Case Study, Part 1: Brand X Through the Funnel - 4 Ps, Positioning Axis, Channel Scorecard, Conversion Arithmetic, and Levers per Stage

Four questions yield the 4 Ps: Product (casual western wear, footwear, accessories for women), Price (clothes Rs 500-800, footwear ~Rs 1,000-1,200, jewellery Rs 200-300), Promotion (new-age, quirky, fun), Place (own site/app plus Amazon, Myntra, Flipkart, Meesho). Consumer: women 18-25, metro and tier-1, students and early jobbers, on Instagram/YouTube/Snapchat, value- but not brand-conscious, heavy OTT. Awareness planning asks: new brand or new product; what do they buy now; where are they active; what is success. New brand = integrated brand campaign; new product = lower-funnel functional message ('trendy t-shirts at Rs 499'). Competitive map: selection vs price - Zara high/high, a low-price high-selection rival, Roadster closest to X. X differentiates on colourfast quality, affordability and monthly new collections. Swap the axes for other categories; keep the method.

Buckets: organic (SEO, ASO, organic social), paid (TV, digital, print, outdoor, OTT, influencers), owned (site/app, landing pages, tracking links, own social, content partnerships). Score each on Reach, Relevance, Uniqueness, Cost, Measurement (trackability/attribution). TV: high reach, lower relevance (audience shifting to OTT), low uniqueness, extremely high cost, medium measurement -> P1. SEO: high reach, relevance and measurement, medium uniqueness, low cost -> P0, 'one of the first channels products use'. Her ranking of parameters: reach, cost, measurement, relevance over uniqueness.

Acquisition for X = install, registration or first browse/product-page/wishlist; not yet activation because repeat behaviour is unproven. Timeline: ad day 1, install day 3-4 (CPM campaigns optimise impressions), account same or next day, browse, product pages 2-3 days later. Numbers: 1,000 -> 600 install (60%) -> 540 register (90%) -> ~380 browse (70%) -> ~300 product pages (80%) = 30% acquisition. Levers: raise frequency to 3-4 exposures (her 'industry benchmark'); retarget installers who did not register; lifecycle messaging for registrants who did not browse. Metrics per step: store visits, branded search, CPC/CPM; installs, click-to-install, CPI; registrations and cost; sessions and duration; product-page views.

Activation: active signals (wishlist, cart, checkout) vs passive browsing; ~50% of 300 wishlist, each later step roughly halves, ~75 cart. Fixes: UX flow, delivery SLAs, ratings and reviews, selection and price relevance, social proof ('added to so many carts'), urgency discounts, similar-item recommendations, payment options (UPI, bank offers). Revenue: smooth payment, welcome and festival promos, AOV nudges ('Rs 100 away from the discount'), delivery-fee promos, segment offers (newbie, second purchase, monthly passes, early access), price-perception tools (card discounts, cashback, partner loyalty, BNPL, EMI). Retention: personalised recommendations, reviews, reasons to return (live shopping, daily games), churn-risk promos, basics subscriptions, lifecycle milestones, retargeting. Churn = signals a user will not shop again; define the window from the category's frequency (one-time, weekly, monthly, quarterly, biannual, annual) and arrest with promos, sale events, retargeting.