We’re launching a new way to score accounts: Intentsity Score.
At LeadSift we track over 15 different types of intent triggers for all B2B SAS. And that gives us a volume of roughly, almost about a million daily intent data points. That translates to anywhere between hundred to thousands of daily leads for a typical LeadSift customer.
And when we have leads at this large scale, the obvious question emerges is how do we know which accounts to prioritize first? And that’s where lead scoring comes into the picture.
One way to approach this problem of lead scoring using intent data would be to base it on the net volume of intent triggers received over a period of time. The higher, the volume of intent triggers, the higher the score.
A net volume-based scoring system suffers from bias towards larger organizations. These organizations have bigger workforces and generate higher digital footprints. And thus, they always score high.
Another problem with a net volume-based scoring system is they treat all the intent triggers the same, but fundamentally, each trigger is unique on its own. To give an example, a funding trigger might be less frequent than an account engaging with the content. And yet the funding trigger might be more valuable in deciding the likelihood to buy than a content engagement trigger. Therefore, if you simply look at the net volume, it’s very easy to miss less frequent, but high-priority triggers.
To address these shortcomings. We designed our new scoring system and we based it on two key components. The first being a statistical model that looks at trends in intent volume rather than the net volume. And the second being a rule engine that assigns higher scores upon finding less frequent, but high-priority triggers.
Let’s take a look at each of them in more detail. The statistical model analyzes 90 days of intent data volume to quantify trends in it. It generates a baseline of usual intent volume and compares the most recent volume against the baseline to compute the intensity of the trend.
How you’d score is similar to Z-score that calculates a values relationship to the mean of a group of values. The score generated by this model is essentially a measure of the upward or downward trend in intent volume. Through this model, we can also generate insights like, “An account received X percent or higher or lower intent volume in the last 30 days than the usual 30 days volume.”
The second component of the new scoring technique is a rule-based component. In this, we look at the last 90 days data to find the occurrence of high-importance triggers. A few examples of high importance triggers are an account receiving funding, or an account seeing leadership changes, or an account entering a new partnership.
Upon finding these triggers, the scoring model factors them in to assign high scores to these accounts. This helps the scoring model to generate insights like, “An account received a high score because it received funding in the last 90 days”.
To sum up our new Intentsity Score, at its core, uses a statistical model and rule-based engines to analyze trends in intent data volume and score accounts.
A higher Intentsity Score would generally mean a higher chance of the account being in its buying journey. From a BANT sales, lead qualification perspective, the Intentsity Score addresses the need and the timing piece.
We are very excited to launch this and would love to have you try it.