In the midst of so much hyperbole, retail marketers must understand how to future-proof their tech investments. Hint: It’s all about the data.

The holiday season is traditionally the time when marketing professionals assess their strategy and their use of technology. Holiday shopping is the litmus test. Actual sales numbers indicate the effectiveness of a campaign, a mobile shopping app, or something as “simple” as catalogs and signage.

It’s also the time for assessing new tools and trends. Searching for “2020 Retail Marketing Trends” will yield a fair number of lists and blogs. Some of the predictions may seem like science fiction.

Top 5 2020 Retail Marketing Trends

  1. Proximity Beacon Marketing
  2. Facial Recognition of Customers
  3. Smart Robot Shopping Assistants
  4. Smart Display Mirrors
  5. Automated/Contactless Checkout

Future marketing technologies may include beacons, facial recognition, robots, smart mirrors, and auto-checkout. Privacy concerns aside, all such technologies will require a massive data infrastructure.

On the more pedestrian side, many of the predicted trends make sense. Mobile app engagement, targeting, and personalization made the top of most lists. The use of video continued its dominance as a future “must have” for retailers, whether the video content was traditional like television advertising or digital, especially found on social media.

Content marketing and SEO also figure prominently, as does the use of multichannel marketing hubs like HubSpot and Adobe Marketo.

The digital technologies named in these lists ranged all along the Gartner Hype Cycle. Some were at the “Peak of Inflated Expectations” phase, like personification and artificial intelligence. Others fit the “Trough of Disillusionment” phase, like native advertising. Only a few fit the “Slope of Enlightenment” phase, like mobile marketing analytics. So, it’s pretty clear that retailers need to set expectations when it comes to sexy marketing tech.

The Common Thread

However, no matter how soon a marketing technology should be embraced, it’s clear that nearly all of it depends on one factor: the retailer’s vast store of data. Whether a product appears in a personalized app, in a digital “smart sign” or in an ordinary catalog or mailer, it is always represented by data – lots of it. Every SKU has multiple data points that must move smoothly, and without error, to the marketing “interface” of the retailer. Every good shopping experience must have a connection to data about relevant products.

Think about it for a moment. A retailer stores product data in a Product Information Management or PIM database. Maybe more than one; pricing could be separate. Add to that all the photos and descriptions for each product, all coming from different manufacturers and landing, somehow, in the retailer’s Digital Asset Management or DAM system. On top of that, there may be separate inventory and sales databases, not to mention marketing planning, customer feedback, and who-knows-what else.

Most of these data sources are proprietary, developed over time on a more or less ad hoc basis. Even if they are robust and versatile, they usually cannot be replaced. They must be made to work well with other databases, no matter what new marketing channel emerges.

The Challenge

Most of the predicted marketing technology trends focus on smartphones and other personal devices. Most existing databases were developed long before smartphones and personalization existed, which meant that companies had to teach old data new tricks.

This will continue to be the case. Enhancing the shopping experience, building customer enthusiasm, and simultaneously building a hybrid of online and brick-and-mortar retail will require that every retailer has a firm grasp of their product related data – no matter how arcane or complicated the database infrastructure may be. Shoppers’ expectations of convenience and personal service, no matter what medium or platform they experience, will only increase. Regardless of the next decade’s new technologies, everything will rely on the data and how we wrangle it to meet the need.