Sales Performance is Becoming More Scientific

Stop leaving your sales forecasts and revenue projections to chance.?

I once worked for a very recognizable and revered consumer direct mortgage brand.?

During that era, we had an algorithm we used to evaluate, in 90 days, whether a salesperson could “make it” or not.?

The model was effective.?

We quickly cut underperformers and reclaimed those resources to invest in the next batch of promising recruits. Our data indicated that most of these folks were likely to remain underperforming.

Think professional sports and salary caps.

However, the only data we had to consider back then was how many leads we gave them and what percentage of them turned into closed deals.

The “art of the deal” between the lead receipt and the closed deal was a mystery.

This is the story of hundreds of sales organizations, thousands of sales leaders, and millions of sales agents. From this experience, sales performance has traditionally been considered more of an art form, gift, or talent.?

Accordingly, executive and sales leadership assume (attribute) most of their opportunity to improve sales production and revenue growth to effective hiring and training programs – more hiring than even training.?

Remember, we assume it’s a gift.

Having been on the inside of several considerable sales operations efforts to leverage the mountain of data that is now spewing out of a robust sales tech stack, sales performance is swinging rapidly toward the scientific.

Here is a brief list of how and where data is being used to predictably improve sales performance down to the individual sales agent level.

1. Everything starts with and is contingent on capturing and managing sales activity data. For any of this to work, you must implement a robust CRM system to track and manage leads, contacts, and sales activity.
2. Set clear sales goals and targets based on individual past performance and predictive aptitude. Think lead scoring for sales agents. Grade individuals and use data to determine a reasonable production expectation for that sales grade.
3. Use data and analytics to identify patterns and trends in sales performance. Use AI/ML technologies to leverage these insights and turn them into strategies. No more using intuition or personal experience to lock in decisions with hard-coded rules. Use real-time data and mathematically validated models to make dynamic decisions in lead and sales management systems.
4. Match leads and sales agents perfectly. Use lead scoring and sales grading to design data-driven lead distribution strategies. Match a lead to a sales agent that will increase its probability of closing. Match a lead almost guaranteed to be close to a lower-grade salesperson as a training opportunity or confidence builder. Match a lead that is unlikely to close to a telemarketing team or lead nurturing program. The possibilities are limitless.
5. Monitor and measure the performance of the sales team and your models to detect changes and the need to retune. Changes in lead generation, lead buying, and sales processes can cause your models to become less predictive.
6. Identify sales agents that could benefit from additional training and coaching. Using the same data fueling your sales performance models, you can pinpoint the exact skills and knowledge that will cause the individual sales agent to perform and grade better.
7. Foster a positive and supportive sales culture that attracts and retains top sales talent. Invest in systems and processes that enable sales agents to scale their engagement with leads and prospects. Give them the data-driven, intelligent tools and resources to help them spend more time with the right leads – leads types they’ve historically been successful with and are predicted to be close to closing.

The perfect combination of increased availability of data, cheap and highly efficient data management and processing power, and rapidly advancing data science makes it easier to make sales performance more scientific.