What are data “fallback strategies”?

At Hull, we often talk about data “fallback strategies” or “fallback mechanisms” as a data management best practice for B2B. So, what are these “fallback strategies” and why do we think they’re important for a sound data strategy?

Let’s first back up and understand the context.

Lots of tools, lots of competing datasets

Your growing business employs a lot of different tools to carry out unique “jobs to be done”. Your CRM hosts records of prospects and customers for your sales team. Your chat tool enables real-time discussions on your website. Your marketing automation platform sends email communications, captures prospect web activity, and form submissions. Your lead scoring tool helps qualify prospects. Your billing and payments systems orchestrate the process of sending invoices and receiving payments.

All of these tools host their own data on your accounts and contacts. While much of the data is unique to the “job to be done”, a lot of the data may overlap.

When unifying your data sources and creating a single source of truth, this creates an issue in selecting what the “truest” source is.

For instance, most B2B companies will collect some form of company name. There may be many sources for it including:

  • Web form submission
  • Chat conversation
  • Stripe subscription
  • Clearbit enrichment
  • Datanyze enrichment
  • Salesforce (sales rep input)

The same situation occurs for common B2B data like job titles, location, company size, and so on.

So, what are data “fallback strategies”?

“Fallback strategies” are the rules that define the data you choose to surface as the “truest” source. In a sense, these strategies provide a cascading hierarchy of data sources for a given attribute. Executing on data fallback strategies ensure that the most accurate is shown and that the dissemination of empty, blank fields in other tools is minimized.

Designing your data fallback strategies

The process of determining which data will be surfaced and shown as the “truest” data starts with understanding what data you have and where it comes from. Taking inventory of key fields and their data sources is a helpful first step that can be done easily in a spreadsheet.

The next step involves evaluating and prioritizing the sources for each field based on how accurate and reliable you find them to be.

For example: we at Hull will generally prioritize a prospect user-submitted source (like a form submission) over other sources like a third party data vendor because we deem self-submissions to be more truthful and up-to-date.

You may end up creating a spreadsheet that looks a little like this:

Source of Data Tool Store as: Priority
Form submission HubSpot job_title 1
CRM record Salesforce job_title 2
Data enrichment Clearbit employment_title 3

Executing on your strategies

Executing on your data fallback hierarchy requires writing your prioritization rules and running them against your data sets. At Hull, we use the Processor to enable programmatic data transformation with simple Javascript.

Don’t know Javascript? Contact us about a solution for you.

From there, make sure to apply the rules to pre-existing datasets as well as to new, incoming data.

Did you learn something new?

We at Hull research how to "join the dots" between your tools, teams and data. Subscribe to follow along with more articles like this, and learn the latest trends, tactics, and techniques.

Angela Sun

Angela brings over a decade of B2B technology marketing experience to her role as the Director of Marketing at Hull. Prior to Hull, she spent 5 years at utility data aggregator, Urjanet, where she held various roles in demand generation, marketing operations, and product marketing.