In the relentless pursuit of increased revenue and profits, businesses tirelessly strive to attract new customers and sell more products to existing ones. The two foundational entities of any retail business are customers and products. Customers have a finite Customer Lifetime Value (CLV) — everyone eventually stops buying. However, the focus on customers often overshadows an equally crucial element: products. Unlike customers, businesses own and control their products. The introduction of new products holds immense potential, and envisioning a scenario where every new product becomes an instant hit is the dream of every retailer. Such a reality would transform the store into a space dominated by best-sellers, reducing the need for an overly customer-centric approach in all business strategies.
Advances in AI within the retail field have brought us closer to the vision of a store filled only with best-sellers. While customers come and go, products that sell today may not be around in the future. What remains constant is the store itself. Every product sold historically serves as a valuable lesson for an intelligent store, akin to the lessons learned from life's daily events that prepare us for a better tomorrow.
The crucial journey toward creating a store with perpetually fast-selling products begins with a deep understanding of all aspects of products currently selling and those sold in the past. "Product Behavioral Analytics," an analytical methodology focusing on and analyzing the various behavioral aspects of products, is the key to unlocking enhanced business opportunities.
Just as all life forms share a basic DNA structure, products have their own unique "DNA" or personality. In a typical store specializing in a particular product type, such as a fashion and apparel store or a cosmetics store, the type of product sold may change over time, but the collective DNA of all products undergoes minimal shifts.
Products, like customers, have personalities defined by their behavioral aspects. These aspects, including attributes like color, size, shape, vendor, manufacturer, and price range, contribute to the uniqueness of each product. By combining this information with natural language processing and advanced vector techniques, an intelligent store can identify and group products based on their distinct DNA.
Understanding the specific DNA of a product enables businesses to appeal to the corresponding segment of customers. Leveraging historical behavioral data, encompassing purchase history, browsing patterns, email interactions, and cart activities, an intelligent store gains insights into which customer is most likely to buy a specific product. This analysis extends to identifying general preferences and the types of products or DNA profiles that resonate with customers, laying the foundation for a store filled with perpetually successful products.
For a store with a substantial existing customer base, selling to this base is six times more cost-effective than acquiring new customers. The primary reason for this cost difference is that social media advertisements, often the primary channel for acquiring new customers, can be a costly endeavor compared to engaging with the existing customer base through channels like email and SMS.
To ensure the success of a new product launch within the existing customer base, a segmented approach is crucial. Segmentation can be based on factors like the same brand, color, or other properties that the retailer deems important in the decision-making process for the newly launched product. This approach is far more effective than broadcasting to the entire customer base, as spamming loyal customers with irrelevant products is counterproductive. Modern retail requires segmented campaigning, and an effective marketing automation tool can combine different behaviors, such as browsing certain product categories without making a purchase in the last month, to create targeted segments.
AI and predictive analytics, as seen in advanced tools like Enalito, take this a step further. These technologies provide the ability to identify the most likely customers interested in buying a specific product, even a newly launched one. The advanced product analytics approach, coupled with a recommendation system engine, accurately targets customers for a new product. By combining product similarity (content-based recommender system) with the purchase and browsing behavior of all customers (collaborative filtering), this hybrid recommender system can precisely pinpoint customers genuinely interested in the newly launched product.
The power of this recommendation system, based on advanced product analytics, significantly improves Return on Advertisement Spent (ROAS) over time. The key difference lies in the segmentation of the existing customer base based on all behavioral aspects related to the new product. While manual segmentation is challenging and requires skilled analytics capabilities, Enalito's recommender system accurately identifies customers most likely to buy a new product from the existing customer base.
The segment of customers auto-identified by Enalito's recommender system becomes the foundation for advanced recommender systems on platforms like Facebook. Targeting people most similar to this segment (look-alike) in social media campaigns ensures much more specific and narrow targeting compared to other segmentation capabilities. This precision can result in a significantly higher conversion rate.
In essence, a store filled with intelligent products, where each product knows exactly who wants to buy it from the existing customer base, is the epitome of success. The integration of top-notch recommender systems, such as Enalito's and those on platforms like Facebook, empowers every product with the intelligence to identify potential customers worldwide. This approach signifies a significant leap toward achieving sustained long-term growth in revenue and profits for the retail business—a store where every product is not just a commodity but an intelligent entity with the potential to become a best-seller.
