How SaaS Companies Can Get More Out of Salesforce (and the Mistakes I See Too Often

"How do we get more value out of Salesforce?"

Simply put, for SaaS and technology companies it’s about simplifying processes, connecting systems that should’ve been connected from the start, and making data actually usable-especially if you're thinking about using AI.

I’ve been asked the value question of high-growth SaaS companies more times than I can count, and the answer usually isn’t about buying more tools or building fancier dashboards. Yet I see these be the go-to solutions they try first and it’s a waste.

Let me give you a quick story:

A few years ago, I was working with a SaaS company whose reps were manually building every renewal. No automation, no entitlement logic … just pulling up the last quote and trying to recreate it line by line.

The kicker?

This company was letting customers access products well beyond what they had actually paid for. Once we integrated their quoting system with their content management platform, we discovered $9 million in unpaid product access!

Once they shut it off, customers started reaching out saying “Hey, what happened to my access?” which led to conversations around what they were paying for. This created $5 million of actual revenue when customers came back to renegotiate!

That was a turning point for the entire team: it revealed how easy it is for revenue to leak when your Salesforce instance isn't wired to reflect what customers own, what they bought, and what they’re actually being billed for.

Those are three separate systems in many companies, and if they’re not aligned, you’re going to leave money on the table just as this client did!

SaaS Company Salesforce Mistakes I See Too Often

Do any of these sound familiar? If so, we should talk.

Overengineering Salesforce

The mistake I see too often is that teams overengineer Salesforce to handle edge cases and one-off scenarios, especially around CPQ or renewals.

You end up with quote templates that are technically correct but practically unusable.

Reps avoid them, build quotes manually, and inevitably things get missed-like an upsell from last year that disappears from the new contract. It only takes one or two small misses per renewal to lose millions over time.

Insight Lag

Back in my operator days, I used to spend the last two weeks of the quarter pulling together spreadsheets to explain what happened: churn reasons, expansion trends, vertical insights.

I’d get told, “This is great! I just wish I’d known this during the quarter.” 

That stuck with me.

What good are insights if you can’t act upon them with business-level impact?

The problem is this is commonplace. Technology companies have the top-level numbers, but typically not more granular details that lead to “ah ha!” moments. 

Such as “Are our healthcare accounts expanding or contracting this month?” or “Are we seeing a drop-off when a customer fails to reach a particular usage threshold?” 

This is the kind of insight you want when it’s most relevant, not six weeks after the quarter closes.

Their quote-to-cash process

I know the Salesforce CPQ transition has created a lot of uncertainty, and some companies are holding back because Revenue Cloud Advanced isn’t quite at feature parity yet. And that’s fair, even if I believe RCA is worth looking into. 

Long term, I do believe moving to native Salesforce functionality - if done right - will give teams more control, better reporting, and tighter integration.

It’s just going to take some patience to get there, but in the interim there are a LOT of out-of-the-box options tech companies tend to overlook. So you want to start there, specifically around Flows, automations, Einstein insights, and leveraging more standard Salesforce objects that will play nice with future product updates.

The AI Opportunity in SaaS

Everyone wants to talk about AI. Well, unless you’ve binged your LinkedIn News Feed, then maybe you want to avoid it for a bit (and apologies if you're reading this after an AI post, blame our marketing team).

What I tell clients is that AI isn’t just about writing better emails or chatbot support. For SaaS, the big unlock is predictive intelligence: spotting churn risks, surfacing upsell opportunities, and streamlining internal approvals.

Imagine a rep trying to discount a product by 10% and the system tells them: “That’s well within normal range for this customer type - no approval’s needed.”

Or, knowing that customers in a specific vertical usually buy a different add-on product six months into their usually contract. Now, you can sell that upfront and close bigger deals!

That’s how AI becomes useful: when it’s tightly coupled to your actual data and processes. (Side note: you can test your Agentforce readiness in 4 mins here)

But here’s the thing: none of it works without clean, consistent, and well-structured data.

I can’t emphasize this enough: data quality is everything. 

You’d be surprised how often we see situations where “Acme” in Salesforce is listed as “Acme LLC” in someone’s Excel file. Or where one rep enters a title as “VP,” another says “Vice President,” and another uses “V.P.” Multiply that by thousands of records across multiple systems, and your AI engine is going to be confused before it even starts.

That’s where tools like Salesforce Data Cloud or a centralized data lake come into play. If you’re serious about AI or even just better reporting, you have to be able to map records across platforms, resolve duplicates, and standardize your data model. 

That means making sure Product A in Salesforce is also Product A in your ERP and support system and that your systems know it’s the same product.

Bonus idea: have you explored everything Salesforce Einstein has to offer? It's a mature product with a ton of excellent applications.

Where SaaS companies should start making progress:

If you're trying to scale or tighten up your sales and renewal motions, my advice is this:

1. Focus on aligning what the customer bought, owns, and is billed for.

2. Automate renewal and quoting processes, especially to prevent accidental revenue loss.

3. Get your SaaS KPIs out of spreadsheets and into dashboards that update in real time.

4. Don’t wait on AI-just make sure your data is clean enough to support it when you’re ready.

5. Standardize data across all your systems before layering in automation or intelligence.

Companies getting the most out of Salesforce aren’t who has the most customizations.

They’re the ones who understand what their data is telling them and have systems that let reps move fast without making costly mistakes.

If that’s not happening in your org yet, it’s likely not a tool issue. I’d argue it’s a signal you need to simplify how you do business and then take advantage of the right parts of Salesforce to support it.

Reach out with any questions!