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Walmart is a big chain retailer in the US. It has an online as well as an offline presence. When we saying, Walmart offline, I believe we are talking about Walmart physical stores? There are certain other store like Walmart’s Sam’s club, Walmart Mexico, Can I assume we are not talking about them at the moment?
NPS: Net promoters score. 0-10 scale. Above 8 is considered a good score.
When we say NPS score, it is a score that is based on the user feedback of the services, experience.
Can we define low here? 3
How was the NPS score collected? At stores when they were leaving.
Has it been low for some time or is this a recent phenomenon? Been low for some time.
I will be trying to identify the problem, how I would go about it is:
Narrow down the problem:
Where are we seeing the problem: Overall NPS or is the store experience, post purchase customer exp, check out experience.
Where is it happening the most? Some particular store or any particular region?
Any particular type of customer that has this low NPS score? Based on age or based on gender or based on the type of customer they are premium with some subscription?
Service that is affected? Store pick ups, deliver from stores, in-store purchases.
Internal Problems:
Walkthrough: A person comes to Walmart, tries to find what they are looking for, they would take the help of the store employees-> wait at the check out -> Pay for his items->leave the store -> Load the items in the car.
Wait Time is too long.
Avg time to check out.
Avg number of people waiting at each counter at peak time.
Avg. basket size per person
NPS score reasons.
Number of instances of this happening.
Avg. NPS score when this happened
They don't accept certain payment methods.
NPS score reasons.
Number of instances of this happening.
Avg. NPS score when this happened
Number of payment methods accepted.
Number of payment methods accepted by Competitors.
Number of times an unsupported payment method was requested by a user.
Price difference on the shelf vs the actual.
NPS score reasons.
Number of instances of this happening.
Avg. NPS score when this happened
A service that we were providing is now unsupported: discount coupons vs store mismatch.
NPS score reasons.
Number of instances of this happening.
Avg. NPS score when this happened
External Problems:
Price difference between competitors and Walmart
NPS score reasons.
Number of instances of this happening.
Avg. NPS score when this happened
Analysis of Number of products that have a significant price difference competitor and Walmart
New services provided by competitor, not provided by walmart: let you walk out and pay later
NPS score reasons.
Number of instances of this happening.
Avg. NPS score when this happened
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)
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
- Filter and sort the data based on the customer's workflow
- 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.
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.
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