If there are two things that always stay same in retail, it is change and growth. Since its inception retail has been through transformational eras. The most prominent and inarguably the biggest game changer in the field of retail is the “Dawn of Digital era”. This has turned the retail industry upside down and the changes have been phenomenal. Digitization has redefined retail at every point in its value chain. It has brought in most fundamental changes in retail business models. Retail is no longer about selling products but it is about selling experiences seamlessly across the platforms. Customer delight is one of the key drivers of growth as against customer satisfaction.
Role of Analytics in retail
Analytics will empower the retailer in building actionable insights for smarter decision making. Analytics is no longer a point of difference, but it became a point of parity, a way of retail life. It adds value at every point in retail value chain which includes knowing your consumer, cost decisions, pricing, marketing, merchandising, supply chain and operations. Be it any aspect of business, analytics has proven solutions to maximize value at every part. Consumer data will be the crucial differentiator in the near term. The company which unlocks the hidden insight from this data stands higher probability to win.
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Consumer analytics: Consumer analytics enables retailers and marketers track customers and have an integrated knowledge of customers across channels and platforms. It unfolds and explains the mysteries behind consumer behavior that will enable the retailers to design an enriching overall customer experience at every touch point and create customer lock-in. Solutions under customer analytics include loyalty analytics and retention, new customer acquisition, behavioral segmentation, churn analytics, cluster identification and prospect analytics.
Cost analytics: Cost Analytics will enable finance and marketing personnel and heads of businesses to analyze profitability of different product lines, consumer segments, to allocate costs, decide the right assortment mix and guide the SKU rationalization decisions accordingly to improve profitability.
Pricing decisions: Price analytics help marketers in designing coupons, markdowns and promotions, dynamic pricing, price sensitivity analysis; improve price realization, competitive pricing intelligence strategies.
Merchandising Strategies: It enables retailers to forecast sales, optimize sales order and purchase order management, assortments, track new product performance, maximize revenue from optimized store space allocation by creating efficient planograms and maximize product availability and visibility with low costs.
Supply Chain Analytics: Predictive analytics empowers the retailer to optimize their supply chain by data-driven decision making. It helps in exact demand forecasting, route optimization, product flow cost minimization, inventory turnouts, product velocity. It enables retailers to derive maximum profits out of minimum inventory costs.
Associate analytics: Predictive analytics in synergy with the power of data mining will work wonders for a retailer to increase their revenues by item basket analysis, POS flow, associate loss analysis, associate sales analysis, customer category mix model, competitor product mix analysis, frequent shopper category mix analysis. This will go a long way in anomaly management.
With the combined power of analytics and business acumen, the retailer that unravels insights from the data and the innovation never seen before is set to create that delightful consumer experience.
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