CEO & Principal Business Consultant, cpq.se
15 articles
CEO of cpq.se — the Uppsala and Stockholm-based consultancy where configure, price, quote for complex products is all we do. I work with European manufacturers on CPQ strategy and business cases: deciding what to invest in, proving it with data, and scaling what works.
Patrik Skjelfoss, CEO & Principal Business Consultant at cpq.se. We are a specialist consultancy — configure, price, quote for complex products is all we do — founded 2019, based in Uppsala and Stockholm, working with European manufacturers.
CPQ strategy and business cases: when to invest, what to expect, and how to prove it. I run CPQ Analysis Workshops that turn 'we should probably do something about quoting' into a decision with a metric, a bet, and a test. Beyond strategy: using the data CPQ generates — quotes, win rates, cycle times, discount patterns — as a management instrument, including applied machine learning on quote data to predict deal outcomes.
CEOs, CFOs, sales directors, and owners of mid-sized and large manufacturers. People who must fund the initiative, defend it to a board, and answer for the result. My posts are written to be forwarded upward — from a champion to the person who signs.
Most CPQ and digitalization initiatives don't fail technically; they fail at the business-case level, because nobody defined what 'better' means before the project started. And most companies sit on a goldmine they never open: their own quote data, which knows how the quarter will end before the CRM does. Data should reduce debate, not create more of it.
Your CPQ doesn't create data conflicts—it reveals them. Before you blame the tool, agree on the one question your business is trying to answer.
Your CPQ feels slow, but the tool is rarely the root cause. The friction you feel is a decision problem, not a software problem.
Your CPQ knows which features cause abandonment. The question is whether your BI is set up to turn that signal into a product or pricing decision.
If an insight doesn't change a CPQ decision, it's just decoration. Learn how to turn the firehose of AI conversation data into an actionable strategy.
Your AI sales rep creates a stream of unstructured signals that expose why buyers choose, stall, or push back. Adoption isn't the advantage anymore. Analysis is.
The true promise of CPQ isn't nicer PDFs; it's more revenue from the same team. Here's how to use your quote history to route effort and prove the ROI in one week.
With high sales turnover, competence rarely compounds. The answer isn't another training deck; it’s codifying what works into the tool everyone already uses.
A simple rule governs slow quotes: you lose about 0.5% win probability per day of delay. Time isn't just process, it is a cost center.
You can win any deal if you take the call yourself. That is the problem. Your system learns nothing, and your expertise becomes a growth bottleneck.
Your visualizer might not be broken, just ineffective. When it fails to answer the buyer's core question—technical fit or aesthetic finish?—it creates friction and erodes trust.
Stop treating CPQ like a calculator. Its data can predict which deals will win—before your team invests hours chasing the ones they'll lose.
You don’t need a two-year plan to prove CPQ. You need a quarter. Ship one product line in 500 hours, prove the value, and earn the credibility to fund the rest.
The best CPQ has learned to disappear. It stops forcing internal complexity on buyers and instead lets them experience possibility while the system handles reality.
When every source system is right but the quote is still wrong, the problem isn't your data. It’s that the selling truth lives in the gaps between them.
ThoughtOS