2019: The year AI and ML become critical to better retail decision-making

Too often today, retail stores are caught between two damaging extremes that can reduce revenue or poison customer experience: out-of-stock situations and waste. Without better customer insights and data, and with so little room for error, either of these extremes can spell an end to profitability for a retailer. But before retailers can avoid these extremes, however, they must deal with the increasing complexity caused by shifting customer demand. Customers now come to expect a personalised experience whenever and wherever they buy, and that expectation can't be satisfied by the traditional statistical methods retailers have applied to projecting demand and setting pricing. The retailers who've adopted those traditional methods have benefited from improved forecasts at aggregate levels, the limits of those "tried and true" statistical methods mean that retailers still have difficulty making day-to-day customer demand predictions.