AI in Retail Marketing

The Power of Predictive Personalization & Automation

A unified data approach is critical to effective, AI-driven personalization and automation—and the profits it can generate.

In a world where customers interact with brands across countless channels, delivering relevant and personalized experiences in real time has become critical for retailers. Artificial Intelligence (AI) is changing the game: it enables companies not only to react to customer behavior but to predict it. This opens up entirely new possibilities for automation and customization. However, the effectiveness of these systems strongly depends on the quality of the data they are trained with. Only precise, current, and relevant data will lead to reliable predictions—whereas incomplete or biased information can result in flawed personalization or even discriminatory outcomes. With the power of this technology comes great responsibility in handling data.

The first thing that retailer CMOs, marketing directors, and creative service managers need to remember is that AI is only as good as the quality of data you feed it. It’s an age-old concept: “garbage in = garbage out.” So, before launching a new AI initiative, especially one involving personalization, ensure your data are accessible, secure, and of sufficient quality to create the right results. That starts with having a single source of truth model for your data. For retailers, it means using robust product information management (PIM) and digital asset management (DAM) systems, such as those found in Comosoft LAGO

Artificial Intelligence is only as good as the quality of data you feed it.

Make it Personal: Redefining One-to-One Marketing

Traditional marketing targeted demographic groups or broad market segments. With AI, the focus shifts to one-to-one marketing: each customer receives a tailored message based on their behavior, purchase history, and interests. Algorithms analyze vast amounts of data in real time to generate personalized product recommendations, dynamic pricing, or customized email content.

Email marketing is a prime example. Instead of sending the same message to everyone, AI generates individualized offers—featuring favorite brands, ideal price ranges, or optimal send times. According to a study by Experian, personalized emails achieve up to 41% higher click-through rates than non-personalized ones.

In a recent case study, AI uncovered shopping patterns that, once expressed as promotional offers in LAGO, resulted in significant revenue increases. In other words, AI can convert your existing data into direct, relevant communication with your customers.

Make it Multichannel: Data-Driven Personalization Across All Touchpoints

Comosoft LAGO already enables retailers to plan, manage, and produce exceptional circulars and flyers, including versioning by region, language, and demographics. Its automated data automation approach achieves up to sixty percent reductions in labor costs and shortens time-to-market by up to thirty percent. But LAGO is not limited to printed materials or a narrow, “one-to-many” messaging formula.

LAGO combines customer data with product data, outputting the results to any digital channel.

LAGO’s
data personalization potential can combine customer data (addresses, recommendations, and purchase behavior) with product information and images, outputting the results to any digital channel.
Today’s customer journey is rarely linear—consumers shift between online stores, apps, social media, and physical stores. The challenge for retailers is to deliver consistent and context-aware experiences across all channels.

AI enables businesses to merge data from various touchpoints into a unified customer profile. For example, if a customer shows interest in a product on Instagram, they might receive relevant recommendations or exclusive discounts during their next website visit. This kind of cross-channel personalization not only boosts relevance but also strengthens loyalty. According to a McKinsey study (2021), data-driven personalization across channels increases purchase likelihood by a factor of 3 to 5.

Make it Meaningful: Relevance Over Noise

Using data to uncover customer’s preferences has its downsides. Personalization is only effective if it provides real value to the customer. Many brands use AI-powered targeting but miss the mark by offering irrelevant recommendations or poorly timed messages. Customers don’t feel understood—they feel annoyed.

“Meaningful personalization” requires more than data—it requires customer-centric thinking: What information is truly helpful in the moment? What content fits the customer’s current life context? Someone on vacation doesn’t want in-store promotions—they might prefer travel-related products or delayed delivery options. The focus must be on relevance, timing, and emotional context. Only then does personalization resonate.

A study by the University of Pennsylvania and the Center for Digital Democracy (2020) reveals that most respondents feel uncomfortable when companies extensively analyze their online behavior—even if it’s intended for their benefit. It becomes particularly problematic when personalized advertising is based on sensitive information, such as health data or political views.

The key, therefore, lies in responsible data usage. Transparent communication, genuine consent, and clear customer value must be at the heart of any AI strategy. This is the only way to build trust and long-term customer relationships.

Make it Meaningful: Relevance Over Noise

Personalization is only effective if it provides real value to the customer. Many brands use AI-powered targeting but miss the mark by offering irrelevant recommendations or poorly timed messages. Customers don’t feel understood—they feel annoyed.

“Meaningful personalization” requires more than data—it requires customer-centric thinking: What information is truly helpful in the moment? What content fits the customer’s current life context? Someone on vacation doesn’t want in-store promotions—they might prefer travel-related products or delayed delivery options. The focus must be on relevance, timing, and emotional context. Only then does personalization resonate.

Retailers are faced with a dilemma: so much data, so little time, and too few tools to handle complex, personalized promotions. However, all three concerns can be addressed with a unified data approach, such as that embodied in Comosoft LAGO. Starting with a single source of truth model, retail marketing planners, designers, and production staff can automate substantial portions of the print and digital workflow. By integrating artificial intelligence and machine learning, savvy retailers can leverage that “single source of truth” to become more relevant to their customers and achieve greater efficiency, customer engagement, and measurable ROI.

Find out more about how LAGO and its AI-driven personalization and automation can revolutionize your retail marketing. Or book a demo to see for yourself.

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