Engagement & Retention project | Ideas2IT Technologies
🍔 About Swiggy's Online Food Delivery Business
Swiggy is an Indian online food ordering and delivery company. Founded in 2014, Swiggy is headquartered in Bangalore and operates in more than 580 Indian cities, as of July 2023. The online food ordering application of Swiggy offers the following enhanced features:
- Swiggy One - Swiggy One is a membership program offering benefits across Swiggy's services, including free delivery and discounts on food, Instamart, and Genie orders, with plans starting as low as ₹75 per month
- Swiggy One BLCK - Swiggy One BLCK is an invite-only, premium membership offering enhanced benefits across Swiggy's food delivery, quick commerce (Instamart), and dining out services, including on-time guarantee, faster deliveries, and complimentary cocktails or desserts at partner restaurants
- Co-Branded Credit Card with HDFC - A co-branded card with HDFC that helps customers avail up to 10% cashback on Swiggy spends, 5% cashback on online spends and 1% cashback on other spends.
- Swiggy Bolt - Swiggy Bolt is a 10-minute food delivery service within the Swiggy app, focused on quick-to-prepare items from popular restaurants, aiming to provide fast delivery of frequently ordered food
- Gourmet Picks - The list shows gourmet restaurants near the user
- Reorder List - The reorder list curates the list of popular food items ordered by customers for them to quickly order from their favourite restaurant
🎯 Swiggy's Core Value Proposition
Step 1: Understanding JTBD
To craft the CVP of Swiggy, let’s look at the JTBD for Swiggy
- Target customer: convenience-driven individuals who value time and ease with respect to having meals. They want to choose from a wide variety of restaurants without the hassle of having to step out
- Needs: Get a wide variety of food delivered quickly and reliably
- Product: Swiggy - online food delivery app
- Category: On-demand food delivery services
Step 2: JTBD of Swiggy
The primary JTBD of Swiggy’s online food delivery app is 🧩 Functional. Swiggy helps users solve the functional problem of:
- Getting cooked food delivered quickly and reliably 🕛
- Reducing the friction of cooking or going out 🍜 🏃🏼♂️
It helps customers save time, effort, and planning hassles, i.e., a task-based job.
Step 2: Core Value Proposition (CVP)
🎯 Hence the CVP of Swiggy is as follows:
For the mobile-first generation seeking convenience, affordability and choice in meals , Swiggy is an on-demand food delivery platform that offers a wide selection of restaurants, personalised recommendations, and consistently fast delivery—making food ordering effortless and dependable compared to traditional dine-in or slower delivery alternatives.
🤔 How do users experience Swiggy's CVP
Swiggy brings its core value proposition to life through a seamless, intuitive, and personalised user journey. Here’s how users tangibly experience the convenience, variety, and reliability Swiggy promises:
- Curated Restaurant Discovery Within 10-12 km: Swiggy displays a wide range of restaurants near the user’s location, segmented by cuisine, discounts, rating, pricing, and popularity. This empowers customers to make quick, informed decisions based on their food preferences, budget, or occasion.
- Real-Time Delivery Time Estimates: Every restaurant listing prominently shows an estimated delivery time before placing the order, enabling users to choose based on how quickly they need the meal—adding control and reducing anxiety around hunger and time constraints.
- Smart Alternatives for Every Craving: While browsing a restaurant, users are shown contextually similar restaurant options, helping them explore alternatives without starting their search from scratch—enhancing the perception of choice and reducing decision fatigue.
- Frictionless Checkout Experience: With a 2-step payment process, Swiggy minimizes friction at the most critical point in the journey, ensuring a quick and hassle-free checkout that aligns with the mobile-first user’s expectations.
- Reliable Order Fulfilment: Swiggy’s logistics and tech stack work together to ensure consistent on-time delivery, building trust and forming the backbone of its promise of dependability.
- Live Order Tracking: Once the order is placed, Swiggy offers real-time order tracking via an interactive map, giving users transparency and reducing anxiety during the wait time.
- Personalized Home Feed and "Eat List" feature: Swiggy tailors the homepage experience based on past orders and preferences. Users can also build their “Eat List”—a personalized collection of favorite dishes—to reorder in seconds, boosting convenience and stickiness.
- Instant customer support: Swiggy provides in-app chat support and quick issue resolution workflows, which is a critical part of the dependable experience.
- Gamified Loyalty (Swiggy One and Swiggy One BLCK): With subscription models like Swiggy One & Swiggy One BLCK, users get free deliveries, discounts, and priority service, which boosts retention and LTV through perceived VIP treatment.
- Post order experience: Swiggy enables users to rate the food and delivery experience, giving them a voice and creating a feedback loop to maintain quality.
🕒 Natural Frequency
The Action that makes the user an Active User
For Swiggy, an active user is defined as someone who opens the app and places a food order within a given time period ( weekly or monthly)
Natural Frequency by Usage
The natural frequency of Swiggy’s online food delivery app can be defined as follows:
User Type | Description | Typical Frequency | Typical traits |
Casual 🌱 | Use Swiggy occasionally—typically during special occasions like birthdays, get-togethers, or when their preferred platform (e.g., Zomato) is unavailable. | 1 - 2 times / month | May not have Swiggy One; price or event-driven |
Core ☘️ | Regular Swiggy users, who order once a week, either to avoid cooking or to try new restaurants. | 4 - 5 times / month | Likely to be Swiggy One users for value |
Power 🌳 | Heavy users, who rely on Swiggy for daily meals, often ordering lunch or dinner to avoid cooking or save time. | 5 - 7 times / week (20+ times / month) | Very likely to be Swiggy One users to maximize savings |
🧠 Engagement Framework
The list of engagement frameworks are as follows:
Framework | Description | Key tracking metric | Framework for Swiggy |
Frequency | How often the product is used | Number of orders placed per day/week/month | Primary |
Depth | The more time/money a user spends, the better the product becomes | Time: Time spent per session, number of items added to Eat List or repeat restaurants ordered from Money: More money spent per order | N/A |
Breadth | Engagement increases as users adopt adjacent products or services | Number of adjacent products used (Instamart, Dineout) | N/A |
Framework for Swiggy: Frequency
Swiggy’s JTBD is to provide meals to customers in a hassle-free and convenient way. Increased frequency of orders indicates stronger habit formation and higher user engagement.
Frequency is key in converting casual > core > power users
🔁 Understanding Retention
🦅 Bird's Eye view of Swiggy
The average natural frequency of Swiggy’s users is about 3 - 4 times / month. Swiggy users would be ordering weekly or bi-weekly. It is a medium frequency utility app. Hence, to look at retention rate of Swiggy accurately, it is most beneficial to look at week-level retention as it is a better indicator of whether users are forming a repeat habit. Day-level metrics (D1, D7) are used for early activation tracking, while month-level retention is used for long-term cohort health analysis.
The following is Swiggy’s Week on Week Retention Table for a cohort size of 100,000 users
Cohort Week | Users Retained | % Retained | Notes |
Week 0 (Signup + First Order) | 100,000 | 100% | All new users install and order |
Week 1 (7 days later) | 38,000 | 38% | Immediate drop after first experience |
Week 2 (14 days later) | 28,000 | 28% | Core users who liked early experience |
Week 3 (21 days later) | 22,000 | 22% | Users building early ordering habit |
Week 4 (30 days later) | 12,000 | 12% | True "retained" users forming long-term habits |
Week 8 (60 days later) | 10,000 | 10% | Power users who order regularly |
Week 12 (90 days later) | 8,000 | 8% | Super loyalists: e.g., Swiggy One subscribers |
Important Points:
- Week 1 & Week 2 : There is a sharp drop in users. It likely includes those users who are extremely price - sensitive. This is normal for a food delivery business.
- Week 4: The retention curve plateaus at 10%. This includes users who are loyal and have high LTV
Source: Estimates from Appsflyer retention benchmarks for food delivery apps; Zomato retention rate in IPO filings
🔬 Microscopic View
🎯 1. Which ICPs drive the best retention?
Retention across ICPs follows the pattern: ICP 3 > ICP 2 > ICP 1.
Swiggy solves a core functional job-to-be-done (JTBD) 👉🏼 delivering meals quickly, reliably, and reducing the friction of going out or cooking at home.
ICP 3 (Convenience Focused users) value convenience, fast delivery, and consistent quality the most. When Swiggy fulfills this through on-time delivery, well-packaged meals, and personalized restaurant recommendations, it builds trust and habit, leading to high retention.
ICP 2 (Value Optimizers) also show strong retention if Swiggy balances quality and speed at acceptable prices.
ICP 1 (Budget Conscious) users are harder to retain long-term without continuous price incentives.
🚪 2. Which acquisition channels drive the best retention?
Channel | Retention Behaviour | Retention Quality |
Organic search / brand ads | High | Users come with strong intent (already know Swiggy), leading to better fit and trust. |
Referral | Moderate | Social proof helps create better initial trust, but loyalty depends on experience after onboarding. |
Performance ads (Discounts driven) | Low | Heavy discount users are more likely to churn after initial incentive is used up. |
🛠️ 3. What features / sub features drive the best retention?
The following features are key drivers of repeat usage and loyalty:
- Swiggy One / Swiggy One BLCK:
Subscription models offering free deliveries, discounts, and priority service boost perceived value, creating strong habit loops and emotional and financial lock-in. - Co-branded HDFC Credit Card:
The co-branded credit card provides cashback on Swiggy orders, nudging users to consolidate more food orders on Swiggy, thereby increasing frequency and stickiness. - Targeted Engagement Campaigns:
Personalized push notifications aligned to the user’s core motivations (e.g., Gourmet Selections for ICP 3, 10-minute delivery for ICP 2) help reinforce usage habits and reduce decision fatigue, improving retention. - Eat List / Favorites:
Allowing users to save and quickly reorder their favorite meals reduces friction at checkout and makes frequent reordering effortless, especially for Core and Power Users.
In summary, convenience-focused users acquired via organic or brand channels show the highest retention, driven by premium experience features like Swiggy One / Swiggy One BLCK, fast delivery, and personalization. Value optimizers show medium stickiness if speed and affordability are balanced,
while budget-conscious users show high early churn without ongoing incentives.
💔 Understanding Churn
📉 Top Reasons of Churn
Churn Reason | Type of Churn | Details |
Poor UI | Voluntary | Users find it hard to navigate the app (e.g., cluttered homepage, confusing flows), causing frustration and abandonment. |
Poor Search Results | Voluntary | If users can't easily find the restaurant or cuisine they want, they get frustrated and leave without ordering. |
High Prices | Voluntary | Users perceive the food as too expensive compared to dining out or ordering from competitors. |
Lack of good discounts compared to other platforms | Voluntary | Competitors like Zomato offering better deals lure price-sensitive users away. |
Hidden costs at the time of checkout | Voluntary | Unexpected delivery charges, packaging fees, or taxes added at checkout break user trust and cause drop-off. |
Delivery promise not fulfilled | Voluntary | When food arrives late despite a delivery time promise, users lose trust and churn. |
Food arrived in damaged packaging | Voluntary | Poor packaging leads to a bad first impression and dissatisfaction with the service experience. |
Food quality is poor (stale / not tasty / too oily / inadequate quantity) | Voluntary | If the food itself doesn't meet expectations, users blame Swiggy (even if it’s the restaurant’s fault) and churn. |
Poor customer support | Voluntary | Delays, ineffective resolutions, or robotic responses from support frustrate users, especially when handling complaints about orders. |
Push notification overload | Voluntary | Users getting irrelevant spammy pushes may also uninstall (a modern reason for churn). |
Competitor app switching | Voluntary | Some users churn only because a friend recommends Zomato or Blinkit (peer pressure/social churn). |
Global pandemic | Involuntary | During COVID-19 or similar crises, external factors (e.g., fear of ordering outside food) caused users to stop ordering, beyond Swiggy’s control. |
Application crash / technical bugs | Involuntary | Frequent app crashes, payment failures, or glitches disrupt the ordering experience and force users to abandon the platform. |
🚨 Negative actions that one would look out for
Negative Action | Details | Metric to track | Frequency of tracking |
Homepage to menu conversion and menu to cart conversion | A low conversion rate from homepage to menu (user sees homepage but doesn’t click into any restaurant) or from menu to cart (user browses menu but doesn't add any item) signals poor relevance, unappealing options, pricing dissatisfaction, or overwhelming choice. It indicates a loss of user intent at critical points of the journey, increasing the risk of churn. | Homepage to Menu Conversion Rate = (Restaurant clicks / Homepage visits) × 100. Menu to Cart Conversion Rate = (Add to Cart actions / Menu page views) × 100 | Weekly |
Cart Abandonment Rate | A high cart abandonment rate indicates friction at the final step of the journey (e.g., hidden fees, second thoughts, slow checkout), which is an early warning of dissatisfaction and potential churn. | Cart Abandonment Rate = (Abandoned Carts / Carts Created) × 100 | Daily |
% of support tickets with unsatisfactory resolution | A high percentage of unresolved or poorly handled support tickets reflects broken customer service, leading to loss of trust and eventual churn.. | % Unsatisfactory Support Tickets = ( Unsatisfactory response / Total Tickets Closed) × 100 | Weekly |
App open rate vs order rate | If users frequently open the app but rarely place orders, it signals that users are not finding relevant, affordable, or appealing options — a key sign of dissatisfaction | Open to Order Rate = (Orders Placed / App Opens) × 100 | Weekly |
RFM (Recency, Frequency, Monetary Value) | By analyzing recency (last order date), frequency (number of orders), and monetary value (average spend), Swiggy can identify users at risk of churning if they show declining engagement over time. | - Recency: Days since last order - Frequency: # of orders in last X days - Monetary: Avg order value over last X orders | Monthly |
NPS | Users who rate low are highly correlated with future churn. | NPS = % Promoters - % Detractors (survey after key orders) | Quarterly |
Support response time | Even if resolved, delayed support response (e.g., 24–48 hrs) causes user dissatisfaction. | Avg Support Response Time (in hours) | Daily |
Order cancellation rate | High cancellation rate (after placing order) shows strong dissatisfaction (pricing, delivery times, packaging). | Order Cancellation Rate = (Orders Cancelled / Orders Placed) × 100 | Daily |
Coupon redemption drop | If users click offers but don’t redeem, it indicates poor offer attractiveness or checkout friction. | Coupon Redemption Rate = (Coupons Redeemed / Coupons Clicked) × 100 | Weekly |
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