Executive Profile & GTM Vision

Who Writes This Site

Everything published here is written by Deeptanshu Sharma. There is no editorial team, no ghost-writing arrangement and no syndicated content — if an article is on this site, it was written by the person whose name is on it. That matters because the material is largely about measurement and paid media, two areas where a great deal of published advice is written by people who have never had to defend a number to a finance team.

The background behind it is roughly nine years of growth and performance marketing work, spanning performance marketing, product marketing and go-to-market strategy across India, South-East Asia, Australia and New Zealand, and the United States. That has included scaling paid acquisition across Google, Meta and programmatic channels, building server-side measurement and attribution infrastructure, and owning commercial targets rather than just channel metrics. The specifics of roles and employers are on LinkedIn, which is the appropriate place to verify them.

How the Content Is Produced

Articles come from problems encountered in actual accounts rather than from keyword research. The recurring pattern is that something behaved unexpectedly — a funnel that did not reconcile, a platform metric that disagreed with the backend, an attribution model that credited the wrong channel — and the article is the written-up version of working out why. That is why the guides tend to dwell on failure modes and edge cases rather than on happy-path instructions, which are already well covered by official documentation.

Where something is uncertain, it is stated as uncertain. Where a figure is an industry-typical range rather than a measured result, it is described that way. Platform behaviour changes frequently, so anything dated will eventually drift out of accuracy; if you find something that no longer holds, the contact page is the fastest route to getting it corrected, and corrections are made rather than quietly ignored.

How This Site Is Funded

This site carries advertising, and it is worth being direct about what that does and does not mean. Advertisers have no influence over what is written here. No article is commissioned, sponsored or reviewed by a third party before publication, and no vendor has paid for a favourable mention. Where a specific tool is recommended or criticised, that reflects working experience with it and nothing else.

Advertising is served through third-party networks, which means the ads you see are selected by those networks rather than chosen here. If an ad appears that seems inappropriate alongside this content, reporting it via the contact page is genuinely useful — that feedback is actionable. How advertising cookies and personalisation work, and how to opt out of them, is set out in full in the Privacy Policy.

The site also functions as a professional portfolio, which is a second and openly stated motivation for publishing. Consulting enquiries and role conversations both arrive through it. That is not in tension with the editorial position above — the reason the writing is useful as a portfolio is precisely that it is honest about what works, what does not, and what remains genuinely unclear.

What You Will Find Here

The library is organised around three kinds of material. The first is long-form explainers that take a single concept — incrementality testing, marketing mix modelling, retrieval-augmented generation, cohort analysis — and work through it properly: what the idea is, where it came from, when it applies, and the specific conditions under which it stops being useful. These run three to six thousand words because the honest version of the answer usually does.

The second is comparison work. A large part of practical marketing operations is choosing between two tools, two attribution models, or two ways of structuring the same report, and most of the material available on those choices is written by one of the vendors involved. The comparisons here are written from the position of someone who has run both, has had to migrate off at least one of them, and can say what the migration actually cost.

The third is the help centre: shorter, procedural troubleshooting entries for the specific failures that consume an afternoon — a GA4 property that stopped recording conversions, a tag that fires twice, a data layer variable that returns undefined in preview but works in production. These exist because the answer is rarely in the official documentation, and when it is, it is phrased in a way that assumes you already knew where to look.

Corrections and Editorial Standards

Every article carries a publication date, and that date is meaningful. Analytics platforms and ad systems change their interfaces without notice, and a step-by-step sequence that was exactly right in March can be wrong by September. Where an article describes a specific menu path or setting, treat the date as part of the instruction. Where a platform change invalidates something substantial, the article is revised rather than left standing.

Corrections are welcome and are not treated as an inconvenience. If a number is wrong, a method is misdescribed, or a claim does not survive contact with your own data, write in with the specifics and it will be checked. Where a correction is material it is made in the article itself, not appended as a note at the bottom that nobody reads. Where a claim turns out to be contested rather than simply wrong, the article says so and presents the disagreement instead of picking a side for the sake of a cleaner conclusion.

Nothing published here is legal, financial, or tax advice, and none of it is a substitute for reading a vendor’s own documentation before making a decision with budget attached. It is one practitioner’s account of how these systems behave in production — useful as a starting point and a sanity check, not as an authority to be cited without verification.