By Shayna Stewart | September 12th

You deserve a big pat on the back if you have successfully shifted your Product and Executive teams to a consumer experience driven mindset — one that prioritizes empathy for the consumer in the product experience.

However, after all that work educating teams and setting up new processes, your Product and Analytics teams are likely experiencing a little bit of friction.

The growing pains occur because the datasets are not evolving as the questions are evolving from business-centric to consumer-centric:

  1. Analytics teams get stuck operating only within the business analytics and marketing analytics paradigms.
  2. Consequently, they have a tough time getting into product and consumer-centric analytics paradigms.

Obsessing over customer satisfaction is a good thing — and a proven game changer for brands across the spectrum.

Often, teams that switch to the consumer experience mindset, accidentally mistake business or marketing data for product data, so you’ll need to stop focusing on quantitative behavioral data.

Here’s how.

Consumer-centricity is tough on data structures

This mostly has to do with when the way data structures were built:

Data structures are a function of the questions you ask.

Historically, the business data sets are the oldest. They were built to answer questions like, “How much money am I making and how many paying customers do I have?

Marketing data sets were introduced to answer questions revolving around campaign performing, reach and impact.

Most companies stopped building data structures beyond those two. Now, brands across the spectrum are struggling through an obsolete system attempting to answer key questions for both marketing and business development teams.

You might be wondering:

How it is possible that we are lacking data sets in a day in an age where the amount of data is increasing exponentially by the second?

Another great question! 🧐

The reality is that data needs a particular structure to answer specific questions. Typically the data is being captured in an unstructured way, and then needs to re-structured to answer critical product and consumer-centric questions.

Evolving Your Data Set

Building these data sets is a cross-functional team sport. It’s a sport because it requires coaching, practice and can create a bit of rivalry across the teams to create a great dataset.

Step 1

👉 Have a clear and concise consumer-centric strategy.

Consumer-centric strategies need to have a consumer journey that is informed by consumer feedback and consumer need based states as the user moves through the consumer journey. Once this is done, make sure that everyone is aware and agrees with this strategy.

The consumer-centric analytics will fail if people start to waver on how much they agree with the strategy, as analytics is meant to provide feedback on how well the strategy is performing.

If people start to disagree with the strategy or follow a different strategy, then the consumer-centric analytics framework will not provide information on how well the strategy is performing.

Step 2

👉 Build your KPI structure from the ground up, starting with consumer-centric KPIs first.

Your consumer-centric KPIs should be descriptive of your consumer’s need based states you identified as a strategic play in your consumer journey research. They also should be predictive of your business and marketing KPIs.

Step 3

👉 Identify the differences between the business, marketing, product and consumer-centric questions.

We recommend that an analytics team member categorize the questions that they get asked on a regular basis. Even further, start to categorize which teams are asking what questions.

This will help set up your data democratization strategy later on. Not everyone is interested in receiving answers to all categories of questions. For a refresher of the different types of analytics, check out this article.

Step 4

👉 Select the right tools and/or update your implementations to ensure all questions are answered.

The consumer-centric questions will always be the hardest to answer as they require the most complex data capabilities to answer. Therefore, your requirements should be led by the consumer-centric analysis requirements and then work backward to ensure your tools can answer the easier three.

In conclusion

There are some key requirements that everyone must have in place to truly have a consumer-centric data set:

  1. Access to data that is summarized around users, not around visits or pages.
  2. A strategy to link user data across platforms, meaning an identity resolution system
  3. A plan and commitment to build cohorts of users and develop for customized marketing and product experiences based on those cohorts

Shayna is a Product Manager who is passionate about consumer-centric product strategy, design and an advocate for consumer-directed data strategies to match.