What does marketing data science do?
Marketing data science uses statistics and modeling to answer budget questions that reports alone cannot. Examples: how many leads to expect next quarter, which customers are worth the most, and which channels truly add sales.
It sits on top of clean data. If tracking is broken or the CRM is inconsistent, a model will only produce confident-looking wrong answers. That is why most engagements start with marketing analytics and data engineering, and why we check data quality before promising any model.
What can we model, and how much data does each need?
Each method has a minimum amount of history before it becomes useful. Here is a plain guide. These are rough rules of thumb that vary by business.
| Method | What it answers | Data it usually needs |
|---|---|---|
| Forecasting | Leads, sales or revenue to expect | Ideally two or more years of weekly or monthly history to capture seasonality |
| Segmentation | Which customer groups behave alike | Hundreds of customers with purchase or service history |
| Lead scoring | Which new leads are likely to close | Hundreds of leads with recorded won or lost outcomes |
| Customer lifetime value | What a customer is worth over time | Repeat purchase history across a meaningful period |
| Marketing mix modeling | How each channel contributes overall | Two or more years of weekly spend and sales, with real variation in spend |
| Incrementality testing | Whether a channel truly causes extra sales | Enough volume to split audiences or regions and detect a difference |
Below those levels, simpler analysis often beats a fancy model, and we will say so.
How does lead scoring work?
Lead scoring ranks new leads by how likely they are to become customers, based on patterns in leads that closed before. Your sales team can then call the best ones first.
We train the score on your own CRM history: lead source, service requested, location, response time, form answers and more. We start with a simple, explainable score so your team understands why a lead ranks high. The score can feed back into ad platforms as a value signal through marketing attribution, so bidding favors the leads that tend to close.
What are marketing mix modeling and incrementality testing?
Marketing mix modeling estimates each channel's contribution using historical spend and sales. Incrementality testing measures it directly by comparing a group that saw the marketing with one that did not.
They answer the question attribution cannot: would these sales have happened anyway? A mix model works across all channels, including offline ones, but needs long, varied history. An incrementality test, like pausing a channel in some regions while keeping it running in others, gives a cleaner answer for one channel at a time. Used together, they keep budget decisions grounded in evidence.
What does a worked example look like?
Here is a hypothetical example. Say a home-services company spends $10,000 a month on branded search ads and wants to know if that spend is needed.
- We pick comparable service areas and pause branded ads in some of them for a few weeks.
- We compare total calls and booked jobs across both groups, including organic and direct.
- If total jobs barely change where ads were paused, much of that spend was buying sales you would have won anyway.
- If jobs drop noticeably, the ads are adding real business and the budget is justified.
You can play with similar tradeoffs in our revenue simulator.
How is data science work priced?
Models and tests are scoped as Engineering projects and quoted per project after a data review. Ongoing forecasting and testing can be folded into a monthly Growth program, scoped to your goals and quoted after a free audit. We work with Houston and Texas businesses and clients nationwide.