Analyzing Product Connections to Increase E-Commerce Revenue
Problem:
A leading global sports apparel and footwear manufacturer was struggling with stagnant sales on its direct-to-consumer website. Although single item transactions were steadily increasing, they noted a stark contrast with in-store transactions where customers usually purchased multiple items. The manufacturer wanted to replicate this in-store behavior online, aiming to increase the number of multi-item transactions on its direct-to-consumer platform.
Solution:
Steven began by examining the underlying factors that were contributing to the higher average item sales per transaction in physical stores. He partnered with local retailers and conducted comprehensive analyses of in-store sales data, customer behavior, and retailer practices.
A critical finding from his analysis was the influence of in-store sales representatives who often utilized their observations of frequently purchased product pairings to upsell customers, especially during the checkout process. They would recommend additional items that paired well with the products customers had already chosen, thereby increasing the items per transaction.
Inspired by this in-store strategy, Steven conceptualized a similar approach for the online platform. He developed a product relationship-based model that examined the patterns and significance of product pairings in online sales. Utilizing transaction history and user behavior data, the model identified pairs or sets of products frequently bought together.
By leveraging these insights, Steven was able to propose personalized product recommendations to online customers based on their shopping history and customer segmentation. If a customer added a particular item to their shopping cart, the website would suggest other products that were often purchased with it, effectively replicating the in-store upselling strategy.
Recognizing the broader applicability of his model, Steven worked to incorporate its insights into product positioning discussions. These insights informed decisions about which products were advertised together and how they were presented on the website, contributing to a more effective online sales strategy.
Steven collaborated with internal teams to develop a new online recommendation engine. Instead of merely suggesting individual items, this engine curated outfit and wardrobe collections based on the model's insights into commonly purchased products. If a customer showed interest in a certain type of sports shoes, for instance, the engine would present them with a collection featuring those shoes along with matching apparel and accessories.
Through this combination of data-driven insights and strategic implementation, Steven was able to significantly influence the online shopping behavior of customers, encouraging them to purchase multiple items per transaction, much like they did in physical stores. The implementation of the recommendation engine based on his product relationship model not only enhanced the user experience but also provided a substantial boost to the sales of the global sports apparel and footwear manufacturer.
Custom Product Relationship Model
As part of his approach to tackle the business problem, Steven built an association-based model that analyzed several years' worth of online customer transactions. The transactions data spanned across various regions and member segments, providing a comprehensive and diverse dataset for analysis.
The core of his model revolved around identifying product relationships at the transaction level. It aimed to decipher patterns and correlations between different products that customers purchased together within a single transaction. This type of analysis, often referred to as 'Market Basket Analysis,' helped to reveal combinations of products that were frequently purchased together.
To make the model more flexible and adaptable to various business needs, Steven coupled it with a user-friendly front-end form. This form allowed business users to modify the scope of the data based on specific parameters such as date ranges, regions of interest, and desired transaction grouping durations. This not only made the model more accessible to non-technical users but also allowed them to customize the analysis based on their specific use cases.
One powerful feature of this model was the transaction grouping parameter. By inputting a specific value into this parameter, users could group individual customers' online and in-store transactions that occurred within a certain timeframe. For example, by entering a value of 90 into the transaction grouping parameter, the model would consolidate all transactions made by each customer, both online and in-store, within a 90-day period into a single grouped transaction.
This feature provided valuable insights into the influence of seasonality on product relationships. By comparing the product relationships across different time frames, the company could understand how the popularity of certain product combinations varied throughout the year. These insights could inform decisions about seasonal promotions, inventory management, and product development.
Steven's model offered a data-driven solution that was not only powerful and insightful but also user-friendly and adaptable to various business needs. It demonstrated the potential of using data analytics to understand customer purchasing behavior and inform strategies to boost sales and increase customer satisfaction.
Actionable Insights to Drive Online Sales
Steven's custom-built association model was successfully integrated into the client's production environment, where it continues to serve as a powerful analytical tool. Its primary function is to identify emerging trends in product relationships across an ever-expanding range of customer segments. These insights have helped the company adapt and evolve its product offerings and marketing strategies to better meet the changing needs and preferences of its customers.
To make the model's insights more accessible to non-technical users, Steven paired it with a user-friendly dashboard. The dashboard was designed to be intuitive and straightforward, allowing users to easily navigate and interpret the model's output. One of its key features is the ability for users to select a specific product and instantly view the other products that are most commonly purchased with it. This feature leverages the power of the association model to provide real-time insights into product relationships.
The dashboard and the insights it provides have proven to be an invaluable asset in a variety of internal discussions and strategic decisions. For example, it has informed product positioning by identifying which products are often bought together, suggesting potential synergies that could be exploited in marketing campaigns or product displays. Similarly, the insight into product pairings has been used to create outfit recommendations, offering customers complete outfit solutions and potentially boosting the number of multi-item transactions.
In the context of inventory management, understanding which products are frequently bought together can inform decisions about stock control, ensuring that popular product combinations are always available to customers. Finally, and perhaps most importantly, the insights generated by Steven's model and dashboard have deepened the clients's understanding of consumer purchase behavior. By illuminating the often hidden relationships between different products, the company can better predict and respond to customer needs and preferences, driving sales and improving customer satisfaction.
Steven's association model and accompanying dashboard have equipped the client with a powerful tool for data-driven decision making, enabling the company to enhance its business operations, product strategies, and customer engagement efforts.