Lead scoring is a common practice for sales teams today. Almost every CRM has a way to look for signals like pages viewed, content downloaded and questions asked to determine how likely it is for a lead to turn into a sale. This is great information for a sales rep to have on hand before reaching out to a prospect. It helps a rep know where to start in the sales pitch, which prospects to reach out to first and where to look for low hanging fruit. But what if the wrong sales rep grabs that lead?
Say you sell marketing automation and a 65 year old, female CEO of a national craft store has downloaded all of your guides, signed up for your newsletter, been to your site daily for a month and hit every signal to put her at the top of your sales team’s prospect list. Then, the sales rep that reaches out to her is a 22 year old, male college graduate whose weekend hobby is World of Warcraft. While the sales rep will very likely know all the features of your marketing automation product, he will not likely have a natural connection about how to integrate the product with the needs of a national craft store owner just learning about marketing automation.
Intuition and human interaction are still large parts of the sales process, especially for large ticket items that require a major investment. Lead scoring analyzes a prospect when they come in by showing how ready they are to buy. However, it doesn’t show who the best sales person is to go after the deal. The most effective sales process not only identifies the most likely person and the most likely time for a conversion, it also connects a prospect to the right sales person.
Machine Learning Takes Lead Scoring to a New Level
Machine learning matches prospects with the right sales reps to take lead scoring to a new level. By taking into account prior interactions, machine learning determines who the best available sales rep is to handle the specific lead in question, therefore improving conversion rates and revenue.
For example, we worked with a company that has a large inside sales team that handles thousands of incoming calls each month. Traditionally, these calls were routed based on first in first out. When a sales rep finished a call, they would pick up the next one in the queue.
By applying advanced machine learning techniques, we looked at a number of characteristics about individual customers and individual sales reps to find best matches. It turns out that the reps who had the highest conversion rate were ranked as the best match for about 6-8% of the calls. However, the reps who had the lowest overall conversion rate were ranked as the best match for about 2-3% of the calls. These “low closers” have tremendous value and can make the company a lot of money when they are matched with the calls specific to their personalities and skill set. It’s not optimal to assign all the calls to the “best reps.”
Machine learning answers the question of, “Who is best suited to take this lead and close the business?” It goes beyond defining “best reps” by close rates to encompass personality traits and skill sets, which results in:
- Higher close rates
- More efficient sales process
- More confident, happy sales reps
- Deeper customer relationships
It’s human nature to want to connect and people are still the ones making purchasing decisions. Try machine learning to help your sales reps connect naturally so prospects feel comfortable with a conversation and excited about becoming customers.