Using AI to Drive More Precise Targeting and Advertising
Discover how AI tools like computer vision and natural language processing allow retailers to gather customer insights that power highly targeted digital ads and on-site merchandising.
Artificial intelligence is increasingly being adopted by retailers of all sizes to gain deeper customer insights and power more precise digital advertising and on-site merchandising strategies. Through tools like computer vision, natural language processing, and predictive analytics, AI allows retailers to analyze customer behavior data at an unprecedented scale and level of detail. This newfound visibility into how customers interact both online and offline is transforming how retailers target customers with digital ads and merchandise their physical stores.
Computer Vision Reveals What Captivates Customers In-Store
Computer vision uses deep neural networks trained on massive image datasets to quickly and accurately analyze visual media like security camera footage. When applied to footage from inside physical stores, computer vision can gain a wealth of insights into how customers engage with different products and areas of the store.
According to a report by RetailNext, stores using computer vision analytics saw a 30% increase in sales from changes to in-store merchandising and promotions based on heat map data. Another study by Trigo found that style and color preferences differed significantly between locations, emphasizing the importance of localized merchandising.
For example, many retailers are using computer vision to understand which product displays and mannequins draw the most gaze time from customers as they browse aisles. Analytics from these systems have found that certain styles or color combinations tend to stop customers in their tracks much more than others. Retailers are responding by changing out less eye-catching merchandising with what they now know performs far better.
One study found aisle endcaps featuring lifestyle products averaged 27% longer dwell times than endcaps with basic consumables.
Similarly, computer vision data on how long customers pause in front of certain areas reveals where customer interest spikes the most. One study found aisle endcaps featuring lifestyle products averaged 27% longer dwell times than endcaps with basic consumables. Armed with these insights, retailers are now able to much more precisely plan where to feature their best and newest products for maximum exposure.
NLP Reveals Customer Intent from Online Search and Reviews
Natural language processing (NLP) allows computers to analyze and understand human language much like humans. In retail, NLP is most commonly applied to analyze the huge volumes of text data created by customers online through search queries and product reviews.
According to an IBM study, NLP-powered search increased online conversion rates by 12.5% by serving more relevant results aligned with customer intent. Another report found that product listings incorporating key phrases from customer reviews saw a 9% increase in click-through rate.
By training NLP models on past search logs, retailers can better understand common product attributes customers search for together or the language used to describe particular categories. This helps them serve more relevant targeted search results and recommendations to shoppers as they browse online.
According to an IBM study, NLP-powered search increased online conversion rates by 12.5% by serving more relevant results aligned with customer intent.
Similarly, applying NLP to millions of customer reviews reveals the specific features, benefits, and attributes that drive purchasing decisions. An analysis of reviews found certain phrases like “long-lasting battery” were highly predictive of sales for electronics. Armed with these kinds of insights, retailers can highlight the most purchase-influencing attributes in product listings and advertisements.
Predictive Analytics Uncovers Hidden Purchase Relationships
Perhaps one of the most impactful applications of AI for retailers is predictive analytics. By analyzing massive streams of customer purchase, browsing, and other behavioral data, predictive models can uncover hidden relationships that humans may miss.
A McKinsey study found retailers using predictive analytics saw a 15–20% increase in operating margin by optimizing pricing, promotions, and inventory based on data-driven demand forecasts. Retailers also increased click-through rates of targeted email campaigns by 120% and reduced inventory costs by 25–50%.
For example, analyses have found customers who buy a new television are also very likely to purchase a soundbar or streaming device within the next month. Others revealed that millennials who purchase certain activewear brands also regularly buy healthy snack items.
Another report found that product listings incorporating key phrases from customer reviews saw a 9% increase in click-through rate.
Understanding these subtle correlations allows retailers to not just target customers with ads for the exact product they recently purchased, but also position “frequently bought together” complementary products. It creates a more seamless and convenient shopping experience that often leads to higher cart values and repeat visits.
As AI tools like computer vision, NLP, and predictive analytics continue advancing, expect retailers of all sizes to unlock even deeper and more personalized insights into customer behavior. With this actionable data, they’ll be empowered to further optimize in-store merchandising and digital advertising strategies. The end result for customers will be a more intuitively tailored, engaging, and convenient shopping experience across all retail touchpoints.
