Globalpraxis and artificial intelligence: Segmentation

Globalpraxis Partners

We continue our journey into the world of artificial intelligence with a key application for managers – segmentation.

Segmentation is fundamental in marketing. It involves dividing a broad target market into smaller, more manageable segments based on various factors such as demographics, psychographics, behavior, or geographic location. It allows businesses to tailor their products, services, and marketing strategies to better meet the needs and preferences of specific groups of customers. Effective segmentation can lead to more targeted advertising campaigns, higher customer satisfaction, and increased sales.

Segmentation has a profound impact on the success of marketing strategies but also on the effectiveness of route-to-market and operational marketing. It allows reaching the right consumer or the right outlet, with the right offer, at the right time, with the right price.

Artificial intelligence, with the aid of big data management, has helped improve segmentation dramatically. For instance, AI involves leveraging algorithms and techniques to better understand and categorize consumer, customers, and outlet behavior.

As is the case for the other topics discussed in our series on AI, advanced data analytics plays a significant role here as well. AI has the ability to analyze vast amounts of data from various sources, including demographics, purchase history, online behavior, social media interaction, and more. A huge difference compared to traditional segmentation based on age, gender, location, interests, etc.

Predictive analytics helps predict future consumer behavior based on historical data, allowing businesses to anticipate trends and tailor their marketing strategies accordingly. For example, Amazon uses AI algorithms to segment its customer base according to past purchase behavior, browsing history, etc. This segmentation helps Amazon anticipate and personalize recommendations, promotions, and messages.

Segmentation refinement is one of the key elements that AI can bring to the equation. We all know how fast-changing and versatile consumers have become, and small changes can have huge effects at the end of the value chain. For example, Coca-Cola has been using AI to analyze consumer data from various sources, including social media to segment their consumer base and identify emerging trends and preferences. By leveraging AI-powered analytics tools the company can adapt its marketing strategies in real time to better target different consumer segments and drive sales.

The same goes when outlet segmentation and activation come into play. It goes without saying, an excellent segmented approach will fail if not correctly implemented at outlet level – a recurrent miscalculation today that we have clearly identified with innumerable clients. Sephora, a leading cosmetics retailer, is a good example of how AI can indeed help succeed at operational level through a precision segmentation that pinpoints customer preferences for highly personalized product recommendations. Walmart, for its part, segments customers by AI-augmented analysis of foot traffic patterns and other factors, tailoring its product assortment and promotions to the different segments.

Similarly, Starbucks utilizes AI-powered customer segmentations to analyze transaction data, loyalty programs information, and social media interaction. This helps identify different customer segments, understand their preferences, and deliver personalized offers, promotions, and rewards aimed at driving loyalty, retention, and revenue growth.

Micro segmentation is also becoming a key element of business success, enhanced by AI. AI can uncover smaller, more nuanced segments, allowing companies to focus on niches overlooked by the competition. It also helps identify which channels certain segments of customers prefer for purchasing, in addition to enabling the optimization of channel selection for specific marketing, activation, or promotions campaigns.

The extraordinary potential of unsupervised learning: Welcome to the future! When AI algorithms are capable of automatically discovering hidden patterns and relationships within data, leading to more nuanced segmentation approaches. Unsupervised learning techniques such as clustering could enable the identification of distinct customer segments without the need for predefined criteria. Unsupervised learning is a type of machine learning where algorithms are trained on unlabeled data without any supervision or guidance. The goal is to find hidden patterns or structures within data. Unlike supervised learning, where the model is trained on labeled data with known outcomes, unsupervised learning algorithms infer the underlying structure of the data on their own.

To be quite honest, we are far from this capability today but it may very well be the future of segmentation.