Identifying sales-ready accounts to reach out to is the heart of freemium sales acceleration. Freemium businesses rely largely on product adoption to trigger the a-ha moment that will ultimately lead to successful sales engagement, so quantifying that a-ha moment in the form of activity scoring is crucial.
For Sales SaaS reps with hundreds or thousands of accounts assigned to them, reaching out manually every three months yields little or no results. What are the odds that today is the day that an arbitrary account is ready to buy? Very little, right. Coupled with that is the fact that an account may have dozens of associated contacts to choose from and engage. Randomly picking based off of job titles and reaching out is a spray and pray strategy that yields equally unpredictable results.
Sales wants to know which accounts to reach out and who within that account to reach out. And we’ve got just the play for that.
Timing is everything: Surfacing sales-ready accounts & the right contacts to engage.
Our goal here is for sales reps to start every day with a list of accounts assigned to them that are ready to have a conversation. We’d also like to give that sales rep a filtered list of the contacts most likely to respond. That way sales reps spend all of their time crafting the most relevant message for their best leads.
This is a great play for freemium SaaS businesses with a large number of accounts where velocity & efficiency are key to success. This play is also good for products with a combination of low-touch and high-touch users – while you may have paying customers already, identifying when that paying account is sales-ready is key.
The bulk of this play is going to sit on MadKudu’s ability to build an accurate account-level behavioral scoring model. Structured data is going to be key – wecan’t build an account-level model if we don’t have account data. When we run this play with InVision, we use Segment for product data & HubSpot for marketing data. We’ll also need Salesforce for our sales data.
Once we have our data piping correctly, MadKudu is going to identify the features & activity that best predict sales readiness by looking at historical product & marketing engagement and the resulting sales outcome. Once the predictive model is build, MadKudu sucks down the latest activity data on a regular basis and looks for triggers.
When a sales-ready account is identified, MadKudu tags it in SalesForce and drops it into a daily report for sales reps. Within each sales-ready account, MadKudu’s models also look at the profile & activity of each contact in order to identify the contacts most likely to engage. Those contacts are recommended to the sales rep.
The Impact: +25% in Pipeline
InVision’s 25% increase in pipeline came from identifying accounts programmatically based on historical customer data. This came without any change to the product, just in optimizing for sales-readiness. If you’re looking at lead activity today, you may be leaving money on the table. With InVision, we identified accounts where no single lead achieved the activity threshold for an MQL, but the account as a whole hit the MQA threshold. It is the combined activity of several users that makes an account ready to talk to sales.
Once this MQA model is built, we can begin to add some of our other plays on top of it. Forcing the funnel by sales-ready accounts by customizing the app or website. Reducing friction on forms or triggering chat for enterprise prospects with a Fastlane play. We can score accounts throughout the buyer journey.