Building an effective account scoring model includes a mass of data points about prospects (demographics and firmographics), web engagement, buying behavior (intent signals), and account engagement. You carefully weighted all of these items to create a score based on your ICP. Then…. A new data source comes along. How do you incorporate this new data into your scoring model? Let’s look at your options.
Use new data as a filtering criteria
You could use a new source of data to filter accounts after they’ve been scored. This is a popular option for a couple of reasons. First, someone (maybe everyone) will be skeptical of a new data source to start. You aren’t quite ready to make it permanent and you want to test its usability. Second, you can start immediately as opposed to waiting for a detailed account scoring update to be decided upon, scrutinized, and finally built in Salesforce.
A great example of filtering we have used involves LinkedIn intent to prioritize accounts. Hopefully, your reps already have a great book of high fit and timing accounts. If they don’t, and they’re focused on a static list, you may want to shift tactics with dynamic books. But these accounts still must be prioritized. Using the new data from LinkedIn ad engagement, reps can filter their books by the companies/contacts showing high ad engagement on LinkedIn to prioritize and then personalize their outreach.
Add new data to your current score
You could choose to incorporate a new data source into your scoring algorithm. This is by far the most complicated process, but you likely already have some processes and procedures to make this work between sales, marketing and operations. That’s because most B2B account scoring models are already pulling from multiple sources to create a score. So what’s one more source?
Take a look at the new data and decide what data you have. It’s likely a mix of demographic data, firmographics, and intent activity. If your new source is simple a more accurate version of an old data source, a simple reap and replace might be in order.
If adding new items, build a plan between sales and marketing:
- What intent signals are relevant to sales readiness? Which ones would marketing use as KPIs for nurturing?
- Set thresholds and rules for those. Accounts exceeding these thresholds would be assigned higher scores, indicating a higher level of intent and potential interest in our products or services.
- Weight it with the rest of your data. If you have 4 scoring categories in your current system, all contributing 25% to the weighted score, adding a 5th data group with make them each worth 20% of the weighting.
Building better books of business
Account books should never be static. The prospects you target aren’t static, so your reps’ books shouldn’t be either. Simply put, accounts move in and out of the “good place” in the fit and timing quadrant. Dynamic books uses the data you’ve built to highlight the highest potential accounts and get them in front of reps. As prospects change, your reps can return accounts whose scores have changed for the worse and receive a better prospect.
Segmentation allows us to prioritize outreach efforts and tailor messaging based on the level of intent demonstrated by each account. Depending on your sales strategy, you can build target books for specific campaigns in addition to an SDR’s main book of business. Between your sales enablement, Salesforce, and other tools, you can then implement trigger-based outreach based on specific intent signals for your SDRs. For example, if an account shows strong intent through high engagement with relevant content, SDRs could initiate personalized outreach to capitalize on the prospect's interest.
Having an approach to incorporating new data speeds up your time to ROI no matter what investment in data you have made. Following either the filter model or incorporating new information directly into your account score, you can reduce the pain of re-scoring accounts and easily revert in case the new data solution isn’t bringing you the results you expected.