Imagine you are PM in Amazon. How do you reduce rate of returning orders ?
Assume current rate of returing orders is 12% and you have to bring it down to 8%.  Based on your analysis you found the most common reason for returning is " Product doesn't match description".  

Assume total different kinds of products listed = 10,000.

How will you proceed from here ?
0 votes
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Assume current rate of returing orders is 12% and you have to bring it down to 8%.  Based on your analysis you found the most common reason for returning is " Product doesn't match description".  

Assume total different kinds of products listed = 10,000.

How will you proceed from here ?
in Problem Solving by | 690 views

3 Answers

+1 vote

I would imagine that the goal of the problem is to see how you think through a problem to identify a root cause, and then implement solutions considering trade-offs.

Understand the problem first to get to the root cause:

Are these products being returned in a particular category i.e apparel versus watches? This might give some clues about how the product descriptions or SKUs are being listed on the website today.

Are these FBA or are these shipped directly by merchants to customers? How much can Amazon control in this process? Is there an error in how the products are being stocked in the warehouse or is there a problem with how the seller has separated SKUs?

Is this problem pervasive across sellers in a single category or multiple sellers in multiple categories?

Are these products being shipped from a new fulfillment center ? Was there a turnover in staff recently in these fulfillment centers?

Is this a new product category for Amazon? Are the product descriptions accurate?

Solutions

1. If the problem is specific to a category i..e tee shirts, see whether the product detail pages for these items identify the right item to ship based on Color (blue, black, white) , Size (S,M,L) and any other factors that can multiply the inventory.

2. If the problem is specific to a seller, investigate Do items have duplicate bar codes even though they are completely different? check if the seller is new or requires better quality control.

3. If the problem is with a specific fulfillment center and not a specific type of product or seller, then identify where in the FC the problem occurs.

4. Is the seller shipping on their own? Do they need to be shut down for breaching customer trust?

 

by
0 votes

Total Number of Products: 10,000 (Given)

Total Number of orders shipped: 1,000 (Assumption)

Total Number of orders returned: 120 (12% of 1,000)

Probable Reasons for Return:

  • Broken Item -20(16.6% of Total returned)
  • Late Delivery -20(16.6% of Total returned)
  • Mistakenly Ordered -20(16.6% of Total returned)
  • Description Mismatch -60(50% of Total returned)
    • Seller
      • Intentional
        • Knowningly uploads wrong Information
        • Knowningly uploads less Information
        • Knowningly uploads confusing Information
      • Unintentional
        • Unknowningly uploads wrong Information
        • Unknowningly uploads less Information
        • Unknowningly uploads confusing Information
    • Buyer
      • Less Information
      • Doesn't read Information
      • Misunderstand Information

Information Based upon which Buyer makes buying decision:

  • Pictures
    • Color
    • Shape
    • Size
  • Description
    • Material
    • Color
    • Size
    • Price
  • Reviews

Our Objective is to reduce returns by 4%(12%-8%) which leads to only 80 returns from earlier value of 120. Since "Wrong Description" accounts for 50% of returns. We need to reduce "Wrong Description" returns from earlier value of 60 to 20 i.e. 66% percent of reduction.

 

Problem Statement: Buyers return items on Amazon because buyer doesn't make well informed decision at the time of purchase. Information is either lacking, confusing, or wrong for buyer. 

 

Solutions: As part of solution, we need to make sure that buyer has all the information required to make right decision and make sure buyer is utilizing that information and making right decisions.

 

  • Less Information: What more information does a buyer need to make a right buying decision.
    • Picture: 3D Imaging.
    • Videos: 3D Video with sound description.
    • Description: Material,Color,specifications etc tagging and comparison with 3D images. Standard scale to compare height, weight, width etc.
    • Written+ Audio+Video Reviews: Amazon points for product reviews and feedback. New review system for customers which can have audio,video, and text functionality. Existing customer can make 30 sec-1 min videos or audios. 
    • Chat: Prospective customers can chat with existing buyers. Algorithm will match customers so as to maintain high customer experience for both prospective and existing customer.
  • Doesn't read Information: How can we make sure buyer reads all the information about product.
    • Mutilingual: NLP will transform reviews, description in language of choice for buyer.
    • Picture: People tend to learn more through pictures so increasing #pictures may help. 
    • Description: Change description to tags instead of written text.
    • Reviews: Sort reviews with negative and positive comments.
    • Scroll Notification: Record scrolling sessions to remind buyer to spend more time on reading description.
  • Misunderstand Information: Make sure buyer understands correctly about product.
    • Picture: Scaled images to highlight specifications of product.
    • Description: 3D imaging can help reduce errors around specifications like height, weight etc.
    • Reviews: Feature through which reviews can alter descriptions. A well written review can act as the secondary product description.

 

Criteria3D ImagingAudio/Video ReviewsChatMultilingualScroll Notification
Value to CustomerHHHML
Value to PlatformHHMML
Resource InvestmentMHMML
Time InvestmentHMMML

 

I would prioritize following three features over others.

  • 3D Imaging
  • Audio/Video Reviews
  • Chat

 

Metrics

  • Business Metrics
    • % Returns
    • Sales Conversion
  • Product Metrics
    • Feature Utilization (#times feature used)
    • Feature Penetration (#times feature used per product)
    • Feature Spread (#products on which feature used)
    • Return-Feature Usage Ratio
  • Ancillary Metrics
    • AOV
    • Retention Rate
    • Repeat Purchase Probability
by (43 points)
0 votes

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C: Clarify the situation

We have 10,000 items that have a total return rate of 12%. First thing I would do is graph the distribution of return rate per type

There are two ways this can come out

1. The distribution is even among all 10,000 items

2. The distribution is biased to a specific type of items.

 

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Regarding 1. This would mean that the categorization is too granular. I would further cluster the 10,000 items to bigger clusters until we see a clear #2 pattern.

 

I: Identify the customers

I would take it General, and Market Prioritized

Generally, what kinds of customers return the most?

                  1. High return for some reason or another customer?

                  2. Try on stuff to see fit customer: Customers who purchase different sizes of the same item and return the wrong sizes?

                  3. Use once and return customer: These customers are the ones that return after using the item once.

 

I would look at the distribution of these customers across the general 10,000 types, and across the largest cluster identified in the step above. Compare them. If they are starkly different. The two approaches that we would want to look at is

 

1. Do we want to target the segment that has the highest return rate?

2. Do you want to target the type of customer that has the highest return rate?

 

 

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by (41 points)
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