In today’s highly competitive B2B landscape, marketing teams are constantly seeking innovative ways to identify and prioritize leads that are most likely to convert into paying customers. This quest for more efficient lead management has led to the adoption of predictive analytics, a technology that leverages data and machine learning algorithms to forecast future outcomes.
In this comprehensive post, we will delve deep into the world of predictive analytics for lead scoring in B2B marketing. We’ll explore what predictive analytics is, its benefits, how it works, implementation strategies, best practices, and real-world success stories. So, let’s embark on this journey to discover how predictive analytics can revolutionize your B2B marketing efforts and boost your ROI.
Table of Contents
- Understanding Predictive Analytics
- Why Is Predictive Analytics Important for B2B Marketing?
- Building the Foundation: Data Preparation
- Developing Predictive Models
- Implementing Predictive Lead Scoring
- Nurturing Leads with Personalization
- Tracking and Monitoring
- Overcoming Challenges
- Real-World Success Stories
- Future Trends in Predictive Analytics for B2B Marketing
Understanding Predictive Analytics
What Is Predictive Analytics?
Predictive analytics is a branch of advanced analytics that utilizes historical data, statistical algorithms, and machine learning techniques to identify patterns, trends, and relationships within data. Its primary objective is to forecast future outcomes or behaviors accurately. In the context of B2B marketing, predictive analytics aims to predict which leads are most likely to convert into customers, enabling marketing teams to focus their efforts and resources on the most promising prospects.
Predictive analytics relies on the premise that historical data contains valuable insights that can be used to make informed decisions about the future. By analyzing past interactions, conversions, and customer attributes, predictive models can identify the characteristics and behaviors that are indicative of a qualified lead.
Why Is Predictive Analytics Important for B2B Marketing?
The importance of predictive analytics in B2B marketing cannot be overstated. Here are several compelling reasons why it has become a cornerstone of modern marketing strategies:
- Enhanced Lead Prioritization: Predictive lead scoring helps marketing teams prioritize leads based on their likelihood to convert. This ensures that sales and marketing efforts are directed towards leads with the highest potential, increasing conversion rates and ROI.
- Improved Marketing Efficiency: By focusing resources on leads that are more likely to convert, marketing teams can optimize their budget allocation and maximize the impact of their campaigns.
- Personalized Marketing: Predictive analytics allows for the creation of highly personalized marketing campaigns. When you understand your leads better, you can tailor your messaging and content to resonate with their specific needs and pain points.
- Shorter Sales Cycles: Identifying high-quality leads early in the sales funnel enables sales teams to engage with them promptly, reducing the time it takes to close deals.
- Data-Driven Decision-Making: Predictive analytics empowers marketing professionals with data-driven insights. It provides a solid foundation for strategic decisions, campaign planning, and resource allocation.
- Competitive Advantage: Businesses that leverage predictive analytics gain a competitive edge by staying ahead of the curve in identifying and nurturing leads.
Key Concepts in Predictive Analytics
To dive deeper into predictive analytics for lead scoring, it’s essential to understand several key concepts:
- Data Sources: Predictive analytics relies on data. The sources of data can vary, but they typically include CRM data, website analytics, social media interactions, email engagement, and third-party data sources. Integrating diverse data streams provides a more comprehensive view of leads.
- Machine Learning Algorithms: Machine learning algorithms form the core of predictive analytics. Different algorithms are suited for various tasks, such as regression for predicting numerical values, classification for assigning labels, and clustering for segmenting data. The choice of algorithm depends on the nature of the predictive task.
- Predictive Features: Predictive features are the variables or attributes used to make predictions. In lead scoring, these could include lead demographic information, past engagement with marketing materials, website visits, email opens, and more.
- Model Training and Testing: Predictive models are trained on historical data. A portion of the data is used for training, while the remaining data is used for testing the model’s performance. This process helps ensure that the model can generalize well to new, unseen data.
- Model Deployment: Once a predictive model is trained and validated, it can be deployed into production systems, such as CRM or marketing automation platforms, to score leads in real-time.
In the following sections, we’ll explore each of these concepts in more detail and provide practical insights into implementing predictive lead scoring in your B2B marketing strategy.
Building the Foundation: Data Preparation
Data preparation is the process of gathering, combining, structuring and organizing data so it can be used in business intelligence (BI), analytics and data visualization applications. The components of data preparation include data preprocessing, profiling, cleansing, validation and transformation; it often also involves pulling together data from different internal systems and external sources.
Data preparation work is done by information technology (IT), BI and data management teams as they integrate data sets to load into a data warehouse, NoSQL database or data lake repository, and then when new analytics applications are developed with those data sets. In addition, data scientists, data engineers, other data analysts and business users increasingly use self-service data preparation tools to collect and prepare data themselves.
A sequence of processes are taken to prepare the data. The data preparation steps stated by different data experts and software providers vary slightly, but the process often includes the following tasks:
- Data collection. Relevant data is gathered from operational systems, data warehouses, data lakes and other data sources. During this step, data scientists, members of the BI team, other data professionals and end users who collect data should confirm that it’s a good fit for the objectives of the planned analytics applications.
- Data discovery and profiling. The next step is to explore the collected data to better understand what it contains and what needs to be done to prepare it for its intended uses. To help with that, data profiling identifies patterns, relationships, and other attributes in the data, as well as inconsistencies, anomalies, missing values, and other issues so they can be addressed.
- Data cleansing. Next, the identified data errors and issues are corrected to create complete and accurate data sets. For example, as part of cleansing data sets, faulty data is removed or fixed, missing values are filled in and inconsistent entries are harmonized.
- Data structuring. At this point, the data needs to be modeled and organized to meet the analytics requirements. For example, data stored in comma-separated values (CSV) files or other file formats has to be converted into tables to make it accessible to BI and analytics tools.
- Data transformation and enrichment. In addition to being structured, the data typically must be transformed into a unified and usable format. For example, data transformation may involve creating new fields or columns that aggregate values from existing ones. Data enrichment further enhances and optimizes data sets as needed, through measures such as augmenting and adding data.
- Data validation and publishing. In this last step, automated routines are run against the data to validate its consistency, completeness and accuracy. The prepared data is then stored in a data warehouse, a data lake or another repository and either used directly by whoever prepared it or made available for other users to access.
Data preparation can also incorporate or feed into data curation work that creates and oversees ready-to-use data sets for BI and analytics. Data curation involves tasks such as indexing, cataloging and maintaining data sets and their associated metadata to help users find and access the data.
In some organizations, data curator is a formal role that works collaboratively with data scientists, business analysts, other users and the IT and data management teams. In others, data may be curated by data stewards, data engineers, database administrators or data scientists and business users themselves.
Developing Predictive Models
Predictive modeling techniques use existing data to build (or train) a model that can predict outcomes for new data. Implementing such techniques enables businesses to optimize decision-making and generate new insights that lead to more effective and profitable actions.
- healthcare organizations apply predictive modeling techniques to optimize diagnostic procedures,
- banking institutions use these techniques to detect and avoid fraudulent activities,
- retail stores implement such techniques to optimize their inventory stock and boost customer satisfaction, etc.
Selecting the Right Algorithm
Predictive modeling is an effective data analytics technique that supports artificial intelligence (AI). With the help of various machine learning tools and techniques, predictive modeling helps predict future events and determines how future decisions affect existing situations. Here is a brief overview of the machine learning techniques that are useful in predictive modeling-
- 1. Linear Regression
One of the simplest machine learning techniques is linear regression. A generalized linear model simulates the relationship between one or more independent factors and the target response (dependent variable). Linear regression is a statistical approach that helps organizations get insights into customer behavior, business operations, and profitability. Regular linear regression can assess trends and generate estimations or forecasts in business.
For example, suppose a company’s sales have increased gradually every month for the past several years. In that case, the company might estimate sales in the coming months by linearly analyzing the sales data with monthly sales.
- 2. Logistic Regression
Logistic regression is a statistical technique for describing and explaining relationships between binary dependent variables and one or more nominal, interval, or ratio-level independent variables. Logistic regression allows you to predict the unknown values of a discrete target variable based on the known values of other variables.
In marketing, the logistic regression algorithm deals with creating probability models that forecast a customer’s likelihood of making a purchase using customer data. Giving marketers a more detailed perspective of customers’ choices offers them the knowledge they need to generate more effective and relevant outreach.
- 3. Decision Trees
A decision tree is an algorithm that displays the likely outcomes of various actions by graphing structured or unstructured data into a tree-like structure. Decision trees divide different decisions into branches and then list alternative outcomes beneath each one. It examines the training data and chooses the independent variable that separates it into the most diverse logical categories. The popularity of decision trees stems from the fact that they are simple to understand and interpret.
Decision trees also work well with incomplete datasets and are helpful in selecting relevant input variables. Businesses generally leverage decision trees to detect the essential target variable in a dataset. They may also employ them because the model may generate potential outcomes from incomplete datasets.
- 4. Random Forest
A random forest is a vast collection of decision trees, each making its own prediction. Random forests can perform both classification and regression. The values of a random vector sampled randomly with the same distribution for all trees in the random forest determine the shape of each tree. The power of this model comes from the ability to create several trees with various sub-features from the features. Random forest uses the bagging approach, i.e., it generates data subsets from training samples that you can randomly choose with replacement.
- 5. Neural Networks
Neural networks are complex algorithms that can recognize patterns in a given dataset. A neural network is helpful for clustering data and defining categories for various datasets. There are three layers in a neural network- the input layer transfers data to the hidden layer. As the name suggests, the hidden layer hides the functions that build predictors. The output layer gathers data from such predictors and generates a final, accurate outcome. You can use neural networks with other predictive models like time series or clustering.
How to Build a Predictive Model?
The predictive modeling process can begin once a set of current and historical data is ready for predictive analysis. To create the prediction model, data scientists or analysts design typical predictive algorithms and statistical models, train them using subsets of the data, and then run them against the complete data set.
Let us look at how to create a predictive model utilizing simple and straightforward procedures –
- Data Collection- The process of data collection is acquiring the information needed for analysis, and it entails obtaining historical data from a reliable source to implement predictive analysis.
- Data Mining- You cleanse your data sets through data mining or data cleaning. You delete incorrect data during the data cleansing process, and the data mining process entails removing identical and redundant data from your data collections.
- Exploratory Data Analysis (EDA)- Data exploration is essential for the predictive modeling process. You gather critical data and summarize it by recognizing patterns or trends. EDA is the final step in your data preparation phase.
- Predictive Model Development- You will utilize various techniques to create predictive analytics models based on the patterns you’ve discovered. Use Python, R, MATLAB, other programming languages, and standard statistical models to test your hypothesis.
- Model Evaluation- Validation is a crucial phase in predictive analytics. You run a series of tests to see how effectively your model can predict outcomes. Given the sample data or input sets to evaluate the model’s validity, you must assess the model’s accuracy.
- Predictive Model Deployment- Deployment allows you to test your model in a real-world scenario, which helps in practical decision making and makes it ready for implementation.
- Model Tracking- Check the performance of your models constantly to ensure that you are receiving the best future outcomes possible. It involves comparing model predictions to actual data sets.
Implementing Predictive Lead Scoring
Predictive lead scoring is a technique used by sales and marketing teams to analyze and score leads based on their likelihood of becoming a customer or making a purchase.
Predictive lead scoring, which is typically an add-on that interfaces with your CRM system, employs a machine-learning algorithm to find trends in lead attributes, identifying which properties most reliably translate into sales. These AI systems track not only behaviors but also firmographic data and negative characteristics to profile the most ideal leads, allowing sales teams to focus their efforts on the leads who are most likely to become customers.
Key Components of Predictive Lead Scoring
Here are the technological components your company should have in place in order to effectively implement predictive lead scoring:
- CRM System
A customer relationship management (CRM) software application is the most important part of predictive lead scoring, because it houses your contacts, records their firmographics, and tracks their behavior. Popular CRM systems, like HubSpot and Salesforce, often include traditional lead scoring in their higher-tier subscription plans but offer predictive lead scoring software as a separate CRM add-on.
Read Also: How to Use Social Media For Branding Success
A CRM system includes several of the components on this list–data analytics, marketing campaigns, and contacts. CRM systems integrate with your phone system or call center, combining multiple communication channels into a workflow and tracking the whole process–including how your customers navigate the system. When the lead-scoring algorithm determines a score, it populates the CRM system’s contact list.
- Predictive Scoring Software or Algorithm
Predictive lead scoring is an AI-based algorithm that combines several machine-learning processes: tracking lead conversion journeys, calculating the most significant data points that precede customership, and scoring each lead based on these data points. As your marketing campaigns evolve and convert high-quality leads over time, the algorithm continually refines its model to score potential customers using the most relevant attributes.
Many CRM systems offer a predictive lead scoring algorithm as a software add-on.
- Call Center Software
Cloud-based call center software is important for lead scoring because it provides multiple communication channels that strengthen and customize your marketing campaigns.
Contact center as a service (CCaaS) platforms provide key communication channels:
- VoIP phone service
- SMS texting
- Web chat and self-service chatbots
- Social media
Omnichannel contact centers unify these channels into one agent interface. Since CCaaS platforms integrate with CRM systems, they populate the agent dashboard with relevant customer data, like sentiment and lead score, providing sales reps with context. Further, higher-tier CCaaS pricing plans enable you to automate workflows–like sending an automated email or initiating an autodialer sequence–once a customer achieves a certain lead score. This enables you to make the most of your predictive lead-scoring AI.
- Data Analytics
Most CCaaS platforms and VoIP phone systems track advanced data analytics: call center metrics and KPIs that track your call center volume, agent activity and performance, channel usage, and more. While these metrics may not directly inform your predictive lead scoring model, they do provide important information about which channels to use in your workflows and which agents provide the best service.
CRM systems, on the other hand, track a wealth of customer behavior and demographic data that directly informs your predictive lead-scoring algorithm. While traditional lead scoring systems require marketing and salespeople to determine which attributes filter quality leads, predictive lead scoring systems calculate this automatically.
- Contact List
A list of contacts, or leads, is important because the predictive scoring algorithm analyzes and scores this list. A critical part of any marketing or sales campaign, a contact list organizes all your company’s potential leads and customers. A CRM system houses the contact list–recording contact information when someone responds to a marketing campaign, gathering historical data, categorizing contacts, and determining how to reach out to them.
Predictive scoring software categorizes and filters your contact list according to their potential to become a customer, before proceeding to the corresponding workflow.
How to Implement Predictive Lead Scoring
Because predictive lead scoring utilizes AI to determine attributes and score leads, it’s easier to implement than traditional lead scoring.
Traditional lead scoring requires tracking lead conversion rates, isolating the metrics that most significantly correlated with lead conversion, and then manually inputting those metrics into your CRM system’s contact properties.
Predictive lead scoring, however, requires just a few steps to implement:
- Step 1: Purchase and Integrate Lead Scoring Software
Most CRM platforms–like HubSpot and Salesforce–include traditional lead scoring in their higher-tier plans. However, most CRM providers sell predictive lead-scoring software as a separate add-on. Once you purchase predictive lead scoring software, follow the software-provided guidelines to integrate it with your CRM system.
- Step 2: Turn on Predictive Lead Scoring in your CRM Contact Properties
Once you’ve integrated predictive lead scoring software, navigate to your CRM system’s Settings menu, then Properties, then Contact Information. From here, you can enable predictive lead scoring. It might be called one of the following: Likelihood to Close, Contact Priority, or Predictive Lead Scoring.
- Step 3: Gather Lead Data
Once you’ve enabled predictive lead scoring, you must gather enough leads and customers for the algorithm to detect patterns in customer attributes. Typically, the algorithm requires between 40 to 100 leads to analyze these patterns. Depending on how many leads your marketing campaigns generate, this process can take anywhere from one day to several weeks. Make sure to gather lead data through all available channels, running your marketing and sales campaigns as normal. The CRM system and predictive lead scoring algorithm will gather a dataset automatically in the background.
- Step 4: Create Workflows
Once the algorithm has gathered enough big data to identify attributes for lead conversion, it will begin scoring your pre-existing and new leads. In order to take advantage of these lead scores, create workflows that automate actions using lead scores as a trigger. You can automate these workflows in your CRM system–or in your call center if it’s integrated with your CRM system.
Example workflows include:
- Sending an email to all leads who score above 50
- Adding all leads who score about 70 to an autodialer campaign list
- Assigning all leads who score above 90 to sales agents, for personalized follow-up
- Step 5: Evaluate and Compare Lead Scoring Models
Although your predictive lead-scoring software should outperform your traditional lead-scoring model, it’s helpful to confirm this by tracking call center metrics like your lead conversion rate. With the implementation of lead predictive lead scoring, your lead conversion rate should be higher and more efficient than it was before you implemented the algorithm.
The most important metrics to track are:
- Lead conversion rate: Should be higher than it was before you implement predictive lead scoring
- Customer purchase rate: Similar to the lead conversion rate, your company’s overall purchase rate should increase when you implement predictive lead scoring
- Agent call volume: Ideally, agents are able to convert more leads with the same number or fewer calls than they were making before
- Communication channel activity: Track which channels your leads, agents, and workflows most commonly utilize. Over time, you might find that certain channels are becoming more popular with your leads, which may inspire you to shift your workflows to emphasize these channels
Nurturing Leads with Personalization
Personalization in the lead nurturing process can speak loudly for your brand; for example, merely appealing to someone’s color taste can persuade them to shop with you rather than another firm.
According to an Accenture survey, 75% of consumers are more likely to buy from brands that get personal, address them by name, and make recommendations based on their previous shopping habits and purchases.
According to McKinsey data, 76% of consumers said receiving personalized messages prompted their consideration of a brand, and 78% said such content made them more inclined to repurchase. In fact, 76% of people are irritated when businesses fail to provide personalized experiences.
Personalizing your lead nurturing is a continual effort, similar to chatting to your prospects throughout the purchase experience and keeping them invested.
The most effective lead nurturing strategies distribute content via numerous marketing touchpoints. Whether you utilize a combination of content distribution techniques such as social media postings, blog articles, or direct mail, each should be created with a personalized focus to ‘nurture’ your prospects into customers.
Personalization begins with determining your prospects.
- Identify Who Is Your Ideal Buyer
Knowing your buyers’ interests, purchasing behavior or history is key yet one aspect of developing a personalized lead nurturing strategy. Additionally, you may want to know a range of other facets, like their identity (gender and age) demographic to even their pain points.
Once all this information is established, you can begin creating what’s called a buyer persona. Buyer personas are the most valuable tools that you can provide to a sales and marketing team. This way we can understand exactly who your target buyers are to then tailor content that will be appealing and relevant to them. This will prompt desirable interactions between you and these prospects and ultimately improve conversion rates.
- Utilize Marketing Automation
Using automated data collection to retrieve your prospects’ details is a must for any lead nurturing strategy. A customer relationship management (CRM) or marketing automation software eliminates manual implementation, to automatically track the actions and behaviors of your audience’s online searches, page visits, clicks, etc. The system will effectively consolidate and optimize this information to provide data insights for you to better communicate and nurture prospects from the very start to the end of the sales funnel.
Marketing automation software, such as HubSpot, allows marketers to effectively utilize data to produce meaningful, personalized content that will help move prospects through the buying journey.
Not only does the system provide real-time data on your prospects, showing the time spent on your site, pages viewed, location, etc. but at the same time, issues savvy call to action (CTA) at the right moments, for instance, “Chat with Us,” “Request a Quote” or “Order Here” as they browse through your site.
HubSpot also delivers automated targeted follow-up emails in a timely way to cross-sell and upsell customers. The buying journey doesn’t end after a conversion is made but is rather picked up post-purchase to keep the lead nurturing process in rotation.
- Use Zero-And First-Party Data Collection
Implementing zero-and-first-party data collection is also a useful way of gaining a prospect’s personal information to boost engagement. While zero-data collection involves a person’s own willingness to provide their data in exchange for something of value (you can offer a discount, coupon, exclusive access, etc.), first-party refers to data that is captured from cookies and other metrics on your website based on the users’ behavior and referral source.
According to a recent Google report, marketers who integrate first-party data across channels see a 150% boost in cost savings and nearly 300% revenue growth. According to Accenture, 83% of consumers are willing to share their data so companies can offer a personalized experience. However, it’s important to balance personalization with privacy.
Accenture analysts advise companies to allow users to self-profile and determine how they want to be known by the brand. “Customers are the data locker. They’ll unlock if they know the exchange is valuable,” according to the report. “If you give them a compelling reason why they should be sharing their data with you, they are more likely to do so.”
- Create Engaging Content
Creating the content of what your prospects will see is one of the most important aspects of the lead nurturing strategy. After all, if your content marketing delivery falls flat so does consumer interest and what would have been subsequent conversions. A personalized and engaging content marketing strategy for lead nurturing will hook prospects all the way through the buying journey. A Demand Gen report stated that an average 20% increase in sales opportunities was achieved through leads with personalized content.
Creating strategic content for multi-channel points, including landing pages, blog posts, email, etc. must be tailored to speak to a prospect’s interests, preferences, and pain points – then balance this engagement while at the same time providing an effective and clear call-to-action (CTA), taking them from awareness and moving them to make a decision.
- Personalizing With Video Connects Better
A solid lead nurturing process is one that gets personal with a consumer on an intimate level. By identifying empathy points and delivering high-value moments while selling a concept, a company can help consumers overcome several friction points – the fact is nothing communicates that better than video.
Video is one of the most powerful marketing tools and the best investment you can make for the lead nurturing process. With the right creative video marketing team, companies can grasp a prospect’s interest in ways surpassing even their own expectations to immediately prompt sales.
A HubSpot survey reported that 76% of marketers said video in the lead nurturing process helped them increase sales. Another, 86% of video marketers say video has been effective for generating leads. Overall 92% of marketers who use video say that it’s an important part of their marketing strategy.
Tracking and Monitoring
Sales and marketing experts employ a variety of strategies to enhance income and broaden the reach of a brand. They help their businesses grow by employing lead tracking to analyse client behaviour and preferences. If you’re a marketing expert or a salesman, understanding how lead tracking works will help you optimise your efforts to attract and convert potential clients.
Lead tracking is a marketing approach that documents and categorises clients based on their in-person or online contacts with the company. Marketing experts can acquire a large quantity of data about client behaviour using a variety of tactics, such as lead volume at each step of sales development and average purchase amount per customer or per visit.
Marketing teams can use this information to categorize leads in the marketing funnel. This way, marketers can determine strategic methods to move leads into more advanced stages in the funnel. This increases the number of conversions between potential buyers to actual customers.
Lead tracking vs. lead scoring
Lead tracking is related to lead scoring, a process where marketers use algorithms to assign a score to individual leads, indicating how likely they are to purchase. While lead scoring specifically refers to evaluating individual leads, lead tracking describes the process of monitoring lead behavior. Lead tracking also helps organizations collect the necessary information to evaluate leads and assign scores.
These two practices use the same customer information to accomplish different tasks. Lead scoring can help sales professionals decide which leads to contact first or what tactics to use with different leads. Typically, lead scoring provides a single, quantifiable label that allows sales representatives to assess the lead quickly. The information gathered from lead tracking can help marketing and sales teams make decisions about overall marketing campaigns. For example, marketers might identify trends that cause lead scores to go up or down during the lead-tracking process.
Methods for tracking leads
The following covers the most effective methods for tracking high-quality leads:
- 1. Prepare an email campaign
Build a structured email sequence to engage with potential leads regularly. Planning a thorough email marketing campaign with multiple entries allows you to track your open rate, click-through, replies and bounce rates. Leads who open emails or click embedded links
have a higher probability of purchase. Many email marketing tools have built-in trackers that collect these details and compile them into reports. When you send more emails, you can also gain more information about the types of content your potential customers are interested in reading.
For example, you might notice that a weekly content-based newsletter with industry news has a higher open rate than a digest email about promotional events. That might tell you that your potential customers appreciate informative content. You might launch a new marketing campaign that focuses on providing information, featuring webinars and blog posts, to appeal to these leads and show them the company’s expertise in their field.
- 2. Conduct follow-ups
Sales teams often have a system for following up with new leads that enter their database. Emailing or calling a new lead allows the sales team to gather more information about them, which helps with lead tracking. For example, they might learn that the lead is located in an area where the company has several locations, increasing the odds of a sale. If you work on a sales management team, consider creating a lead follow-up script for representatives that includes questions about key criteria, like location and budget.
- 3. Practice lead scoring
Using the qualifiers you established earlier, create a scoring system that ranks leads in terms of the likelihood that they might make a purchase. In small sales organizations, lead scoring allows the sales team to focus on high-probability leads. For larger companies, lead scoring highlights the most crucial information from hundreds of data points.
Lead scoring essentially increases the probability of securing a conversion and onboarding new customers. Many sales management tools have adaptive scoring and can change a lead’s score based on new information or interactions.
- 4. Invest in CRM software
CRM software is a single tool that performs all the methods discussed earlier. It reduces the labor-intensive process of sifting through enormous sums of data and collates the information into reports or interactive dashboards. The technology helps sustain lead tracking by storing historical data, allowing sales teams to view changes in customer spending habits over time.
CRM platforms can also update contact information automatically, which can minimize missed conversion opportunities. There are many CRM options for companies of all sizes and industries, and some of them integrate into other business management tools.
- 5. Use website analytics
By using website analytics tools such as Google Analytics, you can track how leads interact with your website, including which pages they visit, how long they stay, and where they leave. This can provide valuable insights into lead behavior and help you optimize your website for lead generation.
Here are a few tips to help you implement an effective lead-tracking strategy:
- Consider your budget. Lead tracking can be as simple as using a spreadsheet or as sophisticated as using CRM software. Think about the size of your organization and its objectives to figure out what you need to create a budget for the process.
- Forecast your lead quota per month. Understanding the volume of potential leads you’re expecting can help you anticipate how much time and energy you might spend organizing the information. It may also help you designate tasks and operate your sales team more efficiently.
- Think about the size of your team. Lead tracking can be time-consuming. It requires you to send out emails, schedule calls, develop content and track analytics. Understanding your team’s availability can help you assign tasks or outsource certain functions.
- Identify challenges in advance. Attend training programs to educate yourself about complex lead-tracking strategies and software. If you’re a member of the management team, work with other directors to predict challenges to lead tracking in your organization.
- Identify the weaknesses of your current lead management system. Examine the quality of your leads to understand the effectiveness of your current strategy. This information can help you choose the right set of tools for your team.
Lead scoring is a technique for assessing prospects based on their level of interest and willingness to purchase your product or service. It can assist you in better prioritizing your sales efforts, nurturing your prospects, and increasing conversion rates. Lead scoring, on the other hand, is not a one-size-fits-all solution, and it might create certain problems and obstacles for content marketing.
- Misaligned criteria
One of the main challenges of lead scoring is to define the right criteria and metrics to measure your leads’ behavior and engagement. If you use criteria that are too broad, too vague, or not relevant to your content marketing goals, you may end up with inaccurate or misleading scores.
For example, if you only count the number of page views or downloads, you may miss the quality and depth of your leads’ interactions with your content. To avoid this pitfall, you need to align your lead scoring criteria with your content marketing strategy and use a combination of demographic, firmographic, behavioral, and contextual factors to score your leads.
- Inconsistent data
Another challenge of lead scoring is to ensure that you have consistent and reliable data to feed your scoring system. If you use different sources, platforms, or tools to collect and analyze your data, you may encounter discrepancies or gaps in your data quality and accuracy.
For example, if you use different email marketing, CRM, and analytics software, you may have different definitions, formats, or standards for your data. This can affect your lead scoring results and cause confusion or errors. To overcome this challenge, you need to integrate your data sources, platforms, and tools, and use a common data model and framework to ensure data consistency and validity.
- Static scores
Another pitfall of lead scoring is to rely on static or fixed scores that do not reflect the changes and dynamics of your leads’ behavior and preferences. If you use scores that are based on outdated or irrelevant data, you may miss the opportunities or risks that arise from your leads’ evolving needs and interests.
For example, if you use scores that are based on a single interaction or a single point in time, you may not capture the full picture of your leads’ journey and decision-making process. To avoid this pitfall, you need to update your scores regularly, and use dynamic or adaptive scores that take into account the recency, frequency, and intensity of your leads’ interactions with your content.
- Lack of feedback
Another challenge of lead scoring is to get feedback and input from your sales team and your leads themselves. If you use scores that are based on your own assumptions or opinions, you may not align with your sales team’s expectations or your leads’ perceptions. For example, if you use scores that are based on your own content marketing objectives or criteria, you may not match your sales team’s qualification or prioritization criteria.
Or if you use scores that are based on your own interpretation or inference of your leads’ behavior or engagement, you may not match your leads’ actual intent or readiness. To overcome this challenge, you need to communicate and collaborate with your sales team and your leads, and use feedback and input to validate and refine your scores.
- Lack of action
Another pitfall of lead scoring is to use scores that are not linked to any action or outcome. If you use scores that are only for reporting or analysis purposes, you may not leverage the full potential and value of your lead scoring system. For example, if you use scores that are only for measuring or comparing your leads’ performance or potential, you may not use them to guide or optimize your content marketing tactics or strategies. To avoid this pitfall, you need to use scores that are connected to specific actions or outcomes, such as triggering a follow-up email, sending a personalized offer, or assigning a sales rep.
Real-World Success Stories
A widely popular design and prototyping platform, InVision operates on a subscription-based freemium model and has amassed 7 million users. While the company successfully identified their Product Qualified Leads (PQLs), converting them to paid plans remained a challenge as most users opted for free or self-serve versions. Sales teams often encountered clients who were hesitant to commit to paid plans.
To address this, InVision collaborated with the marketing prediction company MadKudu. Using historical firmographic and behavioral data, MadKudu’s predictive model identified accounts that were likely to purchase a license from the entire PQL pool. This information was based on successful attribute combinations from past experiences.
InVision implemented a system that sent alerts to their sales team when an account showed potential for purchasing. This enabled their skilled sales representatives to engage with and upsell the most promising prospects.
As a top eSignature provider, DocuSign used to rely on traditional lead scoring methods, assigning letter rankings (A for the most valuable and D for the least valuable) to their leads. Sales representatives had difficulty distinguishing between A leads and often chose them randomly. DocuSign collaborated with Lattice to adopt a purchasing likelihood percentage using predictive analytics to enhance accuracy and offer better insights.
Initially, DocuSign focused on applying predictive scoring models to a single customer persona. They expanded the number of indicators from 4 to 10, leading to a 38 percent increase in Sales Qualified Leads (SQLs). Furthermore, they experienced a remarkable 22-fold return on investment within the first two months.
Prospecting customers can be a daunting and expensive endeavor for sales teams.
Nonetheless, innovative solutions like predictive scoring for leads offer ways to streamline this process and yield greater returns. By harnessing AI-driven lead scoring, sales teams can skillfully evaluate leads and implement well-informed strategies.
Conversation intelligence presents a variety of AI-powered features that can be employed to boost the lead-scoring process. Therefore, it’s high time to leave behind conventional methods and embrace smarter approaches to prospecting and selling.
Future Trends in Predictive Analytics for B2B Marketing
The world of B2B sales is constantly evolving, and 2023 is expected to bring about significant changes. One of the key drivers of this transformation is technology. Technology has already revolutionized the way we do business, and it continues to shape the B2B sales landscape. From CRM systems to automation tools, technology has enabled sales teams to streamline their processes and improve efficiency. But what does the future hold for B2B sales?
- Trend 1: Artificial Intelligence in B2B sales
Artificial Intelligence (AI) is poised to play a major role in the future of B2B sales. AI-powered tools and platforms are becoming increasingly sophisticated, enabling sales professionals to leverage data and insights in ways that were previously unimaginable. Predictive analytics, for example, can help sales teams identify and target the most promising leads, increasing their chances of closing deals.
AI can also automate repetitive tasks, freeing up salespeople to focus on building relationships and closing deals. Virtual assistants powered by AI can provide real-time information and support to sales teams, enhancing their productivity and efficiency. As AI continues to advance, B2B sales professionals who embrace this technology will have a significant advantage over their competitors.
- Trend 2: Personalization and customer experience
In the age of digital transformation, personalization has become a key differentiator for businesses. B2B buyers now expect personalized experiences tailored to their specific needs and preferences. In 2023, personalization will become even more important in B2B sales. Sales teams will need to leverage data to gain a deep understanding of their customers and provide personalized solutions that meet their unique requirements.
This will require a shift from a transactional approach to a relationship-driven approach. Sales professionals will need to invest in building strong relationships with their customers, understanding their pain points, and offering tailored solutions that address their specific challenges. By delivering exceptional customer experiences, B2B sales teams can differentiate themselves and build long-term relationships with their customers.
- Trend 3: Account-based marketing (ABM)
Account-based marketing (ABM) has been gaining traction in the B2B sales world, and it is set to become even more prominent in 2023. ABM is a strategic approach that focuses on targeting and engaging key accounts with personalized marketing campaigns. Rather than casting a wide net and hoping to catch some leads, ABM allows sales teams to focus their efforts on high-value accounts with the highest potential for conversion.
By aligning marketing and sales efforts, ABM enables teams to deliver personalized messages and content to key decision-makers within target accounts. This approach not only increases the chances of closing deals but also helps build stronger relationships with customers. In 2023, B2B sales professionals who adopt ABM strategies will be able to maximize their ROI and drive revenue growth.
- Trend 4: Sales enablement and automation
Sales enablement is all about empowering sales teams with the tools, resources, and information they need to be successful. In 2023, sales enablement will become increasingly important as B2B sales teams strive to stay ahead of the competition. Automation will play a key role in sales enablement, allowing sales teams to automate repetitive tasks and focus on high-value activities.
From email automation to automated lead scoring, sales professionals will be able to leverage technology to streamline their processes and improve efficiency. Sales enablement platforms will also become more sophisticated, providing sales teams with real-time insights and analytics that can inform their decision-making. By embracing sales enablement and automation, B2B sales professionals can work smarter, not harder, and achieve better results.
- Trend 5: Data-driven sales strategies
In the era of big data, sales teams can no longer afford to make decisions based on intuition or guesswork. Data-driven decision-making is becoming the norm in B2B sales, and this trend will continue to gain momentum in 2023. Sales professionals will need to leverage data and analytics to gain insights into customer behavior, identify trends, and make informed decisions.
Predictive analytics, for example, can help sales teams identify the most promising leads and prioritize their efforts accordingly. Data can also be used to personalize marketing messages and tailor sales pitches to individual customers. By harnessing the power of data, B2B sales professionals can gain a competitive edge and drive better results.
B2B marketing analytics involves analyzing data to understand and improve marketing strategies and initiatives in the business-to-business context. It helps companies measure the effectiveness of their campaigns, identify opportunities for optimization, and make data-driven decisions. By tracking metrics such as lead generation, conversion rates, and customer acquisition costs, marketers can gain insights into their target audience’s behavior and preferences.
B2B marketing analytics is vital for businesses as it provides data-driven insights to optimize marketing strategies. By analyzing customer behavior, market trends, and campaign performance, businesses can make informed decisions to drive growth and maximize ROI.
Effective B2B marketing analytics strategies are crucial to unlocking the full potential of businesses. By leveraging data-driven insights, companies can gain a deeper understanding of their target audience, optimize marketing campaigns, and maximize ROI.
This article offers valuable tips and strategies to help businesses make the most of their marketing analytics, covering topics such as setting clear objectives, selecting the right metrics, utilizing technology and automation, and fostering aculture of data-driven decision-making. With these strategies in place, B2B companies can unlock valuable insights and drive growth in their marketing efforts.