Predictive analytics has become more prominent in the last ten years. The market value of the solution grew at a compound annual growth rate of 27% between 2015 and 2020, and this growth is likely to continue in 2022.
Retailers understand the value of using data to predict important trends and behaviors. It enables you to take a proactive approach to decision-making, fuelled by objective data, to stay ahead of the curve.
Predictive analytics work by analyzing data and forecasting what’s likely to happen. For example, a retail analytics solution can analyze customer data and make an accurate prediction of future customer behavior.
Predictive analytics is based on probabilities. It uses data mining, modeling, and machine learning to predict possible outcomes or events.
It’s an alternative to retrospective descriptive analytics, which requires retailers to look back at historical information, such as sales records, to identify findings.
Additionally, predictive analytics lets retailers automate processes. It forecasts what’s likely to happen using all of a retailer’s available data. This means that you can make predictions at scale and in real-time. This has been beneficial for large, multi-channel retail chains that need to make data-based decisions across multiple stores.
To help you better understand its benefits and how it works, we’ve put together the five most common examples of predictive retail analytics in action.
Understanding when a product is likely to sell out helps you better anticipate when it needs to be restocked. It can also tell you how much of a product you need to stock.
However, making accurate predictions is difficult. Seasonal changes, customer preferences, promotions, or new product launches can all impact the popularity of a product. And overstocking and understocking products is equally unhelpful for your bottom line.
Predictive analytics can capture all of these variabilities into a central source of data. This lets it forecast stock availability levels with greater accuracy, compared to a manual prediction.
Descriptive analytics can provide you with an overview of customer behavior. For example, how and when a typical customer shops at a certain store.
However, predictive analytics can take this insight one step further. It can forecast how much someone is likely to spend in the future based on factors that could affect spending. For example, over the festive period people are likely to increase their spending as they purchase presents and stock up. This enables retailers to proactively upsell or market to specific customers or customer groups.
When products aren’t selling very well or aren’t relevant anymore, retailers need to replace them with alternatives.
However, stores have limited shelf space. When you choose to de-list a product and replace it with an alternative, you need to make sure that it’s a worthwhile, profitable decision. For example, what will the old product’s customers purchase when it’s no longer stocked?
Predictive analytics help you forecast what products those customers are likely to purchase instead. This includes predictions for how much spending will be transferred onto the alternative product and if spending would be lost. It also forecasts how much the new or alternative product will sell.
Additionally, predictive analytics can help retailers make decisions about product facings. This is the number of products that should be outwardly faced on a shelf. The higher the number of facings, the more likely a product will sell because it’s more visible.
New products don’t have a historical record of sales. This can make it difficult to accurately understand how popular it'll be, how much it’ll sell, and when it’ll need to be restocked.
Retailers can use predictive analytics to forecast new product sales based on similar products. It can use a predictive model based on existing data from existing products.
When the product is launched, predictive analytics can continuously adjust predictions using sales data generated over time. For example, if customers choose to re-purchase a product a month later, it demonstrates product popularity. Predictive analytics can keep track of this data in real-time, letting you adjust stock accordingly.
With predictive analytics, retailers can create a predictive model to score customers by their likelihood to buy a certain category of products. For example, if a customer often buys cereal, they might be more likely to buy cereal bars in the snack category too.
You can achieve this by identifying predictors of behavior. If the customer is buying cereal, are they in the right age group, are they spending a certain amount in-store, or do they have a certain number of items in their basket?
By analyzing and scoring this behavior, the retailer can target their marketing to specific customers. You know who is likely to buy a product, and who is not. Then, you can provide a coupon at till to customers that score higher on their likelihood to buy certain items – letting you unlock an accurate way of upselling and cross-selling to customers.
Predictive analytics is a powerful way of anticipating outcomes. However, using predictive analytics isn’t the be-all and end-all of analytics success.
Alternatives like descriptive analytics help retailers quickly discover findings in data when they need to. This applies to a broad range of insights, whether it’s stock predictions or customer behavior.
Predictive analytics is useful for forecasting, such as when you’re launching a new product or running a new marketing campaign. It’s important to find a balance between the two because, with analytics, relevancy is key. Both predictive and descriptive analytics can be useful if you use them wisely.
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