Suharsh Mahajan shares practical ways to use data and customer insights to improve e-commerce and retail growth, including customer retention, pricing, and conversion.
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.
Key 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.
Check first: Confirm audience demographics and recent retention-focused post performance before contacting them.
Main ideas
Ask what is driving growth before spending more to get it · Numbers need context to say whether a business is healthy · Test a focused price change instead of reaching for blanket discounts
Suharsh Mahajan shares practical explanations of how businesses can use customer and sales data to make better e-commerce and retail decisions.
In the four reviewed LinkedIn posts, the recurring lesson is to look beyond headline metrics: ask whether customers return, whether discounts are eroding profit, or whether prices and product uncertainty are stopping sales.
The examples come through small-business stories, from a jewelry shop’s repeat purchases to a cafe’s menu prices.
These posts offer a concise, nontechnical starting point for thinking about growth; they are anecdotes, not documented case studies with independently reported test data.
Suharsh Mahajan turns broad business concerns into specific questions readers can follow.
In the jewelry-store post, rising sales and customer totals seem reassuring until they ask how many January buyers returned in February or March.
That cohort view reveals the retention problem behind rising ad pressure.
Other posts apply the same approach to discount-driven skincare orders and a cafe’s pricing.
The fashion post shifts focus: it introduces Trylle and presents expected benefits of virtual try-on without test data.
Readers interested in practical, story-led explanations of customer retention, pricing, and conversion may find the posts useful, but should treat reported outcomes as illustrative rather than proven results.
Across four LinkedIn posts, Suharsh Mahajan argues that businesses should look past headline growth and ask what customer behavior and costs sit behind it.
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Their examples turn that principle into practical checks: track repeat purchases, examine what discounts do to profit, and test whether prices match demand. They usually explain the lesson through a small-business story rather than technical instructions.
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.
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Mahajan’s recurring move is to replace a reassuring headline metric with a question about what customers do next and what growth costs.
In a jewelry-store story, a growing customer chart sat alongside the owner’s worry that more ad spending was needed to sustain sales.
Mahajan asked how many January buyers returned in February or March.
They say the cohort view showed strong first purchases but almost no second purchases; the proposed response was a newsletter and thank-you notes for repeat orders, not simply more acquisition spending.
Numbers need context to say whether a business is healthy
Mahajan distinguishes visible activity from business results.
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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.
In a skincare-brand story, sales, traffic, and engagement were rising while profits fell.
Mahajan says the investigation found that 72% of orders came from heavy discounts and that loyalty was not being built.
They use the example to argue that analysis starts with curiosity, not a particular spreadsheet or programming tool.
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.
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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.
In a cafe story, fancy coffees sold rarely while tea and snacks were popular but, in Mahajan’s account, priced poorly.
The proposed change lowered the price of regular tea and raised the price of fancy coffees.
They report more tea purchases, more walk-ins and online orders, and higher overall sales, and connect the lesson to price sensitivity among Indian buyers.
Make the buying decision easier, not just the marketing louder
Mahajan also describes growth as reducing uncertainty for customers.
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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.
A fashion-store owner is described as getting traffic and cart additions but facing abandoned purchases and returns because shoppers were unsure about fit.
Mahajan presents Trylle’s virtual try-on and AI stylist as a way to address that doubt.
The post states intended outcomes such as higher conversions and fewer returns but does not report evidence that they occurred; ask for test results before relying on those benefits.
The clearest teaching post in the set and the best place to start: it names the exact question a retention analytics partner would want a brand to ask, then shows what the answer looks like.
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Read it to judge how Suharsh Mahajan explains analysis to a non-technical audience.
A jewelry store owner reports rising sales and a growing customer count but says it feels like she must spend more on ads just to hold sales steady.
Mahajan sets aside the total-customers chart and asks a narrower question: how many customers who bought in January came back in February or March?
Grouping customers by first-purchase month shows a strong first purchase and almost no second purchase, meaning ads were filling a leaky bucket.
The fix is modest: an email newsletter with offers and handwritten thank-you notes on repeat orders, followed by steadier growth and less ad pressure.
For brands planning a campaign
Strong candidate for the anchor piece in a co-branded retention series: the diagnostic question is concrete enough to demonstrate with a platform’s own data. The response offers low-tech tactics, so a partner can supply the measurement loop: cohort tracking, repeat-rate benchmarks and follow-through after the first campaign.
Shows how Mahajan separates a good-looking dashboard from a good business.
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The post states the recurring idea most directly and gives newcomers a quick read on the argument they keep returning to.
A direct-to-consumer skincare founder has rising sales, strong traffic and high engagement, but profit falls every month.
Digging in, 72% of orders came from heavy discounts; the brand was buying attention rather than building loyalty.
The lessons: growth without context is noise, metrics show movement but not meaning, and analysis starts with curiosity rather than a tool.
For brands planning a campaign
A natural hook for a vanity-metrics-versus-profitability angle, with 72% discount-driven orders as the illustrative number. Treat it as a story, not verified brand data; for a partner piece, reproduce the exercise with its own cohort or label the figure as reported by the founder.
Shows the smallest, most repeatable version of the method: one business, one measurement, one change, one result.
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A useful example of how pricing and buyer behaviour can be handled without technical apparatus.
A cafe owner with steady footfall and flat sales assumes discounts are the only way out.
Checking the numbers first shows fancy coffees were rarely ordered, while regular tea and snacks sold well but were priced poorly; customers also compared prices quickly online and offline.
The experiment lowers the price of regular tea and raises the price of fancy coffee.
More tea sells, walk-ins and online orders rise, and overall sales grow.
Mahajan draws three conclusions: Indian buyers are price sensitive, small price changes can shift demand, and analysis separates struggle from growth.
For brands planning a campaign
Useful for a pricing or price-sensitivity segment of a co-branded series, and as an example of a repeatable teaching format: one small-business change with a before-and-after. It also signals a focus on Indian consumers and small merchants, narrowing the range of suitable partners.
Shows how commercial mentions are handled: a customer problem, a named product and a list of expected business outcomes.
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Read it to judge whether a co-branded placement would fit naturally into their writing.
A fashion e-commerce owner reports traffic and add-to-cart activity but abandoned purchases and a high rate of returns; customers are unsure about fit.
Mahajan names Trylle, an AI-powered virtual try-on and styling product, and lists its intended effects: higher conversions, fewer returns, better engagement, more confident buying and a better shopping experience.
The post closes on the idea that growth can come from making customers confident before they buy.
Ask for test data before using these product claims in a partner piece.
For brands planning a campaign
Demonstrates the placement format Mahajan uses inside a problem narrative, which suits a partner looking to introduce a specific tool through a merchant’s story. Because the post reads largely as a product recommendation, a co-branded piece would need independent numbers to avoid the same shape.
Commercial work runs through practical analysis, not personal brand posts2
Customer retention and cohort analysis1
Metrics show movement, not meaning1
Pricing experiments and Indian price sensitivity1
Product and tooling explainers inside client stories1
Customer retention and cohort analysis
The most developed post starts with a handmade-jewelry store whose sales and total-customer chart were both climbing, yet the owner felt they had to spend more on ads each month just to keep sales coming.
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Mahajan set the totals aside and asked a different question: how many customers who bought in January came back in February or March?
Grouping buyers by the month of their first purchase showed that first purchases were strong and second purchases were almost absent — ads were filling a leaky bucket.
The fix was retention work rather than acquisition spend: a simple email newsletter with special offers, plus handwritten thank-you notes on repeat orders.
They report more stable growth, lower ad pressure and a repeat-buying community instead of one-time buyers, and close on the claim that growth comes from happier customers who choose to come back.
Cohort analysis — group customers by acquisition month, then measure repeat purchases in later periods instead of watching a running total.
For brands planning a campaign
A co-branded retention series could build directly on this approach: replace the customer-total view with a cohort view, show a repeat-purchase chart, then walk readers through the retention tactics that follow. That gives an analytics platform a concrete demonstration to sponsor, rather than an abstract claim about the value of retention data.
A direct-to-consumer skincare founder reports rising sales, strong traffic and high engagement while profits fall every month.
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On inspection, 72% of orders came from heavy discounts, loyalty was not being built, and the business was buying attention rather than earning it.
Mahajan draws three lessons: growth without context is noise, metrics can show movement but not meaning, and tools show numbers while thinking shows truth.
The conclusion: real analysis begins with curiosity rather than with Excel or SQL.
For brands planning a campaign
Useful for a diagnostics-focused piece: pair the dashboard numbers a brand already tracks with the profit and repeat-purchase measures that explain them. Mahajan’s framing — asking why something is working and at what cost — gives a campaign a clear question to show the platform prompting in-product.
A cafe owner with steady footfall but flat sales was ready to discount heavily.
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Checking the numbers first showed that fancy coffees were rarely ordered, regular tea and snacks were popular but not priced right, and customers compared prices quickly both offline and online before dropping off.
The experiment was deliberately small: lower the price of regular tea, raise the price of fancy coffees.
Mahajan reports more people buying tea, more walk-ins and online orders, and overall sales finally growing.
The conclusion is that Indian buyers are highly price sensitive, even small price changes shift demand, and the answer to a flat month is often reading the customer through numbers rather than adding products or ad spend.
A targeted before-and-after price change on specific items instead of a blanket discount campaign.
For brands planning a campaign
Supports a pricing or elasticity module built for an Indian audience, where Mahajan’s argument about price sensitivity lands with local operators. A co-branded series could use this story as the hook, then show how test design and price monitoring work in the platform.
Product and tooling explainers inside client stories
One post describes a fashion e-commerce operator with traffic and cart adds but abandoned checkouts and heavy returns; the owner says shoppers are unsure how clothes will fit.
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Mahajan introduces Trylle, an AI virtual try-on and stylist product, as a way to remove that doubt, and lists the business outcomes it is meant to produce: higher conversions, fewer returns, better engagement, more confident buying decisions and a better shopping experience. The post closes by comparing the goal to trying clothes on in a real fitting room and points readers to a link in the comments.
The post shows Mahajan explaining another company’s product favorably through a customer problem. It does not say whether the relationship was paid or unpaid, and no disclosure or hashtag establishes sponsorship. Ask Mahajan to clarify the commercial relationship before treating the post as evidence of sponsored work; it does show the format they use.
For brands planning a campaign
This is the pattern a co-branded series would follow: name the operator’s problem, quote the worry, then show the product resolving it in plain business terms. Decide whether you want content in this vendor-explainer style, which puts the tool at the centre of the story rather than the analysis.
Across all four posts, the shape is consistent: introduce someone close to the writer who runs a small business — a jewelry store, a cafe, a fashion store or a skincare brand — quote the worry in their words, list the dashboard signals that look healthy, then introduce the better question.
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The analysis is described step by step, results appear in short bullet lists, and each post ends on a single aphoristic line about what the lesson really was. The language is deliberately non-technical; SQL and Excel appear only as a contrast to curiosity, and readers need no prior knowledge of analytics jargon.
For brands planning a campaign
A partnership can inherit this format cheaply: a named operator, a stated worry, one diagnostic question, and a short list of results.
It is readable for a general business audience while still allowing the platform to show a specific analysis step.
Because the pattern relies on someone else’s business story, co-branded pieces need real customer material rather than invented case studies.
Commercial work runs through practical analysis, not personal brand posts
The posts that name a company describe a software product being explained to a store owner, not a personal sponsorship, event or endorsement.
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They include no product deal, discount code, affiliate mark, event appearance or creator-platform promotion. The commercial angle is one step removed: Mahajan describes how a business makes a decision and where a tool fits into it.
Ask Mahajan for examples of other commercial formats before deciding whether these four posts represent the broader portfolio.
For brands planning a campaign
Frame a platform partnership as a working relationship around analysis content rather than an endorsement slot, matching the register of these posts. Budget onboarding around giving Mahajan a real product walkthrough: the strongest posts show a specific tool applied to a specific operator problem.
Mahajan's posts describe hands-on problem-solving with small online businesses, but name only one company: Trylle, a fashion e-commerce product they present in a post about fit-related returns.
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The other posts work through unnamed businesses—a handmade jewelry store run by a friend, a cafe run by a cousin and an unnamed D2C skincare brand—so they identify no other brand relationship a reader could act on. Ask Mahajan to clarify whether they are connected to Trylle as a client, affiliate, employee or paid partner.
Trylle
Disclosed partnership
Mahajan's post recounts a conversation with a friend who runs a fashion e-commerce store about abandoned carts and high returns, then introduces Trylle as the fix: AI-powered virtual try-on plus an AI stylist to help shoppers judge fit before buying.
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It lists benefits for fashion businesses—higher conversions, fewer returns and better engagement—and points readers to a link in the comments. Ask Mahajan to clarify their relationship to Trylle.
Mahajan's work is easiest to explore through a recurring case-study shape: a small-business owner describes a healthy-looking number that is not translating into durable results, they ask a diagnostic question, then walk through the check and the response.
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Start with the retention and cohort-analysis post for the clearest example; pair it with the dashboard-metrics and cafe-pricing posts to see the same diagnostic habit applied to profit and price.
The fashion try-on post is a product explainer built inside a customer story.
The tags below group the work by topic, method, format, and professional audience.
Coats uses diagnostic ecommerce analysis, drawing on specific reports to question headline metrics.
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They explain how a reported return rate can obscure what actually happened, and how returns mitigation can improve profit, customer experience, and reviews. This resembles Mahajan's habit of asking what sits behind a dashboard number and what action follows.
Coats covers Amazon seller operations, with platform-specific reports, step-by-step instructions, and vendor references. Mahajan's posts are broader small-business stories about retention, pricing, and customer behavior, usually without named reporting menus or platform procedures.
Jabeen and Mahajan both explain ecommerce growth by replacing a surface question with a diagnostic one.
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Their posts ask what makes visitors trust a brand enough to buy, and whether Google sees a store as a strong shopping experience. Both writers present healthy-looking numbers, then redirect attention to the underlying customer or operational condition.
Jabeen's examples centre on Google Merchant Center health, store quality, product data, and conversion issues for makeup ecommerce. Mahajan more often examines retention, repeat purchase, and pricing across several kinds of small business, using narrative case studies rather than platform health checklists.
Hammed and Mahajan both write about how ecommerce discovery, conversion, and retention are changing, and use data points to support a practical takeaway.
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Their posts cover AI-driven discovery, product information, and email automation as areas where brands need to prepare the full customer journey.
Hammed focuses on industry-level trends, AI discovery, and lifecycle or email marketing, often citing survey or platform data. Mahajan tends to focus on one operator's story, using a diagnostic question and a short business lesson rather than a broader market-trend breakdown.
Placement decision: Treat Suharsh Mahajan as a topic-fit candidate for a retention analytics thought leadership series, not yet as a measured audience match.
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The four LinkedIn posts speak to ecommerce owners, D2C operators, and analysts through examples of retention, pricing, conversion, and analytics: cohort retention for a handmade jewelry store, tea-and-coffee pricing for a cafe, virtual try-on for fashion returns, and discount-driven D2C skincare profit loss.
That content maps closely to a B2B ecommerce analytics platform’s co-branded retention series.
Before committing, ask for audience survey results or platform demographics covering industry, seniority, geography, and company size, plus recent retention-focused post performance.
No partnership program, sponsor policy, rate card, or media kit appears in the posts here.
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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.
Good topic and format fit, pending an audience-composition check and agreement on commercial terms.
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Brief
A B2B ecommerce analytics platform wants a co-branded thought-leadership series to help online retailers improve repeat purchasing.
Reasons
Mahajan’s jewelry-store post asks a practical retention question: how many January customers returned in February or March? Cohort analysis shows why rising customer totals can hide weak repeat buying.
The post connects the analysis to concrete follow-up actions, including a newsletter and thank-you notes. A series could explain what to measure and what operators might do next.
Merchant stories and plain-language explanations suit a thought-leadership series for readers who may not be analysts.
The posts don’t establish whether the platform’s target buyers follow Mahajan. Before treating this as an audience match, request data on industry, seniority, geography, and company size.
When to choose a specialist
Poor fit for that promise based on the available evidence.
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Brief
A fashion retailer wants a short-term creator placement promising an immediate lift in conversions and fewer returns, backed by verified campaign results.
Reasons
The fashion post introduces Trylle’s virtual try-on and stylist product and lists intended benefits, but provides no test data showing that conversions rose or returns fell.
The posts offer anecdotal business stories, not documented performance case studies. The product-explainer example shows a possible writing format, not measured outcomes or a confirmed paid partnership.
The posts provide no audience demographics or recent performance figures to support a reach- or conversion-based commitment.
If the retailer instead wants an educational piece about how fit concerns affect shopping decisions, that could be a different brief, with claims and evidence agreed in advance.
A worked campaign example
Evidence and next steps
Use verified, permissioned customer data or clearly labeled illustrative data; don’t present the post’s anecdote as an independently verified case study. Before commissioning, confirm the intended audience match, scope, disclosure, review process, schedule, and fee directly. Ask about a partnership offer and inquiry route.
Why it fits
This builds on the specific diagnostic question and cohort-analysis explanation in the retention post rather than adding a generic product pitch. The chart supports the analysis, while the story explains why the metric matters.
Brief
The analytics platform commissions a hypothetical co-branded LinkedIn post for ecommerce operators whose new-customer totals are rising but who don’t know whether buyers return.
Campaign angle
“Your customer count is growing. Are those customers coming back?” Open with the contrast in the jewelry-store story: sales and total customers rose, yet the owner felt increasingly dependent on ads. Then show how grouping first-time buyers by purchase month reveals whether they return in later months. Use a clearly labeled sample cohort chart from the platform, explain what its figures do and don’t show, and close with questions operators can use to investigate weak repeat purchasing.
Choose Coats when the series needs to teach operators a diagnostic they can run themselves.
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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.
The method is Amazon-specific.
Both posts use Seller Central reports and Amazon's return-rate reporting quirks, so a platform serving a broader DTC or Shopify audience would need to translate the process.
Coats also applies cohort thinking to returns, not repeat purchases.
For a campaign about retention and loyalty, the fit is partial: readers learn to separate purchase-month effects from reporting effects, but not to answer the repeat-buying question at the heart of the jewelry example.
Ask for a repeat-purchase analysis if that is central to the campaign.
Choose jabeen when the campaign is about why storefront signals lose conversions, before any retention question arises.
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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.
These posts frame the right questions rather than demonstrate the analysis.
Both replace a volume question with a trust question, then list what brands should improve.
They offer no cohort split, measurement step, or worked diagnostic readers could rerun on their own accounts.
Neither addresses repeat purchase, so a retention analytics platform gets topic adjacency without the analytical backbone; the case rests on stated numbers rather than a demonstrated method.
Choose Hammed when the platform wants to make the case for retention through channel change.
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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.
The argument relies on third-party report statistics rather than the creator's own merchant data or diagnostics.
Readers learn what the shift means and get four numbered takeaways, but no method to apply to their own lists or orders; the piece informs a decision more than it equips an operator.
The email post also stays at the level of flow-versus-campaign principle.
Ask for a worked cohort or repeat-purchase example if the platform needs to demonstrate that kind of measurement.
Four LinkedIn posts by Suharsh Mahajan cover repeat-purchase analysis at a handmade jewellery store, falling profit at a discount-dependent D2C skincare brand, menu pricing at a cafe, and a fashion product explainer about virtual try-on and returns.
These four posts provide the ideas, brand, and comparison sections.
Six LinkedIn posts by three comparator creators, dated February 2025 to October 2026, help readers assess whether Mahajan's work is genuinely distinct.
No publication dates for Mahajan's own posts appear here, and no biography, non-LinkedIn channel, video, or long-form writing appears here.
The posts do not discuss books, shows, or earlier career work, so name searches that depend on that context are not covered; dated primary material would close that gap.
Reported business outcomes in the posts are the author's own anecdotes, not independently verified test results.
