Business and Product Outcome Mapping
Swiggy Instamart is a quick commerce service offering 10-minute deliveries for a wide range of products.
Objective: To increase average order value by 10% to reach profitability through Swiggy Instamart.
- Customer
- Instamart App
- Delivery Partners
- Warehouse Employees
- Relatives/Friends [secondary actors]
Forecasted revenue of ₹271.80bn in 2024
Quick Commerce Market (India)
25.67%
Expected Annual Growth Rate (2024–2029)
1.8%, projected to rise to 4.0% by 2029
User Penetration Rate (2024)
Market Understanding and User Segmentation
| App | Market Share | Competitive Differentiation |
|---|---|---|
| BlinkIt | 45% | Largest number of categories + item options (Supreme Cataloging) |
| Instamart | 27% | Only loyalty program in the country which covers all at once! [Grocery, Food Delivery, Genie]; Private label for fresh fruits, vegetables and meats |
| Zepto | 21% | Cheaper prices for loyalty members + offers apparel options in quick commerce |
| Big Basket + Others | 7% |
| Order Frequency Segment | % Users | Monetization Segment | % Users |
|---|---|---|---|
| POWER USERS — Users with 15+ orders in a month | 18% USERS | HIGH AVERAGE ORDER VALUE — More than 800 Rs | 13% USERS |
| MODERATE USERS — 6-15 orders in a month | 43% USERS | MODERATE AVERAGE ORDER VALUE — 401-800 Rs | 12% USERS |
| CASUAL USERS — 5 or less orders in a month | 39% USERS | LOW AVERAGE ORDER VALUE — Less than or equal to 400 Rs | 75% USERS |
Problem Hypothesis
H1: Majority of quick commerce users [50%+1] will have an average order value of less than 400 Rs. H2: Less than 15% quick commerce users will have an average order value of more than 800 Rs. H3: Majority of quick commerce users order multiple low value orders based on urgency because of lack of pantry management.
| Segment | Status | Notes |
|---|---|---|
| Rejected segment (the number of orders is already low, hence opportunity size diminishes) | Rejected | N/A |
| FINAL SEGMENT [50% USERS] (AOV ≤ INR 400 and order frequency > 5 times a month) | Accepted | Rejected segment as the average order value is already on a higher side, thus leaves low propensity for increase in order value. |
75% — Accepted
Users with average order value less than 400 Rs (H1)
39%
Users who order 5 or less times in a month
13% — Accepted
Users with average order value more than 800 Rs (H2)
18%
Users who order more than 15 times a month
User Research: A user survey was conducted with 40 participants from Tier 1 city, aged between 18-24 years to identify the segments and the pain points.
Target User Segment: Users with average order value equal to or over INR 400 with an order frequency of more than 5 times a month.
User Research
User Research and Root Cause Identification
Urgency-Based Ordering
Most users order impulsively when they run out of something, not planning ahead or making larger, stock-up purchases. This leads to frequent, small orders.
- Why does this happen? Lack of Pantry Management: Many users don't keep track of their inventory, leading to last-minute orders and missing opportunities for bulk discounts.
- This leads to multiple fragmented orders which leads to low order value. (Root Cause of Low AOV)
Price Comparison
Users feel the need to compare prices across apps and offline stores. They might find better deals elsewhere or perceive the app's prices to be generally high.
- Why does this happen? Price sensitivity: Users are sensitive to price and perception of high prices due to lack of comparison makes them hesitant to commit to larger orders.
- VA
Vaibhav Sharma
Age: 23 · Occupation: Data Scientist · Location: Delhi · Lives: with family · Income Range: 55,000-60,000 Rs · Tech Savvy
Ideal User Persona representing the target segment.
Needs
- Comfort, Convenience, Price (Motivations)
Goals
- To be able to manage pantry without rushing into last minute orders every day.
Pain points
- Limited motivation for keeping a check on pantry items.
- Hectic schedule leading to a lack of organization.
“I wish someone could just take care of my pantry and let me know before I run out of groceries!”
“I usually order when I need something very urgently just enough to avail the free delivery.”
“My work is quite Hectic and doesn't allow me a lot of time to make a consolidated list of things I need. I just order as and when urgency arises.”
“I compare prices across apps and order just enough to get by the day. I am constantly checking when I will surpass the free delivery line.”
Root Cause of Missed Business Outcome
Multiple fragmented orders throughout the days hinder business outcome by reducing average order value and decreasing profitability.
Problem Framing and Impact Mapping
Why should we solve this problem now? The urgency to address this problem is evident due to India's demographic landscape, with over 50% of the population below the age of 25 and more than 65% below the age of 35. The majority of this population segment heavily relies on technology for various tasks. However, traditional pantry management practices, typically handled by relatives or parents through offline mediums, have left this demographic with outdated methods. Given the ongoing expansion of the mobile market and the inevitable penetration of technology into various aspects of daily life, addressing pantry management for youth now is crucial to ensure they are not left behind and to meet their evolving needs effectively.
What is the value generated for the Target Segment by solving this problem? Solving this problem for the target segment, comprising 50% of users, offers significant benefits. Firstly, it alleviates the stress of remembering what to order and when, leading to a more relaxed and organized lifestyle. Additionally, it enables them to avail bulk discounts by consolidating their orders, resulting in potential cost savings.
How do we know it's a real problem? The problem is validated through user survey data, which reveals that 50% of users place orders more than six times a month, yet maintain an average order value of less than 400 Rs. This discrepancy indicates a pattern of frequent but small-value orders, suggesting a genuine need for a solution to streamline ordering processes and potentially increase order value.
What is the true problem?
The true problem lies in the lack of inventory management among users, resulting in last-minute orders and missed opportunities for bulk discounts. This contributes to a perception of higher prices, as users are unable to take advantage of bulk discount offers.
Jobs to be done: When I attempt to purchase affordable groceries online, but struggle due to ordering based on urgency and inability to recall all necessary items, which leads to rushed orders and missed bulk discounts. Help me organize my pantry and grocery needs so that I can avoid daily rushes for orders while taking advantage of bulk discounts.
Who are the customers facing the problem? Tier 1 youth aged 18-24 who lack the time and motivation to manage their pantry yet prioritize cheaper prices and convenience.
Problem Identification & Validation
Users struggle to purchase large quantity of groceries online, due to an urgency-based order behavior and lack of pantry management, leading to missed bulk discounts.
Let's put in numbers to justify our approach.
Target Segment: 50% of total users.
Current Average Order Frequency of the target segment: 12 orders per month.
Assumptions
50% of target segment users will consolidate their orders. These users will consolidate 40% of their orders. Current AOV: 400 Rs.
- If we encourage users in the target segment to consolidate their orders, then: New Number of Average Orders = Original Average Number of Orders / (1 + Percentage Increase in Order Value)
- Percentage Decrease in Number of orders = (Original Number of Orders − New Number of Orders / Original Number of Orders) × 100
- Overall AOV Increase for Target Segment = Percentage Increase in AOV × Proportion of Target Segment Users Who Consolidate Orders
- Overall AOV Increase = Overall AOV Increase for Target Segment × Proportion of Target Segment in Total User Base
- New AOV = Current AOV + (Current AOV × Overall AOV Increase)
New Number of Orders = 12/1.4 = 8.57 orders [Approx]
Percentage Decrease in Number of orders = (12 - 8.57/12) × 100
Overall AOV Increase for Target Segment = 40% × 50% = 20%
Overall AOV Increase = 20% × 50% = 10%
New AOV = 400 + (400 × 10%) = 440 Rs
Impact Mapping
By addressing the identified problem, we can increase average order value by 10% to reach profitability. This is the business value it creates! The business is able to increase the Average Order Value (AOV) by 10% and achieve profitability!
Solution Ideation and Prioritization
Product Solution validation is based on Value, Usability, Feasibility, Viability, and Scalability.
Prioritized Solution
InstaRestocker
Objective: Enhance customer convenience and retention by implementing an AI-based restocking tool that predicts when users will need to restock groceries based on their personal usage patterns, for both perishable and non-perishable items.
- If we implement an AI-based restocking tool that predicts when users will need to restock groceries based on their personal usage patterns for both perishable and non-perishable items, then we will enhance customer convenience and retention because it will ensure users never run out of essential items and they don't have to manage their pantry on their own.
InstaPrice Comparer
Objective: Enhance customer price perception by implementing a price comparer tool that compares prices across quick commerce apps and offline stores for high value items based on market data, shifting perceptions about Instamart price ranges.
- If we implement a price comparer tool that compares prices across quick commerce apps and offline stores for high-value items based on market data, then we will enhance customer price perception and increase order value because it will provide assurance that they are not overpaying.
Solution Ideation & Prioritization
Out of the two proposed solutions, the InstaRestocker is prioritized for implementation.
Prototyping and Solution Detailing
User visits the app
Adds the items, and goes to cart.
Cart page
They see the 'InstaRestocker' card which nudges them to stock up before they run out of products.
Immersive restocking experience
They can scroll up to make it an immersive restocking experience and to view all products. [Helps in activation]
Reorder page
The same feature is also available at the reorder page [Recall + Engagement]
Checkout
Once they are done with adding all the items, they can check out.
InstaRestocker suggests items to add to your cart during checkout, based on your past usage, to help you avoid running out of essentials.
Perishable Items Management
- Data Display: Display expiry date, last purchase date, and estimated depletion date based on user usage patterns.
- Algorithmic Prioritization: Automatically determine the order in which items are displayed.
- Items with a shorter shelf life (1-10 days) are shown 1-3 days before expected depletion.
- For items with longer shelf life, recommend restocking within a timeframe based on usage patterns or expiry/best before date.
Non-Perishable Items Management
- Lifespan Tracking: Record typical item lifespan and usage frequency.
- Recommend restocking based on expected end-of-life or depletion.
- Alert Information: Display item name, last purchase date, typical lifespan.
- Recommend restocking with a message like "You might need to buy it again in a week," tailored to usage patterns.
System Design, Constraints and Second Order Thinking
Constraints
1. Post-Depletion Handling: Keep items on the list for a week after depletion. Provide context-specific notifications like "You ran out 2 days ago". Update algorithm periodically for improved usage understanding. 2. Ethical Considerations: Respect user autonomy and preferences in decision-making regarding data tracking and usage.
- A decrease in order numbers could significantly impact delivery personnel's compensation, necessitating a review of the compensation structure and incentives.
- Additionally, higher order values may result in constant travel with heavy or expensive items, posing security and safety concerns that require careful consideration and planning.
Hypothesis for the Chosen Solution
Opening up a section at the cart that shows when users, who make multiple small average order value orders, will run out of products in their pantry, will nudge them to consolidate orders and will increase average order value by 10% over a period of 3 months.
System Design: Proactive Product Recommendations Based on User Purchase History.
Metrics & Pitfall Mitigation
| Risk | Opportunity |
|---|---|
| The algorithm's accuracy may be compromised if users make purchases from sources other than Swiggy, potentially leading to tracking errors and incorrect predictions. | Implement cross-platform integration to accurately track user purchases from various sources, ensuring data consistency and precise predictions within the algorithm. |
| The absence of nudges for free delivery due to already high order values could result in reduced purchasing behavior, with users opting for need-based items rather than splurging. | Incorporate targeted nudges or incentives beyond free delivery to encourage additional purchases and prevent a shift towards strictly need-based shopping. |
| There's a risk that users may exclusively purchase the same products repeatedly, resulting in the cannibalization of need-based purchases and a limited exploration of new products, potentially missing out on higher-value offerings. | In later stages of product, a 'try this instead' nudge could be introduced to combat this. |
Success Metrics & Potential Pitfalls
Metrics track product success, user satisfaction and, engagement. While pitfalls focus on data accuracy, meeting diverse needs, and user-friendliness.