"How do we get more value out of our platform?" You could be thinking from Salesforce to HubSpot … or Dealhub, Close, Attio, insert your platform here.
I've been asked some version of that question by mid-market SaaS companies more times than I can count and the instinct continues to be to buy another tool, build a fancier dashboard, and reconfigure the CRM again. Sure, that’s the visible layer … but really, it’s just a data symptom of something greater that’s holding back your business!
Data patterns underneath the pain
One client we worked with was a SaaS company with reps in a bind every time a customer wanted to upgrade or modify their agreement mid-term; they have to manually re-create the last quote, rebuild everything, and then get updated pricing (and discounting) approved internally.
Painful? Yes. Slow? Absolutely. And inviting errors? Ohhhh yeah.
So, we dug into the data architecture between their CRM, billing, and usage tracking … sure enough, data wasn’t flowing correctly between them. Customers would stop paying for access to a module, only to retain access when billing and product weren’t synced … for one company, there was $9 million of unpaid access! Discovering this and shutting off access led to $5 million in revenue that came in.
Data friction doesn’t always have that dramatic of a cost, but it always does cost. Three systems weren't wired to reflect what customers actually pay, use, and are billed for … I’d argue that's a data architecture problem disguised as your CRM.
Where SaaS data problems show up most
Overengineered CRMs. I see this especially in the quote-to-cash arena, specifically with sales quote systems built for every edge case but without the rep’s day-to-day in mind. If it takes 53 mouse clicks to build a quote, your rep’s struggling with a sea of admin work (not selling).
Insight lag. At a previous stop, I’d spend the last two weeks of every quarter building spreadsheets to explain churn reasons and flag potential expansion opportunities. And my VP said “this is great … I wish I'd known this three weeks ago."
That’s because you need the right data to act upon; for instance, if a particular vertical (healthcare, in this case) was showing expansion or contraction signs, sales and customer support needed to know that ASAP. After all, what good’s data if you can’t act upon it?
Underused platforms. Got the bare minimum on your initial implementation, and never rectified it? Paying for licenses you aren’t using? Functionality that made sense three years ago? Or just
Happens all the time - as one example, Zylo found roughly half of licenses aren’t used, costing tens of millions annually. If you’re already paying for it, why aren’t you getting the most business value?
If your answer’s “because it’s a mess” then once again, that’s a data-layer problem … not the platform itself.
Why AI makes this urgent
Every SaaS leader wants predictive intelligence right now: churn risk scoring, automated discounts, upsell surfacing, smarter approval routing, and agentic quoting where possible. Imagine a rep discounting 10% and the system telling them, correctly, whether that's within normal range for this customer type.
None of that works without clean, consistent, well-structured data underneath it. This is why we hold data to as the Four Rs … what’s relevant, reliable, revealing, and reusable? If a piece of data doesn't clear that bar, it's not ready to feed your AI agents (unless you want to amplify chaos).
It’s the simple stuff, such as finding "Acme" in your CRM and "Acme LLC" in your ERP … or a title logged as "VP" by one rep, "Vice President" by another, and "V.P." by a third system.
Multiply that across thousands of records and a handful of systems, and any AI layered on top is confused before it starts.
Where SaaS companies should actually start with their data
In a word? Alignment.
- Align what the customer bought, owns, and is actually billed for across every system that touches that answer.
- Automate renewal and quoting workflows once that alignment exists, not before, so you're not automating a mismatch.
- Ask the simple question: what are you trying to answer with your data?
- Standardize your data model across systems before layering on automation or AI and make sure the data architecture
- Don't wait on AI. Just make sure your data would survive contact with it.
Strong data architecture if your competitive advantage in a hyper-competitive market; start with our 844 Data Snapshot to quickly see how your Customer domain of data stands up. Or, get our data & AI consulting services for SaaS companies.
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