We are in the midst of a mass brick and mortar retail extinction at the hands of e-commerce. To survive, retailers need to find new ways to innovate. Most associate the word innovation with new inventions. But there are actually a wide variety of ways for retailers to innovate.
AI is redefining the boundaries of business productivity, with sectors such as retail and wholesale looking to benefit most. With the current pace of AI’s advances, the sky should be the limit. But what if the solutions on offer are too complex for most people to use? And what can we don to combat this?
Here’s what happened when we finally decided to build our very own data farm – including an array of specialized AI processing hardware – completely from scratch.
A considerable imbalance hangs over the human-machine alliance. AI already performs many tasks much better than we do, not least when drawing on data of the past to accurately map out paths of the future. But AI still misses those essential human elements: ingenuity, innovation and common sense. The question we’re now faced with is how we can play to both our strengths to get the most out of the human-machine alliance?
If sales forecasts and inventory distribution models do their job, there should be just enough inventory to meet demand with none leftover piling up on the shelves. This is exactly what our inventory allocation system achieves.
Finding nascent price signals in high-sales-volatility environments is one of Evo’s specialities. Our software can identify price signals as small as a 5% average change even with sales volatility at 30% or higher.
What if you knew what your customers really thought of your product? What if you could identify exactly where you’re going right––in areas of pricing, promotions and perception––and where you’re coming up short? Well now you can through Evo’s Artificial Customer Intelligence. Read on to find out more.
What makes customers decide to buy one particular product over another? We at Evo have developed a successor to conjoint analysis - a technique with high accuracy and tremendous power to predict purchases.
The classic elasticity model is extremely limited in scope. Elasticity depends on many influences, which means it’s highly unpredictable. To generate accurate forecasts, we need to take a closer look at what we actually want to measure.