5 Data-driven Metrics to Measure Customer Experience
3 Minutes Read
In today’s hyper-competitive economy, it has become an imperative for companies to focus on delivering data-driven customer experiences. According to Forbes the benefits are wide-ranging, including revenue generation and cost reduction, as well as enabling process efficiencies and quality improvements. Data-driven CX leads to a more targeted and personalized approach for a specific set of customers and enables organizations to keep the interactions consistent across different touchpoints, provided all functions and LOBs are willing to align, first conceptually and, secondly, functionally. Data-driven CX is not just helpful in delivering positive experiences to customers but benefits the organization as well. It significantly improves decision-making and elevates the engagement with customers to a whole new level.
Now we know the importance of a great data-driven CX. The subsequent question is…how to start improving it?
The best way to do so is to measure your key customer experience metrics and use that data to guide business decisions, across your customer lifecycle. This data will help you:
Understand what you need to change for increasing customer loyalty
Compare your customer experience progress from previous months/quarters
Determine how your products/solutions impacts customer loyalty
Below I highlight 5 data-driven metrics for managing your customer experience:
NPS (Net Promoter Score)
You all must have come across this question “How likely is it that you would recommend our company/product/service to a friend or colleague?” with a choice between 0 (not likely) and 10 (highly likely). It is measuring the what is known as the “Net Promoter Score.” NPS enables you to measure the overall loyalty of your customers based on a number of different factors that go beyond a single interaction or a product purchase. The scale is categorized as
Score: 9-10 Promoters
Score: 7-8 Passives
Score: 0-6 Detractors
NPS can then be calculated as:
The Net Promoter Score = % of promoters (respondents that gave a 9-10) – % of detractors (respondents that gave a 0-6)
NPS is definitely a good predictor of customer behavior as it measures long-term happiness of the customers keeping its focal point on customer loyalty.
Churn used to be particularly critical for companies that operate on a subscription model (customers cancelling their subscriptions in a given time period). For others, it signifies the number of customers that purchased from you the previous quarter/year and have not bought again this quarter/year. We all know that it’s better for businesses to retain their existing customers than to acquire new ones.
Churn rate can be calculated as:
Reducing churn by a few percentage points can lead to a notable increase in revenues. Apart from businesses running on recurring revenue models, a lot of ecommerce companies have started relying on this metric.
Businesses can design test programs to reduce churn. For instance, if lots of your customers are likely to make a repeat purchase within 3 months, automate an email sequence to “re-sell” those who haven’t purchased anything in 3 months just to keep them engaged. The point is to recover a customer before they churn.
Number of Website Visits Before Purchase
This metric is really effective especially for ecommerce businesses. Number of visits a customer makes to your website before purchasing shows how compelling your communication messages are.
It is impractical to expect your customers to purchase on their first visit as they often prefer to research or compare prices elsewhere. However, if the number of visits before purchase goes up, it might be that your prospects are confused or not convinced. Then you can pull in your sales and marketing teams so that they can fix your communication issues and engage with those who are confused and improve conversion rates.
The average number of times a customer makes a purchase in a given period. This enables you to gather insights on how to structure your digital strategy that best suits your audience’s buying behavior.
While the “number of purchases” matters, that figure should also be used to calculate the time between purchases.
Purchase Frequency can be calculated as:
Purchase Frequency =Number of Orders (365 Days)/Number of Unique Customers (365 Days)
By changing the time frame, you can calculate the Purchase Frequency of that time frame. However, looking at the data of a single year period is always preferred. It is crucial to take into account only the “unique” customers to avoid double counting.
An increase in Purchase Frequency leads to higher revenues. But how to do that? Well…Optimizing retention email campaigns, Starting a Loyalty program and Introducing elements of Gamification can surely help.
Average Order Value
Average Order Value not just helps in setting goals but also in evaluating if the new strategies are working. It can be calculated as:
Average Order Value= Total Revenue/ Number of Orders Taken
While this metric is adept for bench-marking efforts, it is not ideal for getting an exact representation of the margin generated per order. To take care of that, make it certain to deduct your expenses as well as the cost of goods sold
Now that I have unraveled the use of five data-driven metrics, you will definitely be able to reexamine your customer experience. It will not just help in identifying pain areas that need immediate attention but will also keep you from unknowingly removing a part of your business that customers love.