It’s time for many organizations to re-think some of their long-term favorite measures. Research has demonstrated that many of the metrics used up to this point have significant faults. The reality is that some of our assumptions around what these measures have been telling us were wrong.
Assumption: A client that is “satisfied” is a loyal client, and this is a primary driver for future purchase intent.
The emergence of net promoter score (NPS) is based on some really profound research which demonstrates quite concretely that it correlates strongly with customer behaviours, as well as loyalty. Armed with this information, one would expect to see satisfaction ratings correlating similarly with the NPS results. Strangely, the correlation exists at a practically non-existent level. Basically correlation is so all over the map that it’s not reliable as a primary customer metric. Of course, there are circumstances where client satisfaction can play an important role (such as if your customer base is small and fixed) however a customer’s likelihood to recommend you speaks far more deeply to what really matters.
Assumption: We know what clients think is important
Too many client surveys are rigid, irrelevant dinosaurs. We love trends, so we fight to maintain consistent measurement criteria. We love predictability so we control what gets measured within it (so we can hit our targets) and we think that we understand exactly what should be measured. The best client surveys allow flexibility for clients to express (either choosing only top issues to rate within a larger set, or completing qualitative research first and using their feedback to build the categories. Better, both.) wherein clients set the criteria and it is allowed to change over time based on what they think is important. What is more important than predictability is receiving information that is meaningful, year over year. Prepare for clients to express dis-satisfaction in different areas over time as you are able to resolve the ones that are most top of mind.
Assumption: If we measure just our performance in the areas we know are important, that will tell us what we need to work on.
Again, not necessarily true. This assumes that every category is valued equally. Without an importance rating on each measure, you won’t know what the true gap is. For example: If something is a 7/10 importance for a customer, performing above 7 may represent excess equity in that attribute… whereas if a client values something at 9.5, performing at a 7 could represent an area of significant discontent. Over-investment doesn’t necessarily create additional value so but re-allocating these resources to an area where real gap exists might.
Assumption: Satisfied employees = engaged, loyal employees.
This parallels with client satisfaction vs loyalty/likelihood to recommend. Many organizations operate under the assumption that the first creates the second, when in reality the correlation between the two is very low.
Metrics are used as guidance for decision making. If you’re basing decisions on poor information, your acuity for emerging risks in those areas is going to be poor also. By moving to measures that tell a more truthful story, you’re positioning yourself for success. Once you know that a measure isn’t telling you what you thought it was, it’s really not much use going forward.