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5 Data-driven Metrics to Measure Customer Experience

POSTED ON: November 28, 2017 TAG: Digital Customer Experience

In today’s hyper-competitive economy, it has become 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, and 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 customer engagement to a whole new level.

Now we know the importance of a great data-driven CX. The next 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 to increase customer loyalty.
  • Compare your customer experience progress from previous months/quarters.
  • Determine how your products/solutions impact customer loyalty.

Below I highlight five 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 measures what is known as the “Net Promoter Score.” NPS enables you to measure your customers’ overall loyalty based on several different factors 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 a good predictor of customer behavior as it measures the long-term happiness of the customers keeping its focal point on customer loyalty.

Churn Rate

Churn used to be particularly critical for companies that operate on a subscription model (customers cancelling their subscriptions in a specific period). For others, it signifies the number of customers that purchased from you the previous quarter/year and have not repurchased this quarter/year. We all know that businesses should retain their existing customers than 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, many 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 three months, automate an email sequence to “re-sell” those who haven’t purchased anything in three months to keep them engaged. The point is to recover a customer before they churn.

Number of Website Visits Before Purchase

This metric is effective, especially for eCommerce businesses. The 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 the 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.

Purchase Frequency

The average number of times a customer purchases in a specific period enables you to gather insights into structuring 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? Optimizing retention email campaigns, starting a loyalty program, and introducing gamification elements 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, deduct your expenses as well as the cost of goods sold.

Now that I have unravelled the use of five data-driven metrics, you will be able to re-examine your digital customer experience. It will help identify pain areas that need immediate attention and keep you from unknowingly removing a part of your business that customers love.

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POSTED ON: November 28, 2017 TAG: Digital Customer Experience

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