Predictive analytics, marketing attribution, and revenue forecasting that show which channels, campaigns, and AI search sources actually produce pipeline, so budget follows revenue.
Get in touch
Used by the world’s leading companies
Most marketing teams can report clicks and form fills. Few can say which channel produced last quarter’s closed revenue, or what next quarter’s pipeline will look like, so budget decisions end up resting on opinion.
Our data science work connects ad platforms, analytics, and CRM data into models that attribute revenue, forecast pipeline, and predict which leads will close. You see where to spend more, where to cut, and how AI search fits into the mix.
The goal is not prettier charts. It is better budget decisions, made faster, with numbers your CFO trusts.
Why do I need data science for marketing?
Last-click attribution can’t see how buyers research anymore.
B2B and high-value ecommerce buyers touch many channels before they convert: search, paid social, email, sales calls, and more and more often ChatGPT, Perplexity, and Google AI Overviews. Last-click attribution hands most of the credit to whatever happened last and quietly starves the channels that started the deal.
AI search makes this harder. AI-referred visits are often undercounted or lumped into direct traffic, even though industry studies from Similarweb and Semrush show AI-referred visitors convert 2–5x higher than classic organic traffic. If you can’t see that pipeline, you can’t invest in it.
Data science closes the gap. Models built on your own CRM and marketing data show what actually drives revenue, forecast what is coming, and help leadership move budget with confidence instead of guesswork.
Marketing data science services
Models that show what actually drives revenue
Data Audit & Readiness Review
We assess your GA4, ad platforms, CRM, and ecommerce data for gaps, duplicates, and broken tracking. Models are only as good as their inputs, so you get a clear list of fixes before anyone builds a forecast on bad data.
Modeling Strategy & KPI Design
We define the revenue questions that matter, the KPIs that answer them, and the models worth building first. That keeps the work focused on budget and pipeline decisions, not science projects that never reach the sales team.
Data Pipelines & Warehousing
We connect ad platforms, analytics, CRM, and commerce data into a central warehouse such as BigQuery, with automated, documented pipelines. One reliable source of truth replaces spreadsheet exports and conflicting numbers between marketing and sales.
Multi-Touch Attribution
Data-driven and position-based models that credit every touch on the path to closed revenue, from the first search or AI answer to the final sales call. You see which channels start deals, which close them, and where budget is wasted.
Marketing Mix Modeling (MMM)
Statistical models that estimate each channel’s revenue contribution, including offline and hard-to-track media, using aggregated spend and outcome data. MMM holds up as cookies and user-level tracking weaken, and it guides annual budget planning.
Pipeline & Revenue Forecasting
Time-series and driver-based forecasts for leads, pipeline, and revenue by channel and segment. Leadership gets a realistic view of the quarters ahead, and marketing can show how planned spend changes translate into expected pipeline.
Predictive Lead Scoring
Models trained on your historical CRM outcomes that score new leads by their likelihood to become sales-qualified and close. Sales works the right accounts first, and marketing learns which sources produce buyers rather than just form fills.
Customer Lifetime Value Modeling
Predicted lifetime value by customer, cohort, and acquisition channel, so you can set smarter cost-per-acquisition targets and bid harder for the customers worth the most. Especially useful for ecommerce brands balancing ROAS against repeat revenue.
AI Search Impact Analysis
We separate sessions from ChatGPT, Perplexity, Gemini, Claude, and Microsoft Copilot from direct and organic traffic, then tie them to leads, pipeline, and revenue. You see what AI search is actually worth to your business and where to invest next.
Incrementality & Experiment Design
Holdout tests, geo experiments, and properly powered A/B tests that measure what a channel or campaign truly adds. Incrementality answers the question attribution can’t: would this revenue have happened anyway without the spend?
Predictive Dashboards & Alerts
Forecasts, attribution, and lead scores delivered in 24/7 real-time dashboards built on your own data, with automated alerts when pipeline or spend drifts off plan. Your team sees problems early, without waiting on manual exports.
Executive Advisory & Budget Planning
Quarterly strategy reviews with senior strategists who turn model outputs into budget shifts, channel plans, and targets your leadership team can act on. Data science that ends in a decision. Strategic partners, not a ticket queue.
Questions
Data Science FAQs
What does data science do for a marketing team?
It turns scattered marketing and sales data into answers about revenue. We build attribution models that credit the channels that start and close deals, forecasts for leads and pipeline, and predictive scores that show which leads and customers are worth the most. The result is a clear view of where each marketing dollar goes and what it returns, including spend that supports AI search visibility.
How much data do we need to get started?
More history helps, but you don’t need a perfect data warehouse. A working CRM with lead sources and deal outcomes, plus GA4 and ad platform data, is enough to start with attribution and forecasting. Predictive lead scoring needs a reasonable number of closed deals to learn from. The data audit tells you which models your data supports today and what to fix first.
How long until we see useful results?
Most engagements start with a data audit and cleanup, and early attribution views and dashboards usually arrive within the first 1–3 months. Forecasts and predictive models improve as they learn from more closed deals, so they typically become more reliable over 6–12 months. We share a realistic timeline after the audit rather than promising specific outcomes up front.
How much do data science services cost?
Pricing depends on scope, data sources, and model complexity. AlchemyLeads plans run from $6,850 to $10,888 a month, and multi-brand engagements start at $15,000 a month. We scope data science work after the data audit, so you only invest in models that answer real budget and pipeline questions.
Will you replace our internal analytics or BI team?
No. We work alongside your marketing, sales operations, and finance teams. Many clients have capable analysts who are stretched thin or lack marketing-specific modeling experience. We build the pipelines and models, document them, and share the outputs in dashboards your team already uses, so internal staff can rely on the work and extend it.
Is AlchemyLeads a good fit for my company?
AlchemyLeads works best with high-value B2B, industrial, technical, and ecommerce brands doing roughly $5M to $150M in annual revenue. We are selective and only take on engagements where we see a strong probability of success, which is a big reason 95% of new clients since 2020 have stayed with us longer than 12 months.
