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.
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 AI and predictive intelligence 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 replenishment, in which retail shoppers purchase the same product at regular intervals (e.g. eye shadow or even basics like underwear), 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 marketing programs.
Critically, in trying to satisfy everyone, these horizontal solutions typically end up satisfying no one. Instead, they usually leave marketing users across different industries frustrated as they piece together workarounds to create personalized experiences for their unique consumer purchase cycle, all because they’re attempting to make the technology work in ways it simply wasn’t intended to.
Retail Marketing Teams 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 continue 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 more shallow, surface-level approach of horizontal solutions. By providing this depth, technology built for retail can drive outcomes around key goals like:
- Surfacing more conversion opportunities through merchandising insights, like replenishment, next best purchase and hidden gems (products with low traffic but high conversion rates)
- Turning casual shoppers into loyal, lifetime customers (versus driving single transactions) by identifying shopper-level optimizations for repeat purchases
- Accounting for engagement across channels, as retail happens on more channels than any other vertical
- Optimizing for increased margins and sell-through
- Reducing return rates by better matching shoppers to products based on critical characteristics, such as sizing
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 types of data 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, retailers who use technology built for retail 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.
Technology Built for Retail 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 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.
The Benefits of Technology Built for Retail Extend Beyond the Solution Itself
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 Technology Built for Retail
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?
Download The Retail Marketing Tech Stack Guide, a comprehensive guide from Bluecore, Google Cloud and True Fit on how to optimize your technology for digital-first commerce, to find out everything you need to know.