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In 2014, I was hot off the back of my own startup. On a drive down to Charlotte to interview for a position leading another software company, I reached out to SPP. We had a successful engagement with them in 2008 when we migrated from on-prem to the cloud when we needed to restructure our packaging and pricing.
At my software company prior to this, we dealt with pricing and costing for made-to-order interior and exterior products. Since profit was being calculated and factored into our customers’ pricing to their customers (consumers), I was always terrified of a mistake. The pricing and costing engine we built was heavily tested, more so than usual, given my paranoia level.
When I first arrived, we had a lot of spreadsheets. I mean A LOT of spreadsheets. Spreadsheets dating back to when Excel was first launched in the mid 80’s.
My Oh SH!T Moment
Early on, we had an error in rounding, which cost a customer a few thousand dollars; had it not been caught quickly, it would have cost them millions.
“To say I was terrified of it happening again
would be an understatement.”
Of course we had our templates with some checks and balances, but I knew it was just a matter of time before a mistake would be made. And if it was made, I wasn’t confident we’d fully be able to trace it back through the nest of legacy spreadsheets and systems.
Right at that moment, it hit me.
How many other software leaders feel this same level of panic?
It’s that nagging feeling when you know you’re skating on thin ice.
You’ve got a nasty, complex challenge in front of you but you keep dodging it,
until you can’t.
First Swings at Defining a Really Hard Problem
The concept for Levelsetter was born. It was 2014 and Levelsetter started with simply querying spreadsheets in AWS Athena. My first mission as CEO was to eliminate our use of Excel entirely. Little did I realize how difficult that would be.
We started with trying to get our customers to adhere to a data format, largely influenced from historical engagements. But things changed over the decades, new technologies, new pricing strategies, and our customers’ systems became more complicated.
We were trying to get to a one-size-fits all data format our clients could adhere to, simple right? Not quite. Each time we met a new client, oddities in their data and unique product pricing would force us to scramble.
“We almost concluded it was just too variable,
too hard,
not doable.”
But we knew that backing off from their transaction data would be a mistake because we wanted that accuracy—we knew their data held the key to our insight generation and future pattern matching to deliver the best results.
It took us a long time. I mean a long time to evolve a data format that worked.
How do we isolate the numerous discount types?
Free months or tiers, bundles with hidden discounts, module caps, free tiers, global pricing … If a deal in APAC gets a 99% discount, is that really a discount or just a rep trying to adjust for currency disparities?
Discounts literally go out of their way to disguise themselves everywhere in software companies.
How do we wrangle dirty data?
Acquisitions, merged data, munged list prices, complicated price books, outliers, data refresh rates … all had to be considered and dealt with in our modeling efforts.
And how the hell do we take decades of our business
logic and know-how and move it to the cloud?
This was the vision, at least for starters, and it would take us until 2021 to achieve it.
It would require back end workflows, custom built tools and a deep understanding of our clients revenue models, downstream systems and the SKU structures they spawn inside of their own quoting systems.
I don’t know how many design sessions I participated in during this time, but I was waking up having dreamt about some aspect of one problem or another.
It was frustrating.
Maddening.
Small wins.
Then bigger losses when the idea didn’t pan out. We’d take our client’s data in at the beginning of the project under the excitement of our newest data format only to conclude we had to customize it later. It was a very painful time, mentally and cost overruns on each engagement were mounting—constantly nagging at us to abandon the journey.
A Glimmer of Light, More Technology Needed
As we continued to attack the problem, a perspective began to emerge. One that influenced our data structures and how and why we ask customers for their information. It required visuals, layered datasets, and the ability to simulate outcomes. We had to build more back end tools and began iterating through multiple proofs of concept.
By 2018 we had enough puzzle pieces and decided to build a SaaS Pricing platform. We hired AWS serverless architects, front and back end developers, data specialists, AI gurus, statistic experts and more. Underpinning this effort was the all-in decision that we would, at least in part, become a software company at our core.
We rebuilt what today is Levelsetter, several times during this stage of development. Each time a more unified and settled design emerged.
Our next goal was simple: we wanted Levelsetter to beat us
when finding the best pricing solution, and to analyze data
faster and more accurately than complicated formulas in Excel.
Excel is no way to live, yet we were still living it. Every analysis required a different spreadsheet. Our algorithms didn’t fit nicely into Excel, not surprising, we had already gone further using Python and other components. But since we were committed to looking at our customers’ transaction data, and all the complexity it brought, we were still modeling database structures inside of Excel forcing it to do all sorts of unnatural acts.
By 2015, I had to buy an expensive workstation just to be able to open damn client spreadsheets. We were generating 40 to 50 versions of each engagement spreadsheet as client data was cleansed and updated. Our Excel journey was a lot like that bad relationship we all experienced when we were younger–you don’t realize what a breath of fresh air is until after you get out of it. Even the sun feels brighter on a cloudy day. We had to break up with Excel.
When you create a pricing model, there are more than just millions of potential answers. But when you’re manually slogging Excel variables, you know you cannot explore all the possible answers. Talk to any pricing professional over a few glasses of wine and they’ll eventually admit they just sort of get close enough and then abandon when the time runs out.
One pricing professional told me
“It’s not like a client would know,
I mean it’s better than what they would do
on their own so you just abandon it at some point.”
That answer really grated on me. I mean, how would you know how good your answer was then? What if a better answer was right around the corner, but you abandoned it?
A job isn’t worth doing unless it’s done right, that’s what my parents drilled into me. So we understood that this was an optimization problem—that if we could solve this we could actually find THE best pricing model for any given situation.
Give Me Fuel, Give Me Fire …
Levelsetter began as a system for us. A way to strip out the labor and retire manual model development and analysis in Excel.
Levelsetter ran its first polished optimization routines, the latest iteration along a long line of iterations, designed to take down our own experts, and I was one of the ones competing against Levelsetter.
So far, I had won.
I had created the best model I could for this bake off and remained hopeful. I imagined a world where crafting pricing models manually shifted to humans reviewing models for final tweaks, improvements, and accuracy tied to the business context for each customer. When Levelsetter spit back its answer, I was floored. Thoroughly trounced. Levelsetter didn’t just pick something better, it computed something significantly better.
Next, we applied Levelsetter to historical engagements. It was finding better answers there too—exploring possibilities that a human practitioner would never get to. Identifying patterns we never knew to look for.
Levelsetter beat everyone.
Even on the most complicated engagements former employees with decades of experience delivered. And all along the way we were learning alongside it.
It felt like the scene from the Thinking Game when Deep Mind sees the AI start to learn and they are screaming and high fiving with satisfaction. It’s a feeling of elation, success and the ability to exhale. It gives you the breath to keep going and at the same time takes your breath away.
Levelsetter gave an answer I hadn’t anticipated,
one that it had found that I had not.
It had explored every possible pricing outcome and chosen the best one —
I would never be able to do that.
In that moment I realized what we had really done: we had automated the thorniest part of our process and in doing that, we set the stage to begin extending our tools to our customers.
We were unlocking massive value for our customers. With all those savings in hours we could innovate our services from spreadsheet jockeys to other areas our clients needed help with.
It wasn’t until Excel was permanently retired, that we sat back and realized the true ramifications of what we had created. In understanding how the software supported our pricing work, we realized we understood how the software would enable our clients to do the same.
We had collectively eliminated thousands of hours
of spreadsheet work that was now
being done in milliseconds.
As we extended our offering into our clients’ go live process, we had automation that would make us more efficient and with better, continuously optimized outcomes.
Our value skyrocketed, while the only thing the other competitors skyrocketed were their billable hours and static outcomes.
We were going head to head with Deloitte, McKinsey, SKP and much larger firms than we were—and we were winning.
LevelSetter Evolves, So.. Many.. Slides.
Stepping back, we looked at our engagement delivery process again. Were there other areas where we could bring more efficiency, deliver better outcomes? Presentations? So.. many.. slides. The creation, the perfection, the time… everybody loves a beautifully designed slide but the labor associated with the final results deck was significant.
And here comes the kicker…the second you think it’s perfect, the customer asks you to change it, create a new data cut or include a new slide… more hours, more cost.
These slides helped execs understand where things were headed but didn’t help the operators, we needed to turn our outputs into outcomes that created action day one.
So we decided to knock out slideware next. That meant leaning into my previous software experience and building a quoting engine. But not just any quoting engine, we could find those on the open market, and they were all dumping grounds of custom fields full of bad data.
We wanted a quoting engine that was simple and intuitive for salespeople. A quoting engine that could make them feel comfortable in the one area of the sales process that gives many software reps the shivers: pricing.
Seeing the Sales Cycle More Clearly
Most reps really love solving problems for their customers and creating lasting relationships. Those same reps hate when they’ve brought the deal to the finish line and pricing turns it sideways. This puts them on defense and often without the right pricing knowledge to successfully put the customer at ease. So they concede too much to save the deal and the discount spiral begins.
We believed that we could change that and create better outcomes.
How could we make talking about the economics of the deal more natural?
Could we get reps to take customers through pricing options transparently as if they were doing a demo?
Levelsetter’s quoting capability started as a way for our clients to really feel their new pricing model. We wanted them to see the benefits and implications of their deal structures and typical sales behaviors.
If I structure a deal a certain way,
then this is the outcome for me,
for the customer and
for the company.
We wanted them to see each individual deal as it evolved until close and visualize all deals so any patterns were quickly visible. We then could inform the sales managers who could stop spirals or flag issues before the end of the quarter report.
It wasn’t until later that clients asked to put their hands on the wheel and use it internally. That was the catalyst for our first API library and we expanded further as customers began embedding our capabilities into their platforms.
To make this real, we added sophisticated time series capabilities to Levelsetter, tracking the pricing events that lead up to a win or loss. What were salespeople really showing clients? And how were their customers exploring packaging and the various trade offs throughout the buying process?
Today, you can build entire applications on top of our APIs and seamlessly integrate Levelsetter behind the scenes with any CRM system, to gather analytics about how customers and salespeople interact with packaging and pricing–before the deal is won– across all channels of the business. This fuels downstream packaging and pricing improvements that drive important profitability gains that we develop in ways others cannot.
For customers that face internal system constraints, Levelsetter also expands the capability of their pricebooks, SKU structures and product dependencies, simplifying quoting for sales and enabling them to rollout new packaging and pricing immediately.
Looking Backward, To Keep Moving Forward
When I reflect on our Levelsetter journey, the thing I’ve enjoyed the most is how the questions we ask have changed. “How should we view discounts?” became “How should our clients be viewing, managing and improving their discounts?” “How should we show the impacts of the new pricing?” Became “How should today’s CFO’s review impact and isolate areas that need validated before rollout?”. This forces us to dig more deeply into how exec teams absorb, adjust and operationalize pricing recommendations.
We enable the executive team to get upfront and personal with our recommendations, the business benefits they will produce and to see the truth on the ground.
Each question broadens our product to further enable our clients to do more of this work within their own teams, at their own pace. We’re continually evolving pricing into the critical capability it was meant to be—not a one-off study by an army of consultants billing hours and stuffing change requests to drain the company coffers.
LevelSetter is a force multiplier for SPP. It allows us to strip hundreds of hours out of a traditional time and materials consulting engagement, without sacrificing quality. In fact, I believe the outcomes are better, faster and more accurate. Deployed within an organization, Levelsetter becomes the way in which teams can galvanize around pricing decisions and deal structures that truly make their products successful.
Because a product is only ever an idea until you couple it with its pricing model. Then it becomes a vehicle to enable software companies to grow more profitably.
And profits are a wonderful thing: they stabilize the business, reduce the need for external capital, create more valuable enterprises and more.
This means the traditional model of expensive service engagements and one time pricing studies is at an end. Customers can now treat pricing as a process and develop it as the internal capability it was meant to be.
This creates better profitability and higher trust ecosystems between sellers and buyers which, in turn, allows the business model to crank the flywheel of deals ever faster—to close multiple deals in the time it takes a competitor to close one. To adjust pricing and packaging before the competition even rolls theirs out at the January sales kick off call.
Others will follow—they always have since SPP was founded in 1982. Levelsetter will ultimately pave the way to an improved way of building today’s software products, one where profitability is designed alongside each sprint.
A customer recently called our approach agile pricing.
We call it continuous monetization.
We are tackling more problems now, finding better ways to extract and present the most important areas of a product where customers perceive the true value in current and future releases. Our goal is to help product marketers and product managers to invest in the right areas, improve their positioning, ensure pricing and packaging evolve to reflect the value received and keep them innovating ahead of their competitors.
When pricing is treated as a process that is fundamental to a company’s core growth strategy, founders will retain more control and ownership while also satisfying investors, giving them the freedom to create wonderful outcomes for themselves and their employees.
It’s not just a story, we’ve worked with many software company leaders and together, we’ve made it their new reality.
