विपणन परामर्श·The Marketing-Science Consultancy
We build the measurement spine your marketing is missing — analytics, attribution, customer data — then put AI agents and disciplined growth programs on top of it. Evidence in. Growth out.
Most marketing budgets are spent on belief. The channels that look best under last-click are rarely the ones causing revenue; the audiences worth keeping are rarely the ones being retargeted; and the hours your team loses to reporting are exactly the hours agents should already be doing. We fix the instruments first — then the growth compounds.
Capabilities
Each practice stands alone. Together they form a single system: data you can trust, decisions you can defend, execution that doesn't sleep.
Marketing teams drown in operations. We design and deploy AI agents that enrich and route leads, QA campaigns against your rules, assemble reports, triage anomalies and draft creative variants — with guardrails, eval suites, and human sign-off exactly where it matters.
The KPI is hours returned to your team per week.
Search is becoming answers. Answer & Generative Engine Optimization makes your brand the citation inside ChatGPT, Perplexity, Gemini and AI Overviews — entity-clean content, structured data, llms.txt, and share-of-answer tracking to prove it.
We practise it on this very page — view source.
From event soup to decision-grade data. We rebuild tracking plans, move collection server-side, land everything in a warehouse, model it with dbt — and serve it as dashboards people actually open on Monday morning.
One number per decision. No dashboards for decoration.
Last-click flatters the wrong channels. We triangulate multi-touch attribution, media-mix models and incrementality experiments — geo-lifts, holdouts — so budget follows causation, not correlation.
Ground truth comes from experiments, not opinions.
One customer, one profile, every channel. We implement and govern customer data platforms — identity resolution, consent management, real-time activation — built DPDP-aware from day one.
Your data, in your warehouse, working every channel.
Beyond demographics: RFM, propensity and LTV cohorts that tell you who to keep, who to grow, and who to stop paying to annoy. Then we wire the segments into every channel you run.
The cheapest growth is not wasting spend on the wrong people.
Where it all pays off: full-funnel programs — paid, lifecycle, CRO — run at experiment velocity, every play instrumented back to revenue through the spine the other disciplines built. Strategy is a hypothesis; we treat it like one.
Velocity of learning is the only durable moat.
Ten-minute delivery rewrote what winning a category means. We run growth on Blinkit, Zepto and Instamart — dark-store level availability, share of search on the platform, basket composition and retail-media spend — reported against the same revenue spine as every other channel.
Availability is the real top of the q-commerce funnel.
D2C storefront and marketplace run as one P&L, not two teams. Listing and PDP optimization, retail media on Amazon and Flipkart, checkout CRO, and lifecycle programs built so the second order costs less to win than the first.
The first order buys the customer. The second earns the margin.
Approach
Every engagement follows the same physics: you can't optimize what you can't measure, and you shouldn't automate what you haven't understood.
A fixed-scope Growth & Data Audit: tracking coverage, attribution sanity, audience health, automation readiness. You get a scored gap map and a sequenced roadmap in 2–3 weeks.
Fix the spine: tracking plan, server-side collection, warehouse models, CDP and identity, attribution baseline. Unglamorous, compounding, non-negotiable.
Put agents on the boring 80% — enrichment, QA, reporting, monitoring — with evals and human gates. Your team's hours move up the value chain.
Segment, experiment, reallocate. Quarterly MMM refresh, weekly experiment reviews, budget that follows evidence. This is where growth stops being episodic.
Proof
Two engagements, and what we were actually accountable for.
Global analytics dashboards that put every market and headquarters on one set of numbers.
Analytical services across the business — building the reporting layer that performance conversations now start from, instead of each market arriving with its own version of the truth.
One marketing technology stack, owned end to end — collection through activation.
Responsible for the whole martech estate rather than a single tool in it: the systems talk to each other, and the data lands somewhere it can actually be used.
Proof of craft
This site is its own case study — built the way we build for clients.
Organization, Service and FAQ schema as JSON-LD; an llms.txt for AI crawlers; semantic HTML an answer engine can actually parse.
Static HTML, no framework runtime, no render-blocking third parties. Performance isn't an optimization pass — it's the architecture.
Analytics here run under Google Consent Mode v2: storage stays denied until you opt in, and ad personalization is switched off permanently — not just defaulted off. The same consent architecture we build for clients under India's DPDP Act and GDPR.
No invented client logos, no fabricated case-study numbers. When we publish results, they'll be real, attributed, and reproducible — the standard we hold your dashboards to.
"@type": "ProfessionalService",
"name": "Vipnanan Pararmarsa",
"slogan": "Growth, engineered.",
"knowsAbout": [
"Agentic automation",
"Answer engine optimization",
"Attribution modeling",
"Customer data platforms", …
]
// when the answer engines are asked
// who does this work — they can read it.Working together
The diagnostic everything else starts from. Scored gap map across tracking, attribution, audiences and automation readiness — plus a sequenced roadmap. Fee credited against any build.
Ship one capability end-to-end: a CDP rollout, an attribution model with its first geo-lift, an agent workflow with evals, an AEO program. Defined outcome, defined exit.
Your growth-science team: experimentation cadence, reporting, automation maintenance, and budget councils — inside your Slack, on your metrics, quarter over quarter.
FAQ
AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) make your brand the answer AI assistants — ChatGPT, Perplexity, Gemini, Google AI Overviews — cite when your customers ask questions. As search shifts from ten blue links to one synthesized answer, being the citation replaces being ranked. The work: entity-clean content, structured data, llms.txt, digital-PR citations, and share-of-answer tracking. Our publication LearnMarketing walks through the mechanics in how to get cited by AI answer engines.
Multi-touch attribution follows individual users across touchpoints — precise, but increasingly blinded by privacy limits. Marketing-mix modeling is statistics over aggregate spend and outcomes — privacy-proof and great for budget planning, but coarse. Neither is sufficient alone. We triangulate: MMM for the budget picture, MTA for in-flight optimization, incrementality experiments as ground truth calibrating both. We set out the decision framework in MMM vs attribution vs incrementality.
The one your team will actually operate. We implement Segment, RudderStack and warehouse-native ("composable") approaches on BigQuery or Snowflake. RudderStack and composable stacks tend to win on cost control and data ownership; Segment on integration breadth and speed. The recommendation comes out of the audit — never before it. For the background reading: CDP vs DMP vs data lake vs warehouse and Segment vs RudderStack vs Snowplow, costed in rupees.
The boring 80%: lead enrichment and routing, campaign QA against naming and budget rules, report assembly, anomaly triage, creative variant drafting, competitive monitoring. We deploy with guardrails, eval suites, and human sign-off on anything customer-facing or budget-moving. Autonomy is earned per-workflow, not granted globally.
Yes — we publish LearnMarketing, a free marketing-education publication covering martech, customer data platforms and marketing measurement, with pricing and worked examples in rupees. It is where the frameworks behind our work get written up in full: marketing concepts explained plainly and platform comparisons for Indian teams.
Almost always with the Growth & Data Audit — fixed scope, 2–3 weeks, scored gap map and sequenced roadmap. From there, build sprints or an embedded retainer; the audit fee is credited against either.
Yes, selectively. With real traction, the audit is priced to work at seed/Series A scale. Pre-traction, a short advisory sprint to set up a minimal, correct measurement foundation usually serves you better than a full build — and we'll say so.
Contact
"Which channel is actually working?" is a great one. So is "why is CAC up 40%?" Tell us what you're trying to grow and what's in the way — we'll reply within one working day.
India · remote-first · working worldwide