Customer Data: Why Quality Trumps Quantity + a 4-Step Approach to Remediation

Blog Post
July 30, 2015


When Less is More

Often times, the discussion about data is around quantity – we think we need more data, more sources, and more insights. But marketers are already inundated with data. Getting more data is not the challenge – getting quality, useful data is.  If we can shift our mindsets to getting more out of the data that we already have, we will open up an easier path of understanding what quality data is telling us.  Quality data results in trustworthy reporting and truer insights, which can help us deliver more effective campaigns thorough better targeting, messaging and content.

But How Do We Find Quality Data?

Over the past two years I have worked with a number of clients to help them improve the quality of their data.  From those engagements and with client input, I developed the 4-Box model to help our clients develop effective data remediation strategies designed to get the most out of their data. At a high level, the 4-Box model provides a methodology to very quickly determine whether data will meet marketing, analytics or campaign requirements. The 4-Box model is a proven approach to evaluating the state of your data and providing recommendations on how to improve and manage the data to ensure that it is fit for purpose.

Step 1: Fit For Purpose

The first step is all about matching your data requirements with your ideal customer profile and marketing objectives. I wrote a blog post a few months ago on how to build the ideal customer profile. The key is to clearly define whom you want to reach based on your marketing or campaign objectives.  It is important to be as specific as possible in defining the ideal profile.  The guideline we use is “what are the minimum attributes” needed to effectively segment customers for a targeted campaign. At the same time, decide which attributes would add significant value if used to append or enrich each of the records at the contact or account level. In addition to standard attributes such as role, title or firmographics, think about using social, digital or behavioral attributes.  These will come into play as part of developing a remediation and enrichment strategy.

Step 2: Data Audit

The data audit is where the 4-Box methodology really starts. By auditing data through the 4-Box lens, you can segment your data based on completeness against a defined ideal profile and engagement.  Engagement may be time based. For example: has contact responded within the past 18 months to a marketing or promotional program? At Harte Hanks we use the Trillium Discovery tool as part of the data audit process to evaluate the integrity and structure of the data.  The data is then matched to a reference file to determine what percentage of records require updating, which is a clue as to the age of the file. We have found that the most effective audit is one that targets a specific segment, such as enterprise customers or customers of certain products or solutions.  


Step 3: Remediation Plan

After the data audit, you should develop a remediation plan that defines clear paths to cleaning, updating, appending and enriching your data.  This may also include a reactivation program for those customers whose data is complete, but who have not engaged in some time. For many companies, this represents 60 percent of their customer base, so reactivating just 5 percent of these customers will yield significant results.  

4-box data remediation strategy

Step 4: Deploy

Once the paths to data refinement are defined, it is time to execute the remediation by fixing data sources and process issues, as well as incorporating new digital and social data sources to add depth to the record and increase the ability to segment and target more effectively. This can be done in a variety of ways, including:

  • Identifying and fixing upstream data capture and issues
  • Selecting third-party data based on the ideal profile framework and marketing strategy
  • Selecting other data sources and types to build out the profile
  • Initiating campaigns based on contact policies for reactivation. Ensure you are compliant with privacy policies and laws.
  • Using contact center and social listening to further profile contacts and accounts and to validate data. This is best used for high value contacts, such as IT or Business Decision Makers or for key accounts.

Quality Data in Action

We worked closely with a global technology client to test the 4-Box methodology.  Our client was looking for ways to improve response rates and, ultimately, MROI.  Using 4-Box, we identified areas for data improvement, evaluated the composition of the roles, titles and number of contacts per account and created a baseline for measuring data quality improvement. The results speak for themselves:

  • 1000:1 ROI for data remediation.  Every dollar spent on data using the 4-Box strategy yielded a $1000 return in incremental revenue.
  • 3.5 percentage point increase in open rates

By improving the overall quality of core data and increasing the data that is “fit for purpose,” the client experienced positive effects on both campaign performance and MROI.

Up Next: Continually Improving Your Data

By aligning marketing requirements with data requirements, we try to ensure that data is “fit for purpose” and therefore unlock more value from existing data assets.  The 4-Box method I outlined above is a very effective and practical way to understand the state of your data and to provide an actionable plan for reactivation and enrichment efforts. By uncovering the value of existing data, you can focus more of your budgets and efforts on creating more powerful customer experiences. Oh, and once you’ve got this part down, we’ll look at a real-time rules-based engine that continually improves your data based on the 4-Box rules that you’ve defined. Stay tuned.