The topic of artificial intelligence continues to make waves across industries. Within retail, it can streamline daily operations, find new customers and prospects, and even optimize marketing initiatives. But when it comes to the latter, marketers still don’t wholly trust putting their strategies in the hands of AI. They want to learn more, start testing, and see how their programs can benefit from this emerging technology while continuing to own overall direction. 

This article lays out Listrak’s recommendations and tips for leveraging AI within your marketing strategies, as well as potential red flags and essential questions to ask.

AI performs tasks using data points, algorithms, and computational power. Its output is only as powerful as the quality of its input. Recently, we’ve seen many vendors claim their tools use top-performing AI. But we know with our tenured AI experience, that doesn’t mean they’re producing top-performing, personalized strategies relevant to each brand.

How Can Marketers Use AI?

When used properly, AI can help marketers scale their programs and become more profitable. For example, Automation has proven to yield a high return with both email and SMS, so combining already-known effective triggers with AI-suggested send times and body copy can take things to the next level without much effort on the marketer’s side. 

Similarly, leveraging Generative AI to put together campaign creative and text can help save time and improve efficiency with multiple options. While the technology and algorithms improve over time, marketers can test how their curated content performs against AI versions for optimal engagement and ROI. 

However, one of the most compelling ways marketers can use AI to their advantage is through data analysis and exploration through Predictive Modeling. Machine learning and AI can help identify customers and prospects most likely to engage with campaigns, purchases, or churn and what positively or negatively impacts overall loyalty. 

What Should Marketers Consider When Evaluating AI Tools? 

When reviewing AI capabilities with current or prospective vendors, the biggest thing to be mindful of is that not all AI is created equal. As we said above, many platforms are marketing traditional functionality as AI-powered, repackaging otherwise table stakes tools that are either automated, rely on a data point or two, or require a third-party integration(s). This includes A/B Split Testing - in which results are automated and aggregated to determine a winner based on a chosen KPI. Time Zone Optimization - when an optimal send time is selected based on historical performance. Both are critical to any marketing program, but there aren't machine learning or AI algorithms in place to determine results or provide other options. Segmentation and Automation - standard functionality allows marketers to trigger personalized campaigns based on recent activities like leaving a cart behind, subscribing to a list, or purchasing. There's not necessarily any machine learning being applied when you still must provide the content, choose timing, choose criteria, etc.

Many martech vendors are just beginning to explore the AI arena, while others, like Listrak, have been leveraging machine learning and AI within their platforms and innovations for years. For instance, Listrak launched one of its signature solutions, Product Recommendations, over a decade ago. This solution dynamically injects products into email creative and onto ecommerce sites based on various goals, making it perfect for cross-selling, upselling, and reinforcing customer loyalty. Marketers can rely on the engine to decide the best products to show for each contact, rather than having to build multiple versions of a product feed themselves. The constant learning and reiteration of our algorithms, combined with the flexibility for marketers to adjust options, should reassure you of our long-term experience in AI.  

Something to additionally watch out for are AI tools that don't necessarily improve program performance or scalability. You want to work with a vendor whose features can help you reach your goals, not hinder them. For example, "AI-based" segmentation capabilities that don't include prospective buyers within their audiences (only contacts with historical purchase data) leave a HUGE opportunity for revenue sitting on the table. Listrak’s Predictive Analytics allows for identifying and targeting email, SMS, and cross-channel subscriber audiences based on the likelihood to convert or engage, regardless of purchase activity. This is incredibly attractive to marketers as they don't have to manually sift through their ever-growing contact and campaign data points to find the best cohort to achieve their various revenue goals. Plus, pre-built analytics make it easy to track changes in each audience.

An additional hangup that AI marketers have encountered is the wrong information sent to their customers and prospects via email and text, including incorrect coupons, sale terms, or irrelevant products. AI Assistant makes it easy to write a text message in seconds while still allowing for the opportunity to gut-check and refine before sending. Marketers write a prompt into the engine with their campaign goals, and they're given multiple options, helping them scale. It even analyzes any link(s) included in the prompt for additional source material. 

What Questions Should You Ask About AI Tools?

When evaluating a platform’s AI toolset, ask questions rooted in use cases and performance goals to sniff out more specifics on functionality. Consider specific scenarios like wanting to identify lapsing email subscribers with a high AOV, or targeting active recent buyers via text with a cross-sell Post Purchase automation, for example. Probe further into the data sources being used. Billions or trillions of data points as part of an AI algorithm sounds great in theory, but not all those data points will be relevant to your brand or related to your customers. Do you want the performance data of a brand entirely outside of your vertical and target demographic to determine how to craft your campaigns?

Additionally, getting a demo of AI tools can help you understand their effectiveness and functionality. See how long the platform takes to generate output and look for errors or timeouts. Case studies and other visual examples are also worth asking for, and you can always push for more details if a case study features extreme results. If something feels off or too good to be true, that might be the case! Asking for references and existing client reviews can tell more of the story, too – See what other marketers say about their experiences with the tools.

Get Started Today

Artificial intelligence is here to stay and will continue to iterate over time. But don’t fret if your team isn’t quite ready to wholly jump into the AI pool and hand over your entire marketing strategy to machine learning models. Continue to try new things, test repeatedly, ask questions, and actively participate in your campaign planning. A great start is to utilize Listrak’s AI toolset! Reach out to your Listrak Account Manager to learn more.