Net Promoter Score is a score which is computed by taking responses from all the customers on "Whether they will recommend company to others?" on 1 to 10 rating scale
Number of ratings >8 are promoters
Number of ratings <7 are detractors
Number of ratings beween 7 and 8 are passives
NPS = promoters - detractors / total responses * 100
I'm assuming that NPS data is collected for walmart offline in 2 ways
- Kiosks at the store while checking out to rate the overall experience and recommend
- Web/Mobile Survey link sent by sms/email etc.
I would like to first check the customer journey. Typical Journey would look like as follows
1. Customer Enter the store
2. Selects items from various categories
3. Ask to service representative if help is needed.
In order to understand where NPS is low we need to couple it in survey questions with customer journey.
There should be rating scale questions targeting spcecific touchpoints in customer journey such as
Store Experience - Layout, Atmosphere etc
Service Experience - Representative Experience, Chechout Experience etc
Product - Avalability, Diversity etc
Once we take input on these questions along with NPS question, we can classify data based on responses in 3 parts
Then we'll calcuate average score given by them for each of the questions mentioned above.
The questions with low rating are likely contributors of the low NPS score.
We can further have category and subcategory relationship in the survey questions itself. For example, if score for store experience is low, then next question in survey will be to drilldown on specifics of the store experience.
Alternatively, we can also ask text questions where customers can provide open ended answers and use text mining and word cloud to understand pain points of detractors, passives and promoters.