Growth has always been important. In times of uncertainty, profitable growth is critical.

The benefits of growth are undeniable. McKinsey’s analysis of more than 5,000 public companies shows that growth champions—companies that profitably outgrow their peers—create 80 percent more shareholder value than their peers over a ten-year period.1 But growth is hard to achieve. Only one out of eight companies grew their revenues by more than 10 percent per year over that span.2





Growth analytics: Scaling smart decisions

Data and analytics is the lifeblood of modern organizations because it’s an engine that drives rapid, smarter decision-making. According to our research, organizations that use their analytical horsepower to turn leads into opportunities for customer growth realize revenue increases of up to 20 percent.5 The most effective approach is to seamlessly embed analytical tools into the sales process, allowing organizations to quickly act on insights and recommendations. The best action to take next may be contacting a top-priority lead, addressing an account at risk of churn, or offering the best possible pricing. That information would be the most useful if displayed in the context of opportunities, quotes, and accounts that sellers work through every day. One important use case for these analytics tools is to display a score for a deal—based on the market, customer, and historical data—within a configure-price-quote (CPQ) solution as the deal is being configured in real-time.

Despite the substantial opportunity, the sustained use of analytics at scale remains a challenge: only 21 percent of companies we studied systematically embed insights from analytics into the tools salespeople use every day, which leads to suboptimal adoption and incomplete feedback—as well as slow improvement—for analytics models.6

A global agribusiness boosted organic growth by integrating insights from analytics directly into frontline sales tools. Decision makers identified about $100 million in additional margin contribution across six advanced analytics use cases, including cross- and upselling and pricing. The system then offered sellers tailored insights and action plans across the customer life cycle in real-time.

Of course, many sellers were used to using their subjective judgment to plan their next moves. To boost the adoption of the system’s recommendations, sellers used a self-serve dashboard integrated with their everyday tools to track their personal performance. Feedback from the dashboard included alerts on performance improvements such as higher margins or conversion rates when sellers implemented the analytics engine’s recommendations. As a result, 100 percent of sellers engaged with the dashboard’s recommendations. The company was also able to refine its product positioning, increase its share of wallets, and identify opportunities for additional product combinations.