Measure twice, cut once. The carpenter’s rule set in stone is now resonating with the retailers’ imperative of measuring customer experience (CX) – just to make doubly sure that nothing goes wrong with customer engagement. Where retailer actions ought to reflect customer preferences, needs and likes, measuring customer experience can go a long way in eliminating costly mistakes that can repel customers and in fulfilling needs of customers in the most appropriate way.
What areas do retailers focus wile measuring customer experience?
Along the customer journey, retailers engage customers across online and physical worlds in so many ways. At every step of the customer journey, retailers’ performance is put to test. For a customer reporting about a defective product, it is the speedy resolution that leads to pleasant customer experience. Any other scenario, as that of ordering products, gleaning details from online and physical sources or returning goods – where retailers engage customers, it is the retailer performance that is weighed to gauge the service, measure customer experience.
In measuring customer journey, retail chains turn proactive by making diligent use of customer data scattered across channels, mediums, external sources and systems through a Hadoop-based data lake, so to speak, to relate performance with various factors and metrics, and find answers to critical questions.
- What interactions matter most?
- What drives customer loyalty?
- What are the important events?
- What are the frequent paths taken by customers?
Using R or Python, machine learning algorithms powered customer journey analytics can provide answers to many a customer journey query. It also helps retailers predict customer behavior and engage customers in the most appropriate way and take critical customer experience decisions.
When customer retention is mapped to CX and when it is held as a critical KPI, customer journey analytics augmented by predictive analytics and ML algorithms can help retailers fathom reasons behind customer churn, pinpoint at-risk customers and reduce customer attrition.
It matters more to know how a retailer fulfills customer needs, how fast the service is rendered to meet customer needs and what does the customer feel about the service offered by a retailer. Customer opinion at the point of ‘experience’ serves as an insightful input for the retailer to transform customer experience.
Measuring perception by way of recording customer feelings through polls and surveys after customer interactions help retailers understand areas that need more focus and know what customers feel about their service. Though NPS allows retailers to measure customer opinion, sentiment analysis augments NPS in tracking customer sentiment about specific features of services or products, roll out measures that appeal to customers and promote enriched customer experience.
What does a customer do after an interaction?
The conversion drive of a retailer is dotted with challenges. With conversions influencing an increase in revenue, it pays to read and analyze customer behavior online or in-store or on any of the channel to understand what makes customers take positive actions and how conversion drive is falling short of expectations and what measures need to be taken to increase conversions.
Beyond measuring the impact of conversions on revenue generation, retailers can make the most of behavioral modelling, collaborative filtering and recommendation engines to make customer conversion drive revenue generation.
Where desirable and positive customer actions breed loyalty and where measuring customer loyalty has grown into an absolute imperative, customer lifetime value driven by machine learning algorithms fills the void to give a measure of customer loyalty and leverage prescriptive analytics for increasing customer loyalty. Other metrics including Net Promoter Score (NPS), Repurchase Ratio, CLI and Upselling Ratio also serve the retailer well in measuring customer loyalty.
How customer action has a bearing on business performance?
For a retailer focusing on a set revenue target, customers defecting to competition or failing to make repeat purchases can be the spanner in the works of revenue generation. Measuring customer churn can help the retailer understand why customers are churning and what prescriptive actions need to be taken to keep customers in their fold.
As satisfied customers bring in more sales, there is a dire need for spotless customer service to yield customer satisfaction while shoddy services can only yield an irate customer who takes action to sever the bond with a retailer. The quality of service can also impact business performance metrics such as sales, revenue, gross margin and market share. By promoting a machine learning led customer service, retailers can evaluate and meet KPIs including NPS, CST, TAT, customer effort, resolution time among others, ensure that their services breed positive customer actions.
By paying attention to the most critical factors that can help gauge customer experience, retailers understand areas that need improvement, areas that are strong and issues that prove detrimental to promote enriched customer experience. More than anything, retailers get to know how they perform and where they stand when it comes to customer experience.