NPS for Walmart Offline is low. How will you go about it?
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in Problem Solving by (14 points) | 247 views

3 Answers

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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

  1. Kiosks at the store while checking out to rate the overall experience and recommend 
  2. 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.
4. Checkout 
 
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
Price Perceptions
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
Promoters 
Detractors 
Passive
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.
 
 
by (61 points)
0 votes

There are three steps I will execute

Explore the Data Source -> Do Data Analysis -> Generate call to actions

  • Exploring Data Source
    • Are all the sources sending data
    • Is the data sampled? Or full fidelity?
    • Is there bias in the NPS group (people who are negative are the ones who leave a feedback) has the trend changed in recent months?
    • Are there verbatims from customers? If not, it should be given
  • Do Data Analysis
    • Filter and sort the data based on the customer's workflow
      • Customer's impression of the store upon entry
      • Customer's ease of finding items, and enjoying the shopping experience
      • Customer's ease of asking for help or being assisted
      • Customer's checkout experience
    • Then we can do categorical clustering on each area's verbatims if it has been collected
      • Taxonomy
      • Categorical clustering based on short word phrases
      • Word2Vec. Specific words refer to a vector of associated words
    • Without verbatims, it may be better to understand the negative and positive NPS distribution is on each area
  • With verbatim, it may be good to take 1% of the data and have a human eye label it to compare it with the machine-sorted categorization.
  • Call to Action
    • Derive features of action based on the most impacted experience area with the most actionable mentions.
by (41 points)
0 votes

Clarify Problem statement

NPS (Net promoter score) = (Promoters - detractors) / Total users *100

  • Promoters = users who scored 9 & 10

  • Detractors = users who scored 1 to 6

  • Passive = user who scored 7 &8

 

Factors affecting NPS rating

  • Right products

  • Right price

  • Right quality 

  • Store / website experience 

    • Discovery / visibility

  • Marketing communication - sms/email, push notification/in app

  • Customer support

  • Brand value

  • Traffic analysis - paid vs free user

 

Sub metrics to track

  • NPS - Region / city wise 

  • NPS - product wise

  • NPS - product category wise

  • NPS - product category & price range wise

  • NPS - product & price wise

  • NPS - user segment wise

    • 1st time purchasers

    • Repeat purchasers

  • NPS - orders where customer support was involved (call, online chat etc)

  • NPS - mode of feedback collection wise

    • sms / email 

    • At store

    • Push notification, in app

  • NPS - user behaviour based on

    • Sales days

    • Free vs paid users

  • NPS - agent who sold the product

  • NPS - wait time 

  • NPS - product discovery (easiness to find product)

 

North Metrics to track

 

  • NPS - product wise

  • NPS - user segment wise

    • 1st time purchasers

    • Repeat purchasers

  • NPS - orders where customer support was involved (call, online chat etc)

by (16 points)
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