The best marketing organizations are the ones that effectively use data in their decision-making processes. Data to inform key marketing decisions such as what qualifies as a MQL and what’s an acceptable CPL had previously been hard to obtain. But with marketing automation platforms like Eloqua, Marketo, and Pardot becoming ubiquitous, that data is easily available.
Sales activity data, on the other hand, is one of the most difficult data to get insights out of for multiple reasons, which we’ll delve into below. Not only is it important to understand the effort being given to each lead, but these data sets also provide the insights necessary to supercharge predictive capabilities. This ensures your marketing team only passes over high quality leads and focuses your sales team to spend time on the leads most likely to convert.
Most high growth businesses have a CRM that marketing either co-owns with sales or at least has access to. So why is sales activity data still so difficult to procure? The most obvious answer is your sales team doesn’t accurately and regularly log all their activities into the CRM. After all, they’re compensated on closing deals—not on administrative work. This makes the data you do have pretty unreliable since you know it’s not likely to be representative of the sales activity as a whole.
But the other issue is the lack of tools to intelligently stitch sales activity data with “who” and “why” data against CRM objects like leads, contacts, accounts and opportunities. Specifically, most existing tools get wonky when your CRM data quality is far from perfect (and isn’t it always?). Common issues include multiple accounts with the same email domain name or even several concurrently open opportunities.
To address these shortcomings, many forward-thinking companies embarked on internal projects to attempt to collect this data. While these projects have noble goals, they also take significant internal engineering efforts and result in one more soon-to-be legacy solution, maintained by overworked internal engineers, being added to the tech stack.
The pursuit of accurate sales activity data has been likened to the search for the holy grail. One company I know has even given this data search mission with the code name “The Final Frontier.” This makes sense since duplicate customer records seem to proliferate in CRMs as fast as tribbles.
Despite the organizational pain it takes to get it up-to-date, clean sales activity data is more than worth it. Once you have the data, you’ll be able on your way to effectively using this data in your decision-making processes, aka magic:
● Better predictive lead scoring—with tools like Infer, 6sense, Everstring—you can productionalize the account intuition of your best sales team members.
● Effective opportunity scoring—gain insight into what’s different between the opportunities in your current pipeline (in terms of activity) and your best or worst past wins.
● Accurate customer and employee churn prediction—gain time to prevent these losses before they happen instead of being blindsided.
To see the impact robust sales activity data can have in an organization and why it’s The Holy Grail of marketing data science, download our Gainsight case study.