Strategy

5 Reasons Retailers Need Marketing Technology Built for Their Business

By Sherene Hilal

Marketers in retail, finance, manufacturing and B2B are all worlds apart. In most industries, marketers focus on awareness, traffic, conversion and retention, but they each serve very different customer lifecycles and face big differences in terms of market saturation and competitiveness.

For example, consumers have nearly unlimited choices when it comes to retail products to buy and brands from which to buy them, resulting in fairly fast and frequent purchase cycles. Meanwhile, consumers have a much more limited set of choices when it comes to banking in terms of both brands and products (i.e. checking accounts, credit cards, mortgages), resulting in longer and fewer purchase cycles.

Given disparities like these, why would marketers across all of these industries use the same technology to engage customers? They shouldn’t. They need retail-specific technology and they need it now.

Let’s take a look at the trends driving this change and why retail marketers must make the shift.

1. Retail Trends Are Shifting Back to the Storefront

For years we’ve heard about the death of retail. How could physical stores compete with behemoths like Amazon and the low cost of the web? Couple that with the pandemic shutdowns and well, it seemed like maybe the death of retail wasn’t all that far off. 

The predictions were wrong. With new technology and better opportunities for omnichannel experiences, retail is actually strengthening. A 2022 Forrester/Shopify study showed that 31% of brands say they plan on establishing or expanding their physical retail footprint in the next year. Not only that, 47% of consumers were more likely to purchase from a brand due to its local presence.

Consumers want that in-store moment and brands need the technology in place to create a unified brand experience. The key is having the right technology.

2. Technology Built for Everyone Satisfies No One

Traditionally, most technology was industry-agnostic or “horizontal,” meaning it was built to work for use cases in a variety of industries. This was acceptable in a world where the role of technology was simply automation, but with the necessity of using artificial intelligence and predictive analytics to scale personalized experiences, the horizontal approach leaves a gap in the customer experience. 

The consumer purchase cycle is different across industries, and this results in varying marketing needs when it comes to data, workflow, testing and optimizations. For example, how consumers buy retail products is quite different from how they buy financial products. Consider the case of product replenishment, in which retail shoppers purchase the same product at regular intervals (e.g. eye shadow), or the frequency of new product arrivals within a retailer’s catalog. These cases are unique to retail and not instances for which marketers in other industries must prepare.

As a result, it doesn’t make sense for platforms that manage consumer data for the purpose of marketing throughout the purchase cycle to have the same workflows and out-of-the-box capabilities across these verticals. This is especially the case as companies move from channel-first to consumer-first strategies, which has made how people buy an essential component of responsive, personalized retail marketing programs.

Critically, in trying to satisfy everyone, these horizontal solutions typically end up satisfying no one. Instead, they usually leave marketers across different industries frustrated as they piece together workarounds to create personalized experiences for their unique customer lifecycle, all because they’re attempting to make the technology work in ways it simply wasn’t intended to.

3. Retail Marketers Need Technology Purpose-Built for Retail Goals

The recognition that technology built for everyone satisfies no one has set in. In response, we are now seeing the rise of vertical technology that anticipates a world in which more and more data about consumers exists and expectations around the experiences brands offer continues to rise.

This vertical focus allows for deeper use cases to solve a more concentrated set of problems and stands in stark contrast to the shallow, surface-level approach of horizontal solutions. By providing this depth, technology built for retail can drive outcomes around key goals like:

Critically, all of these outcomes are highly specific to retailers, which means that only technology purpose-built for retail will natively understand these goals and the retail data model required to achieve them. In turn, this native understanding means that different types of solutions built for retail within the technology stack will more easily integrate with one another. 

As a result, marketers who use retail-specific marketing technology will realize enormous gains that span everything from increased efficiency in launching programs to achieve these goals to increased revenue tied to these types of key outcomes. 

4. Retail Marketing Technology Becomes Even More Powerful in an AI-Driven World

The level of depth achieved through technology built specifically for retail provides value in and of itself, but it becomes even more powerful in an AI-driven world.

AI requires data to train models to optimize for specified outcomes, like identifying next best product recommendations for individual customers to simplify product discovery and increase conversions or determining each shopper’s discount affinity to optimize offers shared in a way that protects margins. And the more data an AI-driven solution has, as well as the more specific that data is to the targeted outcomes, the stronger those models will perform. Getting the necessary volume and specificity of data to strengthen performance requires a vertical focus.

In fact, the key to good AI is focusing it on (and training it to master) one very specific problem set for a specific vertical, use case or user type. That’s because different verticals deal with different types of data and optimize for different goals — all of which limit AI models’ ability to learn and improve over time. Think of it like mastering an instrument: You’ll get much better, faster if you focus on just one instrument versus trying to learn how to play multiple instruments at once.

5. The Benefits of Retail Marketing Technology Extend Beyond the Solution

Beyond the improved power of the technology itself, introducing technology built for retail comes with additional benefits around the team supporting the solution. For example, technology partners that offer solutions built specifically for retail will employ retail experts, resulting in more hands-on advice around key goals and use cases from the account team as well as a high-level strategic view of the latest trends impacting retail

It also means that retail features to help respond to the latest market trends will always get prioritized on the product innovation roadmap, since there’s no competition from features designed to help teams in other spaces (e.g. a compliance feature for finance to obscure credit scores) that provide little-to-no value for retailers.

Building Your Marketing Stack with a Focus on Retail Technology

Once you recognize the power of introducing technology purpose-built for retail, how do you go about optimizing your marketing stack accordingly? And what outcomes can you expect to realize from doing so?  

Our Retail Marketing Tech Stack Guide can answer all of those questions and more. To find out everything you need to know about optimizing your technology for digital-first commerce, download this comprehensive guide from Bluecore, Google Cloud and True Fit.

Sherene Hilal

Sherene Hilal

Sherene Hilal is the Chief Product Officer at Bluecore. In this role, Sherene leads the commercialization of products and business operations, with a focus on product vision, market differentiation, and enterprise excellence. Previously, Sherene was the VP of Product Marketing at Curalate, a content intelligence platform that makes images shoppable. Prior to Curalate, she served as the Senior Director of Product Marketing & Pricing for BlueKai (and later Oracle, following the company’s acquisition), where she defined and developed the “Data as a Service” category. Sherene holds a Master’s Degree in Applied Math and Systems from Columbia University and a B.S. in Applied Math from Cornell University.