“As data piles up, we have ourselves a genuine gold rush. But data isn’t the gold. I repeat, data in its raw form is boring crud. The gold is what’s discovered therein.” ― Eric Siegel, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

Anticipating retail trends is anticipating the future of business and customer response for a product. During COVID-19, eCommerce took the entire burden of running the economy and reaching out to customers. The phase pillion into post-pandemic times, and eCommerce grew exponentially, powered by AI and Machine Learning, it blended naturally with brick-n-mortar stores and brought convenience of shopping either online or offline or both. Since the industry is virtual, a close connect with the end customers is required to predict behavior pattern. Predictive analytics applications forecast demand, trend, customer behavior and expected changes in buying pattern. It has its purpose and uses in healthcare, manufacturing, entertainment, HR, weather, retail and more. Prediction helps in smart strategizing and empowered decision making. Through this post, an attempt is being made to understand the significance of predictive analytics in retail and how it can make a positive impact on the status of an organization.

Significance of Retail Predictive Analytics

Retail Predictive Analytics tally historical data and those being collected in real time. Information gathered is collaborated to make prediction and answer several big as well as small questions related with the prospects of business in future. Retail data forecasting is a practice instrumental in making a huge impact on retail operations. Forecasting is based on numeric data and the factors affecting those numbers. Predictive analytics crystalizes information for holistic decision making.

• It is the safest and surest way for customer behavior analysis as it provides valuable insight on customer behavior.
• With predictive analytics, retailers can focus on customer behavior forecasting. This provides some insightful information on future sales, failure or success of new launches, chances of success for a marketing idea and others.
• Trend prediction strategies calculate several features before making a final decision or taking a call on future practices. It considers data pouring from several sources, compares it with past responses and behavioral pattern.

Predictive modelling in retail essentially is the outcome of the study of customer behavior trends, making a retailer prepared for surprises from the customers. Suppose a customer visits an eCommerce site for a specific item and if unable to find, he or she will move on to another one. This will be a loss for the retailer, the predictive analytics prepares retailers for all situations and scales up the business.

Five Benefits of data-driven Predictive Analytics Applications

By launching predictive analytics applications in the supply chain management system, businesses can solve several problems and pave the path for success as it becomes easy to stay connected with the target customers. This connect improves customer behavior forecasting. Let us evaluate top 5 benefits of retail predictive analytics.

1. Inventory Management Optimization: Predictive analytics provide insights for anticipating retail trends. This helps retailers to plan out inventory requirements, stock up inventory based on past purchases and inquiries. Data-driven decisions streamline supply chain management and curtail any unnecessary or unwanted costs. It also improves order fulfilment time as there is no over-stocking or under-stocking of inventories and bottlenecks have already been dealt with for future demands.
2. Personalized Marketing for Customer: Powered by AI and Machine Learning, retailers can create personalized marketing techniques. Instead of creating generic marketing plans, personalized efforts are always welcomed and garner better response. Once there is an understanding on customer behavior trends, retail data forecasting can make marketing more specific. For example, a customer looking for boot-cut jeans can be exploited with options by retailers if there is an awareness. It is always a pleasure to receive a customized floating message early in the morning. It is sure to win customer interest and keep both customer as well as the retailer in a win-win situation.
3. Customer Service Automation: By leveraging AI-powered forecasting, customer service can be improvised and can be made more personal in approach. Automated marketing gives more personal touch and their acceptance has also grown as they make suggestions based on past behavior of a customer. Chatbots are capable of analyzing sentiments, geographical location and generously contribute towards increasing customer satisfaction.
4. Cross-selling: Based on past trends of customer and purchasing habits, suggestion could be made on related products. Data-driven insights highlights that information and help retailers.
5. Improves Customer Trust and Relationship: Machine Learning engages cookies in collecting information about a customer. Predictive analytics provides accurate forecast about a product demand and help retailers in improvising or withdrawing a product if required. These quick actions build customer trust and reduce chances of losing business.