The retail industry is one of the primary ones directly affected by changing customer behavior and requiring customer analytics. Customers are more informed than ever, with information always available at their fingertips – they are the ones in power. Within minutes they can find alternatives to your products, compare prices and look over reviews. Now, more than ever, the most successful retailers will be the ones who know their customers best. The ones capable of identifying the ever-changing needs of their customers, segmenting them with utmost precision and personalizing customer experiences across the entire customer journey. It may sound extremely difficult, but it’s possible with the help of customer analytics. Through customer intelligence that leverages machine learning, you can be provided with precise and customizable customer segmentation that helps understand and prevent churn, identify customers with the highest lifetime value and personalize communication with each customer segment.
Accurate customer segmentation is an essential part of any retail business, but it is also one that may be the most difficult to get right. Typically, customers interact with a brand across multiple platforms before making a purchase and on each of these platforms, there are factors that influence whether they will buy. Likewise, customers leave clues in the form of data points all the time, the clues can be information about their online habits and preferences, spending power, interests and more. By effectively gathering and analyzing this data, valuable insights can be derived to improve every aspect of the business. Moreover, by leveraging machine learning and predictive analytics you can get an opportunity to not only analyze past behavior but also make highly precise predictions about what your customers will do next!
Now that we’ve established the importance of customer segmentation, let’s go over the specific goals it can help you achieve.
Identifying customers at risk of churn
Churn rate is a metric that directly affects a business’ profitability. It illustrates the percentage of customers that stopped using your company’s product or service during a certain time frame. As it costs more to acquire new customers than retain existing ones, retailers should place churn rate reduction at the forefront of their goals. Of course, identifying customers who are most likely to attrite is not a new practice, but doing so in real-time and getting predictions that are up to 90% accurate, is.
With the help of machine learning technologies, customer analytics tools can analyze data from multiple resources and identify dependencies that will accurately forecast the potential churn of any customer. From there retailers can perform preventative actions that will reduce churn, thus strengthening relationships with existing customers and driving brand loyalty.
Determining customer lifetime value
Customer lifetime value (CLV) indicates the future profits each customer is expected to generate, thus allowing to identify customer segments that are most valuable to the company and determine appropriate costs of customer acquisition and retention. For retailers this metric is especially relevant as it helps identify and nurture high-value customers, increase average order size and shopping frequency, as well as adapt marketing campaigns to each customer segment and increase return on ad spend.
Personalizing customer experiences
With new technologies constantly emerging, customers have come to expect personalized experiences because they make them feel noticed and appreciated. Not to mention, oftentimes they can make their shopping a lot easier and quicker. Relevant product recommendations, personalized marketing promotions, and customized in-store experiences are some examples of customer experience personalization.
How does it work? Machine learning algorithms analyze past purchases, data about previously successful marketing promotions, browsing history (for eCommerce) and more to identify customer interests and make predictions of future behavior and offers that may be most relevant to them. As always, the more quality data the algorithms can go through – the better the results.
At this point, you may be wondering – what results can I expect? While the answer to that is often unique for every business, take a look at the below graph by McKinsey which shows the impact of customer analytics on business performance.
As you can see, customer analytics are invaluable for any customer-centric business, especially a retailer. By extracting ultimate value from your data, customer analytics provide you with forward-looking insights that can inform your strategic decision-making, propel your business ahead of the competition and deliver significant returns on investment.
If you would like to get a consultation on how you can leverage customer analytics - do not hesitate to contact us. We are happy to advise you on your unique business case.
In the meantime, check out our latest eBook on how AI & ML help retailers boost profits!