Brief History of Buyer Engagement Tracking
In the early 2000’s, CRM SaaS offerings began to gain traction with Salesfore.com leading the charge. Now, nearly 20 years later, CRM systems have transitioned from a “nice-to-have” to an essential element of any enterprise sales tech stack.
In theory, the CRM should be the central hub of all customer-related data, however, the industry norm around CRM adoption has been abysmally low—somewhere around 20-25%. Historically speaking, this adoption rate meant that not only were people failing to log in, but they were failing to input data concerning the deals they were pursuing. Specifically, this means that CRM data has been sparse for the metadata around opportunities (deal size, stage, etc.) and the sales engagements that transpired with each of those prospects.
Now, the first data gap mentioned (concerning metadata of a deal) has been addressed through 3rd party data providers (Zoom Info, DiscoverOrg, etc.). These solutions integrate into the CRM and help cleanse and govern account profile data (industry, company size, etc.). And this is helpful as you embark down the path of identifying your ideal customer profile, setting territories, etc., but it still leaves the second gap: Prospect engagement data. In the 2000’s, there was really only one option for addressing this data gap: mandate reps to manually input each activity
Considering the state of technology at the time, this seemed logical. But as we have discussed before, these mandates come at an extreme cost to the organization. To summarize, reps under these mandates either:
- Spend less time in front of prospects in order to adopt the CRM. (Click here to learn about the true cost of manual CRM data entry).
- Continue selling at the same rate and take time out of their leisure to enter CRM data (becoming less happy and more likely to churn).
AI Transforms Data Usability
Over the last decade, software providers (such as RIVA) have offered an alternative solution to the problem through integrations that automatically map activities to the correct records in CRM. While these solutions took a step in the right direction, they still came up short. Sales managers are under even more of a time crunch than their reps. Thus, with either solution (manual entry by the rep or automated entry by a software solution), it was difficult to justify the cost (less selling time for reps or licensing for a software solution) only to have thousands of activity records to comb through as a manager. More data should not mean more time spent sifting through it. What was missing from these automation tools was an artificially intelligent analytics solution that could take that raw activity data and refine it into consumable insights. For example, rather than just mapping 500 raw activity records to an account, today’s big data/artificially intelligent solutions can map those activities to the correct account, and then crunch that data to form opportunity and buyer-specific engagement scores. These scores then make it possible for managers to evaluate sales situations at a glance, rather than having to hunt for insight from textual, line-item data.
Only now, with these advancements in technology, is the cost of getting sales activity data into CRM justified. This paradigm shift, however, is not isolated to only buyer engagement evaluation– it bleeds into other areas of sales as well. One of those areas is Sales Performance Management.