Lift Revenue, Improve Customer Experience, Decrease Returns, and Reduce Shrink
Applying business intelligence and predictive analytics in real-time in the store to influence the customer’s shopping experience is a bold endeavor. Whether building targeted incentives to add incremental revenue or recover lost sales, or monitoring transactions to identify and deter fraud and shrink, we use a scientific approach in optimizing retail solutions.
Verify Return Authorization and Incent Targeted Incentives are based on this premise: the data to predict and shape consumer behavior can be collected, analyzed and optimized, and an immediate response can be sent to point-of-sale for every customer transaction.
The goal of artificial intelligence (AI) in retail is to apply a complex mathematical model to the retailer’s data and learn, using thousands of variables, the most useful elements that are most likely to result in an action. AI involves the computer’s doing the work in place of a human, finding things previously unseen or overly complex. Instantly running “what to do next” analytics has helped Appriss Retail clients accomplish 25 percent more in 25 percent less time.
With an expanding customer base consisting of many different retail verticals, our data repository of sales and return transactions exceeds 45 billion records.
This data set enables our team of PhD statisticians the capability to develop of sophisticated revenue lift models, return fraud models, and best practices for transaction optimization. In addition, ongoing refinement is made possible by searching for changes in return behavior across all retail formats—particularly new internal/external fraud schemes and organized crime rings—far beyond the scope of any individual retailer.
Predictive analytics encompasses a variety of techniques from statistical modeling, data mining and scientific techniques that analyze current and historical facts to make predictions.
In a retail environment, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many consumer behavior factors to allow assessment of risk or potential associated with a defined set of conditions, guiding decision making for shopper-specific retail transactions.
Our data offers a wealth of predictive analytics insights into customer behavior. Our team of statisticians can design and test a variety of targeted incentive models to help determine those most likely to drive sales revenue in all types of shopper segments, retail formats, product assortments, and more.