Welcome back, marketers. I’m your host Eric Westerkamp, CEO at CaliberMind, bringing you another installment of Funnel Lab Fridays, our weekly LinkedIn Live session where we chat about the latest use cases and challenges for data-savvy marketers.
Today, I’m joined by Misha Salkinder, our Director of Customer Data Strategy at CaliberMind, and guest speaker Drew Smith, Founder and CEO of Attributa. We’re discussing the pros and cons of in-app versus data warehouse approaches to marketing attribution.
Setting the Context: The Great Attribution Divide
So, the big question we keep hearing, especially from customers, is: “Should my attribution solution live inside Salesforce (or another CRM) or should I anchor it in a data warehouse environment?”
For an in-app approach, you would install a native application (or package) that sits directly in Salesforce. It leverages the CRM’s objects, fields, and reporting environment. With a data warehouse approach, you store your marketing events and CRM data in a data warehouse, like Snowflake, BigQuery, or Redshift, and your attribution tool performs advanced processing on top of that environment.
We’ll explore what’s good, bad, and maybe a little ugly about each approach.
Drew’s Take: Pros & Cons of the Data Warehouse Approach
Drew shares his thoughts on the big question for this session: “You’re working with tons of customers who face this decision. What are the advantages and disadvantages of data warehouse-based attribution?”
Data Warehouse Advantages
- Flexibility: Drew cites an example of a client who wanted to marry product data to attribution data. In Salesforce, if the product-level data is too many relationships away from the Opportunity or Campaign Member objects, you can’t easily tie them together in a single report. A data warehouse doesn’t have that structural limitation.
If the data exists, you can join it however you like. Salesforce is powerful but it has a certain structure—object relationships, fields, etc. In a data warehouse, you can model data in more creative ways to support deeper analytics.
- Advanced Enhancements: If you want to drop advanced analytics (think AI, machine learning, generative text summarization) on top of your attribution data, a data warehouse is your best friend. You’re not limited by the CRM’s ecosystem or licensing constraints.
- BI Tools & Multi-Use: If you’re using a solution like Tableau, Domo, or some custom reporting, a data warehouse is your central source of truth. You can feed multiple downstream tools without needing to replicate the data in each system.
Data Warehouse Disadvantages
Drew is quick to note that the data warehouse approach also means more overhead—like setting up the ETL (extract, transform, load) or ELT pipelines, building data models, and ensuring that your marketing, sales, and product data are all properly stitched together. It also means your day-to-day user has to step outside Salesforce to see certain attribution metrics, which might slow adoption for folks who live in Salesforce all day.
In-App Approach: Why People Love It
Flipping the question, I ask, “Okay, if a data warehouse is so flexible, why do some folks still choose an embedded in-CRM app for attribution?”
In-App Advantages
- System Familiarity: Salesforce is the foundation of B2B organizations. If your sales and marketing teams already live in Salesforce, it’s simpler to adopt a solution that’s basically just another tab in their CRM. There are no new dashboards or separate logins to learn.
- Quicker QA & Troubleshooting: Let’s say you see a weird pipeline number in your attribution report. If it’s inside Salesforce, you can just click through to the actual records, like the Opportunity or the associated Campaign Member data. You know your typical debug flow: “Let me open the opp, check who’s on it, see the campaigns, etc.” It’s all right there. With a data warehouse approach, you might have to jump into a BI tool or run a separate query in your pipeline orchestration tool.
- Speed to Market: If you’re a smaller org or you don’t have a dedicated marketing ops/RevOps function, you might prefer a simpler system that’s easy to install, uses standard objects, and delivers a quick ROI. Instead of building out heavy-lift data engineering, you can rely on the CRM’s object model and reporting environment.
In-App Disadvantages
But again, if your marketing data eventually outgrows the CRM’s structure or you need advanced funnel modeling, you’ll likely discover you’ve hit a glass ceiling. It’s not easy to see item-level data or do a specialized funnel analysis that references multiple steps across different product lines purely in Salesforce objects.
Misha’s Perspective: The Middle Ground
Misha notes that sometimes the conversation isn’t all or nothing. For some, an in-app solution might suffice if they have a simpler marketing org, fewer data sources, and simpler questions. But as you mature, your funnel questions get more complex and your data sets grow in volume and diversity (web analytics, social channels, third-party data, product usage data). That’s typically the turning point. You can’t do advanced funnel metrics or item-level analysis inside an in-app solution easily.
Another benefit to data warehousing is that you can do more than just attribution. You can also run engagement scoring, intent analysis, advanced funnel modeling, or even feed that data into an AI-driven pipeline forecast. If everything sits in the CRM, you might risk performance hits or run into object relationship limits that hamper your ability to produce the analytics your team truly wants.
The Machine Learning & AI Angle
We can’t talk about data warehousing without acknowledging how quickly the machine learning and AI field is evolving. I ask both of my guests: “If we see big leaps with generative AI, how does that differ between an in-app approach versus a data warehouse approach?”
Drew hammers home that Salesforce does have Einstein, but it’s still somewhat limited. You can’t just feed it raw data from a hundred different sources. It’s primarily built for certain use cases (like predictive lead scoring). With a data warehouse approach, you can bring in your own large language model (LLM), or adopt something like Google’s Gemini, embedding it directly to do text-based insights or advanced forecasting. The “bring your own AI” concept is far easier when you have a flexible environment.
Misha reminds us that “AI is only as good as the features it trains on.” In-app solutions can’t easily glean data from across your entire org. So, if you want to incorporate product usage data, or do super advanced pipeline modeling, you might find yourself out of luck if it’s an in-CRM approach that doesn’t store all that info.
Tool Time: A Live Peek at Data Warehouse Attribution
In the spirit of “show, don’t just tell,” I hop into a brief demonstration. Imagine you’ve got a data warehouse system orchestrated behind the scenes. I shared a buyer journey screen from CaliberMind that summarized a complicated account’s activity over many months.
You could see how top-of-funnel awareness started with G2 or Google Ads. Eventually the prospect filled out a form, or engaged with a drift chat, and the data was all joined. For me, that’s the holy grail: a single timeline or buyer journey that we can display in a user-friendly interface.
Then I showcase how a large language model, integrated with our data warehouse, could summarize the entire timeline in plain English. Think of it as letting your attribution data “tell the story” automatically. That’s the kind of synergy you get when your data is all in one place, you’re not restricted to a single object model, and you can do fancy transformations or AI-based summarization.
You can push some of these summaries back into Salesforce for the comfort of your sales team, because you want them to adopt it. But your advanced data transformations and modeling are all happening outside the CRM, letting you do all the magic under the hood.
Evolving from In-App to Data Warehouse Attribution
At the end of the day, the choice between an in-app attribution solution and a data warehouse-based approach boils down to your organization’s complexity, data maturity, and analytical ambitions.
If you’re just dipping your toes into attribution, have a modest marketing stack, and want a quick, easy solution that your sales team can “click around” in, an in-app approach is a good starting point.
If you’re orchestrating large data sets—complete with web analytics, product usage logs, external intent signals, multiple marketing channels, and you want to break the bounds of CRM objects—then a data warehouse approach is likely the more scalable, future-proof solution.
I can’t emphasize enough how important it is to think about your long-term marketing analytics roadmap. As Drew points out, you might be fine with in-app for a while, but once you start wanting to do more advanced funnel analysis or multi-product correlation, you’ll need a data warehouse solution.
A final note: I’ve seen time and time again that marketing teams can get a better seat at the table once they deliver truly advanced insights. If your ultimate aim is to become a data-savvy marketing org that uses AI for buyer journey summaries or does deep funnel velocity tracking, you’ll probably want the freedom of a data warehouse approach at some point.
See you next time on Funnel Lab Fridays.
