How to get your sales team to embrace data analytics

Buying shiny (and expensive) new tools won’t do much good if no one trusts them, says McKinsey’s Sebastian Kerkhoff.

by Sebastian Kerkhoff
Last Updated: 10 Mar 2020
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Down to business

It’s been nearly 15 years since “big data” broke into the business lexicon. But only recently has the B2B sales world started to appreciate the scale of the benefits data-driven sales can deliver, from prioritising leads to enhancing existing relationships with customers, by offering the right product at the right time for the optimal price.

Analytics isn’t likely to identify a whole new way to do business; rather, it will provide insights and opportunities to sell more and do so more efficiently. Here are five critical lessons to ensure big data serves your business’s needs:

1. Focus on clear business objectives—and ignore shiny objects

We’ve seen it time and again: too many sales organisations start their analytics implementation by asking what tools they’re going to use. Instead, companies need to keep their eye on the fundamentals of the business and what problems stand in the way. 

2. Help your sales team “trust the data”

Overcoming sales-team scepticism requires a dedicated approach to building trust. We have found four elements to be important to success:

-- Create transparency. By providing transparency into how the algorithm is built and how insights are derived, companies are much more likely to persuade salespeople to trust the analytics 

-- Involve your salespeople. The best analytics teams work with sales reps as partners and evaluate solutions, such as ways to improve their relationships with customers, from their point of view

-- Start simple. Even the simplest analytics programmes can uncover insights, such as underlying inefficiencies in market structures across suppliers, distributors, and customers. Start simply, and ramp up on the back of small wins (improved funnel conversion, for example)

-- Show the value. Sales reps ultimately want to sell more, so be clear about how analytics can help them do that. The best tools can give salespeople and their managers a window, for example, showing where there are performance gaps, and then identify specific opportunities to close them

3. Make it easy to use

The best teams use design thinking to develop tools that put sales-rep experience at the centre of the process. That means developing tools that are simple to use, delivering information that’s easy to understand, and providing insights or recommendations that are easy to act on. 

4. Start with the data that are easy to get

Combining data to create a perfect data set can be frustrating. Call it ruthless pragmatism, the 80 per cent solution, or common sense, but experience has shown us that successful programmes start with the data that are easily accessible in one system or in systems that are already communicating well with each other. Now is not the time to seek out third-party information and invest the time necessary to negotiate access and merge the feeds.

5. Build a team mind-set

Building a successful analytics programme often means removing long-standing silos of data and analysis. For example, we typically see that companies can overhaul their sales-pipeline approach only after breaking down cross-functional barriers between sales, marketing, and their product teams. To get there, a particularly effective technique is to launch a series of test-and-learn pilots to identify and target new customers, accelerate pipeline growth, and improve salesperson and channel conversion rates for a specific product group.

B2B companies have been slow to embrace all that big data has to offer, often because they’re unclear about what’s possible or are intimidated by the complexity. But those that are ready to move are empowering their sales team with insights that will translate to the bottom line. And that can only be bad news for ignoring the benefits it has to offer.

Sebastian Kerkhoff is senior expert at McKinsey Marketing & Sales. He wishes to thank  Charles Atkins, Till Großmaß and Georg Winkler from McKinsey & Company for their contributions to this article

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