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HTPM Project.pptx
- 1. 2 Business Objective To enhance user engagement by creating personalised apparel e-stores for users for different occasions based on their preferences thereby increasing brand loyalty, customer retention & revenue. Market Size • Size of the fashion e-commerce market in India is around $8-10 billion & is set to grow 35% to around $30 billion in next 5 yrs • Flipkart & Myntra continue to hold 60% market share in online fashion retail where 56.40% of Myntra users are male while 43.60% are female • In July 2022, about 90% of monthly website traffic of Myntra was from India • Myntra claims to have on-boarded 6,000 brands showcasing around 15 lakh styles on its platform for "Big Fashion Festival" sale • Tier-1 customers make 55% of the current BPC customer base & the rest from Tier 2 cities & beyond
- 2. 3 Competitors Features Nykaa This year Nykaa had a ‘Festive Edit’ section to help users find the desired product easily & numerous options owing to various partnerships Ajio Launched campaign to celebrate the changing face of Kerala in particular and to position Ajio as the top fashion shopping and gifting destination during Onam; also planning it to extend to other festivities Flipkart Added AR-powered 'view in my room' feature to offer an immersive ecommerce experience in categories like furniture, luggage & large appliances; planning to extend it to beauty products Purple Users can filter items/outfits by descriptors like colour, stats tool to help surface clothes that user never wears/overlooked, clothing catalogue & ability to assemble and save looks Stylebook Powerful auto categorisation capabilities to organise items in the wardrobe by filters like season, occasion, colour & brand. It enables you to plan outfits in advance Smart Closet Build you own personal lookbook, share your pairings, shop for items that will complement your personal style & browse outfit ideas posted by fellow fashionistas Research Methodology ● As part of primary research we checked Myntra’s existing features, market share, target audience, competitor analysis ● As part of secondary research we conducted a survey and in-person interviews to gauge the need of personal apparel store for different occasions ● Nearly 95% respondents were aged between 18- 34 ● 20% check for new features while 61% check new features sometimes only ● Only 27% users like Myntra's recommendations while 69% sometimes like it ● Price, brands and reviews are major levers that drive user’s purchasing decisions ● People usually shop on Myntra at "No Particular Occasion" & "Festivals" ● People buy for others in this order: self > family / relatives > friends ● Women shop for family/friends while men tend to shop for self/loved ones only Survey Findings
- 3. 3 Stylist Sonam Age - 21, College student Coder Chinmay Age - 28, IT professional Purchasing Habits ● Loves the idea of fast fashion ● Buys outfits for all festivals majorly based on the looks ● Wants to match her outfit with peers Challenges Spends lot of time and energy to find desired outfit among the massive ocean of suggestions which often leads to wrong or no buying at all Purchasing Habits Challenges ● Possesses ample interest in trying out trendy outfits ● Lacks knowledge, time & patience to find the perfect style Usually prefers quick online purchases without browsing many options and ends up buying clothes that doesn’t serve his purpose/style Myntra’s target audience is essentially young, fashion-conscious online consumers with a significant online presence Target Persona
- 4. 4 Twinning: Users can select with whom they want to coordinate (besties, couple, cousins, etc) their outfits and Myntra would suggest apparels for both Style Me: User needs to select for whom to shop, what occasion and Myntra would suggest apparels for that occasion. Additionally, user can also post picture of their outfit/part of outfit and Myntra would help to style it better Region Based: Based on the user’s region, occasions would be more specific to that region thus more apparel options for that specific occasion Solutions Virtual Trial: See how your selected apparel suits you in our virtual trial room through auto-generated avatars via photos/previously mentioned dimensions
- 5. 5 Prioritization - RICE Framework Feature Strategic Alignment Reach Impact Confidence Effort Total Twinning 1.00 5.9 6.2 6.1 8.8 25.4 Style Me 1.00 6.6 6.6 6.4 8.2 33.9 Region-based 0.20 7.6 7.2 7.4 4.2 19.4 Virtual Trial 1.00 8.6 8.2 7.6 8.0 66.9 Strategic Alignment x Reach x Impact x Confidence Effort RICE Score =
- 6. 6 Feature - MVP Style Me Get to know what suits you with our personalized recommendations 2 3 4 Experiments to run ● A/B test with select set of users to understand if this feature is accessible and being used as intended ● Flow analysis to understand how users are interacting with UI ● Observe how users are interacting with the feature 1
- 7. 7 Feature - MVP Virtual Trial Use virtual trial to check out how the clothes you selected will look on you Experiments to run ● A/B test with and without the feature to understand if the feature is working as hindrance for user experience ● A/B test to understand the total volume of orders placed and time spent before placing with and without the feature 1 2 3 4
- 8. 8 Feature - Future Scope Region Based Recommendation Twinning Myntra will recommend for region specific festivals to the users Get matching apparel with your partner for the special occasion
- 9. 10 Success Metrics Feature Description Style Me Positive Metrics ● Number of recommendations: Count of the number of times the feature is being used by the end users per week. ● Average recommendations per user: This will indicate the stickiness of the feature. If a user is using the same feature repeated number of times then it indicates stickiness. ● Average interaction time per user: This indicates if the users are finding value out of the feature. ● Number of photos uploaded: To track feature adoption. ● Total number of items: To track if uses finds utility. ● Volume of business: To track if uses finds utility. ● Daily active user: Total unique active users using the feature. Negative Metrics ● Bugs reported: Total bugs reported for this feature. ● Social media sentiment: Overall and feature specific sentiment. ● Bounce rate: To track utility of feature. Virtual Trial Positive Metrics ● Daily active users: Total unique active users using the feature. ● Average time spent: This indicates if the users are finding value out of the feature. ● Total number of items: To track if uses finds utility. ● Volume of business: To track if uses finds utility. Negative Metrics ● Bugs reported: Total bugs reported for this feature. ● Social media sentiment: Overall and feature specific sentiment. ● Bounce rate: To track utility of feature. North Star Metric: Total number of orders placed per day
- 10. 11 Associated Risks & Mitigation Features Associated Risks Mitigation Strategies Style Me ● New user journey ● Increased reverse logistic ● Expectation management ● Unable to categorize occasion ● Use demos to explain user journey ● Collect feedback and rate products ● Depending on user response increase/decrease occasion categories Virtual Trial ● New user journey ● Expectation management ● Increased reverse logistic ● Use demos to explain user journey ● Collect feedback, product ratings and use returned products data in personalization Region Based ● Expectation management ● Collaboration with vendors ● Use Myntra’s credibility to encourage vendors to collaborate Twinning ● Expectation management ● Difficult to find appropriate pairing for other person ● Technological complexities ● Work extensively on user preferences to find appropriate apparels ● Give different size options ● Repetitive testing
- 11. 11 Go-to-Market Strategy Cluster 1 Introduce “Style Me” Cluster 2 Introduce “Virtual Trial” Cluster 3 Assemble both the features Based on the target persona aforementioned, we choose tier-1 cities to launch the beta version of the application. The potential customers are divided into 3 clusters randomly and introduce our features as mentioned below : After a quarter of introducing the proposed features, the Success metrics for 3 different clusters can be compared. Based on the result, final feature(s) will be decided and rolled out to tier-2 and 3 cities.
- 13. 13 https://www.figma.com/proto/0axa4YClm1wVqgZqqP1YhC/Untitled?node- id=10%3A497&scaling=scale-down&page-id=0%3A1 ● Survey Link: https://iimcal.au1.qualtrics.com/jfe/form/SV_6XS3HLNiIQMq2iy ● Prototype Link: https://www.figma.com/proto/0axa4YClm1wVqgZqqP1YhC/Untitled?n ode-id=10%3A497&scaling=scale-down&page-id=0%3A1 ● Myntra Studio (Myntra’s current solution): https://www.myntra.com/studio/home?appRoute=%7B%22url%22%3A %22%2Fstudio%2Fhome%22%7D ● https://www.statista.com/statistics/1246475/myntra-monthly-website- visits-by-country/ ● https://techcrunch.com/2013/04/04/myntra-buys-fitiquette-a-disrupt- finalist-with-a-virtual-fitting-room-as-indias-online-fashion-market- heats-up/ ● https://retail.economictimes.indiatimes.com/news/e-commerce/e- tailing/fashion-e-commerce-will-become-a-30-bn-market-in-india-in- five-yrs-myntra-ceo/92049482 ● https://cdn.upgrad.com/uploads/production/ecc292fb-333f-4ed9-9c97- 003d885e15d7/Competitive+Analysis+of+Myntra.pdf ● https://www.similarweb.com/website/myntra.com/#outgoing-links References
- 14. 14 Thank You!