As retailers plot the next phase of their offerings, following COVID-19 lockdowns, they face growing pressure to deliver an in-store experience that attracts and retains customers. Many innovative brick-and-mortar retailers are turning to data engineering and analytics to better understand the optimal customer insights for their stores. This includes measuring customer satisfaction, frequency and volume, staffing needs for high and low traffic time periods, and which setup of checkout methods would lead to faster throughput and increased basket size. Customer satisfaction is known to have direct impact on the bottom line, along with significantly influencing the customer perception of the brand/store, as a September 2018 study from Popspots found that 70% of customers feel checkout experience influences their perception of a store more than any other factor, and about 79% of customers said a negative checkout experience makes them less likely to return.
Today’s customers no longer accept the down time of waiting in line. If retail businesses cannot keep their lines moving as quickly as their customers want, they will lose out to stiff online competition. So, what’s the secret to keeping the lines moving and getting customers to return every time? To understand how retailers can identify key drivers of customer satisfaction and how to improve their customer experiences, we performed a comprehensive analysis that combined customer experience survey data, store level operational data, customer transaction and behavior data across both satisfied and dissatisfied customers. One of the key outcomes of the study was that checkout experience had the most negative impact on overall shopper satisfaction. Factors impacting a poor checkout experience were further analyzed to identify if any of the following components had a significant influence: self-checkout versus assisted checkout, store location and shopping hours.
In looking at the customer satisfaction data, we were able to understand a shopper’s expectation of an ideal checkout experience. Transactional, operational and observational data revealed the gap in the current state of checkout versus customer expectation. For example, lower satisfaction — as per the consumer experience survey results — was heavily correlated to a longer payment processing time (derived from transaction data).
From there, the friction points were grouped into shopper issues, staffing issues, process issues and technology issues, so that relevant business stakeholders could take ownership in resolving the issues. The checkout process of key retail competitors was also analyzed to identify qualitative avenues of improvement.
The outcome of this work was a set of actionable recommendations, along with short-term and long-term monetary impact of every recommendation so that retailers could properly evaluate the changes they would want to implement. For example, a recommendation to reduce payment processing time by six to 10 seconds for card transactions could significantly improve checkout satisfaction. Another recommendation was the use of machine-learning triggers — apply for credit card, warranty upsell — to cut down unnecessary upsell prompting for customers at checkout that could help save time during transactions and potentially improve acceptance rate.
Overall, data-enabled processes can help retail stores capture and use customer transaction data and behavioral data in tandem to outline solutions and design a better checkout experience for the customer. With ever-evolving customer behavior and the advancement in technology, retail is changing at an accelerated rate. Retailers need to integrate analytical insights with the latest tech innovations to offer a better customer experience.