Maximizing the Value of Consumer Data: Part 1

/ 9 November 2021

Discussions around consumer data often focus on capture and generation – methods both overt and covert to build greater and grander data sets. However, a recent Forrester study confirmed what we already suspected – companies don’t actually know what to do with all that data. According to the research, 81% of professionals surveyed felt that they needed to improve their ability to use and act on their consumer data. And worse, 40% noted that the customer insights they currently have are not meaningful or relevant.

The importance of utilizing data to improve the customer journey cannot be overstated. As a result, the key marketing focus and priorities are shifting towards generating actionable insights from their customer data. In this two-part article series, we’ll discuss why.

What is customer data?

Before we look into the value and importance of consumer data, it’s key to understand what it is and how it’s captured.

Consumer data is essentially any data point that reflects a customer’s activity, preference, or intentions. In the digital space, countless touchpoints can inform your overall view of a consumer, including:

  • Preferred communication and purchasing channels – which platforms are your customers visiting? When are they visiting them, and which are driving the highest purchasing rate?
  • Communication preferences: are they opted into email, text, or other communication channels? How do they prefer to communicate with you?
  • Campaign engagement: which campaigns are they engaging with? Which content is most relevant to them?
  • Behavioral, psychographic, and demographic data: how do their age and background inform their purchasing decisions?

This data can be captured and stored through a variety of systems. Customer Relationship Management (CRM) and Point of Sale (POS) systems such as Salesforce and Pipedrive are commonly used to gather customer data together and track purchasing patterns and behavior. Email platforms, such as Mailchimp and SendGrid, offer greater insight into customer responses to your marketing efforts.

Customer feedback and survey platforms, like Medallia and Survey Monkey, have a focus on primary research. With so many systems operating within organizations, one of the key challenges for marketers is preventing data silos and connecting the dots across systems.

Structured vs. Unstructured Data

When it comes to measuring the value of your customer data, there are two key types to keep in mind, each with its own pros and cons.

Structured or quantitative data reflects answers or actions that are chosen from a fixed set of predetermined fields or results. This could be the answers to a multiple-choice question within a survey or a review of the most visited pages on your website. Structured data has the advantage of being easier to ingest into your CRM system and often receives a higher engagement rate due to the ease of response. Both make it an attractive option for companies looking to extract quick and clear insights that drive impactful action.

The downside is that the consumer needs to adapt their often detailed and complex answers into simplified statements. For example, tracking the website pages that a customer visits will tell you what they may have been interested in, but not where they are in the buying journey or why they left without purchasing. It’s this lack of detailed information that presents the biggest drawback for structured data.

Unstructured, or qualitative data, reflects answers or actions that are not constrained by fixed responses – in other words, they are provided ‘freestyle.’ This could be the ‘any other comments’ section of your feedback form or the unfiltered questions received by your customer service teams. Unstructured data provides information in much greater detail and can reveal new and unsuspected opinions or perspectives.

Giving unlimited scope to feedback will often elicit a smaller but more impactful response. The key drawback of this style of data is that it is challenging to incorporate into analysis schemes without advanced analytic solutions.

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