Enhance Retail Loyalty via Data Insights

Enhance Retail Loyalty via Data Insights

The Gist

  • Inconsistency alert. Inconsistent transactional history could indicate decreasing loyalty and potential brand abandonment.
  • Poor connection. Analyzing behavioral data, such as website activity and campaign engagement, reveals vital context for customer purchasing decisions.
  • Lack of trust. Uniting transactional, behavioral and campaign data into a 360-degree single customer view enables personalized pre-lapse communications to reengage at-risk customers.

Inconsistency, poor connection, lack of trust. Just like in any relationship, a customer can fall out of love with a brand too. If you’re in retail, you might have noticed this more frequently in the past few months; customer loyalty isn’t earned the same way it was 20 or even just three years ago. Soaring inflation has applied pressure to purse strings, and competition over consumers’ discretionary income remains hot.

That’s why, right now, every company should be watching out for customer churn — when customers stop paying for services or products, and eventually abandon the brand.

Since attracting new customers can cost five to 25 times as much money as activating existing customers, it has never been more important to monitor your customers’ transactional and behavioral trends. Increasing customer retention rates by only 5% can increase profits by up to 95%, so it’s an effort worth making.

Transactional Frequency Is Just a Small Piece of the Puzzle

Identifying signals that indicate churn can be tricky for retailers. Especially, for those who have infrequent product cycles. There isn’t a formal end to the relationship, such as with a banking or telecommunications provider, making it difficult to decipher if a customer is truly considering leaving a brand.

Of course, transactional history is key to establishing customer churn rate. When a customer buys from your company less frequently, it indicates that loyalty might be diminishing and brand abandonment could be on the horizon.

But, unfortunately it’s not always that clear-cut. Instead, retailers must contextualize transactional trend data in terms of the customer’s previous transactional behavior, taking into account their historic purchase frequency. This will help uncover if a customer is just slightly delayed in making their usual purchases, or could be on the brink of lapsing.

But even with contextualization, to accurately identify the warning signs that your customer might be about to break up with you, behavioral data is fundamental.

Related Article: The 5 Stages of Predictive Analytics for CX Success

Behavioral Data Is Where True Insights Lie

Website activity is a key indicator as to whether your customer is likely to lapse. Yet, many customers will have online sessions on your website that don’t result in a purchase. This is standard. And, doesn’t necessarily mean they’re disengaged. Rather, it’s important to dig deeper and look at how customers are using your website. Are they switching between product categories? Has the number of products in their wish list or basket decreased? Is session length decreasing?

All these data points share crucial insight into the context surrounding your customers’ purchasing decisions and their brand affiliation. If there’s plenty of items in a customer’s basket, but the number of purchases are declining, then it might be a lack of finances preventing customers from checking out rather than a lack of engagement. Yet, if wish lists and baskets remain empty, and website visits are fleeting, a lapsed customer might be in the cards.

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