We discussed lead generation before. After lead generation, it is necessary to determine priority of leads.
What is predictive lead scoring?
Predictive lead scoring is a novel approach to prioritizing customers. No sales rep wants to lose time with unqualified leads and these systems enable reps to focus on qualified leads.
There are a few ways to name this activity: predictive lead scoring, predictive scoring, customer behavior modeling. It is a subfield of predictive sales analytics.
Why is predictive lead scoring relevant now?
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 greatest sales tactics will not work on a customer who is not 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.
What are the benefits of predictive lead scoring?
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.
How does lead scoring work?
The most critical data for lead scoring is of course sales data. All other data are essentially indirect measures of interest and without sales data, no one can scientifically tell whether your leads will collect. So the first step is always to sell to a significant number of customers and then analyzing the data for patterns.
After such macro scale analyses, some obvious patterns became even more visible, for example the correlation between a lead’s position in the account and their likelihood to buy. These were used to build rules-based systems which historically 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. Many parameters such as location, size, interest, historical buying habits are also being analyzed . And this is only that company’s data.
There are 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?
A solution is to aggregate all the available data, hire data scientists, build a learning model, pilot and test it and reap the benefits in months after you started this project.
Thankfully, there’s an easier way! Work with a vendor that has done all of the above. Even better, most vendors combine anonymized data across millions of customers to truly understand customer behavior.
Who should be using predictive lead scoring?
Ideal companies have
- a repeatable sales process that already sold to hundreds of customers
- well managed CRM data including all sales rep interactions like both successful and failed sales
- not changed their target market or product recently
Unless you have high quality sales data for a few months covering hundreds of sales, it is difficult to build a good system. Modern machine learning algorithms that predictive lead scoring engines rely on are data hungry.
You should already be selling to your target customers. A company doing frequent pivots with its product may not have enough sales data for a good lead scoring system. Since system will work on existing sales and since existing sales are not representative of the current direction of the company, the prediction system will likely underperform.
What are the limitations of predictive scoring?
As we identified below, predictive scoring is not for all companies. Here are the current shortcomings and how you can overcome them:
- Predictive scoring systems are data hungry: Until you have a few hundred sales, your knowledge of the market, your sales force’s relationships in the market, your product quality are far more important than sales systems. Focus on the product while casting a wide net, selectively spending on marketing and hiring the best sales reps that have experience with emerging products
- Most predictive scoring systems are black-boxes: Your reps won’t know why a certain individual is a qualified lead while another is not. Prescriptive sales systems try to fix these problems by providing not only data on leads but also recommendations on how to approach the lead. We will be covering that category soon
- Your target market and product should be stable: If your target market changes, your sales data loses its value for analytics. However, if you know the critical junction after which your company decided to focus on a certain market, you could take data from that point on to have training data for the prescriptive model.
Identify top vendors in lead scoring
It is difficult to talk about sales or marketing software without mentioning Salesforce. Salesforce not only provides lead scoring service but also many customized solutions based on their CRM platform.
Thanks to their CRM system, they have an enormous database of contacts and sales. 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.
Salesforce is so powerful and common that it is not possible to launch a lead scoring solution that is not compatible with Salesforce. Insidesales.com is one such product.
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. Based on millions of sales, Neuralytics can identify not only which leads are more likely to convert but also when and how to reach out to those leads.
Understandably, not every company is happy to share its data to make a central sales database better. Because while that database serves you and increases your sales, it could also be making sales of your competition better. And in the long run, the benefits to your competition could be more than the benefits for you, reversing your gains in the market.
While this sounds like a far-fetched scenario, it is also clear that while smaller companies have almost nothing to lose by sharing insights, things are not so clear for large companies. For example, if you already own 60% of a market, you may be more interested in protecting your data then trying to gain insights from the data of the remaining 40% of the market.
This fuels growth of companies like Absolutdata that enable companies operationalize AI driven lead scoring systems without sharing their data with other companies.
Now that you know the benefits of lead scoring and how it works, you can focus on generating leads. Check out our comprehensive article about lead generation.
Finding the right predictive lead scoring partner is crucial for the success of your business and we can help: You may be wondering: “So what are the most important parameters?” 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.
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