Today, I offer you a guest post from Brian Kardon, CMO at Lattice Engines. Their tools leverage Big Data to help those market & sell intelligently. In this post, he shares how Big Data uncovers key LeadGen insights. Specifically, how Lead Development Reps can close more business by doing less. Brian shows how to fix low lead conversion rates with insights from Big Data. At the end of this post, download our Big Data Source Checklist. Use this to make sure you are building a world-class data foundation.
The Folly of Persistence by Brian Kardon
Persistent sales people are praised, honored, and even given swanky “President’s Club” trips to the Caribbean. As Gordon Gekko (Michael Douglas) famously said to Bud Fox (Charlie Sheen) in the movie “Wall Street”: “This is the kid, calls me 59 days in a row, wants to be a player. There ought to be a picture of you in the dictionary under persistence, kid.”
The rep that calls a prospect 50 times and stalks him on LinkedIn is the very essence of a modern, successful sales person. Right? An analysis of millions of pieces of data challenges the near universal celebration of persistence. In fact, the data show that quitters, who know when to quit, win in the end.
There is a dearth of data-driven insight about the process of selling. We wanted to apply data science to one of the last bastions of folklore and anecdote – sales. Working with my colleagues Paolo Massimi, Ph.D., and Shashi Upadhyay, Ph.D., we looked at the performance of more than 1,000 sales reps over a thirty month period. The call logs we analyzed had more than four million entries: number of outbound calls, messages left, completed calls, scheduled meetings. We then looked at the actual deals that closed from those efforts. Our hypothesis was that persistence pays off. But the data told a completely different story.
- Sales reps are simply too persistent. There is a point of diminishing returns after which the conversion rate drops quite dramatically. Reps should call a prospect less than seven times. The effort beyond seven outbound calls, on average, does not correlate with better win rates.
- Sales reps spend too much time with accounts that will never close. 97% of effort is spent with prospects that will never close. Reps should stop calling a prospect after a certain point and focus on their uncalled accounts. If you ask a sales rep what their biggest time-waster is, they will likely say some variety of “administrative tasks” – entering data into the CRM, doing expense reports, booking travel. They hate that stuff! But the real time-waster is time spent with the wrong prospect: one that will never close.
- The best performing reps instinctively know when to quit. In our study, the reps having the highest quota achievement came closest to the “optimal” number of outbound calls. They knew when it was time to move on. Poor performing reps spent too much time trying to connect with accounts with low conversions. The long tail of effort (phone calls beyond the ninth) accounted for 50% of their time.
- The number of days between calls matters. Concentrated calls to a prospect are more effective than spreading calls out over more days. Some reps talk about a “drip call” strategy: they call once a week, or every 2 weeks. Our analysis shows that this is a poor strategy. The best call strategy is a concentrated one – focus calls for multiple days in a row. A fast pour beats a slow drip every time. Bud Fox got it right when he persistently called Gordon Gekko every day. Of course, data science would have advised Bud to stop calling after seven attempts, not fifty nine.
Most sales professionals face the “curse of abundance” every day. There are too many people to call. How do you prioritize? Ironically, reps can close more business by doing less. Sales reps should be “intelligently persistent” by knowing when to stop trying to connect with an account. Companies can help their sales teams by analyzing their own big data to find the right moment to quit. Data science can help figure this out.
For peak sales performance, the true winners are often quitters. To all you quitters out there, see you next year at “President’s Club.”
Thank you, Brian for this Big Data case study.
To get started with building your data foundation, download our Big Data Source Checklist.
To get started with building your data foundation, download our Big Data Source Checklist.
