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Effective Customer Feedback

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Author: Sean Burton
Updated: 07 Aug 2019
Effective Customer Feedback

The old adage "the Customer is always right" has been a mainstay of offline customer experience for decades. But is it correct? A core component of the work I used to be involved in at Seren, related to digital Customer Experience trackers or Voice of Customer programmes: typically an on-site survey to see what your customers' think. Online surveys, if implemented well, can be extremely powerful especially when integrated with other data sources and used as part of a customer feedback mechanism.

For example, the Net Promoter Score (NPS) is an industry standard measure that has been shown to correlate to financial performance. This score is created based on analysing the responses to the question "Would you recommend this site/service/product to your friends or family?". The responses are measured on a scale from 0-10: scores of 0-6 being classified as 'Detractors; scores 7-8 as 'Passives'; and scores 9-10 as 'Promoters'. The NPS score is then calculated as the percentage of 'Promoters' minus the percentage of 'Detractors'. The advantage of NPS is that it's pretty sensitive to minor changes in the average recommendation score as it actually takes the distribution of the scores into account.

One of the approaches we encourage our clients to implement is to actively measure the shifting patterns of this distribution as part of their wider customer service strategy. By integrating the survey responses with backend CRM (ensuring you have the users permission) and web analytics data enables you to: firstly, scale the size of the problem; and secondly, to proactively contact the Detractors to help resolve the problem. This proactive resolution can be very powerful as you can convert a former Detractors into a Promoter and thus an evangelist for your brand. This strategy can be implemented across all customer touch points, i.e. Retail, Call Centre & IVR, online, etc. and can then help drive channel specific service adoption by developing the services and channels that has the most benefit to your customers.

Forbes published an interesting article on how for every hour that Apple spent calling their Detractors (based on customer feedback from in-store visits) they generated $1000 in additional revenue or $26M in the first year.

All sounding rosy so far?

Well, the problem with the above, it that there is obviously a cost associated with trying to convert a Detractor into a Promoter - the customer was a Detractor for a reason and to make them a Promoter will likely involving fixing something.

Enter Econometric modelling. Essentially, this is about forecasting the financial impact of various actions. By bringing in spend data from your transactional or CRM systems you'll be able to see the value of the Detractors, as a group, to the organisation and thus the financial impact of converting them to Promoters. For example, by overlaying CRM/transaction data onto your Detractors you can determine their average spend or lifetime value. Using Econometric models will enable you to confirm the potential uplift in these values and thus the overall financial gain. You can then weigh this against the cost of actually fixing the problem to determine whether it will deliver business value.
The recent hire of a Chief Data Scientist by the New York Times to help them predict unscribers is additional testiment to the above (

By using this approach you may actually find that trying to convert your Detractors to Promoters is not cost effective, allowing you to investment in more profitable areas. Without Econometric modelling you would simply be working on overall volumes and you may find that, although your Detractors may be numerous, they may not be high-value and may never be. Therefore focusing elsewhere, for example by upselling the Passives, may yield better returns.

Of course, I would also advocate that fantastic customer experience for all your customers is vital as the low value customers of today, may be the high-value customers of tomorrow. However, the commercial reality is that there are always finite resources - both personnel and financial - and so it is vital to focus on what will generate the most value initially. If you don't then you'll likely fall at the first hurdle as the business will start to question the value of the project before it ever gets a chance to generate a return.

Naturally, to do the above you need to have your data house in order and it is for this reason why 'Big Data' projects often fail (and therefore suck), because people try to fly before they can even crawl.

Get the basics right and then build on that foundation.

So, what do you think? I'd love to hear from you


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