Machine Learning Lead Scoring

Sales should never be a passive process. Although it boils down to the customer's decision to complete a purchase, sales teams can effectively develop strategies to push a lead further down the sales funnel. Leveraging data and predictive analysis can help ensure the success of your strategy.

What Is Lead Scoring?

Lead scoring aims to help marketing and sales teams optimize their processes and efficiently allocate prime resources (time, attention, and money) to the leads that are most likely to result in a sale.

Traditional Lead Scoring

Traditional Lead scoring is the process of ranking leads to determine which are most likely to convert into paying customers and, thus, should be prioritized.

Lead scoring systems assign values to a lead's actions in the sales funnel. If companies can identify which leads are hot prospects, their sales team can focus on positive leads, improving their ability to complete more transactions and conversions.

Traditional lead scoring is a process of determining the quality of a lead - based on specific personal and professional criteria. Then, manually, a score is assigned to them (A, B, C, ... or similar). This score is then used to determine whether or not that lead is qualified to make a purchase.

But traditional lead scoring is not based on any statistics; it is a time-consuming and inefficient process.

Machine Learning Approach to Lead Scoring

One of the most effective ways to perform lead scoring is through machine learning (ML), which uses predictive modeling algorithms to analyze customer data from previous interactions and forecast future customer journeys and outcomes. Read on to learn more about its benefits and how you can best utilize it in your organization.

Lead Scoring Benefits

Machine Learning lead scoring is now favored in the marketing automation industry as it improves overall conversion and helps teams align acquisition and sales objectives. Its benefits include:

Quick and Comprehensive Method

Machine learning lead scoring is quicker compared to manual customer data gathering and analysis. Sales teams can instantly access their data to plan their approach to a promising lead. Furthermore, it equips sales personnel with comprehensive information relevant to the customer's journey. This allows them to prepare for every opportunity to make a sale, as they can customize their approach and offers based on what they think could best engage the customer.

Less Prone to Errors

As mentioned above, predictive or ML lead scoring is based on data gathered from customer behavior, transaction history, and demographics using the company's customer relationship system. When you use this data-driven method, lead scoring becomes more accurate, with little to no error.

Focused Campaigns

Machine Learning lead scoring allows a company's marketing team to create and run more focused ad campaigns and promotions based on their target customers' common behaviors, demographics, and interests. With more targeted campaigns, you can maximize your marketing and advertising budgets. 

Datasets Needed in Machine Learning Lead Scoring

Creating a lead scoring model using machine learning requires an appropriate dataset. Here are some examples of what type of data should be available. The more, the better!

Customer Profile Data

This dataset is used to measure customer and prospect attributes. These include demographic information such as age, job title, industry, location, and persona segment.

Account Profile Data

These are the essential info about the customer, including the size of their company, the industry it is in, and the type of account (personal or business).

Customer Intent Data

These cover interests and activities that customers and leads reveal through their customer journeys when purchasing or viewing specific products and services. This dataset also measures their willingness to accept marketing and promotion materials and communication.

Customer Engagement Data

This data captures customers' activities when interacting with the company's website and other collateral. Customer engagement data includes:

• Email marketing views.
• Click-throughs.
• Click-to-open (CTO) rates.
• Click-to-reply (CTR) rates.
• Website activities.
• Form and survey responses.
• Product page views and downloads.
• Free or paid trial activations.
• Online event participation or attendance.

Customer Purchase Data

This includes all transactions and purchase activities of existing customers. Customer purchase data also details the amount they spent on a company's services or products, as well as the time and frequency of their purchases. 

Marketing and Sales Performance Data

This dataset provides information on the effectiveness of sales and marketing campaigns. 

This data also includes how and where prospects were sourced, whether they're from organic searches, paid advertising, direct advertising, referrals, social media marketing, or email marketing. Getting this data helps marketing teams identify which channels rake in more quality leads.

Marketing and sales performance data usually include the following:

• The lead's name
• Demographic data (age, gender, job title, industry)
• Tags, comments, and posts made
• Contact details (phone number, email address, etc.)
• Lead source (the channel through which the lead discovered the product or service)
• Time spent on the website
• Number of clicks, emails sent, phone calls, trials, and demos

Machine Learning Scoring Usage

Having the lead scoring attributes listed above may or may not help convert a lead, but only by gathering a comprehensive list of customer and engagement data will a company see what actions lead to conversion.

Machine learning also identifies the activities and interactions that resulted in a conversion. When a company successfully pinpoints trends and common behaviors, it can utilize lead scoring to identify "hot leads," which are targets or leads that will potentially convert.

By working out these hot leads, marketing and sales teams can focus their strategies on prospects who are more likely to become customers, repeat buyers, and product ambassadors.

Wondering how this is possible on the backend? A no-code machine learning platform Graphite, for instance, uses binary machine learning classification. This means that they use two values in their algorithm: 1 (yes/converted) and 0 (no/not converted).

Once a dataset is uploaded, teams can create no-code ML models that include 

• features (attributes or engagements) and 
• binary target column (value for predictions and forecasts, converted yes/no).

After building the model, users can "ask" the Graphite model for new leads that are more likely to convert and which activities or engagements can potentially convert them. This is done by running the ML model through a scenario, with which it will use all the data available to make a prediction.

Machine Learning Scoring Model - Easy Way

When done manually, gathering data and scoring or categorizing lead attributes and activities can be a tremendously tedious task. 

Fortunately, with the use of ML algorithms and software like Graphite, a company can maximize comprehensive customer data to create a machine learning lead scoring model that generates accurate results. The best part is that since Graphite is a no-code machine learning platform, teams can train, build and deploy models without writing a single line of code!

Whatever your target is, whether it is to retain, yield, or convert, building a lead scoring model with machine learning allows you to efficiently analyze your target customers' lifestyle, interests, and buying behavior. You can identify quality leads through this, allowing you to focus your marketing campaigns on the right people - with less wasted time, energy, and money.

Knowing your leads better can convert more of them, resulting in more sales and a higher return on investment (ROI).

Further Reading

- Predictive Lead Scoring with No-Code Machine Learning

- Use Case: Predict Lead Conversion

- Popular Applications of Machine Learning in Business

- How to Create a Leads Scoring Model: Definitive Guide