If you handle customer complaints, and are sick of constant flurry of grievances; you've got Nanni to blame.
Who is this Nanni person you ask? Well, he is the one who started it all.
At least as far back as recorded history goes, Nanni wrote the very first written complaint about his unsatisfactory experience with a Mesopotamian Copper merchant called Ea-nasir.
This complaint inscribed on a clay tablet, which now proudly resides in the British Meuseum; included matters such as:
- Sub-standard quality of copper sold
- Rude behavior towards Nanni's servant (who handled the transaction).
Now if our merchant Ea-nasir would have been sharp-witted; he would have signed himself up for a "how to be a people person" seminar among other things. Unfortunately, we don't know much about what happened.
In present times, we have a solid framework of collecting customer feedback.
For specific interactions CSAT is used, and for a more forward looking assessment on brand satisfaction etc. NPS is used.
While such research is great, it may not necessarily help us take specific and precise action to drive customer engagement, where the engagement is focused at meeting customer's specific needs and fulfilling our organizations business objectives.
Another challenge is the fact that you've got to get folks to share their feedback, which in-turn poses the following challenges:
- Customers with negative experiences are highly likely to share their experiences*.
- Only about 1 in 10 happy customers, may end up sharing their experience*.
- Finally; Perceptions, needs and expectations change over time. And the likelihood of getting precise feedback from the same customer on a recurring basis may not be possible.
Instead, enterprise can complement their customer / brand satisfaction scoring framework with a customer data driven action oriented scoring model. I call this Customer Experience score, and most enterprises should have the building blocks to calculate, track, and action on such a score.
With banking data as an example, we can combine product portfolio with behavior and intent data to obtain:
- Rows that score for each dimension
- Scores across dimensions add up to create a unified score
- Instead of forcing a pre-conceived score band based categorization; let the data decide based on distribution of values.
Finally you end up with categories in which you can bucket these scores, but most importantly every customer.
This isn't the end, but actually the beginning of where things get interesting. You want to start taking snapshots of these scores, compare changes and act accordingly.
For instance, some % of your customers may drop out of the top bucket. And you can immediately react to this data point and course correct your engagement strategy.
Again, most of this; if not all, should to be automated. The final score, and score change based triggers can go from a centralized system to various engagement and delivery systems within the enterprise.
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