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3 ways to use data analytics to identify and target high-value customers

What gets measured, gets done

We have all heard the saying “what gets measured gets done,” and in the fast-paced world of retail, that phrase takes on a whole new meaning. Store metrics truly drive the success of a retail business, from units sold to people on the floor to feet in the door. 

And analyzing that data is essential to knowing what to do with it. 

Data analytics can be used to identify and target how much product to order, how many people to schedule, and perhaps most importantly, who your high-value customers are. Knowing who to target and how can help your business grow quickly.

Data analytics is a powerful tool that can help you make informed decisions about your retail business. By tracking and analyzing the right metrics— and most importantly, knowing how to use it— you can identify areas where you can improve your business and grow your profits.

Here are three ways to use data analytics to identify and target high-value customers:

Segment your customers based on their purchase history and behavior

Segmenting your customers based on their purchase history and behavior is a great way to identify groups of customers who are more likely to make repeat purchases or spend more money. This can be done by looking at factors such as the products they have purchased in the past, how often they make purchases, and how much they spend each time. Once you have identified these groups of customers, you can target them with specific marketing campaigns or offers that are tailored to their interests. This can be a very effective way to increase sales and revenue.

Having reports that allow you to filter on highly-specified data— like shoppers who like leather, neutral colors, and plaid— can help you segment your customers without coming off as segmentation. This information can then be used to create targeted marketing campaigns that are more likely to resonate with your customers.

For example, if you have a report that shows that your customers who like leather, neutral colors, and plaid are also interested in traveling, you could create a marketing campaign around a new line of brown, plaid leather suitcases that targets these customers. 

By understanding your customers and their needs, you can create marketing campaigns that are more likely to resonate with them and result in a purchase.

Use predictive analytics to identify potential high-value customers

Predictive analytics can help you identify customers who are likely to become high-value customers in the future by analyzing their past behavior. This information can be used to target these customers with special offers or promotions that are designed to convert them into high-value customers.

For example, if a customer has bought several linen suits from the summer collection over the past few months, you might predict that they are likely to buy several more from the fall collection that will be rolled out in the next few weeks. You could then target them with a personalized text saying you think they would be interested in the soon-to-be-released fall collection, and offer them a sneak preview in order to encourage them to make a purchase.

You could also use predictive analytics to identify customers who are likely to churn, or stop doing business with you. This information can be used to target these customers with special offers or promotions that are designed to keep them from churning.

By understanding their customers’ past behavior, businesses can identify those who are most likely to be valuable in the future and target them with offers that are tailored to their needs.

Use data analytics to measure the effectiveness of your marketing campaigns

Data analytics can be used to drive business for fashion retailers by tracking key metrics such as website and store traffic, email, text, or social media engagement, and customer interactions. 

This data can then be used to identify which marketing campaigns are most effective at attracting and retaining customers and inspiring them to make purchases, set appointments for in-store styling consultations, or even just browsing new products. 

By understanding what is working and what is not, retailers can make changes to their marketing campaigns to improve their results. For example, if a retailer finds that a particular campaign is generating a lot of website traffic but few customers are actually making purchases, they may want to consider adjusting the campaign to focus more on conversion. 

By using data analytics to measure the effectiveness of their marketing campaigns, fashion retailers can get a better return on their investment and attract more customers.

Bringing it all together

Data analytics are an integral part of the retail business, as they can help retailers identify and target high-value customers in several ways, including:

  • Segmenting customers based on their purchase history and behavior to identify groups of customers who are more likely to make repeat purchases or spend more money.
  • Using predictive analytics to identify potential high-value customers and target them with special offers or promotions.
  • Measuring the effectiveness of or marketing campaigns in order to make changes to improve results.

Learn more

Want to learn more about how to use data analytics to identify and target high-value customers? 

Watch this 25-minute webinar Inside the world’s best store experiences to dive into the systems and insights behind creating memorable customer moments and explore the art of crafting end-to-end exceptional store experiences.

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