3 Best Practices for Predictive Lead Scoring to Increase Sales Efficiency

Imagine your marketing efforts bring significant results, inundating your insurance agency’s sales team with new leads. The problem is that you lack sufficient sales representatives so the agents lose the potential customers calling them too late. Or another challenge: only a small percentage of these leads appear qualified, and your sales team has already wasted several days reaching out to them.

Predictive lead scoring could be an answer for insurance companies in this scenario. Using AI and machine learning, predictive lead scoring models evaluate and rank leads based on their quality, pinpointing the most motivated leads for conversion.

As with any other solution, the implementation of predictive lead scoring has its own nuances. So below we offer you 3 best practices for predictive lead scoring that will allow you to increase conversion rates.

1. Pay enough attention to data collection

For some reason, many businesses overfocus on technical points like choosing the algorithms or fine-tuning the parameters. But this doesn’t matter if your data is incorrect or outdated, as well as if your company has incomplete data, missing and repeated values. Since machines learn from data, the accuracy and performance of your lead scoring model totally depend on the quality (and amount) of insurance data that you feed the model with.

The logical question here is where to get this data. As a business owner, you might worry that your insurance agency doesn’t have enough data or the required data for predictive lead scoring. Your fears are understandable but usually ungrounded.

“Unless you’ve just started the business (and even in this case, a skilled ML team will be able to help you) or changed the target market or product, there is a high chance that your company is sitting on mountains of insurance data. You just need to know where to look for it.”

Volodymyr Mudryi, DS / ML Engineer @ Intelliarts

Learn more about predictive lead scoring

So what insurance data to collect for a predictive lead scoring project? Pay attention to:

• Demographic data: Age, geography, job title, income, etc. define a buyer persona and increase the chances of a successful sale. For example, “insurance near me” Google requests have grown by over 100% in the past two years.
• Firmographic data: As a subcategory of demographic data, this type is especially useful for B2B insurers to categorize organizations. Here we identify the type of company, its size, location, revenue, etc.
• Customer behavior data: Get advantage of web analytics and tracking tools to collect data related to user interaction history, such as page views, number of downloads, emails opened, CTA reactions, and others.
• Purchase history: A valuable source for the ML model includes data about past purchases in the insurance domain, including claims records, lead’s policy information, and payment history.

2. Invest time in data preparation

Data preparation and cleansing tasks are the way to improve data quality and, hence, the accuracy of your lead scoring results in the future. A team of professionals can help you with data preparation or you can do this in-house and prepare data upfront.

Here are some of the steps that insurance companies can undertake to make raw data suitable for further processing and analysis:

• Put together the insurance data that you have and randomize it to make sure the data is evenly distributed, and the data order doesn’t interfere with the learning process
• Clean the data to remove unwanted, incorrect, and irrelevant data and, thus, get rid of noise and ambiguity in the training dataset
• Handle missing values and redundant information Standardize data formats across different sources
• Standardize data formats across different sources
• With data labeling, identify raw data and add meaningful labels to provide the context to your data for the ML model to learn from it
• For large datasets, perform data aggregation to organize data in a more consumable and comprehensive way
• In contrast, if you work with insufficient data, enhance and augment it, for example, by using third-party data, to reduce overfitting. (This happens when the model memorizes the data it was trained on and becomes unable to generalize it.)
• Anonymize personal and other sensitive data, which makes up a significant part of your insurance dataset
• Visualize the data to see how it’s structured and explore the relationship between valuables Split your cleaned data into two datasets: training and testing ones
• Split your cleaned data into two datasets: training and testing ones

3. Implement continuous improvement of the model results

As the saying goes, there is always room for improvement. Your target audience, market conditions, the industry, and model features are prone to changes. The historical data you fed the model with can quickly become irrelevant, affecting the performance. This could cause model staleness when the data is outdated and/or the model doesn’t satisfy the business requirements. Another possible issue includes data drifts — unexpected changes to data structures and infrastructure.

To avoid all this, consider implementing continuous training after you put the predictive lead scoring model into production. This means regularly monitoring the model results and updating/retraining the predictive model as the new data arrives.

Some metrics like lead conversion and purchase rates, your insurance company could track on its own. Also, enlist the support of the ML team to monitor the ML metrics to bring more efficient results. For instance, they could set up dashboards for real-time performance monitoring.

In case you want to know more about predictive lead scoring and explore its real-life examples, Intelliarts has published the white paper “Elevating Your Insurance: Top Best Practices for Predictive Lead Scoring”. In this research work, we describe 7 more best practices for implementing predictive lead scoring shared by the Intelliarts ML experts. Feel free to download it using the link above.

Wrap up

Important for your lead generation strategy, lead scoring implies sorting through leads to supply a consistent flow of quality leads to your sales team. Predictive lead scoring automates this process without compromising accuracy, ensuring that your agents don’t invest time in low-performing leads. Instead, they focus on what truly matters — reaching out to potential prospects and customers and delivering value.

In case you’re thinking about improving your sales efficiency and want to implement predictive lead scoring, don’t hesitate to contact Intelliarts. We’ll be happy to introduce machine learning to your insurance value chain.