Let Us Explore How Predictive Algorithms Are Helping in the Retail Industry

Predictive algorithms are everywhere these days. Now, we are in an age when data holds the key to a company’s success and power to transform an industry. It is no brainer that enterprises are eager to invest in using proper tools that can mine data, analyze it and predict the future trends, growth curve, forecast the success of a marketing campaign among many possibilities. All these are possible because of predictive algorithms used in data analytics and machine learning. These predictive algorithms can also be leveraged to augment the retail sector, where most applications of this technique exist viz. buying patterns, fluctuation of customer behavior as per demand season, etc.

Earlier, the retail decision making revolved around a retroactive dashboard of KPIs. These KPIs were on the study of past customer activity behavior. But, as per current times, retailers need real-time analytics for improved response capability to stay relevant in the market by aligning themselves as close as possible to customer demands, with careful attention to sub-divisional and demographic breakdown. Besides, for retail, one cannot use the same predictive model that was implemented in other sectors. For instance, say a retailer wants to estimate the probability of customer churn, he cannot use the predictive model that was employed at a logistics company trying to calculate the possible shipping routes. The chief goal is to go beyond the limited identification of what has happened to give the best review of what may occur in the future.

While gendering past purchase history to create personalized experience later, may make the customer feel that retailers are genuinely interested in offering their best services, it may not always yield fruitful results. Apart from that, communication with customers has always been difficult irrespective of the scale of the company or business. So while data existed, retailers failed to make most of it before. Thanks to predictive algorithms, retailers have a chance to take a proactive approach based on real-time data and predict future trends. It further enables them to come up with new strategies and offers to attract more business. It not only helps them identify the most popular products but also reveals popular products or combinations preferred by the customers. Moreover, by collating demand, product pricing history, competitor activity, and inventory levels, predictive algorithms can automatically set ideal prices to respond to market changes in real-time.

Using predictive algorithms, retailers can also predict the online return propensity based on historical trends in transactional attributes and customer demographic parameters. This is crucial to making differential returns policy, sales strategies, return control measures as well as loyalty program refinements. It even helps in deciding the better of the proposed marketing campaigns that target a particular niche of the audience. A McKinsey study had stated that targeted campaigns could deliver 5 to 8 times the ROI on marketing spend and lift sales 10 percent or more. Lastly, devising predictive algorithms allow forecasting the potential revenue for a selected store location-based on several factors. These include local demographics, zip code prices, remoteness from existing locations, product preferences, competitive market conditions, customer purchase power. These attributes go a long way in selecting the next retail outlet.