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Use Cases | Retail | E-Commerce
Discover moreExperian Convex | Use Cases | Retail | E-Commerce
Retail/e-commerce companies can benefit from Convex to better manage supply, demand, marketing, human resources, fraud and operational efficiency.
Available Use Cases
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Retail / E-commerce
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Revenue/ sales prediction
Challenges
- Having the right products in the right place at the right time is one of the biggest challenge for retail. Purchasing more inventory than needed is a poor use of working capital; however, if the store doesn’t have enough inventory, there may be lost sales as well as customer frustration.
- Current solutions are limited to aggregated levels such as by department or category. These methods fail to capture the nuances in the data such as holidays, special events etc.
Opportunities
- Machine learning models can create forecasts for up to 1 million series in each project.
- Different products could have vastly different sales history, seasonality, and trends. Automated machine learning can automatically perform unsupervised clustering to find like-groups of products, and model each cluster separately (e.g., by store, region, category, correlation, etc.).
Product assortment and placement optimization
Challenges
- Traditionally retailers have stocked their stores with the same products based on the season. However, different store locations have different customers, weather, display and inventory capacity resulting in very different needs. The result of one-size approach is “stock outs” on hot items and markdowns on others, both costing the retailer hard-won profits. Customer satisfaction and loyalty are impacted when shoppers cannot find items in the store.
Opportunities
- AI models can look at a variety of factors: past sales, store display space, local trends, online behavior, predicted weather patterns, and more to determine which products would be the best fit for a given store location. This AI based optimization prevents stockouts by sending more inventory to stores where products are most needed and minimizes markdowns by making sure that products are on display where they can be sold at full price. AI models can reroute inventory between stores to ensure that retailers can take advantage of local trends.
Churn prediction
Challenges
- Loss of customers without prior notice
- No action for the customers based on their leaving probability or value to the company
Opportunities
- Identify customers who tend to leave in the near future with the AI algorithms
- Take actions to retain valuable existing customers based on churn prediction models
- Maintain revenue from the existing portfolio
Propensity prediction
Challenges
- Limited number of products and limited revenue from the customers
- No proactive cross-sell or up-sell actions
- One-fits-all product campaigns
Opportunities
- Identify customers who tend to buy additional products or increase usage in their existing products
- Take proactive cross-sell or up-sell actions to get share in the market for specific products from the valuable customers
- Grow the portfolio and product coverage
Segmentation
Challenges
- Lack of granular customer segments
- Traditional customer segmentation without analytical methods
Opportunities
- Granular customer segmentation including risk segmentation or marketing segmentation concepts utilizing advanced clustering AI algorithms
- Customized customer management or risk activities based on customer segments
Customer lifetime value estimation
Challenges
- Limited information regarding the value of the client
- Standard actions to approach each client independent of the value of the client
Opportunities
- AI models can help calculate the lifetime value of the clients
- Increased revenue
- Customized client actions for affluent clients
Fraud
Challenges
- Lack of strong fraud models
- Lack of adequate number of fraud events in the data
Opportunities
- AI is ideal for anomaly detection which helps to capture unusual events in the behaviour of the customer
- Fraud data events can be increased within the portfolio sample with oversampling, undersampling or class weight features