Acquisition project | Zomato

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Acquisition project | Zomato

INTRODUCTION

Zomato is a consumer tech company focused on discovery and delivery of food from your favorite restaurants. The project that I have chosen is for a cuisine that I am working on - Mithai or Indian sweets. While Zomato is at a matured stage, the product flow that I am working on is a new flow that we have recently launched in Delhi NCR and is currently in testing phase. We are working on acquiring users to this new product. Hence I have taken the product to be in the PMF/Pilot stage and designed my project based on that.

Why mithai?

Overall online penetration for mithai is very low.
Overall mithai cuisine market in India is very large ~ INR 58,900 Cr (Business line report, Bikaji market report on sweets) For reference, Zomato GOV for 2022-23 is ~INR 26,300 Cr across 50+ cuisines (Zomato annual report 2022-23)

Customer pain points

(Understood this through multiple customer surveys)

Information available on Zomato for mithai is not sufficient for users to make a decision.
The UI for mithai on Zomato is not very visual as you would see when you enter a Mithai shop.
Mithai is expensive on Zomato compared to when users buy offline.

What is the core value prop for the new flow?

The new flow has a newly designed menu and aims at making buying of mithai easier with sufficient information. Sharing some glimpses of the flow below:

WhatsApp Image 2024-06-08 at 5.16.04 PM.jpeg

Some of the features of the new flow :

More information for the user to make an informed decision - No. of pcs in each variant, Shelf life and storage conditions.
New content design where high quality premium images with exact boxes which will be delivered is displayed making it easier for users to make a choice for gifting.
Some other features like price match guarantees which are live in smaller cities will also be added. These are currently not visible in the screenshots here.

IDEAL CUSTOMER PROFILES

Let's take a look at a few profiles

	ICP 1	ICP 2	ICP 3	ICP 4
ICP Name	Rajeev Kumar	Radhika Sharma	Ashok Kumar	Ishika Gupta
Age	35-45	25-35	50-60	18-25
Income levels	80k-100k/month	100k-150k/month	50k-80k/month	40k-50k/month
Gender	Male	Female or male	Male	Female or male
Location	Middle class areas in Tier 1 cities like Ramesh nagar in Delhi or Dadar in Mumbai.	Upper middle/Posh areas in Tier 1 cities like Sushant lok in Gurgaon or Byculla in Mumbai.	Tier 2 cities like Chandigarh, Jaipur, Kanpur	PGs and shared flats near colleges, Corporate hubs in tier 1 cities
Companies	Non-tech roles - Administration, managerial positions in manufacturing units, Traditional sales	New age tech companies, marketing or branding roles in established companies	Government services, Academic services, Manufacturing units, Retired	Studying in college, White collar roles in tech or non tech firms
Marital status	Married with kids	Single or married	Married	Single
Living situation	Living with wife, retired parents and kids. Has grown up in the city	Living with flatmates, alone or with her partner. Both partners are working, Could be a migrant to the city.	Living with wife, Kids have moved out to tier 1 cities for work	Living with flatmates or in a PG
Where do they spend time?	Facebook, Whatsapp, TV, OTT, Friends meeting offline	Instagram, Twitter, OTT, Cafes and pubs, Shopping, Whatsapp	Facebook, Newspapers, Instagram, TV, Parks	Instagram, Twitter, OTT, Cafes and pubs, Shopping
Pain points	Lack of time to go and buy mithai for occasions and gifting, Parking in crowded markets in front of mithai shops, Variety of options, Doesn’t trust the prices online	Lack of time and a personal vehicle to go and buy, Remembering the need to buy mithai last minute and immediate requirement, Very high rush during festivals at the offline stores which is the highest use case for her to buy mithai, Lack of information about the mithai she is buying, Lack of awareness of good mithai shops since she could be a migrant	Parking in crowded markets,Limited movement outside their homes, Lack of upgrade to premium options for gifting at their local shops	Is conscious about sugar intake so has mithai very occasionally, Movement within the city, Budget constraints
Current Solution	Corner shops in the area for personal use, Bigger stores like Haldiram’s or Bikanervala when for occasions and gifting needs, Occasionally Zomato for self consumption (Rasgulla, Gulab jamun, Rasmalai etc.)	Take an auto/public transport to the nearest mithai shop, Use Swiggy/Zomato sometimes, Plan the purchase when she is already out, Ask the partner to go and get mithai, Call the mithai shop and get delivery post enquiring about prices, other info and freshness	Use a rickshaw/auto to go and buy offline, Plan the purchase when shopping for supplies	Order using Zomato, Walks to corner shops
Use cases for Mithai	Enjoys occasional mithai, Pooja, Festivals, Visiting relatives, Saturday evenings, Celebrations like kids passing their exams	Festivals, Self consumption	Festivals, Visiting relatives, Pooja	Small celebrations, Cravings,
Influencers	Wife for pooja, occasions and festivals, Kids for self consumption	Parents and relatives	Kids pushing parents to order online	Flatmates
Blockers	Parents who don’t trust mithai being delivered by Zomato, Muscle memory since childhood because of which you think of going offline first, Other pain points mentioned above which make it a difficult experience to order mithai online	Other cuisines which can be substituted for mithai like bakery, desserts	Behaviour to buy offline post checking on information and freshness, Extra mark ups on mithai on Zomato	Alternatives to mithai like bakery
Current Zomato usage (Does not include mithai orders)	Moderate (1-2 orders in a month)	High (3-4 orders in a month)	Low (1-2 orders in 6 months)	High (3-4 orders in a month)
Current mithai orders per year (online + offline both)	10	5	8	2

ICP Prioritization

	Adoption curve	Mithai consumption frequency	Appetite to pay	TAM	Distribution potential
ICP 1	Medium	High	High	High	High
ICP 2	High	Medium	High	High	High
ICP 3	Low	High	Medium	Medium	Low
ICP 4	High	Low	Low	High	High

Based on the above framework, we can see that ICP 1 and ICP 2 seem to be the best profiles to target.

ICP 1

Adoption Curve :

This set of users have adopted the Zomato app fairly well but are not power users.

Mithai consumption frequency :

Mithai is consumed on a lot of occasions at their homes. Their go to celebration food choice is mithai.

Appetite to pay :

Appetite to pay is high for mithai. On an average, a mithai order is Rs 800 (assuming 1 Kg mithai) and the consumption is around 1-2 times per month. Mithai consumption is ~ 0.02-0.01% of their monthly income.

Distribution potential :

Penetration of the Zomato app is fairly high in this group. What we need to work on the acquiring this user to the new mithai flow.

TAM:

Large TAM, Calculation shared below

ICP 2

Adoption Curve :

This set of users have a high adoption curve. A substantial number of users are power users in this cohort.

Mithai consumption frequency :

Mithai is moderately consumed within this cohort. Mostly festival and occasion based.

Appetite to pay :

Appetite to pay is high for mithai. On an average, a mithai order is Rs 800 (assuming 1 Kg mithai) and the consumption is around 1-2 times in 2 months. Mithai consumption is a negligible percentage of their monthly income.

Distribution potential :

Penetration of the Zomato app is fairly high in this group. What we need to work on the acquiring this user to the new mithai flow.

TAM:

Large TAM, Calculation shared below

TAM/SAM/SOM Calculation:

	ICP 1	ICP 2	Calculation	Sources
Total population (NCT of Delhi, Census 2011 survey, In lakhs)	167.9	167.9		- Economic Survey of Delhi 2021-22
Total population (NCT of Delhi, 2024, In lakhs, Growth rate @1.7% YOY)	205.5	205.5	Actual Growth rate from 2001 to 2021 is 1.94% CYGR, Population growth rate has seen a decline in recent years. Adjusted growth rate assumed at 1.7%. Source - Economic survey of India	- Economic Survey of Delhi 2021-22
Population which eats mithai (In lakhs)	115.4	115.4	Mithai market in India - ~INR 5890000 lakhs Mithai Market in Delhi @ 9.8% - ~INR 577220 lakhs AOV for Mithai - INR 500 Average annual order per users - 10 No. of users - 115.44 lakhs	- Business line report - Bikaji sweets market report
Average order value for mithai (In INR, Approx.)	₹500	₹500
Male population in age group 35-45 @ 7.68% (In lakhs)	8.9			- Census Reprort Delhi (Assuming Sex ratio to be constant)
Female population in age group 25-35 @8.75% (In lakhs)		10.1		- Census Reprort Delhi (Assuming Sex ratio to be constant)
Occassions per year where mithai is eaten for respective ICPs (Mithai Affinity)	10	5
TAM value (INR, In lakhs)	₹44,329	₹25,253
Smart phone adoption (In lakhs)	7.09	8.08	Pan India adoption 2023 - 71% Assumed higher side adoption for Delhi NCR - 80%	- Smart phone penetration
App adoption (In lakhs)	2.8	4.8	- Total annual transacting users for Zomato 2022-23 with assumed 10% YOY growth- 638 lakhs - Assumed users in Delhi NCR @ 5% - .05*638 = 32 lakhs - App adoption - ~16% (32/198.7) (Delhi NCT app adoption across population strata) - ICP 1 and ICP 2 however have higher propensity to use the app - App adoption @ 40% for ICP 1 - 40% of 7.09 = 2.8 - App adoption @ 60% for ICP 2 - 60% of 8.08 = 4.8	- Zomato annual report
SAM value (INR, In lakhs)	₹14,000	₹12,000	Taking into account Mithai affinity
Current penetration for annual mithai orders on Zomato (Approx.)	1	2
Target penetration for annual mithai orders on Zomato	4	4	40% penetration for ICP 1 and 80% penetration in ICP 2

What is the Jobs to be done for the new flow for these ICPs?

JTBD	ICP 1	ICP 2
Functional	Ease of getting mithai, Saving of time, Not getting stuck in traffic	Ease of getting mithai, Last minute purchases, Not getting into the hassle of using public transport or standing in a queue on heavy festival rush days
Personal	To perform the pooja on time at home, To look at a variety of options from multiple shops before making the decision of buying a mithai for gifting	Feeling equipped to do some traditional things in her busy modern schedule
Financial	Getting the same price online as offline	Getting the best service for the money that she is spending
Social	Being tech savvy	Expectations of the family are fulfilled sometimes when the required pooja/rituals are sorted

How does the competitive sphere look?

Competitors	Positives compared to competition	Negatives compared to competition
Swiggy	Better UI More information and enhanced content for users	Could have better discounts than Zomato
D2C brand websites	Variety is much larger	Best prices Extra website only SKUs
E-Commerce (Amazon/Flipkart)	Delivery of fresh mithai	Delivery of items beyond hyperlocal stores
Offline stores	Low effort than going to the shop Good offers	Lower prices for large quantities

ACQUIRING USERS

I have designed 3 experiments for 2 acquisition channels keeping in mind that the stage of the product flow is finding PMF, have to identify the right marketing pitch and users which have to acquired will be a subset of existing zomato users.

1) Zomato only launch

Hypothesis: Users find it difficult to order mithai online because of lack of essential information like pieces per kg, shelf life of items, how to store them and current images lack enticing visuals.

Experiment: Test new UI and gain initial feedback. Also test, how predictable the new flow is for an existing user of Zomato but non buyer of mithai online.

Marketing pitch understanding: Effectiveness of pitch on better UI and more information for the user.

Channel: Internal slack group

No. of users targeted: 3000+

Expected feedbacks: 300+

Target ICP: ICP 2 and some other early adopters who might not be very high frequent buyers of mithai but good insights on UI and other aesthetic and technical aspects can be expected.

Duration of internal test: 2 weeks

Post on: Internal new product feedback groups on internal IDs

Launch city: Delhi NCR (Corporate office location)

Feedback channel: Slack threads, one on one conversations

What do we want to get feedback on?: UI of the new flow, Did you notice the new information added?, How helpful and accurate is the additional information to make the buying decision and accuracy, Quality and clarity of new images uploaded, Ease of navigation from opening the menu to checking out, Any other feedback

Timelines:

Day 1	Launch announcement with value proposition and features Clear any doubts that users might have
Day 4	Collate initial feedback and reach out to users to delve deeper into what is shared if required Connect with internal teams to discuss and alter any minor changes to improve experience
Day 7	Reminder announcement to share more feedback Start analyzing data to check how the funnel is performing,
Day 10	Speak to more users offline to understand how they felt while using the product Form hypothesis about next steps - Major changes or next step of launch
Day 14	Thank users for sharing their feedback Collate offline findings with data Finalize next steps - Any major changes or next step of launch

2) Whatsapp campaign 1

Experiment:

Test if users find the enhanced content and UI for the new flow ideal for their mithai buying needs.

We will do A/B testing on the targeted users. 50% of the users will be seeing the new flow and 50% the older flow. Same whatsapp communication will go to both sets of users to understand the difference in their user journeys. This will help us understand if users find the new product flow ideal to solve for the information gap from offline to online ordering.

Marketing pitch understanding: Effectiveness of pitch on better UI and more information for the user

Channel: Whatsapp campaign

No. of users targeted (Assumed): 45,000 - 50,000

Expected feedbacks: 4500 - 5000 users

Target ICP: ICP 1 and 2

Duration of : 4 weeks

Share from: Official whatsapp account

Launch city: Delhi NCR, Specific pockets

Feedback channel: Internal data, one on one calls with select users

What do we want to get feedback on?:

Feedback will be taken through the following methodology -

Comparison of CG and TG behavior, no. of orders, GMV, items ordered and upgraded etc. - Intent here is to look purely from internal data POV for any specific difference in user journeys for the groups.
Delving deeper into CG behavior with data and finally some one on one interviews.

User cohorts for one on one conversation -

Early drop offs in the funnel - Menu opened, did not click on the mithai.

Were they looking for mithai? Why were they looking for mithai? Did they like the thumbnail image while scrolling the menu? Was the pricing that they did not understand? Did they finally buy it and if yes from where?

Mid drop offs - Menu opened, explored variants and then dropped off.

Why were they looking for mithai? Did they not like images of variants? Was the variant they were looking for not found? Did they finally buy it and if yes from where?

Late drop offs - Menu opened, explored variants, cart built and then dropped off

Why were they looking for mithai? Was the cart value not as per expectation? Were they looking for an offer? Did they finally buy it and if yes from where?

Successful order -

Why were they looking for mithai? How was the experience? Any other relevant information needed to make the decision of buying easier?

Timelines:

Day 1	App release post testing 1st whatsapp communication to identified users falling in prioritised ICP buckets
Day 7	L1 funnel data analysis start Minor tweaks, if any
Day 14	Identify users from CG group where we can start speaking Connect with users for feedback
Day 21	Continue monitoring funnel data Continue speaking with users for feedback Minor tweaks
Day 28	Conclude the experiment Finalize on insights gathered and initiate changes if any Plan for scaling

3) Whatsapp campaign 2

Hypothesis: Users find the prices expensive online

Context: New tag of “Offline price match guarantee” is introduced in the new flow.

Experiment:

Test if users who are active Zomato users but non mithai buyers online, start buying mithai online.

Targeted users will receive whatsapp comms that this set of restaurants are on a price match guarantee

Channel: Whatsapp campaign

No. of users targeted (Assumed): 45,000 - 50,000

Expected feedbacks: 4500 - 5000 users

Target ICP: ICP 1

Duration of : 4 weeks

Share from: Official whatsapp account

Launch city: Delhi NCR, Specific pockets

Feedback loop: Internal data

What do we want to get feedback on?:

Funnel data to understand drop offs especially users who have interacted with items/restaurants where price match guarantee is shown.
Additional GMV per user on the partners
Contribution change of highest priced variant for an item

Timelines:

Day 1	1st whatsapp communication to identified users falling in prioritised ICP buckets
Day 7	L1 funnel data analysis start Minor tweaks, if any
Day 14	Track identified metrics Understand discoverability of the new tag. Minor tweaks if that is a problem
Day 21	Continue monitoring funnel data
Day 28	Conclude the experiment Finalize on insights gathered and initiate changes if any Plan for scaling

This project is made with data available in public domain. No internal company data has been used here. Few assumptions have been taken and mentioned alongside.