Learn how AI is changing Predictive Sales (Lead Scoring) in <5 minutes

Learn how AI is changing Predictive Sales (Lead Scoring) in <5 minutes

We discussed lead generation before. After lead generation, it is necessary to determine priority of leads.

Poor lead prioritization can decimate companies. Sales targets get missed, teams get demoralized. Most importantly, sales personnel fail to learn from their experiences. They can’t gain generalizable experiences as they encounter leads with wildly varying intent. Even the great sales tactics will not work on a customer who is willing to buy. A sales person who has seen only customers who are not willing to buy, can lose hope on even the best sales tactics and processes.

Learn the latest approaches to lead scoring

Historically, rules-based systems were prevalent in lead scoring. The lead’s location, company and position within the company were used to determine lead quality. Later, more dynamic criteria like behavior of the lead on the company’s website started to get analyzed. There are still many parameters to analyze such as location, size, interest, historical buying habits. So what are the most important parameters?

Luckily, this is a topic of research interest. In the article, “On Machine Learning towards Predictive Sales Pipeline Analytics, authors approach this with profile-specific two-dimensional Hawkes processes model. They use geography, deal size, sector, industry, product as input data. Then, with the Seller-pipeline interaction modeling and profile-specific Hawkes process, they calculate the possibility of conversion of an opportunity to an actual sale.

However, there’s limits to what one company can do with its own data. Data aggregation can improve the quality of lead scoring. Online behavior of the lead on other websites can reveal whether she is really interested in buying or just trying to learn about a topic. So how do you analyze all relevant data that you have access to, to create accurate lead scores?

Salesforce is one of the leading lead scoring companies. They not only provide lead scoring service but with their CRM systems, they offer many customized business solutions. Thanks to their CRM system, they have an enormous data. The product, Sales Cloud Einstein, analyzes all fields attached to the Lead object. Then it tries different predictive models like Logistic Regression, Random Forests, and Naive Bayes. By finding the best model, it predicts the best leads. According to changes in lead’s information, it updates itself automatically to keep the predictions up to date. Additionally, Einstein Opportunity Insights, which is customized for the sales team using it, automatically match their selling process. With machine learning, natural language processing, statistical analysis, it suggests the best follow ups, meeting times, key moments automatically. Moreover, Einstein Account Insight analyzes thousands of articles each they, identify major changes in the market and companies.

However, the most critical data for lead scoring is of course sales data. All other data are essentially indirect measures of interest. So how can you bring together all sales data on a lead to estimate her score? One of the most advanced lead scoring systems that leverage other companies’ sales data is InsideSales.com’s Neuralytics. Neuralytics combines data from its customers to build one of the largest datasets on sales.

Identify benefits of lead scoring so you can convince stakeholders

Now that you know about the relevant approaches, it is important to learn benefits of lead scoring so you can convince sales leadership to invest in these systems.

Ultimate benefit of lead scoring is increased sales. With prioritized leads, sales personnel will be able to spend more time on leads that are likely to convert which will result in increased sales. Working with fewer customers, yet closing more sales is a sales rep’s dream come true. This will result in increased morale for your sales team, leading to improved retention of sales reps. And these predictive systems improve with more data. As more data is accumulated in the system, prediction quality will increase, further improving sales.

 

Now that you know the benefits of lead scoring and how it works, you can find out more vendors on predictive sales by visiting our comprehensive list of predictive sales vendors.

If you have questions on how to generate leads, check out our comprehensive article about lead generation.

Leave a Reply

Your email address will not be published. Required fields are marked *