# Suharsh Mahajan

Suharsh Mahajan shares practical ways to use data and customer insights to improve e-commerce and retail growth, including customer retention, pricing, and conversion.

- Profile: https://b2binfluencer.marketing/creators/suharsh-mahajan/
- LinkedIn: https://www.linkedin.com/in/suharshmahajan
- LinkedIn followers: 5,947
- Topics: ecommerce, pricing-strategy
- Based on 11 dated posts, latest 2026-10-09

## Summary

Suharsh Mahajan is most useful for story-led explanations of e-commerce retention, pricing, and conversion questions.

- Best for: A B2B ecommerce analytics platform planning a co-branded thought-leadership series on repeat purchasing.
- Not for: A fashion retailer seeking a short-term placement with verified conversion or returns results.
- Check first: Confirm audience demographics and recent retention-focused post performance before contacting them.

### Takeaways

- A jewelry store post asks how many January buyers returned in February or March.
- A D2C skincare dashboard looked healthy while profit fell from discount-driven orders.
- A fashion post presents Trylle virtual try-on benefits without test data.

## Main ideas

- **Ask what is driving growth before spending more to get it**: Rising sales or customer totals can hide a business that depends on constant advertising or discounts. Mahajan’s recurring move is to replace a reassuring headline metric with a question about what customers do next and what growth costs.
- **Numbers need context to say whether a business is healthy**: Mahajan distinguishes visible activity from business results. A dashboard can show movement without explaining whether that movement is profitable or durable, so analysis should ask why a metric is changing and at what cost.
- **Test a focused price change instead of reaching for blanket discounts**: Mahajan presents pricing as something to examine against what customers actually buy and compare. A small, targeted change can be more useful than discounting everything or adding products. The post reports an anecdotal result, not a measured test design.
- **Make the buying decision easier, not just the marketing louder**: Mahajan also describes growth as reducing uncertainty for customers. This fits their focus on diagnosing the obstacle to purchase, but the fashion post differs from the others: it introduces a named product and lists expected benefits.

## Work to read

- [Cohort analysis for a handmade jewelry store: why repeat purchases stall](https://www.linkedin.com/posts/suharshmahajan_business-ecommerce-growth-activity-7378284943514398720-bf17)
- [A D2C skincare dashboard that looked healthy while profit fell](https://www.linkedin.com/posts/suharshmahajan_dataanalysis-business-strategy-activity-7383358336156700672-eum1)
- [Repricing a cafe menu: regular tea against fancy coffee](https://www.linkedin.com/posts/suharshmahajan_analytics-pricingstrategy-businessgrowth-activity-7379372067709923328-F1Hb)
- [Virtual try-on for a fashion store: cutting returns and abandoned carts](https://www.linkedin.com/posts/suharshmahajan_ai-fashiontech-ecommerce-activity-7477216797490962432-heGj)

## Brand relationships

- **Trylle** (disclosed_partnership)

## Partnerships

No partnership program, sponsor policy, rate card, or media kit appears in the posts here. The four posts include no disclosed paid brand relationship, stated deliverables, or published contact route for commercial enquiries. The sole brand-facing example is a post about Trylle, an AI virtual try-on and styling tool for fashion retailers. They open with a store owner's cart-abandonment and returns problem, present the tool as the answer, list benefits for fashion businesses (higher conversion, fewer returns, more confident buying), and point readers to a link in the comments. The post states no payment, terms, or disclosure, so it shows the shape of their brand content, not a confirmed commercial offer. The other three posts are standalone customer stories about retention, discount-led revenue, and tea-and-coffee pricing, and name no brand. For a co-branded thought-leadership series on retention analytics, ask directly about scope, exclusivity, review and approval, disclosure wording, and scheduling. The only public contact point mentioned is their LinkedIn profile, a personal profile rather than a partnership or inquiry page.

## Alternatives

- Eric Coats: Choose Coats when the series needs to teach operators a diagnostic they can run themselves. The two posts walk through cohort-based returns analysis on Amazon, naming the reports, showing how to link a return to its purchase month, and providing worked numbers: a December cohort return rate of 6.6% against Amazon's reported 4.6%, and January at 2.6% cohort against a reported 22%. The second post turns the same approach into a four-step returns-reduction process, from checking return rates to reworking product detail pages. A platform serving third-party sellers and marketplace operators gets a tighter, more operational piece from Coats than from a merchant-story framing.
- Aman jabeen: Choose jabeen when the campaign is about why storefront signals lose conversions, before any retention question arises. One post assesses store quality in Google Merchant Center, naming the weak signals that drag a store down: missing return information, slow mobile pages, weak product data, unclear shipping details, poor ratings, and the settings that fix them. The other examines a makeup store with 15,022 visitors, 992 orders, 626 new customers, and over $60K net revenue, arguing that the opportunity lies in swatches, shade guides, user-generated content, reviews, product pages, and checkout. For a series aimed at brands whose immediate problem is conversion rate, this is a closer fit than retention analysis.
- Aminat Hammed: Choose Hammed when the platform wants to make the case for retention through channel change. One post explains that AI-assisted discovery is moving product decisions ahead of the brand's own website, citing 203% year-over-year growth in GenAI referrals to retail ecommerce and 51% of U.S. consumers using an AI shopping tool in the past month. It then explores the consequences for product information, brand-controlled discovery, conversion, and first-party data. The other makes the case for retention economics directly: Omnisend's 2026 report, drawn from 150,000 brands, found automations were 2% of email sends but 30% of email-driven revenue. That is a strong, current hook for a series about why retention spending deserves its own budget line.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/