Mapping the current end-to-end experience of online shopping with reviews — as it exists today WITHOUT True Review App — across all three personas. Based on data from Contextual Inquiry (5.3), Diary Studies (5.4), User Interviews (5.1), and Focus Groups (5.5).
Scenario: Mani wants to buy wireless earbuds under ₹5,000 for daily commute.
| Dimension | Detail |
|---|---|
| User Actions | Old earbuds stop working → searches "best wireless earbuds under 5000" on Google → sees sponsored articles and Amazon links → opens Amazon India |
| Thoughts | "Let me find something good. Last time I got burned, so this time I'll be more careful." |
| Emotions | 😊 Mild excitement + ⚠️ Pre-emptive caution |
| Touchpoints | Google Search, Amazon India app |
| Pain Points | Google results are SEO-optimized affiliate articles, not genuine recommendations. Top results are paid placements. |
| Opportunities | ✅ A trusted review platform could become the first touchpoint — users search Google for "best [product]" and find True Review instead of affiliate spam. |
| Dimension | Detail |
|---|---|
| User Actions | Browses Amazon search results → filters by price (₹2,000-5,000) → sorts by "Avg. Customer Review" → shortlists 3-4 products with 4.3+ stars and 1,000+ reviews |
| Thoughts | "These all have similar ratings — 4.3, 4.4, 4.5. How do I even differentiate? More reviews = safer... I think." |
| Emotions | 🤔 Confused — too many similar-looking options |
| Touchpoints | Amazon search, filter system, product listings |
| Pain Points | Star ratings are clustered (4.2-4.6 range) making comparison meaningless. Products with fake bulk reviews appear more popular. No way to filter by "verified long-term users." |
| Opportunities | ✅ Usage duration badges and verified-only reviews would immediately differentiate products. A "3-month usage" review is worth more than 500 Day-1 reviews. |
| Dimension | Detail |
|---|---|
| User Actions | Opens first product → scrolls past product description to reviews → filters "Most Recent" → scrolls to photo reviews → reads 15-20 reviews → checks for suspicious patterns (same dates, similar language, generic praise) → reads 1-2 star reviews specifically |
| Thoughts | "Wait — 12 reviews posted on the same day? Same writing style? This feels planted. Let me check the negative reviews — those are harder to fake." |
| Emotions | 😠 Suspicion → 😤 Frustration → 🕵️ Detective mode activated |
| Touchpoints | Amazon review section, review filters, photo reviews |
| Pain Points | Spends 8-10 minutes PER PRODUCT reading reviews. Has to manually detect fake patterns. Genuine reviews are buried. No usage duration info. Photo reviews are optional and rare. |
| Opportunities | ✅ Mandatory verification eliminates the detective work. Mandatory photos for high ratings ensure visual proof. Usage duration badge answers "how long have you used it?" automatically. |
| Dimension | Detail |
|---|---|
| User Actions | Opens Flipkart → searches same product → reads reviews there → opens YouTube → searches "[product name] review" → watches 2-3 unboxing/review videos → opens Reddit → searches "[product name] reddit india" → reads threads → opens Google → searches "[product name] problems" |
| Thoughts | "Amazon reviews say 4.5 stars. Flipkart says 4.1. YouTube guy says it's decent but bass is weak. Reddit says the company plants reviews. I don't know what to believe." |
| Emotions | 😫 Exhaustion → 😵 Information overload → 😤 Anger at the system |
| Touchpoints | Amazon, Flipkart, YouTube, Reddit, Google — 5 platforms for ONE product |
| Pain Points | 23 minutes average spent cross-checking. Conflicting information across platforms. No single source of truth. YouTube reviewers may also be sponsored. Reddit opinions are anecdotal. The entire process is a time tax caused by distrust. |
| Opportunities | ✅ True Review as a SINGLE trusted source eliminates multi-platform checking entirely. If every review is verified, there's no need to cross-reference. This is the core value proposition. |
| Dimension | Detail |
|---|---|
| User Actions | Returns to Amazon → compares his 3 shortlisted products based on all gathered data → eliminates 1 (too many suspicious reviews) → stuck between 2 → reads 10 more reviews for each → picks the one with "least suspicious" reviews and best YouTube feedback → adds to cart |
| Thoughts | "I'm not 100% sure about this. But it's the best option based on everything I've checked. Fingers crossed." |
| Emotions | 😰 Anxious uncertainty — making a ₹4,000 decision based on incomplete trust |
| Touchpoints | Amazon cart, final review comparison |
| Pain Points | Decision is based on "least suspicious" rather than "most trusted." Even after 30+ minutes of research, confidence is LOW. The purchase feels like a gamble, not an informed decision. |
| Opportunities | ✅ Verified reviews create HIGH confidence decisions. When you know every review is real, you can decide in minutes, not half an hour. Decision changes from "least suspicious" to "genuinely best." |