- Companies that effectively use analytics in service of marketing and sales performance are 1.5 times more likely to achieve above-average growth rates than their peers.
- B2B companies historically lag behind their B2C counterparts in adopting and deploying commercial analytics, but the ones who engage with the tools already outperform their peers; their return on sales are up to five percentage points higher than that of their counterparts.
- B2B companies are increasingly ready to invest in—and execute on—analytics. They should learn from the outperformers.
If outsized growth is the holy grail, adept use of commercial analytics is one clear way to get there, fast. Our research shows that B2B companies that effectively harness analytics in service to marketing and sales performance are 1.5 times more likely to achieve above-average growth rates than their competitors. Other B2B companies have taken notice.
Many business leaders seek to harness analytics to grow their businesses. But B2B companies have historically lagged behind their B2C counterparts in adopting and effectively employing commercial analytics. However, those that use the technology more effectively outperform their peers, using many techniques of B2C sales to engage prospects and customers and increase the lifetime value of each customer relationship (Exhibit 1). These outperformers also achieved up to five percentage points higher return on sales, a common measure of how efficiently companies convert sales into profits.
We have found that outperformers not only distinguish themselves by their methods, but by their consistency and clarity in strategizing for the challenges of the transition from the very beginning. Companies ready to invest in the pursuit of outsized growth would do well to take a page from the outperformers’ playbook by investing in these four critical areas.
The hurdles of analytics
B2B outperformers succeed at translating commercial analytics into profitable growth even as many B2B sectors are under threat from B2C companies, such as e-commerce companies with more advanced and sometimes superior e-commerce and analytics capabilities that have begun to encroach on B2B territory by offering industrial supplies.
B2B companies and their leaders realize that analytics are important. In a 2021 McKinsey survey of more than 2,500 respondents in six countries and more than ten industries, 64 percent of B2B companies indicated that they expect to increase their spending on predictive analytics. But they tend to fall short when it comes to applying the right resources throughout the entire overhaul of their commercial operations.
The problems start at the beginning of the process, when many companies struggle to identify goals for their analytics programs, such as preventing churn or increasing cross-sells. As a result, while every company has significant volumes of data, many B2B companies lack the capabilities to translate data into relevant, usable insights that help them to sell more effectively by improving their understanding of their customers’ experiences, needs, and triggers.
Efforts to boost commercial performance also often fall flat if frontline sales teams do not buy into new sales-support tools or incorporate them into the regular habits and cadences of their work. In short, many companies fail to plan for and execute on the ways analytics will create end-to-end changes in their commercial operations.
Four behaviors for commercial analytics success
Our study of B2B outperformers’ commercial analytics programs revealed four common behaviors. Before launching a program, they create internal agreement on the sources of commercial value for their organizations. From there, they assemble or develop the right analytics workforce. They facilitate the work of the analytics team by bringing together the right data tools to facilitate faster decision making and to foster capabilities and execution. Although many companies put similar efforts in place, the key to outperformance is effective, coordinated execution in all four areas.
Build consensus on sources of value
Analytics can do a lot if used effectively. But without agreement on what analytics to pursue, companies can waste resources and energy on programs that do not deliver value. Before embarking on a commercial analytics program, cross-functional teams should collaborate to figure out where the greatest value is, then work with commercial leaders to shift resources and effort to focus on those areas. Crucially, leadership should expect to iteratively improve their approach to analytics. For instance, not every use case, tool, or model is a good fit for every situation, but quick experimentation and decision making—in other words, failing quickly and investing in demonstrably viable use cases—can maximize learning and minimize waste.
A review of the customer lifecycle—acquisition, product-customer fit, pricing, and retention or reacquisition—can yield significant insight into the most important sources of value (Exhibit 2).
The most promising use cases can get sponsorship from at least one executive whose team can benefit from it relatively soon, can generate value quickly, and—particularly in larger companies—are clearly scalable. For example, a distributor realized that it had a significant churn problem. It lost customers who accounted for up to 30 percent of its sales every year, enough to offset its new-customer acquisition. The organization’s leaders decided to invest in decreasing churn before addressing other issues. This focused application of analytics resulted in an additional $5 million to $10 million of run rate (annualized revenue) in eight months.
Assemble the right analytics talent
Top analytics talent produces outsized returns: the best data scientists and engineers are ten to 50 times more productive than average performers.1 As a result, analytics talent is in demand in almost every sector. However, B2B companies can attract and retain the right talent by making their value proposition as employers clear. This proposition will vary for each company. For instance, companies whose analytics functions are less mature might highlight the opportunity to build a high-performing analytics team. While not all analytical problems are glamorous, many data scientists and engineers are interested in gaining industry-specific knowledge. One energy and materials company discovered that the data scientists and data engineers were keen to learn more about the industry and get involved with business teams, so it provided opportunities for the analytics workforce to develop a sense of connection to the company and industry in the form of cross-functional squads, who worked hand-in-hand during weekly sprints to solve a business problem. Another conglomerate initially failed to attract candidates to a far-flung office, but the number of applications skyrocketed when it moved the analytics hub to a major city.
To build an analytics team, companies should begin by hiring an analytics leader who can anchor the function and attract other analytics talent. Senior academics and analytics leaders from major tech companies are good candidates.2 The focus should initially be on hiring the right people instead of filling as many roles as possible. In addition to data scientists, companies should recruit strong data engineers and analytics translators to build the data foundation.3
While most B2B companies have in-house recruiters that can help find this talent, it is often critical to create a new way of working to hit the growth aspirations. For instance, one logistics company hired more than 50 digital roles within months, taking the average time to hire from ten months to three weeks for key roles such as data scientists and engineers. To do this, they used a blend of automated assessments and screening, upskilling recruiters through an in-house “war room” using agile principles to manage the candidate funnel and accelerate hiring, while supplementing with seasoned external recruiters for senior roles.
Working in the same location is often helpful in building and reinforcing team cultures. Companies should therefore ensure that the analytics team has the structure needed to work in an agile way in the first few years, even if not everyone is co-located (Exhibit 3).4
Use flexible data architecture, algorithms, and tools
Effective data architecture, algorithms, and tools are already standard at companies that are successfully using analytics. The key is for companies just starting analytics work to move decisively with the data they have to begin gleaning insights as soon as possible and avoid the trap of sinking into a lengthy IT project.
For beginners, perfect is the enemy of the good. Almost all companies complain about their data quality. But in our experience, almost all companies also have good-enough internal data that can be immediately put to use in a minimum-viable-product version of a data lake. Companies typically only use only a small portion of their available data, but the new insights lie in the other 90 percent. Data such as inbound inquiries, on-time shipments, and call center notes can generate significant insights if they are properly linked. One organization had planned on a two-year data-lake project that would eventually support its work on alleviating global hunger. However, an initial analysis revealed that the organization really needed something that would support its initial set of analytics models. Readying the required data only took a month and allowed the preliminary models to run in 30 minutes at a cost of $2. In the meantime, the data-lake overhaul, an investment in infrastructure, proceeded in parallel.
Finally, any insights from analytics models should be paired with business judgment. In a cross-functional team, this input should come from a business-function leader who has direct responsibility for the commercial outcomes related to a customer, region, or group. This person should consult any other experts as needed. Having the voice of the business on the team can ensure that any insights are logical and ultimately understandable and actionable for the sales team.
Use change management to boost front-line execution
Analytics programs’ ultimate test is whether they are accepted by front-line sales teams, which makes the difference between boosting the company’s performance and having its efforts wither.
In our experience, even highly predictive models can be rejected by the front line if a tool they had not heard of until they were instructed to use it generates an incorrect recommendation. In those cases, the model’s failure is often a reason for sales teams to abandon a model that they find no reason to trust. The remedy for this pitfall is to involve frontline teams in the development of analytics tools. This approach builds trust in the tool and yields insight into the needs of both front-line teams and their customers.
Once tools are ready, front-line leaders and high performers should model and champion their use to reinforce sales teams’ knowledge of the tools’ efficacy. One company gathered its sales managers for a two-day training summit each time it launched new analytics tools for use cases such as pricing, churn, and cross-selling. This methodical approach helped the company grow its earnings before interest, taxes, depreciation, and amortization (EBITDA) by more than 10 percent for several consecutive years.
Once tools are past the pilot stage, integrating them into the company’s roster of standard tools with which reps are familiar make them more acceptable to the front line. Indeed, an integrated insights dashboard has an air of permanence compared with the spreadsheets and ad hoc dashboards commonly used during pilots.
However effective or beneficial new tools are, it may be difficult for the salesforce to form new habits, especially when old habits are comfortable. Weekly routines can help frontline reps integrate new analytics tools into their working cadence. One company implemented a set of weekly reviews designed to integrate analytics insights into the sales function’s work. Another company tracked sales reps based on the frequency with which they use their analytics tools and how often they acted on analytics driven insights. The sales reps who consistently used the insights achieved better results over time, which accelerated the adoption of those tools.
Companies should also use insights to personalize rep-specific targets. For example, one company used pricing analytics to set specific price-increase targets for each rep. The reps didn’t have to implement every new insight to hit their targets, so they retained a sense of control.
Finally, new insights tools can reveal opportunities for cross-functional collaborations. Insights often have implications for nonsales teams. For instance, one company that built a tool to analyze churn found that 50 percent of its open service issues remained unresolved, a significant failure that contributed to customer defections. In response, leaders organized weekly district reviews of the customers that most at risk of churn. Supply chain, sales, and customer service all participated in rectifying the situation. The use cases identified in the first six months were worth $20 million to $30 million.
The timely and effective adoption of commercial analytics tools and techniques offers B2B companies undeniable strategic advantages. By learning from the outperformers, others in the sector can improve their performance and even build defensible advantages.