Improve Sales: Less Big Data, More Big Questions
The analytics revolution sweeping across many organizations has yet to be fully incorporated into Sales organizations. Despite recent progress, having tools like Salesforce.com and Google Analytics is simply insufficient. Far too few organizations are combining proven Sales approaches with a true analytical mindset to ask and answer the right questions.
We have seen 3 waves of analytics in Sales: the late 1980s, the late 90’s with Siebel Systems, and more recently, with Salesforce.com and similar tools. Surveys show widespread dissatisfaction with CRM implementations, and Big Data tools like Salesforce.com are not well utilized or even understood. Despite this, executives are encouraged to invest just a little bit more – Big Data goodness is just around the corner.
Clearly there’s a major disconnect between Big Data promises and the reality in most Sales organizations today. Before investing more resources, senior executives should make sure they’re on the right path, by taking the following steps:
- Use segmentation to identify customer traits correlated with their KPIs.
- Prioritize segments based on the incremental impact from sales.
- Tailor KPIs (and salespeople’s incentives) for each segment.
To see how this might work in action, let’s explore a recent engagement where a leading enterprise confronted this situation. The data is disguised to protect confidentiality.
The company has seen impressive growth based on a model of marketing driving leads and sales turning leads into customers. With growth slowing, it is considering significant investments in marketing and sales, but is concerned about the impact on profitability. What should it do?
We started with a deep dive into the analytics, correlating 3 primary KPIs (conversion, average revenue per user (ARPU), and LTV) with 17 variables contained at the user level within Salesforce.com and proprietary databases. We used this segmentation to calculate expected values on each KPI based on the type of user. A sample of these values – conversion rates – is listed below.
While interesting, this analysis begged the question of where the sales team should focus. To find out, we broke each segment into a control and a test group. The control group received the standard outbound email and phone contacts from the sales team. The test group received no outbound contacts from the sales team – essentially Sales was turned off. The difference in performance is listed below.
There were several interesting insights. The first was an eye opener: The sales team’s impact on conversion wasn’t 20% but only 5%; surprisingly, 15% of customers in the test group converted without contact from Sales. About half the leads were from Broadcast media, very unqualified, but still took 60% of the sales team’s time. Leads from online marketing channels had a very high conversion rate when contacted by sales, but saw a more than 50% decrease when not contacted. And the most active leads – using the product most frequently – actually converted worse when they were contacted by the sales team!
The last step was to move beyond conversion as the sole KPI, focusing on LTV for each segment. For segments with very low conversion, we moved further up the funnel, testing micro-conversion events (such as a whitepaper download or attending a webinar) and allowing users, with minimal direct guidance, to move down the funnel at their own pace. For segments with already high conversion, we focused lower in the funnel, exploring ways to increase ARPU (via upsells and cross-sells) or decrease future churn (via onboarding).
A scoring model was automated within Salesforce.com, with a recommended content and contact strategy put in place for each segment. The least qualified leads were assigned to an auto-responder campaign, and outbound contact from the sales team was significantly reduced. This freed up time for the team to focus on other opportunities, including retention, ARPU, and outbound prospecting. It also saved more than $1M by demonstrating an expanded sales team was unnecessary. Meanwhile, marketing shifted their mix from broadcast to online marketing channels, increasing ROAS by 120%.
Many companies are reluctant to tinker with their approach to sales – even minor inefficiencies can jeopardize revenue. Plus, incorporating new ways of thinking requires coordination across the entire organization, from sales, marketing, analytics, support, and finance – and the consistent support and direction from the top. Much easier said than done.
However, substituting analytics for gut instinct and basic CRM reporting helps take the guessing game out of important investment decisions. Using test-and-control techniques popularized in the direct response world can show the true impact of the sales team and better prioritize their focus and objectives. Executives need to understand what’s going on within the sales organization, starting with some basic questions around analytic competencies and capabilities. Rather than invest in systems, tools, and training, many companies would be better off hiring a couple of very inquisitive, analytical problem solvers, and provide them cover to ask all sorts of interesting and (potentially) uncomfortable questions. The end results will very likely be faster, cheaper, and better than any outsourced or automated solution.