Strategies to improve salesforce effectiveness have historically come from two sources: consultancies and internal analytics groups doing manual analysis. The approach to optimizing salesforce effectiveness is now undergoing a paradigm shift, as innovative organizations internalize a rigorous and consistent process for using advanced data analytics. To effectively leverage advanced analytics, organizations need to have three kinds of tools:
- A customer relationship management platform (CRM)
- means for aggregating this data and providing actionable recommendations
- a test-and-learn capability to isolate the incremental effectiveness of each action and tailor and target it for maximum impact
All organizations now have a means of capturing detailed customer interactions via a CRM system – however, only recently are B2B organizations beginning to leverage the other two pieces of the analytics puzzle.
Understanding Performance Across Your Network
Though it may sound simple, the first step in improving salesforce performance is to gain a comprehensive view of activity across the network. It would be ill-advised to develop strategic hypotheses without a lens into how salesforce activity varies over time and by segment, and without a view into what factors are correlated with stronger financial outcomes. At the core of this process is a central repository for all data, including sales activities, data about each sales rep and customer, and financial performance metrics.
The system for aggregating the data and generating insights should be easy and flexible to use, highly automated, and most importantly, focused on generating actionable recommendations. Executives must be able to identify the answer to a wide range of critical questions, including:
- Which customer segments are being under-served by our sales team?
- Which customer incentives are associated with the most growth in average contract value?
- Which outreach tactics are correlated with higher sales?
- Based on “uncontrollable” factors for each rep – such as territory demographics and number of customers in her territory – what should we expect each rep’s performance to be?
Answering the last question can help better set performance goals, while also identifying which “controllable” factors – such as call volume, message used, etc. – drive over-performance. These strategies can then be incorporated into training and compensation strategies moving forward.
As executives use data to answer these types of questions, they begin to develop a set of ideas for improving performance. That’s where experimentation comes in.
Improving Performance With Test and Learn
Each customer interaction can have a large impact on existing or potential relationships. Rolling out a new idea to the whole salesforce is a risky endeavor, as a wrong decision could lead to immediate and sustained profit loss. Instead, leading B2B organizations have found that the best way to isolate the cause-and-effect relationship between any strategic shift and key performance indicators is to use a test-and-learn approach – try an idea with some reps or customers and compare the performance of the “test” group to the performance of a well-matched “control” group.
This idea is simple in theory but challenging in practice. A large number of factors, including competitor actions and economic trends, lead to volatility in key performance metrics. This makes it difficult to isolate a small – but potentially valuable – change from the volatility of normal business operations. To isolate this signal, B2B organizations need a platform that algorithmically suppresses noise and eliminates biases, while also identifying which reps to target and which versions of the idea to implement.
Test and learn is not limited to designed tests – executives can also generate causal insights by mining data for “natural experiments.” The inherent differences in activity caused by a large salesforce lead to natural test vs. control opportunities, and analyzing these natural experiments is a risk-free way to generate actionable strategies. For example, data can be efficiently mined to identify instances in which some sales reps increased in-person visit frequency compared to their historic behavior. These reps can then be compared to a group of similar reps who did not exhibit changed behavior to accurately identify the incremental impact of increasing in-person visits. In fact, any activity that happens differentially throughout the network – such as local client events or trainings – should be measured test vs. control.
Recently, a global top 20 bank generated over $60 million in incremental revenue by employing this approach to optimize bankers’ sales strategies.
The organization had been tracking banker behavior with a CRM system. Despite this wealth of data, the bank was unsure which outreach tactics and which leads generated increased revenue. Management needed a way to understand the impact attributable to changing sales strategies, while also identifying which bankers would benefit most from each change.
The bank analyzed sales rep activity data to find instances in which reps increased their frequency of in-person visits, due to the fact that they had been given a greater number of leads. The reps identified with higher frequency activities were considered “test.” The test reps were then compared to a group of similar “control” reps based on factors such as tenure, segment focus, and financial metrics. The bank measured a 2.4% increase in revenue for test reps relative to their well-matched control.
Breaking down these results, the bank learned that reps who received leads for two specific customer segments experienced the largest sales lift. In-person visits were far more effective than any other outreach strategy – increasing calls and emails had no statistically measureable impact. The bank also learned that specific bankers, such as those geographically closer to their clients, benefited more from in-person visits. The bank was able to target the right bankers and clients for expanding the program, generating an incremental $60 million annually compared to the original targeting plan.
B2B organizations have a broad range of strategies at their disposal to increase the effectiveness of their sales force. To make decisions in an accurate, efficient, and targeted manner, these organizations need to move beyond simply reporting data from their CRM, to generating actionable insights that can be validated and improved through a test and learn approach. This process is worth tens of millions of dollars per year in incremental profits to the organizations that effectively leverage it.
Paul R. Monasterio, Principal of Applied Predictive Technologies, advises leading companies across the globe to develop data-driven strategies to improve profitability and corporate value.
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