Five Lessons B2B sales leaders can learn to make analytics work

Till Großmaß, Partner at McKinsey Marketing & Sale
- Leadership - Mar 06, 2020

Till Großmaß, Partner at McKinsey Marketing & Sales, shares the learnings from his experience of B2B analytics, that can make the difference between success and failure for B2B sales leaders. 

It’s been nearly 15 years since “big data” broke into the business lexicon, often upending long-held assumptions and elevating the critical role played by data scientists and suites of analytic tools. In that time, most of the attention has been on the B2C world. 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.

The limited experience B2B businesses have with analytics puts them at risk of making some of the same mistakes their B2C colleagues suffered through. Going into an analytics programme with only a vague sense of objectives, becoming enamoured with specific technology, or building with scant concern for how tools will be used in the field are all likely to lead to wasted resources and poor results.

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. But through our own experiences working with customers across a wide range of sectors we’ve identified 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. It’s easy to fall into the trap of bringing in an off-the-shelf solution favoured by an operating unit or key stakeholder. And sometimes it does pay off, but only in the short term.

Instead, companies need to keep their eye on the fundamentals of the business and what problems stand in the way. Start analytics implementation by defining those challenges, and then build internal knowledge through testing and iterate with tools on hand. 

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. It’s essential to work with the sales team to determine what they really need. This goes beyond standard adoption protocols for releasing software or tools. 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. These insights can then drive significant changes in how the organisation engages with clients. Start simply, and ramp up on the back of small wins (improved funnel conversion, for example). This helps sales reps become more comfortable with the insights and understand how data can direct their decisions.

  • 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, into individual performance against targets, show where there are performance gaps, and then identify specific opportunities to close those gaps while providing concrete recommendations on pricing.

3. Make it easy to use

Even the strongest insights can only translate into measurable impact if sales teams are able to act on them. 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. Make sure that the insights you want to make available are convenient to access and easy to understand for both reps and managers. 

4. Start with the data that are easy to get

Combining data to create a perfect data set can be frustrating. That fact becomes painfully clear when an analytics programme is trying to run across multiple systems that typically don’t communicate with one another. This is where too many analytics efforts fail, incurring delay after delay as they try to make the combined data perfect.

Call it ruthless pragmatism, the 80 percent 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. Typically, each team has its own tools and sources of data. This creates multiple blind spots. To overcome this, functions need to form an integrated team to share data and improve sales analytics.

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. With each “win” (goal achieved), the cross-functional team integrates additional existing internal data sets across customer relationship management, enterprise resource planning, and relevant product streams and can then enrich that with external data on competitors, intent, and other signals. This provides a richer capability, which makes adoption easier.



Getting ahead of the competition with analytics

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.

*The author would like to thank Charles Atkins, Sebastian Kerkhoff and Georg Winkler from McKinsey & Company for their contributions to this article

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