Qualify and prioritize leads with customer behavior data

As a marketer, you likely already know the merits of using third party data enrichment providers to gather additional information (like job title, company industry, and company revenue) about your prospects. While attribute-based data points like these are certainly valuable when it comes to lead qualification, your company may be in a good place to get even more refined in your prioritization and targeting efforts with the help of behavioral data.

What is behavioral data? Behavioral data is information pertaining to an individual’s interactions with your company. What those specific data points are will depend entirely on your business, but some examples include things like: account creation, account cancellation, purchase and upgrade history, webpage visits, ad clicks, plan usage, and more.

Some behavioral data can be stored in places like a backend database, data warehouse, javascript tracking tag on your website, or a data lake — which historically have not been marketer-managed assets, making the data that lives within them harder to get to.

A customer data platform like Hull acts as a bridge for go-to-market teams to access and utilize this data. By supplementing attribute-based data with behavioral data in your lead qualification process, you can begin to paint a clearer picture of who may be more likely to buy your product. What they do (on your website, during a free trial of your product, etc.) becomes a more meaningful indicator of likelihood to buy than who they are.

With this data at hand, you can begin to filter and stack-rank your prospects, helping your sales team prioritize where to focus their efforts.

Consider this example from Hull customer, Pusher, who prioritized and sent leads to their Sales team based on a combination of event data from their backend data warehouse and data from their data enrichment vendor, Clearbit.

Pusher had hundreds of “freemium” users signing up on a monthly basis, without a scalable, efficient way for their go-to-market team to target and convert them. What they ultimately did to help prioritize these users was combine data that helped them identify fit and data that helped them detect interest to stack-rank individuals to focus on.

In order to generate data points that would help them in this prioritization endeavor, Pusher leveraged Hull's Processor, a real-time data transformation and editing tool, extensively.

With the Processor, Pusher was able to:

  • Compute new attributes: Generate a new customer plan ranking when customer changes their plan
  • Log and timestamp any behavior: Date customer first created a new app or instance
  • Log and timestamp key milestones: Customer created first 10 connections

To dive deeper into Pusher’s use case, visit: How Pusher Uses Customer Behavior Data to Drive Growth & Retention.

Looking for ideas?
Other use cases for Lead Scoring & Qualification

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