As lockdown restrictions ease and shops begin to reopen, modern retailers are facing growing pressure to deliver an in-store experience that attracts and retains customers. Retail records an abundance of behavioural information, and innovative brick-and-mortar stores are now turning to Data Engineering and Analytics to better understand the optimal customer insights for their stores. This includes measuring the 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.
Combining store-level (transaction) data and customer experience (survey) data can help with store-level insights such as average basket size, preferred payment options per customer, and checkout line optimisation, without significant expenditure at the outset.
Customer satisfaction is known to have direct impact on the bottom line, along with significantly influencing the customer perception of the brand/store. A positive checkout experience is one of the major drivers of overall satisfaction and customers now demand the ease and speed of an online checkout experience, forcing brick-and-mortar stores to adapt accordingly. Popspots, one of the largest digital advertising networks in grocery, conducted a national study and found that 70% of customers feel checkout experience influences their perception of a store more than any other factor. Additionally, about 79% of customers say a negative checkout experience makes them less likely to return.
Even though the final moments of the in-store experience provide one of the most significant opportunities to drive awareness, engagement and conversion, there has been minimal innovation around the retail checkout experience beyond improvements to point-of-sale (POS) systems.
How can Analytics and Technology help in this process?
With growing competition from online and offline stores, retailers have realised that customer satisfaction plays an even more significant role in the success of a business. With the plethora of checkout options, the need to reconcile and validate transactions has increased, and analytics companies are using anomaly detection algorithms to isolate transactions that require reconciliation. Similar algorithms also help detect and isolate fraud.
Through advancements in dashboarding and reporting, analytics companies can arm store associates with simple and clear dashboards that are helping them make smarter decisions with better accuracy, with real-time forecasting and create impactful promotion strategies. Based on behavioural information, customer profiling and product features, analytics companies identify markers that record customer loyalty, product preferences and forecasts for future purchases.
Example: Helping a large retailer to deliver a powerful and actionable solution that improved the checkout experience
Kantar's Analytics Practice performed a comprehensive analysis for a retailer to identify key drivers of Customer Satisfaction and how to operationally improve customer experiences. The study was conducted combining CX survey data, store level operational data, customer transaction and behaviour 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 analysed to identify if any of the below components had a significant influence:
- Self checkout vs Associate-assisted checkout
- Store location
- Shopping hours (peak vs non-peak)
Through a study of customer satisfaction data, we could show a shopper’s expectation of an ‘ideal’ checkout experience and the different drivers of checkout satisfaction. A study of transactional, operational and observational data revealed the gap in the current state of check out versus customers expectation. For example, lower satisfaction (as per the CX survey results) was heavily correlated to a longer payment processing time (derived from transaction data).
The friction points were grouped into shopper issues, staffing issues, process (operational) issues and technology issues so that relevant business stakeholders could take ownership in resolving the issues. To supplement the learnings from the CX-CRM analytics, the checkout process of key retail competitors was also analysed to identify qualitative avenues of improvement for our client.
The power of transactional data facilitated granular analysis, which helped provide customised recommendations at a customer and store level. For example, a recommendation to reduce payment processing time by 6 to 10 seconds for card transactions, which account for 60% of all transactions that happen in a day, had a potential to improve Checkout Satisfaction NPS up to 600 basis points.
The customer impact
Based on customer transaction history, Machine Learning-enabled triggers (apply for membership, upsell) were recommended to be built into the prompting system at the checkout, which could save 10 to 12 seconds per transaction by cutting down unnecessary upsell prompting and potentially improve prompt acceptance rate from current levels of 4% up to 21%.
In addition, predictive analytics-based store optimisation solutions were recommended to estimate traffic and queue length to facilitate optimal lane scheduling and cashier staffing. Improved Self Check Out Adoption (an estimated increase by 10% in adoption) could reduce wait time at POS (assisted check out) transactions, and has a potential to improve NPS from 34 (point-of-sale) up to 54 (self-checkout) for at least 10% of all transactions.
Our solution helped the retailer deliver a frictionless checkout experience, which influenced overall satisfaction and had a ripple effect on customer engagement with the retailer.
Data-enabled processes can help retail stores capture and use customer transaction data and behavioural data in tandem to outline solutions and design a better checkout experience for the customer. With ever-evolving customer behaviour 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.