Get Ahead of Fragmentation in Retail: Key Data Orchestration Tips

One of the biggest challenges retailers must navigate is fragmentation. As digitization accelerates, the number of touchpoints in the customer journey has amplified. In 2021, executives need to outsmart and outpace the real-time speed at which shoppers make buying choices on and off screen. The key is to create more meaning and value from data.

Get Ahead of Fragmentation in Retail: Key Data Orchestration Tips

At Maven Wave, our most actionable advice is to move to an agile, more sustainable way of doing business — to focus on what’s constant in the midst of ever-present change.

Here’s how to transform your data into meaningful business value.

Getting Clear on Gaps

Many companies have a customer-centricity problem without realizing it. This trend isn’t new. In fact, it began prior to the COVID-19 pandemic. In one study, Capgemini found that while 75% of organizations perceived themselves to be customer-centric, only 30% of buyers agreed. For consumer products companies, that number was 80% versus 14%.

Come 2020, the pace of digital activity accelerated tremendously, which resulted in a completely transformed set of shopper expectations. Ready or not, retailers had to shift gears and continually turn corners without a clear understanding of what’s on the other side. Imagine driving a car on a race track without a clear line of sight into upcoming obstacles.

Having a strategic advantage in retail means embracing uncertainty — you don’t know what you don’t know. So, how can retailers navigate uncertainty while removing barriers to staying profitable?

Stitching Together Data

The bottom line is that your data needs to deliver value, in the story that it communicates, in real-time. Being agile means having a continuous understanding of what’s happening across time zones without any lag.

Success is more than the sophistication of your analytical models and the inputs that you have in your data warehouse. How many people at your organization can effectively interpret that information, in real-time, to make their best judgment calls? One solution is to implement pre-built connectors that make it easy to unlock an organization’s critical insights.

Consider the use case of a fictitious retailer: Acme Retail is seeking to enable demand forecasting as an example. Here’s what that retailer would need to do:

1. Determine Goals

First, determine the specific marketing problem you want to solve. Start with a use case that is impactful that you can not solve today with your traditional environment. It should be a use case with the goal of focusing on the results, not the mechanics, of producing something right away.

2. Collect Data

Next, analyze the customer and marketing data (both internal and external) available throughout the organization to determine the potential data source value. From website analytics to inventory and search marketing data, there are many places to look. It’s important to do an audit of all the silos the organization is storing data in and determine which datasets are important in creating a holistic view of the customer.

3. Infuse Machine Learning

With clean data and a good picture of your current marketing performance, you can apply machine learning (ML) models and predictive analytics capabilities. Some of the most effective uses of machine learning for digital marketing include:

  • Developing more advanced customer segmentation models.
  • Creating more effective marketing mix models.
  • Using behavioral data to focus marketing campaigns only on those who are most likely to be interested in their products or services.
  • Using Sentiment analysis on social media data in real-time to understand how consumers are reacting to specific campaigns, allowing companies to react quickly when they miss the mark or hone in on that perfect ad.
  • Predicting which customers are more likely to churn, providing marketers with time to focus efforts on retaining customers before they leave.
  • Help marketers personalize ads, offers, and campaigns for segments of consumers.

4. Create the Right UX for Your Team

Now that the data is correlated and cleansed and machine learning models are applied, it’s time to get to the exciting part: visualizing those insights. Data and analytics are only as good as they are presented to key decision-makers. That “last 18 inches” from the computer screen to a user’s eyes are critical to the success of any program, yet it’s often an overlooked factor within many analytics projects.

Last But Not Least

Data orchestration means uncovering stories, not crunching numbers. These stories are about people (your shoppers) for people (your team) to interpret. Given the uncertainties that exist in today’s landscape, the ability to listen and learn will be mission-critical now more than ever.

For retailers looking to learn more about how to unlock the secrets to digital transformation, use your data, and prepare your organization for the future, download our guide, “Welcome to the Agile Retail Era: Mission-Critical Ways to Strengthen Your Digital Acceleration Footing.”


*Chris Daniel was the lead author of a publication team that included Paige Krzysko (editor & lead strategist),  Kylie McKee (editor & contributor), and Ritika Puri (writer).

About the Author

Kylie McKee
Kylie McKee is a Content Marketing Strategist at Maven Wave with more than eight years of tech industry experience and five years of content marketing experience. Prior to joining the Maven Wave team, Kylie worked as a Content Marketing Specialist for WebPT, Inc. and earned an Associate in Applied Science in Motion Picture, Television, and New Media Production with a CCL in Screenwriting from Scottsdale Community College.
August 25th, 2021

Get the latest industry news and insights delivered straight to your inbox.

Sign up for our Newsletter