CPG, Retail & eCommerce
Consumer-oriented companies have tremendous opportunity to maximize profits at every stage of the value chain, from optimizing pricing to perfectly aligning supply and demand.
Why AI Now?
No industry has weathered change in recent years as much as retail, eCommerce and consumer goods. However, change presents new opportunities. The digital transformation of the customer journey means machine learning can be used to accurately segment customers and target them with personalized and relevant messages and offers. Social media in particular is a treasure trove of customer insight, both at the individual level — where brand advocates and detractors can be addressed one-on-one — and as a resource to understand broader sentiment towards brands and products. RapidMiner can help companies take these industry shifts in stride and excel past their competition. From understanding buying behaviors to setting the optimal price to ensuring supply meets demand, RapidMiner can help consumer goods companies and retailers maximize profits at each stage of the value chain.
CPG, Retail & eCommerce Use Cases
Highlighted RapidMiner Impact
An ecommerce company used predictive analysis of market baskets and purchasing patterns to tailor mobile incentives, boosting repeat purchases within 30 days.
A consumer products company used text analytics on social media posts about competitors’ products to find opportunities to differentiate and stand out in the market.
A retailer optimized its SKU mix by merchandise segment and channel, eliminating unprofitable products and maximizing distribution efficiency.
A retailer increased campaign effectiveness with better targeting of customers with offers and promotions, increasing purchase frequency and size.
An ecommerce company improved its demand forecasting with predictive analytics, reducing its overstocks and inventory write-offs.
A retailer optimized its new store location decisions using predictive planning, helping it achieve more per-foot productivity from each location.
An online retailer reduced fraudulent ecommerce orders by identifying patterns and applying them to each order, immediately suspending suspicious orders.
A consumer product company improved its maintenance services and customer satisfaction based on what predictive analytics said were the most common attributes leading to 1-star product reviews.
A retailer conducted call center analytics and improved its workflows, particularly around customer complaint remediation, making customers happier and decreasing the risk of negative reviews.
What Our Customers Say
“Allows folks with various levels of skill to be successful. New features are aligned with the business challenges we face. The vendor listens and is very close to the users.“
– Director Data Science in the Retail Industry