Gain better retail insights by supplementing data analytics with loyalty card information

Communications and Distribution Writer

Loyalty card programs give retailers valuable insights about their customers' purchasing patterns, providing marketing teams with a way to target promotions and giving purchasing departments information on what keeps customers coming back for more. However, inaccurate data can undermine the effectiveness of these retail insights. Retailers can solve this problem and use their loyalty card data to its full potential by supplementing the information with their other analytics insights.

The inaccurate data problem

Consumers signing up for loyalty cards sometimes provide incomplete or inaccurate data, including false contact information, age, marital status or income.

"When you have thousands and thousands of these stores all throwing data in, their data could look good. It could be 90 percent correct, but portions of it could be horrible," Andrew Robbins, CEO and founder of Paytronix, explained to Datanami. a survey of data management professionals conducted by Experian earlier this year, 91 percent of the respondents said their revenue and ability to gain customer insight is negatively affected by poor-quality data. On average, U.S. organizations believe that as much as 32 percent of their customer data is inaccurate.

Not surprisingly, retailers are often unable to effectively leverage the data from their loyalty cards to figure out which merchandise creates loyal shoppers among various demographics. If 30 percent of customers provide false addresses, incomes and ages, simple data analysis may lead to inaccurate or inconclusive retail insights.

The big data promise

However, new advanced data analytics techniques offer merchants a way to derive more value from their loyalty programs. The latest technologies allow retailers to combine information from customer tracking programs with data from external sources like social media, mobile applications and in-store sensors. The information gathered from these outside sources can help fill in the holes in loyalty card data. According to a report from Oliver Wyman, retailers that analyze multiple sources of data will find valuable insights about consumer behavior that can be applied to retention and cross-selling efforts.

According to Supermarket News, these insights can also be valuable from a merchandising perspective. Retailers can use loyalty card information to align their prices, promotions and SKU allocations with the needs of loyal customers. When shoppers consistently find what they need at a store, they're more apt to return. Leveraging this information can lead to a 1 to 4 percent increase in overall sales.

Understanding a customer's future value

It's important to keep in mind that not all customers in a loyalty program will be valuable customers in the long run. Recent statistics from COLLOQUY show that the average American has 12 active loyalty cards that they use at least once a year; this might include memberships at competing stores. Retailers need to figure out which of these customers have the most value if they want to effectively target their merchandising strategies.

Data analytics gives retailers the ability to use information collected from various sources and identify the leading indicators of a consumer's purchasing habits, thereby identifying the individual's future buying value. This allows companies to more accurately target merchandising and marketing strategies to those who are most likely to become life-long customers.

Importantly, retailers using this strategy are able to predict a consumer's purchasing behavior more accurately by comparing customers against other individuals with similar profiles. Sometimes a customer who appears loyal and devoted to the store might be even more devoted to another brand. When applied correctly, data science can help unravel such nuances.

Retailers that use data gathering and analytics tools to supplement their loyalty card information will be in a good position to figure out which customers are most valuable to their store and then tailor merchandising accordingly to keep their dedicated shoppers happy.

To optimize your business with big data, explore IBM's Solutions for Retail.