Blog
How to Start Event-Based Marketing: A Bottom Up Approach
A bottom-up approach to event-based marketing using data integration, customer behavior analysis, and automation to optimize marketing spend.
By Ross KatzRead
Your marketing team is spending. You have no idea what's working.
We connect your ad platforms, e-commerce data, and CRM to show you which channels drive revenue, which customers are worth acquiring, and where your budget is wasted.
Marketing Analytics
Marketing teams make spending decisions every day. Most of them are guessing. Not because they lack tools but because those tools do not talk to each other, each one claims credit for the same conversion, and none of them connect to revenue.
33%
Product SKUs cut after profitability analysis
28
Average marketing touchpoints per purchase discovered in attribution analysis
7+
Marketing channels unified in a single attribution model
Use Cases
Engagements where this is the right work.
Multi-touch attribution
Your customer touched six channels before buying. We build attribution models that distribute credit across touchpoints so you know which interactions drive conversions and which are noise.
Marketing mix modeling
Platform-reported attribution inflates every channel. We build marketing mix models that use statistical methods to measure the true incremental impact of each channel, including offline and brand spend that never gets click-attributed.
Customer segmentation
Not all customers are equally valuable. We build segmentation models that combine quantitative purchase data with qualitative behavioral signals to identify your best customers, predict churn risk, and inform acquisition targeting.
Product profitability analysis
Revenue is not profit. We connect product-level cost data to sales data so you can see which products make money after marketing spend, COGS, and fulfillment.
Channel ROI reporting
Google Ads says one thing, Meta says another, and neither matches your revenue. We unify the data and build reporting that shows true cost per acquisition and return by channel.
Our Process
A structured approach that delivers results at every stage.
01
Unify Your Marketing Data
Your ad platform says you spent $50K and generated $200K in revenue. Your finance team says total revenue was $150K. Your e-commerce platform shows a different number. The discrepancy is not a rounding error. It is a data architecture problem: your marketing data, transaction data, and financial data live in different systems with no shared source of truth.
Output: Single source of truth across CRM, ad platforms, and product
02
Build Attribution and Segmentation Models
We connect your ad platforms, e-commerce systems, CRM, and financial data into a unified analytics layer. That means multi-touch attribution models that account for cross-channel journeys, marketing mix models that measure the true incremental impact of each channel, customer segmentation built on purchase behavior and qualitative signals, product profitability analysis that includes marketing spend and fulfillment costs, and reporting that your CMO and CFO both trust.
We have built segmentation models from demographic, behavioral, and purchase data that replaced gut-feel targeting with data-driven customer personas fed directly into CRM for campaign execution. We have unified e-commerce analytics across Shopify and Amazon so companies could compare channel performance with a single set of numbers. We have analyzed product profitability across entire catalogs to identify which SKUs look good on revenue but lose money after marketing and fulfillment, leading one client to cut a third of their catalog and improve margins.
From our podcasts: Maximizing Sales Efficiency with AI and Data Tools, and Transforming CPG with AI at TICKR.
Output: Multi-touch attribution and cohort segmentation in production
03
Operationalize Marketing Intelligence
Marketing analytics is not a reporting project. The goal is a system that connects spend to revenue and gives your team the data to make better allocation decisions every week. For teams ready for it, we build agentic workflows on top of campaign data that automate performance monitoring and surface optimization opportunities without manual dashboard review.
Output: Reverse ETL of audiences and triggers into your activation tools
Insights
Perspectives from our team on marketing analytics.
Blog
117 Key Questions for E-Commerce Leaders
117 questions e-commerce leaders should answer across finance, marketing, sales, operations, support, technology, and BI — prioritized by business impact.
By Ross KatzRead
Blog
How to Understand Your Customer Acquisition Cost and Lifetime Value
A practical guide to calculating Customer Acquisition Cost and Lifetime Value, and using the CAC:LTV ratio to manage growth and profitability.
By Ross KatzRead
Frequently Asked
Questions
We already have Google Analytics. Why do we need more?
Google Analytics tells you what happened on your website. It does not connect that activity to revenue, customer lifetime value, or profit by product. We build the data infrastructure that ties web behavior to transactions, customer records, and financial outcomes so you can see the full picture.
How do you handle data from multiple ad platforms?
We ingest data from Google Ads, Meta, TikTok, Amazon Ads, and other platforms into your data warehouse, normalize it, and build unified reporting. Each platform inflates its own attribution numbers. We build models that reconcile those claims against your revenue data.
What is the difference between attribution and marketing mix modeling?
Attribution assigns credit to individual touchpoints in a customer journey, typically using click-path data. Marketing mix modeling uses statistical analysis of aggregate spend and outcome data to measure the incremental impact of each channel, including channels that are hard to track at the user level like TV, radio, and brand campaigns. Most mature marketing analytics programs need both.
Can you work with Shopify and Amazon data?
Yes. We have built marketing analytics platforms for e-commerce companies selling across Shopify, Amazon, wholesale, and direct channels. We consolidate order data, ad spend, and fulfillment costs into a single reporting layer so you can compare channel performance accurately.
What does a marketing analytics engagement look like?
We start by mapping your current data sources: ad platforms, e-commerce systems, CRM, and financial data. Then we build the pipelines and data models that connect them. Most clients have usable attribution reporting within 6-8 weeks. Deeper analysis like customer segmentation, marketing mix models, and lifetime value models typically follow in the next phase.
How do you approach customer segmentation?
We combine quantitative and qualitative approaches. Quantitative segmentation uses purchase history, frequency, recency, and monetary value to group customers by behavior. Qualitative inputs like survey data, support interactions, and engagement patterns add context. The result is segments your marketing team can target with specific campaigns, not abstract personas that collect dust.
Ready to see what your marketing spend actually produces?
We’ll connect your ad platforms, e-commerce data, and CRM so you can stop guessing and start measuring.
Book an Intro CallOur Other Services
Or, see what else we do.
Business Intelligence
Turn data into decisions with BI platforms that your team will use. CorrDyn builds dashboards, reports, and analytics workflows.
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RevOps Analytics
Unify your CRM, billing, and pipeline data into a single revenue model. CorrDyn builds the data infrastructure that makes RevOps work.
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Data Engineering
Build reliable, cost-effective data pipelines on AWS, GCP, and Azure. CorrDyn designs and implements data infrastructure that scales.
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Managed Data Team
Get a full data team without the hiring timeline. CorrDyn embeds the specific skill sets you need and owns outcomes, not just hours.
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