At L'Oréal, I helped bring vendor-run analytics studies, multi-touch attribution, A/B testing, and marketing mix modeling, in house using a custom system I built in Python and SQL. The result was over a million dollars in savings, and a faster, more accurate view of what was actually driving sales.
Why Agencies Overcharge For This
Multi-touch attribution and structured A/B testing used to require specialized vendors and expensive licenses. That's less true every year. The statistics behind these models are well understood, and the tooling to run them, Python, SQL, and basic ML libraries, is free. What businesses are usually paying for is convenience, not capability.
That convenience has a cost. Outside vendors are slower to iterate, they don't know the business as well as the people inside it, and every question outside the scope of the original contract turns into a change order.
What In-House Actually Looks Like
Bringing this in house doesn't mean hiring a full data science team. It means building one well-designed system, a real-time data pipeline, a set of attribution and testing models, and dashboards that speak the same language as the marketing team, once. After that, every new question gets answered in days instead of waiting on a vendor's roadmap.
That's the model VQuants builds on for clients now, whether it's a Fortune 500 marketing team or a single ecommerce founder trying to figure out if their ads are actually working.