Success in sales has always come down to the numbers, but that’s even more true today. Innovative sales leaders are using big data analytics to accelerate revenue growth, including better identifying potential buyers, advancing leads, and improving sales engagement.

Is your company using data analytics to fuel data-driven growth?

If your answer is no, you’re not alone. A recent McKinsey and Company article reports that business-to-consumer (B2C) companies are the ones that have placed the most attention on using data analytics to fuel growth—reaping the benefits ahead of business-to-business (B2B) companies. In fact, the consulting firm argues that “only recently has the B2B sales world started to appreciate the scale of the benefits data-driven sales can deliver.”

The piece goes on to offer five lessons for sales leaders to help build big data analytics into their sales model, including:

  1. Focus on clear business objectives—and ignore shiny objects: Rather than starting with a technology solution and then looking for a problem, the McKinsey article suggests focusing first on business fundamentals and obstacles to success. This includes defining challenges and building knowledge with current tools on hand to make the business case to top management. In the end, this approach helps secure budget and company-wide buy in to bring in the right technology solution or platform.
  2. Help your Sales team trust the data: Involving Sales at the outset is critical to success and long-term adoption. Offer tips and examples to help bring them along and develop trust in data analytics outcomes. This includes creating transparency in how sales algorithms are built and how insights are derived, as well as starting out simple and showing value. It’s especially important to demonstrate how the new tool provides insights into performance against targets and how to close gaps with the right sales opportunities.
  3. Make it easy to use: Like any big data analytics initiative, success requires users to be able to act upon the data and insights derived. For salespeople, this includes “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,” according to the article. 
  4. Start with the data that are easy to get: Building the perfect data set can be challenging and time-consuming. Rather, the McKinsey article recommends using data that is easily accessible—either in a single system or systems that are already connected. From there, build up the data sets over time, using a “test-and-learn” process to better target new customers, accelerate the sales pipeline, and improve sales conversion rates.
  5. Build a team mindset: Finally, breaking down silos is key to improving the adoption and accuracy of sales-driven data analytics initiatives. Using the test-and-learn methods, teams can work together at “breaking down cross-functional barriers” across the Sales, Marketing, and Product organizations, incorporating more data sets into the process over time, including CRM and ERP data. With joint analyses and broader data sets from across the business, teams can leverage predictive analytics to refine sales strategies and targeting even further, identifying micro-segments and prioritizing leads based on value potential and buying preferences.

If you’d like to learn more, check out the complete McKinsey & Company article.