1. Mine the customer journey for deep insights into perceptions and habits
2. Make loyalty programs more effective by predicting uptake
3. Create omnichannel analysis across customer touchpoints
4. Optimize product mix and pricing with algorithm-driven decisions
5. Conduct market basket analyses to find powerful product combinations
6. Boost the results of every campaign with more precise offers
1. Enhance merchandise planning with machine to auto-prune unprofitable products
2. Optimise supply chains and eliminate stock outs without additional inventory
3. Plan the most successful store sites and optimise in-store demand against staffing with AI
1. Identify patterns that enable immediate detection of fraudulent purchases and returns
2. Inject customer voice into decision-making with analytics-drive insights
3. Enhancing product quality assurance programs with predictions of likely defects
4. Find and retain the right staff by predicting which candidate hires will be the most successful employess
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.
Let us show you how data science can capture the new opportunities in your changing industry to drive revenue, cut costs, and avoid risks.