Sales managers are becoming more reliant on data to make better decisions. But what are the common traits of the data-driven sales manager?
Working at a company that specializes in helping sales managers hit (and, in many cases, exceed) their targets, I regularly discuss what tools they use. There are the basic ones like CRM, e-mail tracking software and LinkedIn. All are great tools and should be used daily, however only give you half the story: negotiation skills, positioning and previous interactions.
The other half of the story is the way that a sales manager leads and makes decisions. The best sales managers use data to influence their actions before they make them. The key traits of a data-driven sales manager are as follows:
Champions diversity
Pull data from different sources, such as transactional data from ERP systems and contact and pipeline data from the CRM. Add that together with interactions from the website, email open rates and social engagement and that gives you a powerful picture of every prospect and customer.
Example:
Your sales team are struggling to cross-sell additional products. By delving into a customer’s purchasing history you find that they buy product A from you but their purchases of product B have been dwindling for a while. This suggests that they could be buying from your competitor, giving the data-driven sales manager a clear course of action for a counter-campaign targeting all the companies who have stopped buying product B.
Quality-focused
A data-driven sales manager will base their decisions on the data they have access to. This means that their decisions are only as good as the data they have.
As a result, the top sales managers invest time, effort and resources into making sure data is of a quality that they can trust. This reduces uncertainty and makes it easier to understand patterns.
“Without quality data, you are blind and deaf in the middle of a freeway.”
Geoffrey Moore,
Management Consultant and Theorist
Forward-thinker
Sales managers who use data will acquire even more data every time they execute a decision.
Therefore, the data-driven are constantly re-evaluating and refining their tactics along the way.
This creates a fluid approach where they’re quicker than others to pull the plug whenever evidence suggests that a decision is wrong.
It also gives them scope to introduce new technologies and processes to help support their actions.
Example:
A sales manager may know that he or she has already captured data in their business system but is unable to extract it in a format that’s useful. Top sales managers will, therefore, throw away the spreadsheet and invest in a Business Intelligence solution to do the analysis for them, meaning their salespeople can get to digging for opportunities in an otherwise impenetrable goldmine of data.
Asks the right questions
It’s pointless looking for answers if you’re not asking the right questions.
A data-driven sales manager sits, pen in hand and finger on chin, philosophically asking the right questions to help improve their team’s performance and understand their current situation.
Examples:
How many opportunities were closed or won compared to last year? How many opportunities were closed compared to the lost ones? Which salespeople have the highest close rate? Which salesperson is most consistent at generating opportunities? Do specific lead sources provide shorter sales cycles than others? How many cold calls do we have to make to generate a customer?
All of these questions and more are easily answered through data analysis.
Thrives in uncertainty
The best data-driven sales managers know what they don’t know. Even the most robust and extensive data collection process is not an all-knowing oracle – there will always be uncertainty in the sales world.
Yes, they’ll be given ‘predictable’ data, but it’s how comfortable they are with the uncertainty that truly drives them.
Example:
A data-driven sales organization may pump their sales pipeline full of various data filters and risk factors to gain a comprehensive ‘probability’ of an opportunity closing. Understanding common risk factors like close dates, the likelihood of closing or recent momentum can help data-driven sales managers better determine the outcome of an opportunity. However, these values aren’t 100% accurate but they are ok with this as they are critical components of sales.
Finally, and most importantly…
Learns from previous mistakes
“We cannot solve our problems with the same thinking we used to create them”
- Albert Einstein
This mantra should be adopted by every data-driven sales manager.
Data gives sales managers a second chance to rectify previous mistakes by highlighting a specific area that caused the error.
Learning from your previous mistakes will reduce the number made in the future, thus making you better at what you do and, as a result, the rest of the team more profitable.
Example:
Advanced sales analysis of your sales funnel highlights specific areas of weaknesses among salespeople.
For instance, a salesperson with a high conversion rate from stage 1 to stage 2 but an abruptly low rate from stage 2 to stage 3 might have challenges with the nuances of that stage and need additional guidance.
The sales manager who brings this actionable information to a sales training session with that salesperson will find out that that person is more receptive to any constructive criticism, given that it’s supported by data.
