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How to use Net Promoter Score with Customer Analytics and CX

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When it comes to customer analytics Net Promoter Score (NPS) is at the top of the list for KPIs. We see our retail and restaurant customers especially use this metric, combined with some other drivers to act as a baseline for management reporting and a needle that drives an influx of campaigns and other all-hands conversations. Ultimately, it becomes a question of how to use Net Promoter Score (NPS) with customer analytics to make decisions about where the organization should go in order to satisfy existing customers and convert new ones.

NPS is the rank that shows the percentage of your customers that would recommend your company, product, or services to others. Recommendations come in all formats where others can be any other company, friend, family, colleague, twitter respondent, really anyone who would listen or want to hear the message of your praises being sung.

You’ve seen the smiley face ranking system before. These are now even outside (or inside) restrooms. It’s basically the idea that you want promoters, passive customers/buyers, and detractors, respectfully giving high to low scores. To codify a “promoters” (“promoter”) score means that they have ranked your service or product on the upper 20%, so on a scale of 0-10, its a score/rank of 9 or 10. And ultimately your score is an equation of the percent of your promoters minus the percent of your detractors. So with this easy equation in mind any NPS focus group or platform can be used to collect the scoring and apply the ranking.

At the end of the day its all about providing a better customer experience (CX). As any engineer will tell you, it’s very challenging to improve something that you cannot test or measure. It is difficult enough to get customers, and where it should be an easier task to retain customers and get them to become repeat customers, it is not always the case. Using NPS and other ranking such as Customer Satisfaction (CSAT) scoring helps guide the ship. These metrics are part of the overall Customer Analytics and CX journey. It’s also part of the belief in a Customer Data Platform (CDP) approach to consolidate metrics that enable CX and other business aspects into a single combined source of truth, accounting not just for a holistic customer view but also to blend in other operational metrics on a customer’s transactions, how those metrics relate to sales (impact via the POS system, for example), or their interaction aligns with Accounts Receivables. Since most CDP systems, and customer NPS and CSAT systems, don’t provide the full blended picture of CX to Cash (a pun on order to cash), a Customer Analytics system is needed to create the data pipelines, merging the disparate data, and providing a holistic view through an analytical lens.

If a company is only using CSAT and NPS there are other critical ‘keep the lights’ on metrics for marketing, customer success, sales, that should be used to measure how customers are impacting the business, for example:

  • Customer Churn Rate
    • How may customer stopped using your product or service, ie.: cancelled in a given period of time
  • Customer Effort Score
    • Typically the score via survey after a customer completes a transaction/event aligning with how difficult to easy the transaction was for them.
  • Average Customer Resolution Time
    • They time to completely resolve a customer issue, from first notice to conclusion, often referred to as Time to Resolution
    • Omni-channel systems are good at capturing this metric typically
  • First Contact Resolution
    • Each touch point a customer makes should be captured, as such if a customer percentage of touch points is high without resolution, that’s usually a sign of a bigger problem, conversely, solving a customer problem on the first attempt should be recognized as an area of efficient, and other not first contact resolutions should learn from where customers were able to resolve their issue on first touch
  • Average Customer Spend
    • Obvious, but many organizations cannot yet provide this information, mainly because of lack of omni-channel systems or lack of access to data and the means to consolidate. This metric tracks a very important KPI to help segment customers and find the “Top n” customers, conversely the “Bottom n” to understand the customer demography better
  • % of Repeat Customers
    • Ideal and necessary from our experience with customer analytics is the number of customers that come back to use your product or service. This can be further broken down by type of service plan (recurring revenue, one time, contract, subscription) etc. but understanding this metric has real impact in “Sales Flash” reporting as a barometer for customer retention. Again, after you’ve worked hard to gain the customers, the belief is you should continue investing in them as you would like them to do with you.
  • Average Time Between Customer Engagement
    • Just because you have repeat customers doesn’t mean you couldn’t accelerate their transaction/events with your products and services. This metric helps create a cadence and understanding for a min and max threshold that you can anticipate from repeat customers. Sure you can push the envelope to increase the frequency of transactions/events but if you don’t understand the purchasing cadence then you have no idea what the organic vs. non-organic traffic baseline is naturally, so how would you know if you’re pushing to hard and ostracizing some customers.
  • Average Customer Recurring Revenue
    • Another metric that the “subscribed” age of businesses are looking to guide their ships is ACRR. This takes your idea of MRR or ARR and allows segmentation to determine an umbrella KPI to understand where the business sweet spot is and what it should target. In a subscribed Recurring Revenue business, or one where a partial segment of the business is recurring revenue, using this KPI is something any board member or executive is going to want to see.
    • Tie this in with AR and Average Revenue Per Employee and you’ve got a quarterly board book slide that steals the show.

It is super important to measure to improve. More now than ever customer attention spans are short. Even if your product is amazing, and your customer tribe is loyal to the end, you have to understand their experience, and how it is impacting your business. A customer data platform (CDP) and an omni-channel system are becoming table stakes for box store retailers and online e-commerce shops alike. No business is immune to customer churn. But the addition of a Customer Analytics solution, usually some form of a data pipeline, a data warehouse, and executive reporting, allows for the persistent, consistent business logic to tie all the metrics and key aspects of the business together for a 360 degree view of the customer and the business. It would then allow the customer journey to surface beyond just the marketing, and sales team, to a more cross-functional stage at the operational and executive level to make more informed decisions, not to mention alleviating some time from those on-the-ground teams to focus more on the customer rather than crunching and wrangling data, sweating over how to combine for example Salesforce CRM data with Marketo data, etc.

Let us know which customer metrics you use? Do you use a CX system or an omni-channel solution? What about a CDP? We’re happy to help and discuss this topic more.

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