5 Ways Liquor Retailers Can Use Predictive Analytics to Grow Their Business

April 11, 2018 In Latest News

This is Part 2 of a 2 part series covering business intelligence and predictive analytics and why they are important for liquor retailers. Click here for Part 1.

For liquor retailers looking to get an edge up on their competition, predictive analytics is a powerful tool for extracting valuable insights from your pre-existing data. Even those who are unfamiliar with the nuts-and-bolts of running a statistical analysis have a lot to gain from making a conscious effort to leverage this increasingly popular form of data analysis.

As opposed to data analysis that takes place after-the-fact, predictive analytics is used to predict future outcomes using historical data – this can include things like customer behaviors, promotional impact, the price point that moves the most units, and more.

Using predictive analytics effectively can have wide-ranging benefits on your business. Here are five ways that you can use predictive analytics to make smarter decisions and increase profitability.

1. Product and Price Selection

Predictive analytics can enable you to take a data-driven approach to both product and price selection in your store. For example, predictive analytics can give you valuable insight when making pricing decisions, such as:

  • The most profitable price point for a product, so you don’t discount a product too much or too little
  • How much you need to discount a product to clear out unsold inventory
  • How different types of customers will respond to price changes

Here’s one example: price-elastic customers will still buy a product even if it is priced higher than normal, but price-inelastic customers are more sensitive to price changes and more likely to move to a competing or generic brand.

Beyond just helping you set prices, product selection is another area where predictive analytics can flex its muscles. With predictive analytics, you can better understand which products will appeal the most to your customers and have a better idea of how well a product will sell based on historical trends for similar products.

Moreover, given the importance of catering your offerings to your local market, it is also extremely useful to be able to predict how well a product will sell in your area versus national trends. If you know that your customers tend to stick to their preferred brand instead of trying new products, even if a distributor or supplier says something is “the next big thing,” you can make an informed decision about whether or not to carry that product. You might opt to avoid picking that particular product up if your data shows that it wouldn’t appeal to your customers, for example, or you could at least know the right amount to order for your local market.

[bctt tweet=”Product selection is another area where predictive analytics can flex its muscles. With predictive analytics, you can better predict how an item will sell based on historical trends for similar products.” username=”3x3Insights”]

2. Segmenting and Understanding Your Customers

The next type of predictive analytics that we will discuss is predictive segmentation, which slices and dices your customers into different groups, and uses the characteristics of these groups to determine which segment new customers fit in most closely. The data used in this method can come from an extremely wide range of sources, such as demographic information, but also includes things like payment method and purchase history.

Once you have segmented your customer data, it can then be used to predict future customer behavior, giving you insight into which certain customer characteristics that are associated with specific outcomes.

For example, you might find that customers who pay using Apple Pay are more likely to respond to an offer on social media or in an App, or that customers that buy primarily clear spirits also buy mixers. Therefore, predictive analytics would tell you that you should target Apple Pay users with social media promotions and that you should stock your selection of mixers next to your gins and vodkas.

3. Identifying Your Most Valuable Customers

Predictive segmentation also helps you answer a very important question: Who are my most valuable customers, and what do they look like?

One additional benefit of using predictive analytics to segment your customers, by dividing them into groups with similar characteristics, is that you can uncover your most valuable customers and have evidence to back it up. Being able to look at your customers and figure out which ones are the most important to your business is extremely valuable, but having the ability to do this in real-time can be even more vital.

For example, if your data indicates that a new member of your loyalty program resembles your most profitable members, possibly by analyzing what types of products make up their basket, you know that it is in your best interest to ensure this customer receives excellent customer service and has an enjoyable shopping experience each time they visit.

4. Personalizing Product Recommendations

Predictive analytics can also be used to drive personalized offers and experiences, and can help indicate which products you should recommend to certain types of customers. While you must still keep in mind each customers’ unique traits, understanding the general trends of your customers can help you have a product recommendation in mind before you even begin helping a customer.

If you have a way to reach your customers directly—like a loyalty club, website or App—offering personalized recommendations and even deals can be extremely successful at driving people into your stores and increasing the effectiveness of your promotions. According to marketing personalization vendor Dynamic Yield, in a study of 50 million online shoppers, personalized product recommendations yield 2.8X higher revenue per user.

Another important aspect of personalization is timing, and with the power of predictive analytics, you can align the timing of your promotions and recommendations with the behavior of your customers and maximize profitability.

[bctt tweet=”An important aspect of personalization is timing, and with the power of predictive analytics, you can align the timing of your promotions with the behavior of your customers and maximize profitability.” username=”3x3Insights”]

5. Optimizing Inventory Management

Finally, the last area we will discuss where predictive analytics can be extremely valuable is in optimizing your store’s operations, merchandising, and purchasing decisions. It is important to understand when you might need to stock up on merchandise or if there are times when sales of certain categories are down so you need to adjust your weekly orders.

Many retailers rely on intuition for making these decisions, but being able to use your sales data to make smarter ordering decisions can help minimize shrinkage and increase product turnover.

To get a sense of how helpful this can be, think back to how much leftover product you had last time you ordered in bulk to prepare for a special promotion or holiday. Or, think about all the seasonal beer offerings that customers typically only buy during certain times of the year, and how slow those products move after those dates have passed.

If you are able to accurately forecast sales based on past trends, every time you are buying for a promotion or stocking up on seasonal products, you can have a much better idea of how much inventory is too much.

We predict that we may have you convinced that predictive analytics can be a powerful decision-making tool that could have a significant impact on your store’s bottom line.

Predictive analytics is simply another way businesses can use information and data to guide decision-making and drive growth. Just like Business Intelligence—which provides multiple points of view drawn from various, disconnected sources of data—predictive analytics doesn’t make the decisions for you. It can, however, empower you to make informed decisions and the right judgment calls needed to grow your business.