Power to Predict
While working as a technology and strategy consultant with retail giants, we often asked about how predictive analytics is going to help them with future trend and customer management.
Retail companies are generating and storing the humongous amount of data via POS, CRM and loyalty programs, Inventory and staffing system, and other sources. In-store and online analytics system collects, and measures this data to provide an insight about consumer behaviour. Advancement in analytics will help retailers for accurate forecasting for sales and operations. It gives insights about targeting new customers, forecasting store traffic on big days and festival to maintain better staffing, maintain efficient inventory level.
Predictive analytics is a part of data science that is extracting information from data and using it to predict trends and behaviourpatterns. In business, predictive models and results are based on historical and transactional data to identify risks and opportunities. Predictive tools builds the relationships between multiple factors to allow assessment of potential outcomes/possibilities for existing situation, guiding decision making for senior management. Predictive analytics uses statistical methods, machine learning algorithms, and heuristics.
Now the customer has competitive options available with retail shops, retailers now need to focus efforts on maintaining long term customer satisfaction, rewarding consumer loyalty towards brand and shop, this will help to minimize customer attrition.
Retailers usually respond to customer attrition reactively, taking efforts to retain only after the customer decided to go with other retailers. At thisstage, the chance of retaining a customer is difficult. Proper application of predictive analytics can lead to a more proactive retention strategy. With predictive analytics, retailers are becoming proactive rather than reactive by analysing the customer shopping pattern and taking additional steps to cope up with any situation analytics
Predictive analytics attempts to do exactly that, the retailers are increasingly creating it to better understand the needs and behaviours of shoppers, gain more precise data about customer future purchase and how we can increase profit out of it.Retailers are now able to build personalized loyalty programs, real-time POS discount system, personalized product suggestion which ultimately leads to customer retention.
Recent advancements in analytics have introduced predictive behaviour analysis for web base fraud detection with the help of heuristics in order to study the normal web user behaviour and detect patterns indicating fraud attempts.
At the time of big events, festivals retailers in America have faced a problem of returning the products after using for event, game shows, this leads to big loss on product sell and massive disruptions in inventory management.Using predictive analytics models of real time data, including understanding upcoming big, seasonal events, helps retailers to predict and tackle the rise of fraud cases.
This gives managers the ability to channelize the store staff with effective guidelines and rules to prevent return fraud. From human resource management prospective, employee fraud can be predicted by looking at the past history of employee and fraudulent returns and comparing these with employee working schedule, accordingly management can take effective measures to overcome fraud situations.
With the advancement in the system now, Predictive fraud analytics are integrating with predictive queuing analytics— where retailers can gauge the likelihood of a queue developing based upon the number of people in store at the present time, external economic and social conditions, the weather and multiple other factors that can contribute to rise in the number of cases in In-store theft.
Marketing and Promotions
While marketing consumer products, there is the challenge of keeping up with competing products and seasonal discounts by competitors. Not just understanding the customer behaviour, Predictive analytics can also help to build an effective strategy for to multiple product release, pricing, marketing communication, across all the segments of consumers. The ultimate goal is keeping order cost of the product as profitable as possible without disturbing the product sales cycle.
Smart Pricing and Product Collaboration
Different product have different selling points, some with discounted prices and other with optimal relative price, or any add on promotional price for ex. Winter hand gloves sales increase when it is discounted and combined with winter coat.The advanced predictive analytics solution will tell you what drives sales of every specific product unit down to the store level to create smart pricing and product collaboration strategy. This way, retailers can clear their remaining holiday inventory without making major lose.
Replenishment & Allocation
The process of product replenishment and allocation can be challenging for seasonal fashion retailers. With new variations, style and demand in every season, it’s difficult for fashion retailers to get rid-off the old inventory stock on profitable margin. The even more challenging part is seasonal stock from a vendor which can be hampered by lead – time variability leads to out of stock situation for high demand products. This is the reason why retailer should always need to have safety stock for high demand products.
The major challenge to maintain the safety stock is multiple factors affecting the demand of seasonal products such as weather, price, change in taste of the consumer, launch of new product from competitors this can lead to over/under stocking of the product. Retail analytics can help retailers to choose the optimal way of replenishment and allow to build stock only when it adds the value.