higher plans
• Cross-buying relevant products

And, expenses can be incurred from the following sources which also you need to consider:

• Cost of customer service
• Maintaining a customer success team
• Loyalty perks
• Operational cost

Finally, when you have the annual profit, the customer profitability analysis calculation goes like this:

CPA = (Annual profit) x (no. of years customer stays with company)

Is it a Good Strategy to Focus Most Marketing Efforts on The Most Profitable Customers?

Targeting your most profitable customers is a common strategy for increasing your sales revenue. Some customers will contribute more to your profit margin than others. The key is to identify those customers early and adapt your sales strategy accordingly. For example, you may want to:

• set higher sales targets for your best customers
• find similar products or services to sell them
• find similar customers to sell to

Identify your most profitable customers

You can use different measurements to identify your business’ most profitable customers. For example, you can analyse your previous sales to find out who they are. Take note of what they buy, and when they buy it.

You can then segment your customers and the products or services they buy into one of four categories:

• high sales and high profit
• high sales and low profit
• low sales and high profit
• low sales and low profit

It’s a good idea to focus on customers that provide high sales and high profit. However, customers that provide high profit on low sales can also help boost profits.

If customers are providing low profit from high sales, you should think about adjusting your pricing to see if you can generate more revenue from these sales.

It may not be worth focusing any efforts on customers who generate low sales and low profits.

Methods for selling more to your best customers

You can try the following ways to sell more to your best customers:

• up-selling – selling them premium products with higher profit margins
• cross-selling – offering complementary products to those already sold
• diversifying – identifying a need and developing new products/services to meet them

What Other Considerations Should a Company Look at When Measuring Customer Profitability?

All people may be created equal, but the same can’t be said for customers. Everyone knows that some customers are more profitable than others. Conversely, some are downright unprofitable. Knowing which is which is the all-important question.

Despite enormous variations in profitability, many companies continue unprofitable relationships with customers, often providing them with pricing and service levels identical to those received by the most profitable ones. Why? In most cases, companies simply do not know who the unprofitable customers are. As such, they cannot develop marketing strategies or manage costs accordingly

Companies don’t necessarily need a state-of-the-art database or analytics technology to improve customer profitability. Rather, they can follow a comprehensive approach for measuring and managing customer value called the customer value management cycle.

Because of their unique qualifications and abilities, financial managers should take the lead in translating analysis to action and creating the culture of value.

What is The Meaning of Customer Profitability Analysis?

It may sound counter-intuitive in a competitive business world, but sometimes a customer can be doing a business more harm than good. Let’s look at some examples. In 2007, Sprint-Nextel sent termination letters to a small percentage of their customer base that had been over-using customer service, in some cases calling hundreds of times per month.

Allstate Insurance has dropped thousands of homeowner policies in Florida over the last decade or so due to a high risk of policy losses from hurricane damage. In both instances, the companies performed a customer profitability analysis to determine if certain groups were causing more harm than good. In these cases, the old adage, the customer is always right certainly does not apply.

The premise behind customer profitability analysis is simple – does it cost more to do business with certain clients than what is brought in? Some customer groups provide profit for a company, and these are the groups that marketers target with advertisements and fantastic deals.

But with other customer groups, the company may be far off better without them, even if it causes potential public relations strife.

There can be multiple motivations for examining the idea of customer profitability, and it is more complex than just selling a product or service at a loss, or telling a customer goodbye. Is the customer unprofitable? Is the way they are serving the customer unprofitable? Maybe there are internal problems in the company to blame?

Before a company decides to say goodbye to a whole segment of customers a customer profitability analysis is a must. Let us look at the advantages and disadvantages of performing one.

What is CRM Process?

The CRM process is a strategy for keeping every customer interaction personalized and meaningful that consists of five main steps. A customer relationship management system (CRM system) provides the data and functionalities your team needs to execute this strategy—and ultimately turn leads into customers.

To understand the steps of the CRM process, you have to understand the customer lifecycle. It’s one of the first concepts you learn as a sales rep to understand how a person becomes a loyal customer.

The CRM cycle involves marketing, customer service, and sales activities. It starts with outreach and customer acquisition and ideally leads to customer loyalty.

There are five key stages in the CRM cycle:

1. Reaching a potential customer
2. Customer acquisition
3. Conversion
4. Customer retention
5. Customer loyalty

The CRM process is that concept in action. It’s the tangible steps an organization must take to help drive consumers through the cycle of learning about your brand and ultimately becoming repeat customers.

According to the customer lifecycle, we know that the first step in the CRM process is maximizing reach with leads. In practice, reach is using your CRM platform to generate brand awareness through targeted marketing campaigns.

Why is Customer Profitability Analysis an Important Topic For Manager?

Customer profitability analysis helps managers to see whether customers who contribute sizably to total profitability are receiving a comparable level of attention from the organization.

Beside this, what is the usefulness of conducting a customer profitability analysis?

1. CPP helps assign the cost and revenues to different products and customers helping identify the profitable and loss making ones. 2. CPP helps in retaining customers as programs are put in place to retain the most profitable ones hence resulting in customer satisfaction and loyalty.

How Can ABC be Used to Track Customer Profitability?

Businesses rely on strategic and inventive accounting methods to stay profitable in competitive markets. There are a variety of cost accounting methods available to collect, analyze, and evaluate a company’s spending and investing habits, all of which inform management of how production processes can be streamlined and costs can be reduced.

Of all the tools available to accountants and business managers, the activity-based costing (ABC) method is able to increase profitability by saving the business time, money, and resources.

Activity-based costing is an accounting method that assigns costs to products or services based on the activities and resources that make up the overhead of manufacturing a product or providing a service, whereas traditional methods allocate production costs based on specific factors, such as labor, materials, marketing and other sources of overhead.

Activity-based costing is more logical and efficient for companies making customized products because overhead costs are not spread evenly across all products. For example, a low-volume product may necessitate minimum machine hours as well as multiple indirect costs and a high-volume product may require maximum machine hours with no indirect costs.

If the overhead of both products is based solely on machine-hours, as is usually the case with traditional costing methods, then the overhead costs of the low-volume product would not be accurate, which could result in the company suffering significant financial losses.

Implementing the ABC method requires an investment of time and resources from management as well as focus and dedication from all members of an organization. It can be a complicated and detailed task, as every business activity must be broken down into its essential components.

• The first step is to identify the products for which costs need to be allocated. Companies may find it helpful to start with one product that is easily approachable from an ABC perspective in order to see if the method is beneficial before implementing ABC in all aspects of their business.
• Next, companies will have to identify all of the direct costs, activities, and indirect costs associated with each activity required to manufacture a product. All aspects should be investigated, which may include negotiating with suppliers, handling complaints, issuing purchase orders, and more. Companies can hire consultants or utilize activity-based costing software to help organize information and coordinate with the existing accounting system.

When Can Determining Customer Profitability Activity Based Costing be Used to Analyze?

Customer profitability reporting, using activity-based costing (ABC) principles, aids in determining which types of customers to retain, grow, win-back, and acquire and how much to optimally spend doing each action.

Read Also: 10 Effective ways to Earn Customers Trust

Activity-based costing provides a more accurate method of product/service costing, leading to more accurate pricing decisions. It increases understanding of overheads and cost drivers; and makes costly and non-value adding activities more visible, allowing managers to reduce or eliminate them.

ABC enables effective challenge of operating costs to find better ways of allocating and eliminating overheads. It also enables improved product and customer profitability analysis. It supports performance management techniques such as continuous improvement and scorecards.

Questions to consider when implementing ABC

• Do we fully understand the resource implications of implementing, running and managing ABC?
• Do we have the resources to implement ABC?
• Will the costs outweigh the benefits?
• Can we easily identify all of our activities and costs?
• Do we have sufficient stakeholder buy-in? What will it take to achieve this?
• Will the additional information ABC provides result in action that will increase overall profitability?