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Using scoring models to gain customer insight

8th Jun 2015
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When it comes to measuring the customer experience and gaining insight into what consumers really think, scoring models are a vital part of the job. Analysing the quantitative data they collect and produce provides the necessary understanding to make business decisions that impact loyalty, engagement and ultimately revenues. And this data doesn’t have to be numeric when it is entered by the customer. You can use slider scales or even emoticons to make it simpler for consumers to provide feedback – with each value having a numerical equivalent that makes analysis easier.

From the likes of Net Promoter Score to ranked and ratio scoring, there are a number of models out there. They all have their plus and minus points and this article looks at what they bring to CX and marketing professionals.

However it is important not to use them in isolation. Qualitative analysis, looking at what customers actually say, rather than where they rank you on numeric scales, is equally vital. In the past analysing qualitative data has been difficult and time-consuming, as each piece of feedback had to be manually read and understood. New technology, such as systems that use sentiment analysis and natural language processing, mean it is now possible to automate this to a degree. While it isn’t 100% accurate it does highlight appropriate areas for targeting more detailed manual analysis.

Here are the three most common quantitative scoring models:

Ranked scoring
The so-called 80:20 rule holds remarkably true for many organisations. In many cases as little as 20% of customers deliver the lion’s share of revenues. So identify these, focus your efforts on their needs and you’ll benefit from their continued loyalty. This approach does have some strong advantages. Nurturing your biggest customers, particularly in the B2B space, should give insight into what they want and enable you to deliver it.

But it ignores the other 80% as ranking-based approaches create arbitrary boundaries between, for example, your 20th and 21st highest-value customers. In reality the difference could be just a couple of pounds, but if you focus too rigorously on ranking, you’ll miss out on looking after them. It also assumes that your customer base is static – what about the new consumer that has spent relatively little to date, but with a bit of nurturing could join your top revenue generators?

It’s therefore important for companies to think carefully about how and why they use ranked scoring – and to use other models to supplement the insight this method brings.

Ratio scoring
As the name suggests ratio scoring assumes you have a fixed total, and that increasing one measurement will decrease the others. It is essentially like cutting a cake – the individual slices can’t total more than the size of the total. Therefore if you collect 180 positive and 60 negative responses in an opinion poll, the ratio of positive to negative is 3:1.

You can also express ratios as fractions or percentages. A 2% product return rate is a 1:98 ratio, while a 1-in-10 conversion rate is a 1:9 ratio.

Ratios are helpful as they are simple for people to understand. Based on a ratio you could see that “8 out of 10 restaurant diners plan to return in the future.” However ratios can fluctuate quite drastically from one day to the next and it’s not always easy to see what has influenced that. Keeping with the restaurant example you could find that on a particular day the rate of customers who plan to eat with you again has fallen to 5 out of 10. But this could have been because there was a big event in town that day, meaning your regular diners were replaced by out of towners attending a conference. Takings might actually have been up, as they filled the restaurant, but wouldn’t visit again as they weren’t local.

This means that ratio metrics should act as a start point. Use them to identify areas that require further analysis, but then use other models to interrogate and explain the data to avoid missing anything.

Net scoring
The best example of the above is Net Promoter Score™, which is designed to measure the loyalty that exists between a provider and a consumer. It works on a principle of difference scoring with an emphasis on analysing strong ‘for’ and ‘against sentiment. Consumers answer the simple question “Would you recommend this company/service/product to friends and family?” and provide an answer on a scale of 1-10.

So-called ‘passive’ ratings of 7 or 8 out of 10 are ignored. Above that, they are a positive promoter, below 7, they are a detractor.

To get your overall score, you subtract the percentage of below-passive detractors from the above-passive percentage of promoters. If the resulting number is higher than zero then this is favourable and indicates more customers like a company, brand, product or service than not. Likewise, below zero is negative and indicates room for improvement.

The issue with NPS is that it merely provides a snapshot. Analysing customer insight is far more complex. For example a net score of +15% looks great, but could have been reached in multiple ways. You might have had 20% positive and 5% negative feedback, or 50% positive and 35% negative or even 15% positive and 0% negative. Headline NPS scores won’t tell you if you had 0% or 35% unhappy customers, which shows why you can’t rely on the model alone.

Accounting for the fallibility of humans
Numeric scoring models may appear to be objective, but the very fact that they involve humans makes them subjective. One consumer might see giving you six out of ten as positive – another that it is average.

How it is then interpreted, by yourself and others, is equally subjective. You might believe an NPS score of 75% positive is amazing – but the CEO might castigate you for the 25% of negative feedback.

To truly get a view of what is going on it is therefore important to combine multiple forms of insight. Look beyond just the numbers and get qualitative information from customers and the staff that interact with them. What do you they like about a product or service and why? Is there a minor niggle that drags the whole offering down? It’s this insight that can very often make the difference – however scoring models can tell you where to look deeper so ensure they are part of your insight toolkit.


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