Everything Else

Why SaaS Prices Are Increasing, and Will Continue To

We’ve all felt the squeeze these last 3 years. Higher prices at home, higher prices at work, smaller budgets. Software subscriptions haven’t been exempt from this. In fact, they’ve outpaced almost everything else you’ve been feeling the squeeze on.

This SaaS inflation has been around longer than these last 3 years though. It’s been accelerating for the past 6 years, even when times were better economically.

And this isn’t going to change. In fact, it’ll probably keep accelerating. I’ll explain why.

Success! You're on the list.

The Vertice Report

After looking for more data behind my anecdotal experiences around software negotiations the last few years, I stumbled across Vertice‘ SaaS Inflation Index: 2022 report.

Two key snippets from the report that we’ll dive into:

  1. Software is a significant expense on the P&L
No alt text provided for this image
Vertice’ SaaS Inflation Index: 2022 report

2. Software pricing inflation is outpacing market inflation significantly

No alt text provided for this image
Vertice’ SaaS Inflation Index: 2022 report

Why These Two Points Matter

1 in 8 dollars being spent on SaaS means that this line item should be at the forefront of executive focus. Software is eating more and more budget every year. According to SVB’s State of SaaS 2022, around 37% of companies expect a 6% increase in IT budget. But that also includes IT Services, not just software costs.

Companies will have to eliminate software or renegotiate contracts to align with those budgets.

For the second point, SaaS prices are inversely correlated to 1 year after Global VC stagnations or decreases in funding. Basically, SaaS companies start playing debt catch-up when funding opportunities start becoming scarce. Valuations have been set high but the market has become more competitive.

This means paying attention to fundamental business economics since that next funding day might not come for a while, or will be significantly reduced. To satisfy current investor interests and continue growth in valuation, fundamental business economics must outshine other SaaS companies competing for less available funding.

When this happens there are several levers that can be pulled. Oftentimes multiple are pulled at the same time.

  • Increasing prices
  • Reducing headcount
  • Reducing marketing spend
  • Eliminating discounts

Macroeconomic conditions around rising interest rates, shaky banking situations, and rapidly overcrowded marketing channels due to AI capabilities are suggesting more

One Justification For Higher SaaS Prices

There is a counterpoint that doesn’t make the future look so bleak. After all, not all SaaS companies are just raising prices because of high expectations.

Many SaaS platforms started as a niche solution. Then as they tapped that market, they expanded capabilities to continue growth. This is a great reason to increase prices. The customers save money on a simpler tech stack with less tools to maintain, and the SaaS platform solves more business problems with their solution and can charge more. Win-win.

HubSpot is a great example of this. They started as a Marketing platform, then added their CRM, Service Hub, CMS Hub, Operations Hub, and Sales Hub along the way. Their pricing evolved and increased with these evolutions. They made more money to grow into their expectations, and customers saved money by eliminating other subscriptions and the staff to maintain the tech stack.

The Future SaaS Inflation and What To Do

My hypothesis is that this won’t change in the future. SaaS will continue to become more expensive. And the risk for investing in the wrong SaaS company is rapidly increasing.

If you pick the wrong one, you could be locked in and subsequently squeezed for huge price increases year after year. You also will have significantly less ROI on tools if they increase price without providing additional value to your organization.

AND the more complex the SaaS is to set up and maintain, you’ll also suffer from wasted labor spend to get those tools set up and running. Only for the investment to flop.

SaaS inflation will continue because we’ve seen a stagnation in VC funding. Valuations were super high leading up to 2022 and companies are trying to catch up to those valuations to raise again and deliver for their current investors.

Plus, Marketing and Sales is becoming more expensive for these companies as channels become even more difficult to cut through noise at scale.

Expect pricing to outpace general inflation the next several years.

What To Do When Choosing SaaS

Software is an investment. This is not a metaphor, it needs to be treated like an actual investment where you do due diligence behind the company you’re investing in. Because your business operations literally depend on the software to run. Otherwise, why are you investing in the software in the first place?

This means that when you’re looking at software vendors, you should get as much info as possible on their financials. Obviously for private companies this is more difficult, but it doesn’t hurt to ask the people you’re talking with in the company.

Figure out what their revenue looks like, their operating expenses, revenue per employee, NRR (net revenue retention), average customer LTV, etc.

If they’ve been valued at 20 times their ARR, you know they have to grow into that somehow. They need to increase their number of customers with the same pricing, or increase their pricing with slowing/stagnating customer acquisition.

If they are focused on increasing customers, you might get less support when needed. If they’re going to increase pricing due to slower customer acquisition, make sure you get value in those increases. Ask for better SLA’s or additional functionality after those price increases.

Another major risk to consider is companies going bankrupt/shutting down because of poor business economics. What if they can’t raise again? What if they cut headcount in the wrong places and the platform stops working? Does that company have enough cash reserves to weather the storm?

What To Do During/Between Renewals

If you’ve already passed the evaluation stage and are in a contract/up for renewal, there are still opportunities.

Start with a Tech Stack Assessment/Tech Stack Audit. List every single tool, what you spend, how many users, and their feature sets. Then look at which feature sets overlap, which tools aren’t actually solving a specific business problem, which ones don’t contribute to your company strategy, etc.

Then cut redundant ones. Explore ways you can consolidate your tech stack to make it easier for employees. Talk to vendors that you’re renewing with and see if they have additional modules/features you can upgrade to so you can replace other tools.


Software is getting more expensive. Low interest rates flooded the market. This resulted in unsustainable pricing and now pricing has to catch up with tightening economic conditions.

Avoid the hurt of investing your operations in SaaS products with poorly run businesses behind them. Do your due diligence.

If this article made you think about your Tech Stack and the subscriptions you have, check out the Tech Stack Audit at

We see and work with hundreds of different SaaS tools every month on behalf of clients. Skip the confusion of trying to evaluate software subscriptions, overlap, and usage in your organization. Reach out to our team instead.

Everything Else

Warning: The bottom 50% of BI devs will be on the chopping block

Like others, I’ve been experimenting with OpenAI technology and all the associated tools that have popped up.

The rate of adoption and integration with existing platforms has been astounding. It’s both awe-inspiring and terrifying.

Mostly, the terrifying part is related to the uncertainty it poses for many economically. OpenAI tech is fundamentally changing how humans work, think, and interact.

It’s making our already crowded social and information channels more crowded. We’re probably spending more time reading AI-generated content with AI-generated images than we’d like to acknowledge. But it’s also vastly increasing the speed to market for internal and external products.

For example, a few months after ChatGPT exploded, HubSpot and Salesforce both announced companion “ChatGPTs” of their own. After testing, it generates quick answers to questions about my specific CRM data that I would have had to hire someone to build or take a couple hours to build myself.

Is the technology perfect? No, and neither are the human workers that it’s been slowly replacing. Does it make mistakes and lie? Yup. But at a fraction of the cost compared to an equivalent behaving human.

Will it replace experts in specific niches, make their caliber of work easy to produce with some short AI training and some refined prompts? Not in the short term (most likely).

What this means for the beginner tier of white collar workers

This is where things get dicey. Do companies cut workers that were doing admin/low-level type of development work? With one person paired with ChatGPT and other similar tools, they’ll be able to be as productive as 3 or 4 of the beginner tier of white collar workers.

Will their output be great for the long-term economics and stability of the business? Probably not, but the majority of companies will take the short-term gain as long as things can still get done to make more money in the short-term.

But as we saw with off-shoring, sometimes the juicy margins are just too juicy to care about the degradation in quality that can occur.

The bottom 50% of BI devs

This sucks to say, because we all were at the bottom 50% when we started learning. But the future doesn’t look bright for those who build BI solutions to simple questions.

With HubSpot and Salesforce, it’s already possible now to ask it a question and have it build simple reports for you. This capability will only creep further into cloud platforms that house your data. I wouldn’t be surprised if in a couple years, you’ll be able to load all your data into Snowflake or AWS, and ask it to generate insights for you.

So what’s going to happen when you need some charts for straightforward KPIs and metrics inside of Tableau? Or PowerBI? Or Excel? Or any other tool? Are you going to go out and hire a $50-$80k/year employee, a $100/hour+ contractor, or ask your AI companion that can spit out an answer that an executive gut checks and then moves on?

More advanced BI solutions will still be valuable. Ones that provide answers driven more by complex architecture and knowing where to look for previously unexplored connections in data. But even that might not be far off (look at the Data Guide in Tableau for example).

Final Thoughts

This topic impacts my own business ( and the consulting areas we focus on going forward. It certainly is going to make our demonstrated expertise beyond the basic topics more and more important

Overall, ChatGPT and other OpenAI tech brings up a lot of questions for the near future of humanity.

Will future generations be handicapped or elevated because they no longer have to struggle through the learning process of the basics?

Economically, how will we restructure to compensate for an explosion in data and less need for humans to manually generate all of that?

What will happen to perceived low value employees? Will they be reassigned to other work or cut entirely?

p.s. Was this article written by me or by ChatGPT? How can you tell? Does it impact how you feel about reading it if I told you it was written by ChatGPT? What if I told you it was written by me?

This is where our future is heading. Real and virtual are getting blurrier. The question is how humans will adapt to the furthering distrust of anything virtual. After all, how can you know it’s real and from a human, for a human? beep boop boop beep

Dashboards Tableau

Bad bookmark behavior – an inside look at a personal habit

Click here to jump straight to the dashboard

Some people are normal when it comes to saving things they care about. Some people have problems physically hoarding objects that link them to past memories. But me… I have problems with digital hoarding. It’s probably related to some combination of FOMO and consumerism that drives me to hitting that save button. Because… you know… I’ll eventually get back around to it.

So naturally, instead of actually revisiting some of my past bookmarks, I built a dashboard to analyze my bad habit.

I use a bookmark tool called, which has an API I can easily pulled structured data from. Using this API, I extracted the bookmark data to a spreadsheet with a low-code automation tool called Integromat. It took about 2 minutes to do and I can run the automation again in the future or schedule it to run if I want to update my data.

Integromat is something I use frequently in my consulting business ( to automate business processes. It’s a great tool you should check out if you have automation needs but don’t want to code custom solutions.

So what did I build?

This was a fun and simple visualization. I just wanted to look at how frequently I was bookmarking things over the past year or so with So I did breakdowns by month, day, weekday, and hour.

Aliens and UFOs seem to be a hot topic ever since the Pentagon declassified some Navy videos in 2020. So why not make a viz with that theme?

The Dashboard

Thanks for reading. Any questions or comments? Feel free to reach out to me via email on my contact page.

Dashboards Tableau

Where Did PPP Loans Go? Taking a Deeper Dive.

Recently, I published a dashboard that took a broad look at PPP loan data. Over the past few weeks, I looked deeper into that data set in order to pull more interesting findings from it.

Before you take a look, here are the most significant highlights in my opinion:

  • Spelling mistakes were prominent. Out of some of the 9 largest cities in the USA, there were more than 100 spelling variations.
  • About 4000 investment-related firms took out loans totaling between 1 and 3 billion dollars. This is interesting because investment fees and activities were at record highs during lockdowns.
  • The top 10 industries by loan count were heavily in-person industries, indicating that loans were distributed largely to businesses who needed them most.

While the dashboard has been developed for mobile as well, the viewing experience will probably be better on desktop.

Like this post? Enter your email below to subscribe for more visualizations and Tableau related posts.

Dashboards Tableau

Looking at PPP Loan Data – A Visualization

$667 billion dollars were set aside in 2020 for businesses across the US. This program was called the Paycheck Protection Program (PPP for short). It was meant to be a stimulus package to hold over businesses who were impacted by COVID-19 lockdowns. Without revenue streams, many businesses had to lay-off workers en masse, providing a potentially catastrophic destabilization of American society. According to the Bureau of Labor Statistics, there were close to 18 million unemployed people in June of 2018. The PPP data indicates that over 31 million jobs were retained due to the program.

Without getting into the inaccuracies of the standard unemployment numbers provided by the Bureau, we can still see that laying off potentially 10% of a country’s population wouldn’t be healthy for society. In the past, the US government has provided stimulus and bailouts primarily via public spending increases and buying toxic assets from big corporations. But this stimulus was meant to directly support businesses of all sizes who were directly impacted by the lockdown. Did it server its purpose? You can be the judge of that! I’ve created a data visualization meant to help show where the money went across the nation. This visualization includes the ability to search and filter on columns of the data. In the near future, I’ll be releasing a dashboard that digs into the… more suspicious loans taken out.

Here’s the dashboard, it’s interactive and has some additional info when you hover over data. It is configured for desktop and phones, but is better to view on desktop.:

This data was recently released regarding PPP loans. It was split into different data sets. For loans greater than $150k, data for the whole nation is provided in one data set. For loans less than $150k, data is split by state or territory into its own data set. I’ll be providing a combined data set of all loans here in a little bit, but for now this visualization is for loans greater than $150k.

Some interesting takeaways from this visualization:

  • Allegedly nearly 10% of the US population’s jobs were retained due to the program
  • There were TONS of misspellings and bad data entry
  • The majority of loans were in the $150k-$300k range

P.S. Check out the next visualization here, where I take a deeper look at the PPP data.

Like this post? Subscribe to be notified of future dashboards and Tableau tips.


Data Visualization is Not Even 25% of the Work

What Did You Just Say?

I’m a Data Visualization specialist at this point in my career, and I’ll tell you an unpopular opinion… The visualization part of data projects really isn’t the hardest part of the project, it’s not the most important, and it’s the least time consuming. Even with all of these factors considered, it’s often the most visible and emphasized part of the project (no pun intended) in many businesses. This is wrong.

I’ve estimated that not even 25% of the work on data visualization projects has to do with the visualization. Are there any studies or hard numbers to back this up? Nope. Just an estimation which I’ll go through now.

Estimating a Data Project Timeline

When it comes to a full-scale data project that ends in a visualization, the hard work and complexity happens behind the scenes. Gathering business objectives, setting scope, listing deliverables, data collection, data exploration and availability, data cleaning, data structuring, exploratory analysis, and maybe some additional modeling and data science before we even begin crafting an end visualization. That right there is up to 10 different steps and could be broken down further. If we (wrongly) assumed that each step took the same amount of time, then data visualization only takes 9.1% of the time (1 out of 11 steps).

In reality, the longest and most difficult portion of the project happens in the “unsexy” part. That would be data collection through data structuring. These steps are the bulk of the work. Unless the data set you’re working with is simple or there has already been significant effort in building clean and structured data sources, you’ll spend significant amounts of time exploring and verifying data. The reality is usually about 30-70% of the project timeline is spent in these phases. The other reality is that you’ll go through these phases of the projects several times, since the first few presentations of the data visualization will bring up many more questions on data quality and how the project ended up with the values presented in the data visualization.

What is the amount of time then for the data visualization part? If you have your business questions laid out during planning and you are aware of the analyses that need be done, there are only so many paths to take. There are a limited number of visualizations you can choose. The meat of your data story is already outlined with the business requirements. Sure, you can spend more time digging for additional data stories to tell, or on the UX/UI components to make it look better. But additional analyses are cherries on top of the outlined deliverables. And once you meet a certain design threshold, a slightly better look won’t fundamentally change how the visualization is received.

Why Isn’t The Data Visualization the Most Important Part?

Ok, maybe I was a little harsh. The data visualization portion of a data project is important. Striking out on the data visualization can make a project bust. But an excellent data visualization can’t make up for a poorly executed planning and data collection phase. It can’t make up for bad data science or inaccurate data sets. That’s why it isn’t the most important part. What a good data visualization can do is: surface bad data, surface inaccurate data science methodologies, answer business questions that should have been part of the planning phase, and much more. Yes, data visualization is important in data projects. Maybe 2nd most important, but it’s not the be-all and end-all.

But That’s My Job…

Building impactful data visualizations can provide great value to your organization and data projects. So being efficient and good at what you do can certainly provide great value to your company, job security, and a fruitful career. That being said, if you want to make your job more resilient to economic forces, you need to keep some things in mind.

If you’re a Data Viz specialist like me, you need to constantly work at delivering additional value outside of simply your visualization. The one exception to this would be a consultant hired specifically for data visualizations while everything else is perfectly prepared (ha!). Even as a consultant you’re arguably always better off delivering more value than anticipated for your customer.

But I digress. Deliver extra value. Somehow, some way. Whether it’s through your data visualization or through other parts of the project you’re working on. Having a diverse skillset that provides a significant multiplier of your cost to a company will make you a prized member of your company.

For some this is easy. Maybe it’s because you’re actually a BI developer, which encompasses many more responsibilities. Maybe you’re simply the data person in a smaller organization and therefore handle more of the data pipeline. If you’re not explicitly in a situation that pushes you outside of data visualization, just beware the fragility of your position and the need to diversify your value contribution in order to make your position more resilient. Just because you’re job box has a label doesn’t mean you can’t break out of that box or just pop the top open and relabel it yourself.

Give Me A Parting Analogy

Constructing a building is a great parallel to a data project and its steps. The planning, permits, surveying, laying of the foundation, erecting of the frame, installing the mechanicals, and putting up the drywall and insulation take the bulk of the time. Especially before the frame goes up, how many construction projects have you looked at and said ‘they never make any progress on this, they’ll never finish!’ This is only before you return a couple weeks or month later and everything is finished! It’s not that nothing was happening before that last stretch of time, it’s just that the progress wasn’t visible to the untrained eye.

This is equivalent to the business planning through data structuring part of a data project, especially from an end-user’s perspective. Nothing happens… nothing happens… then boom! The data visualization presents itself with all the work wrapped up into the visible end-product. Like the finishing steps of a construction project, the data visualization is what everyone will see and pay attention to. As long as there aren’t horribly obvious mistakes or incredibly artistic/unique details, most people won’t be too moved. The end-product will simply serve its purpose.

On the flip-side, if the foundation and frame of the construction was poorly or improperly done, the looks and/or functionality of the interior and exterior finishes won’t matter. In fact, the construction will eventually become unusable due to its improper foundations. Data visualizations are no different. Without high quality data, structure, and planning, the whole thing falls apart. Then your visualization answers the wrong business questions or answers the right questions incorrectly.

So remember, get the foundation right and spend the most time on it. It’s the most important part. Then focus on the visuals, because that’s what the people will appreciate.

I’m always looking for feedback, tell me what you think of this post! – Dan


Success! You're on the list.