efficiency gains in number of
leads to conversion
delta in incremental revenue over accounts
prioritized using traditional models
overall number of accounts
A Fortune 500 company uses advanced customer intelligence insights to drive sales from busy SMB customers.
Small and medium-sized business (SMB) owners don’t have time for much in their busy days for anything besides running their shops. Time is an asset they can’t afford to waste. So when a Fortune 500 logistics company wanted to reach them with new delivery services or changes to their current contracts, they needed to do so in ways that SMBs would find valuable. The company was looking for a better way to collect, segment, and analyze its many data points to uncover untapped revenue potential and sales opportunities with its SMB segment.
We created a customized sales engine for the client with our Revana Analytic Multichannel Platform (RAMP) solution. RAMP uses data analytics and algorithms to analyze patterns in customer behavior and deliver relevant sales offers to increase conversions and ultimately, loyalty. We also added enhanced data science to get a better understanding of SMB client needs and what types of offers would resonate most. Enhanced data categories include firmographics, social accounts, technology usage, online marketing sophistication, current online footprint (websites, keywords, descriptions, etc.), and behavioral signals that capture purchasing intent. More than 20,000 data variables, combinations, and permutations to make the insight deeper and more interesting.
This level of hyper-segmentation and micro-segmentation added context to the data that was generated. For example, we learned that companies with a lot of Facebook followers were more likely to use our client’s services than its competitors. And certain types of attorneys were more likely to purchase delivery services, based on the amount of paperwork they have.
We applied propensity models and other segmentation logic to the list to create customer profiles of high-value customers and those who would be more likely to purchase services, as well as those who have “high seasonality” (i.e. propensity to ship during specific time of year). We also identified highly qualified companies modeled after our most desired customers. This allowed the team to be more productive by focusing resources on the companies and people that matter most, passing leads to the sales team at the right time, and allowing the sales team to spend time on the right accounts.
We tested the initiative during a two-month pilot, and compared it to a control group and a group with limited analytics insight. The results showed both wide and deep growth. The enhanced insight group signed up more accounts than the others, had significantly higher average revenue per account, and achieved a greater than 25% efficiency gain (fewer leads needed to achieve same revenue target). In addition, the group showed a greater than 150% delta in incremental revenue over accounts prioritized using current models.