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Why using AI to drive retail performance requires the right data, strategy and partner

November 13, 2023

The market for AI technology and services is expected to reach $407bn by 2027, and already companies are evaluating, experimenting, or executing AI-driven strategies. According to Salesforce, 68% of marketers in 2022 claimed to have a fully defined AI strategy, a huge leap over the 29% that said the same in 2019.

But whether those strategies are creating impactful results is another story.

AI is not some magic wand you can just wave over your marketing stack for triggered email or SMS messages and expect to move the needle. You have to apply AI technology to two things first: good data and smart strategies.

It’s about the data

All the tech talk and big predictions aside, AI is just a highly efficient tool for organizing and surfacing data. The “Intelligence” part of AI relies on data, both yours and that of your solution provider.

For instance, brands often have massive amounts of data about both customers and products, most of it unstructured. Tapping that data into actionable, structured information is like tapping into a goldmine. AI could extract customer attributes such as gender, age, product taxonomy, components, color, and more which brands can leverage to make better decisions.

AI can help predict a customer’s probability of purchasing a particular product, and then make more relevant product recommendations. It can assist with product design, pricing, and even messaging. But it all relies on good data to start. Not just a large database, but a quality database.

For marketers, that means relevant first and zero-party data collected through direct customer interactions from sales, communications, and other points of direct contact.

But that’s not the only data marketers can leverage. Working with the right marketing technology platform can provide a wealth of additional consumer behavior data as well, gained from powering thousands of campaigns across a range of companies and industries.

Wunderkind for instance has petabytes of consumer behavior information gained from trillions of individual data points accumulated over 13 years. In just the last 30 days, we’ve tracked over 170 billion e-commerce events and captured nearly 950 million unique device or email associations, and over 11 billion e-commerce site page views.

That’s not boasting. That’s an example of the vast volume of data that brands can tap into, which can act as a force multiplier to rapidly scale the effectiveness of their own internal data sets.

But strategy matters too

Data is the foundation, but what you build on it matters as well. It’s easy to get caught up in the AI hype and get distracted by fancy use cases that don’t necessarily address your core needs as a business.

The most practical use case for AI is to improve the efficiency of existing operations and deliver actionable predictions. Just be sure to focus your AI execution on your most valuable interactions, not your highest-volume ones.

That may sound counterintuitive. Sure, it’s tempting to use AI at scale across millions of lower-value interactions just because it makes someone’s life easier. But focusing efforts on more high-value interactions like customers adding items to their cart in real time will deliver a far better ROI on your AI investment.

Interestingly, AI can help with this as well. Propensity modeling uses machine learning to help ensure you’re reaching the best customers by examining past open, click, and purchase behavior. It essentially lets you determine which customers are most likely to buy your products or perform other actions, calculate a customer lifetime value, determine churn potential, and evaluate the value of leads.

Other targeted uses of AI include:

  • Recommendation engines: Make product recommendations based on previous behavior and stated preferences.
  • Collaborative filtering: Analyze customer tastes, preferences, and browsing history to predict what they might buy next, find new customers, and reduce customer churn.
  • Strategic upselling: Convert an abandoned cart into a purchase by either upselling to a luxury option or down-selling to a bargain option, or redirecting to suggested alternatives if an item is out of stock.
  • Complementary cross-selling: Identify products frequently bought together and offer a bundle recommendation, increasing both customer satisfaction and cart size.

These are just some of the ways AI can play a role in e-commerce execution and marketing beyond simply triggering a generative email. These are powerful, revenue-generating tools with far greater strategic potential than limiting AI to “simple” communications.

That’s why it’s important to select a marketing solution partner that not only understands the bigger picture, but has the ability to deliver on it as well. When evaluating marketing technology providers, be sure to ask not only whether they have an AI solution, but how it integrates with your existing technology, and how it will increase efficiency and drive results.

In a world where AI's disruptive force is reshaping the fabric of traditional business models, companies should be trading in tangible outcomes, not promises or whiz-bang tech. Don’t settle for mere solutions. Demand results and outcomes from a partner that not only shares your long-term vision, but has the means to deliver as well.

For more actionable insights and advice on how to evaluate and integrate AI capabilities into your e-commerce marketing strategy, download Wunderkind’s free report, Revolutionizing Retail: How To Navigate The AI Landscape To Drive Performance.