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CASE STUDY - 6 MIN READ

Rectification Regarding the Technische Unie Reference Case

Published on June 18, 2025

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Rectification Regarding the Technische Unie Reference Case

Published on June 18, 2025

At SYMSON, we place great importance on transparency and accuracy in how we communicate about our solutions and customer collaborations. Recently, we published a reference case on our website describing our work with Technische Unie. In that case, we incorrectly stated or implied that Technische Unie uses SYMSON’s AI pricing models.

We would like to take this opportunity to formally rectify this information.

What Was Incorrect?

In the original publication, it was suggested that Technische Unie also uses our AI-powered Algorithms or AI Powered Pricing strategies.  

This is not the case.

What Is the Actual Use Case?

Technische Unie does not use any of SYMSON’s AI-based pricing functionalities.

Instead, their engagement with SYMSON consists of the use of our:

  • Competitor price monitoring tools
  • Webscraping services for collecting competitive market price data

These solutions allow Technische Unie to keep track of competitor prices, but do not involve any form of AI or dynamic pricing algorithms.

Our Apologies

We sincerely regret that the original reference case may have given the impression that Technische Unie utilizes SYMSON’s AI models. This was incorrect, and we take full responsibility for the misrepresentation.

We have also communicated this directly to Technische Unie. Below is the content of our email:

Originele e-mail aan Technische Unie 🇳🇱

Beste Technische Unie,

Tot onze spijt plaatsten wij op deze website een feitelijk onjuist artikel waarin werd gesteld dat Technische Unie B.V. gebruik zou maken van onze AI-Powered Pricing Partner tool. Dit bericht was inhoudelijk onjuist en werd zonder toestemming van Technische Unie B.V. gepubliceerd. Het artikel is inmiddels van onze website verwijderd. Wij betreuren deze gang van zaken en bieden hiervoor onze excuses aan.

English Translation 🇬🇧

Recently, a reference case was published on our website stating that Technische Unie uses SYMSON’s AI models. We would like to inform you that this information was unfortunately incorrect. Technische Unie does not use our AI models. In reality, your company only makes use of our price monitoring and price scraping functionalities. We deeply regret this mistake and would like to offer our sincere apologies for spreading incorrect information. Transparency and care are of utmost importance to us, and we take this very seriously. In addition to this email, we will soon publish a rectification on our website to publicly correct this information. Should you have any questions or comments in response to this message, we are of course happy to discuss them.

Our Commitment

At SYMSON, we are committed to honest and accurate communication about the scope of our services and the use cases of our clients. We thank Technische Unie for their understanding and will continue to hold ourselves to the highest standards of clarity and transparency.

For any questions regarding this rectification, please reach out to us at dennis@symson.com.

Do you want a free demo to try how SYMSON can help your business with margin improvement or pricing management? Do you want to learn more? Schedule a call with a consultant and book a 20 minute brainstorm session!

HAVE A QUESTION?

Frequently Asked
Questions

1. What is a price sensitivity experiment?

A price sensitivity experiment is a structured method to test how customers respond to changes in pricing. By adjusting prices for a selected group of products or customers (test group) and comparing the results to those who experience no changes (control group), businesses can measure the true impact of pricing on sales and margins.

2. When should I run a price sensitivity experiment?

You should run a price sensitivity experiment after completing a price sensitivity assessment. This initial assessment helps identify which products or customer segments are likely to react to price changes. The experiment is the next step to validate those insights with real-world data.

3. How do I split my test and control groups effectively?

Split your data in a way that creates a fair comparison. This could be by product, sales channel, or customer segment. The key is to ensure that both groups are similar in all relevant aspects except for pricing. This helps isolate the effect of the price change and avoid misleading results.

4. How long should a price sensitivity experiment run?

It depends on your business model:

  • For B2C companies, results can often be seen in about a week.
  • For B2B companies, the buying cycle is longer, so it might take a quarter or even six months to gather meaningful results.

Choose a timeline that fits your sales cycle and allows for enough data to evaluate impact properly

5. What should I measure in a price sensitivity experiment?

Start with a clear and measurable objective, such as increasing product margin or boosting conversion rate. Then compare performance across the test and control groups to evaluate success. Metrics might include sales volume, revenue, margin, or customer churn—depending on your goal.

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