Mastering the art of sales requires effectively sifting through a multitude of leads to determine the prospects worth pursuing conversion, which remains a common challenge for businesses.
It is also why an increasing number of successful companies today are investing heavily in understanding their customers, products, and services by leveraging robust data-driven models and insights.
This is where predictive lead-scoring models come in!
Predictive lead scoring is an approach that can allow you to streamline the end-to-end process of lead conversion and focus on the high-intent prospects with more chances of conversion.
In this blog, we will further explore the concept of predictive lead scoring, its importance and benefits, and how it can help you manage as well as prioritize your lead generation strategy and drive more sales.
What is predictive lead scoring?
Lead scoring is an important marketing task that helps you target the right prospects to improve the productivity and efficiency of marketing and sales teams.
Predictive lead scoring takes this concept of the traditional lead-scoring approach a step further. It applies machine learning algorithms to identify and evaluate the key behaviours of prospects and accurately rank them in a way that distinguishes those who are more likely to convert.
Put simply, predictive B2B lead scoring is an approach that is predominantly used in sales and marketing to help direct their efforts on your High-Intent leads first. These are the users who have the potential to convert into leads in a stipulated amount of time.
The process of predictive lead scoring is designed in a way where each lead takes a specific numerical score or ranking. This is based on aspects such as their characteristics, behaviour, and interactions with any of the customer touchpoints, including a website, emails, or social media platforms.
Some of the main attributes of predictive lead scoring are-
- Profile Data – Data points such as demographic details, including name, age, location, and customer address, come under the profile data.
- Engagement Data – This includes aspects such as customers’ online preferences, website activities such as web visits and downloads, or email activities such as CTO (click to open) and CTR (click-through rate).
- Purchase Data – Customer purchase history information includes product purchases, frequency of purchases, mode of payment, etc.
Once these key attributes are identified, the predictive lead scoring approach uses ML algorithms to create a powerful model that can accurately score users based on the above data points.
The model evaluates the relationship between the prospects’ attributes and their optimal profile and qualifies them depending on their propensity to achieve the desired behaviour.
How does predictive lead scoring work?
Predictive lead scoring works by leveraging machine learning algorithms and automation capabilities to identify the ideal customer types in a quick and unbiased way.
It then uses historical data to identify various attributes of the qualified leads. The process helps to ultimately find correlations and predict the prospects or leads with high chances of conversion.
This kind of deeper analysis results in a robust lead-scoring system that helps marketing and sales teams improve their response time and lead prioritization strategy.
Difference between traditional and predictive lead scoring
ince not all leads are equal, you need to identify the leads that are ready to convert instead of those that need more nurturing by adopting a lead scoring model.
There are mainly two types of lead scoring methods as described below-
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Traditional lead scoring |
Predictive lead scoring |
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| Traditional lead scoring is the process of creating a lead scoring point system manually for a prospect or customer to be able to qualify leads that should be passed to your sales or marketing team. In the traditional lead scoring model, predetermined criteria assign a score value to each lead. This criterion includes explicit as well as implicit data. Explicit data here focuses primarily on the data the prospect provides. For instance, industry, company, title, and budget. In contrast, implicit data reflects mainly on the prospect’s behaviors, such as click-through rates and web visits. | Unlike traditional lead scoring, the predictive lead scoring method does not require you to assign values to each criterion. The model uses an algorithm instead to determine the quality of the lead and the probability of whether that prospect is ready to make a purchase. What is important to keep in mind is that predictive scores make sense only when you have enough data about how prospects interact with your marketing or sales efforts before making a purchase. |
 You first identify a numeric value for each criterion |
 The predictive lead scoring model uses an ML algorithm that incorporates behavioral and historical data from the CRM. |
How it works |
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 The combined numeric value here determines the lead score of the prospects. |
 The model also takes into account the big data to build the ideal lead profile. |
 If the score meets a certain threshold as decided by sales/marketing, the lead moves to the sales team for further follow-up. |
 Once done, the incoming leads are scored against this ideal lead profile to determine the leads most likely to convert and those needing more time. |
 If the score does not meet the threshold, the lead continues with further nurturing (based on their interest) until the lead score meets the minimum threshold. |
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How is the predictive lead scoring model built?
The process of building a predictive or advanced lead-scoring model can be broken down as below –
- The process starts with identifying data that considers various variables to suggest a lead’s or prospect’s probability to convert, including demographic data, industry, job title, previous purchase behaviour, etc., and other scoring attributes generated by the existing customers and prospects.
- The next step is to identify your target metrics for optimization, such as conversion, yield, retention, or service quality.
- The last step is to train the lead scoring model based on the identified set of key attributes to help you capture various aspects of the customer’s lifecycle.
The predictive lead scoring model will then evaluate the relationship between different attributes associated with customers/prospects and the identified behaviour. This is followed by scoring them based on the propensity to achieve the respective behaviour.
Data sources used here for predictive modelling include CRM and analytics data (e.g., Google Analytics 360). These data sources include both users’ behaviour and journey on the website/app, such as the different channels (direct, paid, organic search, social media, referral, etc.) they have come from, page views, hits, sessions, user device and custom dimensions used for storing specific data.
Predictive Lead Scoring Example – Tatvic’s Propensity Model
Lead prioritization and scoring are a few of the critical aspects of managing sales. Poor lead prioritization can have severe consequences for businesses, including missed sales targets, demoralized teams, and more.
The predictive lead scoring methodology not only helps teams manage the above consequences effectively but also provides useful experiences to sales personnel as they encounter every single lead with varying intent.
For instance, a salesperson who has encountered only users unwilling to buy can quickly lose hope despite using the best sales tactics and processes.
Benefits of predictive lead scoring
Saves a great deal of time
An automated predictive lead scoring ML model or system allows your marketing and sales teams to save a great deal of time as they no longer have to work on getting potential customers.
Data-driven approach to lead prioritization
In the traditional lead scoring methodology, the lead scoring factors to be evaluated are primarily based on subjective aspects such as human assumptions and biases displayed by marketing and sales departments. Whereas the advantage of the predictive lead scoring approach is that it is based on accurate data points to make predictions.
Better lead targeting
Predictive lead scoring also helps you identify leads that are most likely to convert. This allows you to focus your resources on the ones with the highest conversion potential. Thus, you can improve your lead conversion rates significantly and increase your sales by targeting your efforts.
Aligns sales and marketing
Predictive Lead scoring is an excellent approach that offers sales and marketing teams a common platform, allowing them to work together. Both departments can work collaboratively to offer their perspectives and create a mutually beneficial system that will work in favor of both the company and its customers.
Accurate predictions
Predictive lead scoring systems work on the data collected from different sources, thus enabling the analysis to go deeper. The approach allows you to create a richer and more accurate buyer profile.
Because of a more scientific, data-centric approach and the use of various artificial intelligence, machine learning and automation, the predictive lead scoring model reduces the chances of subjective errors due to different human biases or judgments.
Better return on investment (ROI)
Another benefit of predictive lead scoring methods is improved ROI because the methodology calculates the score for all leads using machine learning algorithms. This allows you to identify as well as maintain your focus and efforts on the customers or prospects who are more likely to buy or are ready for sales to help you maximize your overall rate of return.
Competitive advantage
Using a predictive lead scoring approach, you can ensure a distinct competitive advantage for your business as it allows you to identify and convert high-potential leads much faster than your competitors. This kind of competitive edge helps you further grow your market share, revenue, sales numbers, and overall profitability.
Limitations of predictive lead scoring
While a predictive lead scoring approach has several benefits, it also has some limitations. Some of these are listed below-
Needs strong technical skills
Successfully implementing a predictive lead scoring model from the start requires a strong technical understanding and skill set in Data Science and Machine Learning.
The target market and product need to be stable
In case the target market changes, the respective sales or marketing data loses its complete value for analytics. However, if you can identify the point after which your business decided to focus on a specific niche market, you could start taking data from that point on to have the relevant training data for the prescriptive model.
Requires a large amount of data
Predicting lead scoring with greater accuracy requires access to massive amounts of customer data to train the machine learning model and identify customer behaviour patterns.
Model accuracy
Model accuracy refers to the degree of correctness in predicting the outcomes of a predictive lead-scoring model. It is crucial as it ensures the reliable identification of high-intent leads, leading to improved sales targeting and higher conversion rates.
The Bottomline
Lead scoring is one of the most important processes for any business looking to enhance or maximize lead quality and minimize wasted sales or marketing efforts.
The predictive lead scoring model is an advanced version of traditional lead scoring that works as a powerful data science model. Based on customer behavior profiles and engagement patterns, it scores the contacts or leads on their likelihood to convert.
In this post, we have discussed how the predictive lead scoring model generates numerical scores for all the sales leads based on profile, behavior, and other similar data. This allows you to target customer segments more effectively and better align your sales and marketing strategies.
