We live in the age of data; and yet, in the world of sales, relying on trusted instincts, luck, and self-proven professional strategies are still successful strategies for many “salty dog” sales pros.
And, for good reason. They get results.
Up until now, data (even big data) has not really informed the sales process until after the fact. And even then, those insights have been fairly rudimentary in their usefulness -- for reasons we’ll explore in a bit.
Shift now to the world of the data scientist.
Data scientists rely on, well, data. There is no room or reason for instincts, luck, or gut-informed, trial-and-error proven strategies. The data is the source of truth, and, from that, actions can be determined.
While these two worlds have been mostly separate; in the data era, the world of sales and data scientists are intersecting.
Why data scientists need the salty dog sales pro
Data is now capable of being made useable and useful, and it can and does inform the sales process -- not only with historic context, but predictive “next-best-step” insights. Machine learning and artificial intelligence (AI) are created with data and are already available to sales professionals as a revenue intelligence toolset.
So, the question of whether sales is a science or an art has finally been answered.
And the salty dog sales pro and the data scientist need each other. Here’s why.
Where sales insights come from
Sales insights originate with sales pros. Data scientists rely on the sales pro’s interactions with contacts and their activities that then get converted into data in the CRM software. This is the source of data that data scientists analyze to derive sales insights.
Now, let’s say you want to improve sales team performance. You need data on current sales activities to inform this.
Sales only know what they track in the CRM. (“If it’s not in salesforce, it didn’t happen.”)
Data scientists only know what sales tracks. They are completely dependent on sales for the quality of the data they are analyzing and basing recommendations on.
There’s just one problem with that.
Data is only as good as its source
Sales pros are notorious for not entering all their contact and activity data in the CRM. In fact, only 40 percent of it ever gets entered.
Data is only as good as its source, and when the CRM is not reliable, it loses its value as a “single source of truth.”
Sales pros know the CRM is not reliable because they are the ones not entering the data. So, they ignore it and keep on doing what they have always done by relying on instinctive methods to win deals.
Organizations that want to get an edge on their competitors through a high performing sales org driven by data come to a crossroads here.
If the data isn’t accurate, how can they get trustworthy insights that can improve sales performance?
One option is to manage sales pros to be more disciplined at entering all contacts and activities into the CRM. But, as Dr. Phil would say, “How’s that workin’ for ya?” Not too well, right?
Salty dog sales pros are not going to get on board the data entry train.
The good news -- and here’s where data scientists rejoice -- is that with AI, they don’t have to.
AI empowers sales and data scientists
Organizations can now deploy AI tools that automatically capture all contact and activity data and map it to the right opportunity accounts in the CRM.
Sales is no longer burdened with data entry, and data scientists know that the data they’re analyzing is accurate and reliable. They can formulate better algorithms to solve problems for sales pros.
With accurate data, data scientists and sales pros can enter into a true partnership where each informs a continual feedback loop that works to solve real problems and improve revenue.
Data scientists still need salty dog sales pros in the world of AI. Remember, it’s the sales pros' activities that inform the data that gets captured. Open dialogue about what needs to be tracked and measured and the issues that need to be solved for should become standard practice for both parties.
Questions data scientists and sales pros can solve together when they have accurate CRM data:
- What activities need to take place, and when, to win/close larger deals?
- Who needs to be involved in the buying group, and how often do they need to be contacted to win/close deals?
- What activities correlate to pipeline?
- Where are reps not selling higher into the buying group?
- What activities do high performing reps all have in common?
These are just a few examples of questions that organizations have solved when they use AI to generate accurate, continually updated CRM data.
Sales is a science and an art. By leveraging AI revenue intelligence technology, organizations can increase their selling capacity and create high performing sales organizations that operate off of solid, predictive data.
The data scientists need the salty dog sales pro and the sales pro needs the data scientist to meet revenue goals in today’s digital era.