How to Use Predictive Analytics in Retail
How to Use Predictive Analytics in Retail
Retail is a field where businesses succeed by effectively uncovering what customers will like next. Predictive analytics can be the difference between a strong revenue stream and a dwindling sales pool.
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Retailers today produce more data than ever before, but their massive pools don't always translate into successful outcomes. Because there is so much information and because competition continues to increase, retailers are more hard-pressed to convert information into unique insights that give them an edge in attracting future sales — feats that are often easier said than done.
How can companies find actionable information that will help them track not just what's currently selling, but what will sell in the future? More and more, businesses in every industry have been turning to predictive analytics. However, few fields may be as optimized for the technology compared to retail. In a field where businesses succeed by effectively uncovering what customers will like next, predictive analytics can be the difference between a strong revenue stream and a dwindling sales pool. By taking advantage of these easy-to-implement strategies, you can employ retail predictive analytics to enhance your operations.
Improve Engagement and Personalization for Consumers
One of the biggest challenges retailers face in a commoditized industry is turning one-time shoppers into brand loyalists. Even so, the amount of data a single sale produces today can help generate significant insights you can use to convert customers into followers. Massive retailers like Amazon already track users' habits, search histories, shopping preferences, and more.
It's not just massive e-commerce giants who can use this data, though. For smaller retailers, combining these insights with predictive analytics can reveal new potential sales, display emerging trends, or even give an idea of new products prospective customers may want. By incorporating retail analytics into predictive models, you can more readily foresee customers' needs and encourage shoppers to come back for a personalized experience.
Enhance Your Inventory and Store Management
The days of having a fully stocked inventory all the time are quickly fading. Having too much of an item that isn't selling or not having enough of a popular product can be equally damaging to your bottom line. However, most companies still use the same standard method of basing future orders on historic patterns. This isn't always a problem but can prove challenging when you're left holding a container of products you can't move without taking a loss.
Using predictive analytics grants you a path to both reduce expenses on inventory and ensure that the stock you're buying converts into sales instead of sunk costs. Retailers who deploy analytics can focus their efforts to highlight areas of high demand, quickly pick up on emerging sales trends, and optimize delivery to ensure the right inventory goes to the correct store. Predictive analytics can help you stay ahead of customer preferences, streamline your supply chain management and reduce your inventory expenditures while helping expand margins.
Better Target Your Marketing Campaigns
More and more, consumers are swayed by personalized campaigns. When Facebook and Instagram can show you relevant ads based on the smallest details shared, broad-strokes campaigns start to fall somewhat short. Retailers are uniquely poised to collect a range of individual data including preferences, search or inquiry history, shopping patterns, spending habits, and even the most successful engagement strategies.
With this breadth of information, it's easy to start assessing consumers on a more granular level. Instead of creating a massive campaign that costs thousands and has limited impact or reach, predictive analytics can personalize the marketing process. Offering more direct messaging also means that you can control not just the message, but when, how, and why it's displayed. This helps improve ROI and efficiency while creating a better customer lifecycle and building loyalty.
Make Better Pricing Decisions
Setting prices for many smaller retailers remain more of an art than a science. To date, many companies still base their prices on historic data and established notions such as seasonal tendencies and trends. However, eCommerce has done away with many factors that affect prices, including traditional times such as seasonal sales. Most retailers still wait to drop prices until traditional sales periods, losing out on advance sales. This in turn impacts revenues due to the massive price fluctuations.
Instead, using predictive analytics can help find the best times to start reducing or pushing prices slightly in either direction. Studies have shown that gradual price changes are more effective than sudden spikes. AI and predictive analytics can track inventory levels, competitor prices, and collate demand to determine what prices should look like. Being proactive in moving prices can help differentiate your store and give you better control over promotions while staying a step ahead of the industry.
Retail has become as much about anticipating customers' needs as it is about simply stocking nice products. Companies that innovate with the times and harness analytics can optimize their efforts and garner better results thanks to proactive strategies emerging from real-time insights.
Published at DZone with permission of Shelby Blitz , DZone MVB. See the original article here.
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