We’re tempted to suggest that it’s only a matter of time before your competitors catch on because the predictive analytics market is expanding at a pace of 23.2% annually and we know that predictive analytics can help you make better marketing decisions (which equals more income).

To predict marketing trends, consumer behavior, and campaign results in marketing, one uses data mining, predictive modeling, and machine learning. Predictive analytics assists marketers in understanding why something occurred in the past and what can be done to better those results in the future by thoroughly examining enormous amounts of consumer and market data.

This article focuses on the many common applications for predictive analysis when you are looking into sales and marketing to help you get the best of results.

What are the Most Common Sales and Marketing Applications for Predictive Analytics?

Predictive analytics collects data from data sets to detect associations, forecast trends, identify patterns, and locate linkages. We may predict the future and make wise decisions with this strategy.

Read Also: How to Build a Data Analytics Program?

There are countless ways that business intelligence can use predictive analytics in sales and marketing. We list a few of the most typical ones below:

1. Customer targeting

Customer targeting separates a client base into groups of people who are comparable in particular marketing-relevant aspects, such as age, gender, interests, and purchasing patterns.It lets businesses to provide customized marketing messages to clients who are most likely to purchase their goods.

It has been demonstrated that predictive analytics outperforms conventional methods at identifying potential clients.The following are some examples of the types of factors utilized in consumer targeting:

• Socio-demographic factors: Age, job, marital status, education, etc.
• Engagement factors: Recency, frequency, monetary, etc.
• Past campaign factors: Contact type, day, month, duration, etc.

There are three benefits for the business in this situation: I improved customer communication; (ii) cost savings in marketing; and (iii) an increase in overall profitability.

The improvement of direct marketing campaigns in a financial institution is an illustration of this. Predicting which customers will sign up for a term deposit is the objective.

2. Churn prevention

Churn prevention seeks to foresee which consumers will leave our company, when they will do so, and why. It is significantly less expensive to keep an existing customer than to find new ones. Customer attrition can therefore be quite expensive for businesses.

Companies can create prediction models that enable preemptive action before it’s too late by utilizing the power of large customer data sets. The following are some examples of attributes used in churn prevention:

• Socio-demographic variables: Gender, age, education level, job category, marital status, nationality, etc.
• Products contracted: Credit cards, insurance policies, etc.
• Engagement variables: Recency, frequency, monetary, time, etc.
• Product/service usage: Mobile, web, physical, call center, etc.
• Technical incidents: Customer service calls, etc.
• Stationary variables: Season, date, time, etc.
• Competitor variables: Price, quality of services, etc.

The business then investigates the reasons for customer attrition and takes the necessary steps to keep those clients. For instance, we may provide a discount or an added function to clients. Predicting telecom customer attrition using account information is one example.

3. Sales forecasting

Analysis of past sales, seasonality, market-moving events, etc. are all part of sales forecasting. It produces a forecast of the demand for a good or service that is reasonable. Short-term, medium-term, and long-term forecasting can all be done using sales forecasting.

By taking into account all relevant elements, predictive analytics can in this case forecast customer reactions and shifting sentiments. The following are some examples of the variables utilized in sales forecasting:

• Calendar data: Season, hour, bank holidays, etc.
• Weather data: Temperature, humidity, rainfall, etc.
• Company data: Price, promotions, or marketing campaigns.
• Social data: Economic and political factors that a country is experimenting.
• Demand data: Historical sales.

The foundation of a business’ planning is its sales forecasts. In fact, it enables accurate revenue forecasting and efficient resource allocation. For instance, accurately predicting power consumption in the electric industry. As a result, forecasts are more accurate, providing better information to choose the best course of action.

4. Quality improvement

Businesses can better meet client needs by analyzing market research, which boosts revenue and lowers attrition. The following are some examples of the types of factors utilized in quality improvement:

• Product characteristics: Components, presentation, etc.
• Customer characteristics: Gender, age, etc.
• Customer surveys: Tastes, preferences, etc.

Once the business has created the predictive model, it can look for characteristics that suit consumer preferences. As an illustration, consider basing wine quality models on physicochemical tests (e.g., pH values). Here, the results are based on sensory data, such as assessments by wine specialists.

5. Risk assessment

Risk assessment enables businesses to examine potential issues related to a specific industry. By using predictive analytics, decision support systems may be created that can predict which operations will be profitable for the business and which won’t.

A generic word, risk assessment can mean different things to different people. In fact, we may want to assess the risk posed by clients, businesses, etc. The risk assessment can examine the following kinds of data in the instance of a client:

• Socio-demographic factors: Gender, age, education, marital status, etc.
• Product details: Credit amount, bill statement, etc.
• Customer behavior: Repayment status, previous payment, etc.
• Risk metrics: Default, etc.

The banking industry provides the example of choosing which clients will use a credit. Here, we use many forms of data to determine whether an application is qualified to obtain credit. To reduce the impact of default risk, we evaluate a customer’s likelihood of not repaying a loan in more detail.

6. Financial modeling

Financial modeling converts a collection of theories about the actions of markets or agents into numerical forecasts. These predictive models assist businesses in making decisions regarding investments or returns.

Predicting the stock market trend using both internal and external variables is one example. Numerous sectors utilize predictive analytics to enhance their outcomes and foresee future events so that they can respond appropriately. Retail, finance, insurance, telecommunications, energy, and other industries all have successful uses.

What are Examples of Predictive Analytics in Marketing?

Predictive marketing is not exactly a recent development. Smart marketers have long employed certain techniques and tools to improve the quality of their products and the targeting of their advertising efforts. Predictive analytics, however, is now available and reasonably priced for practically every firm thanks to a tremendous evolution in data science and analysis over the past few years, as well as the development of supporting technologies like cloud computing.

The following are some ways that predictive marketing analytics might help contemporary marketing teams:

1. Product development

What if a business could foresee with greater accuracy than its rivals what products will be in demand in the future? Such concepts were formerly regarded as delusions with justification, but today, industry leaders employ predictive analytics to outperform their rivals.

Example: L’Oréal and Synthesio

For instance, L’Oréal, the top cosmetics company in the world, employs a consumer intelligence platform powered by AI that Synthesio built to remain on top of fashions in the beauty industry and to supplement its product creation with predictive analytics. L’Oréal would have to forecast beauty trends at least 6 to 18 months before they arise in order to maintain leadership in such a cutthroat sector.

The AI engine at Synthesio gathers information from over 3,500 websites, including YouTube, fashion blogs, beauty forums, and all of the major social media sites. L’Oréal can predict future trends in lifestyles, product ingredients, and packaging by examining millions of data points relevant to the beauty industry.

2. Customer segmentation

An ML model may automatically cluster customers based on a variety of data points when predictive analytics-enabled customer segmentation is in place, as opposed to marketers who usually spend hours manually doing so.

For example: Aydinli and Acquia

For instance, to rapidly and precisely identify audiences for their focused advertising, Aydinli, a major brand distributor with operations in Asia, the Middle East, and Europe, turned to Acquia, a digital experience business.

Modern machine learning models from Acquia found behavioral- and product-based clusters, enabling Aydinli to categorize clients into groups such as high-return customers, digital-only purchasers, and other groups. As a result, Aydinli saw an increase in revenue of $50,000 per campaign and a ROI of over 3,500%.

3. Uplift modeling

Today, being able to predict marketing campaign results effectively is a crucial marketing talent that mainly relies on data analysis. Marketers may significantly reduce the time it takes to predict campaign uplift with the help of machine learning models, which are actually far more effective when processing massive volumes of data.

For example: IDT and Optimove

In order to tailor consumer communications based on their history, language, and responses to prior campaigns, IDT, a telecommunication and financial services firm, turned to Optimove, a company that helps businesses enhance their marketing efforts with the aid of AI and predictive analytics. Instead of taking weeks, IDT was able to determine campaign uplift thanks to Optimove’s predictive analytic-enabled technology.

IDT marketers were able to improve the number of consumers acquiring new services by 50% and achieve a 17% increase in the lifetime value of active customers with the aid of Optimove’s predictive customer model, lifecycle segmentation, and churn forecasting models.

4. Recommendation systems

One of the most common applications of predictive analytics in marketing is a recommendation engine. Several of the biggest companies in the world, like Amazon and Spotify, have dominated their respective industries by correctly forecasting what consumers want to watch, listen to, or buy next.

For example, Itransition

Itransition assisted a large, international e-commerce company in implementing a recommendation engine that uses AI and computer vision to offer customers more individualized experiences and boost customer engagement.

Our engine can sort through huge collections of customer data using a collaborative filtering algorithm to determine which product a specific consumer will be most interested in next. The system’s adoption helped our customer’s conversion rate from visitors to purchases rise by 8%.

5. Lead prioritization

Prioritizing leads typically requires an internal marketing staff to manually examine user data. Due to the lengthy nature of this difficult procedure, opportunities to fortify relationships with potential clients are lost as a result of delayed judgments. This process can be greatly accelerated using predictive analytics, leading to more reactive decision-making and higher conversion rates.

For example: WNS

A top digitally native company was aided by WNS, a company that utilizes data analytics to improve business outcomes, to increase the conversion of potential leads with the aid of a predictive analytics platform. Leads were divided into three categories by the machine learning-based model: hot, warm, and cold, with hot having the highest possibility of conversion. The marketing team was able to quickly identify high-quality prospects, nurture them, and noticeably raise consumer engagement as a result.

6. Churn prediction

The churn rate is a crucial indicator of client satisfaction in the marketing industry. The relationship between the two is very obvious: customer happiness declines as the churn rate rises. Marketers may correctly anticipate the likelihood of churn for a specific client in real time and take preventative action by applying predictive algorithms.

Example: Lityx.

In a recent article, the New York Times reported that the U.S. Department of Agriculture (USDA) has a program to help farmers grow more food. The retail business discovered that many visitors to the website would only make one visit before leaving. They planned to take proactive steps to reduce churn through early identification of these at-risk clients.

Read Also: How to Choose a Data Analytics Platform?

Lityx developed a number of predictive models based on the retail company’s data on consumer demographics, line-item transactions, marketing activity, and other indicators. These models aid marketers in determining the likelihood of recapturing lost customers. As a result, the client noticed a 265% improvement in forecasting re-purchase behavior after initial visits, as well as a 330% improvement in the accuracy of identifying at-risk customers.

7. Ad personalization

The degree of personalization in marketing has a direct impact on click-through rates (CTR) and, consequently, ad performance. To put it mildly, creating effective creatives for each demographic and campaign requires a significant amount of resources. Predictive analytics makes it feasible to deliver customized ad campaigns at scale using real-time consumer data.

Example: IBM and Mastercard

For instance, Mastercard promoted their cooperation with “Stand Up to Cancer” and its initiative to donate $4 million to cancer research by using IBM Watson Advertising Accelerator. Based on geography, device type, and time of day, the marketing team at Mastercard used IBM’s AI-based technology to identify the most captivating creative components for each target demographic.

The marketing team at Mastercard not only saw a remarkable 144% boost in click-through rates, but they also gained important knowledge regarding creative development in general. Customers were most receptive to provocative and original CTAs like “Start Something Priceless,” whereas generic CTAs like “Learn More” had significantly less of an effect.

Using data-based predictions, predictive analytics has emerged as a key tool that can alter every area of marketing, from lead creation to churn prediction. Marketers can eliminate irrelevant data and use only valuable insights to drive their sales and marketing initiatives thanks to smart data analysis. They enhance their relationship with clients and ensure long-term growth by taking use of these advantages.