If a customer walks out of your store without buying, looking at the security camera footage won’t tell you why. You can analyze the video (Data) all day, but you will never know if they left because the price was too high, or because the music was too loud.
To find out, you have to run out to the parking lot and ask them.
In the corporate world, we rely heavily on ‘Security Camera’ data—web tracking, AI analysis, and big spreadsheets. These tools are excellent at telling us what happened. But they are terrible at telling us why.
Here is why the most profitable companies still invest in human conversations, and how you can use them to fix your pricing strategy.
AI is a Mirror, Not a Window. AI can only tell you what customers have said in the past. It cannot tell you what they would pay for a new, innovative product. That requires a human “Store Owner” intuition.
The store owner talked to people – he or she never missed the value of human conversation.
How to make qualitative interviews worth the effort
Qualitative Interviews done by a good interviewer who makes the interviewees talk in the right direction gives direct access to the thoughts of our clients and target groups. This includes thoughts which do not exist in a written language.
The Cost of Not Knowing
Compare the 300 € interview cost to the cost of launching a product with the wrong price. “Is it expensive to pay 300 € to find out your customers would have paid 20% more? No. That 300 € just made you 20.000 €.
This additional publicity pays for a part of the survey. The other one is additional sales following an improved strategy.
Stupid interviewers are a no-go
For best quality of interviews the interviewer has to dive into the world of the interviewed person. The interviewer has to direct the interview and add questions if necessary. This requires a lot of experience and knowledge for the interviewer. Hiring a random person without knowledge is the first step to disaster. Less experienced interviewers need the first interviews in a project for training, experienced interviewers do not need that. They can relate to previous experiences
References
This article is based on about 20 years experience with qualitative interviews and the usage of some form of artificial intelligence since 1992.
A market research consultant is supposed to count endless statements, run text analysis and much more. This sales “secret” looks like an beautiful island in the data lake: the ones who use the tools they sell have more success.
Sophisticated sales strategies are often about overcoming resistance, getting attention and having efficient sales talks.
Its in doing and selling market research and consulting, or in my side jobs in real estate and agriculture. It is always the same: the products or product category I used myself or literally I ate sold better.
In this article I talk about EYOD in general and its implications, at the end I discuss the implementation in to Software-as-a-Service (SAAS) sales.
Numbers That Support the Eating Your Own Dogfood Strategy
The statistics fall into three categories: Product Quality, Sales Productivity, and Customer Trust.
1. Enhanced Product Quality & Cost Savings (The “Proof” Part)
The primary result of EYOD is a better product, which is a prerequisite for simple sales.
Early Defect Discovery: EYOD helps uncover bugs, usability issues, and performance problems that traditional QA often misses because employees use the product in real-world, unscripted scenarios. This prevents embarrassing sales demos and post-launch failures.
Reduced Cost of Fixes: The cost to fix a bug discovered post-release is exponentially higher (often 10x or more) than fixing it during the development or internal testing phase. EYOD shifts detection left, saving significant time and money.
Support Ticket Reduction: Companies that effectively use internal testing and dogfooding often see a reduction in post-launch support tickets because the major friction points have already been addressed. This frees up support to focus on sales-related questions.
2. Improved Sales Productivity & Confidence
The sales team is a huge beneficiary of EYOD.
Time Spent Selling: Studies show sales reps spend less than 30% of their time actually sellingduring an average week. The rest is often spent on administrative work, data entry, or research.
EYOD Argument: When a sales rep uses the product daily, they spend less time researching answers and more time speaking with authentic, first-hand knowledge. This effectively increases the percentage of time they spend selling because they are more confident and less reliant on external knowledge bases.
The Power of Anecdote: Sales professionals who use their product daily can pitch with genuine, personal use cases and real-time product knowledge. This first-hand credibility is invaluable and beats any pre-written script.
The Credibility Metric: Buyers are now extremely sophisticated and value credibility above all else. Research shows that sales reps who demonstrate a deep, personal understanding of their product—not just its features, but its value in practice—are significantly more likely to close a deal than those who only recite marketing points.
3. Customer Trust and Loyalty (The “Simple Sales” Part)
Simple sales are about trust. EYOD is the ultimate trust signal.
Employee Experience (EX) & Customer Experience (CX) Link:90% of employees say the experience they have as an employee directly impacts the experience they provide customers.
EYOD Argument: If employees find the product difficult, buggy, or uninspiring (i.e., they aren’t “dogfooding” it successfully), they cannot authentically sell or support it, leading to a poor customer experience. EYOD ensures the employee experience is positive, translating to better Customer Experience.
The Profitability Connection: Companies with a highly engaged workforce are 21% more profitable than those with low engagement.
EYOD Argument: Dogfooding fosters product-centered engagement across the company, linking the team to the core product mission. This engagement fuels the superior service and authenticity that drives the 21% profitability increase.
That is a perfect strategic pivot for your consulting message. You are absolutely right—manufacturers have already internalized EYOD as an operational necessity. The biggest opportunity for you is in SaaS, where the challenge is systemic and the “Does the guy use the product?” question is a critical sales filter.
Here is a revised, high-impact section you can add to your article to specifically target the SaaS audience and position your consulting services:
The SaaS Blind Spot: When EYOD Becomes a Strategy
You’ve identified the core problem: while using your own product is an operational given for a farmer or manufacturer, it’s a strategic choice (and failure point) for most SaaS companies.
The SaaS Credibility Gap
For a B2B SaaS buyer, the market is overwhelming, and every vendor promises efficiency. This forces buyers to rely on one simple question to cut through the noise: “Does the team selling this product actually use it in their daily workflow?”
Industry Type
Status of EYOD
Customer’s Filter
Manufacturing
Operational Necessity (Must use their own equipment to produce/operate)
Does the product work?
SaaS/Intangibles
Strategic Option (Often use internal, legacy, or competitor tools)
Does the company believe in their own product?
The credibility gap emerges because a SaaS company’s internal teams often have an “easy escape.” If their product (e.g., a marketing automation tool) is buggy, the internal marketing team can quickly switch to a different tool, use an API bypass, or get a quick developer fix. This immediately destroys the authentic feedback loop that outside, paying customers rely on.
My Consulting Value: Enforcing Authentic Friction
My consulting service is not about telling you to use the product; it’s about scientifically structuring the process to ensure they experience the same friction their customers do.
This involves establishing a rigorous, company-wide “Forced Use” framework that answers complex questions:
Which teams must use which features to fully replicate a customer journey?
How is the feedback quantified and categorized to inform strategic pricing and development sprints?
How is the company prevented from using “backdoor” internal workarounds that mask poor UX?
By focusing on the strategic, systemic implementation of EYOD in the SaaS world, I transform simple advice into a sophisticated, necessary business project—the exact challenge you might want to hire mee.
My $200 Marketing Mistake and the Lesson That Redefined My Consulting” or “Why ‘High Traffic’ Can Be a Useless Metric: A Case Study on Targeting ‘Doers’ vs. ‘Dreamers’
I use this as a simple example for something much bigger: a year ago, I launched a promoted post for an article I had written. I spent a modest budget on Facebook… The results came in: thousands of views, plenty of clicks. I was thrilled. Then I checked my sales dashboard: Zero. Nothing.
The article was a philosophical piece... It was perfect for attracting an audience I call ‘Dreamers’—people who enjoy thinking about business in the abstract.”
I thought publicising a philosophical piece of text will help my brand and so elevate my sales. It did not – there were many people who like my way of thinking, but hardly any of them needed my service. The connection trom the philosophical article to my services in market value, marketing ROI and brand value calculations and research was not present for the readers, or they did not need it.
How This “Failure” Makes Me a Better Consultant
“This experience is why I am so passionate about data-driven validation. Before my clients spend a single euro on ads, it is important to ask the tough, practical questions that separate ‘Dreamers’ from ‘Doers’:
The Message Test: Is my content speaking to the person who thinks about the problem, or the person who pays to solve it?
The Alignment Test: Does my Call to Action (CTA) on this post align perfectly with the service I’m selling?
The Market Test (The ‘Doer’ Question): Have I calculated my Serviceable Addressable Market (SAM)? Do I actually know how many people are in my target niche and what their potential value is?
The Metric Test: Am I measuring the right thing? (Stop tracking ‘Views’ and start tracking ‘Qualified Leads’).”
The market test is not expensive – see my order page. You, the reader, might even do it yourself and decide if you want professional assistance. Most successful campaigns are the result of contributions of multiple persons:
The business owner or Chief Marketing Officer
The consultant helping defining the target group and messaging with the help of AI
The responsive clients who help developing strategy and product by answering questions
The designers who do the final graphic appearance
Lets start it and find the marketing mistake.
Measuring water depths does not necessarely help with marketing mistakes.
We are looking for people and organizations with a web presence or at least a listing in Facebook, Linkedin or some municipial business directories. AI cannot find others.
Recommendation networks – do they have qualified leads?
The power of recommendation marketing lies in its ability to effortlessly drive sales when a product aligns with a broad audience, eliminating the need to vet leads. Personal referrals, customer reviews, and influencer endorsements pave the way for seamless transactions without added complexities.
Ideally the other members of your recommendation network are looking for customers for their network and are warming up leads before. The prospect expects to be contacted when the seller reaches out.
This is an ideal setup. In practice most recommended leads are not warm, sometimes it is required to hire service people with bad quality just to please the guy who gave the recommendation.
However, the flip side emerges when the product caters to a niche market or appeals to a limited segment of potential buyers. In such cases, relying on recommendation marketing with personal endorsements can result in frustration. Vendors like myself may find themselves inundated with unqualified inquiries, while those offering recommendations witness their efforts to connect buyers and sellers go unrewarded, whether in commissions or praise.
AI is more than just talking about AI – Artificial intelligence in sales
How it is supposed to be: Professionals in this area use AI-powered tools to personalize customer experiences, optimize marketing campaigns, and predict sales trends. They understand how to leverage AI for tasks such as customer segmentation, lead scoring, and content creation. A creative mindset combined with an analytical approach is key to suc The reality: Many of the ones selling products claiming to be AI-powered tools do not use AI for their own outreach to prospects. So it stays less personal, the approached clients get annoyed. AI applied correctly helps finding the ones who are open for the products.
AI is really good in finding information about prospects – it can read and digest complete web presences in less than a second. So just use it.
Human and artificial intelligence observe the environment. Observations are interpreted and used for finding trends and experiences are processed and result in learning experiences. Humans learn social behavior by observing their fellow humans; animals learn where food is, how to behave in a herd, and where dangers are present. Learning can be dangerous. If the underlying relationships are not understood, false conclusions are drawn. In the US state of Maine, for example, margarine consumption and the divorce rate developed in parallel. So, is stopping eating margarine doing anything for your marriage?
People seeking orientation in this confusing world look for guides and signposts. That’s why they tend to see trends where there are none. And some sellers of products of dubious utility for the customer load their products with predictions about the future. They say: “This is the current trend” or “soon everyone will be doing it this way” or simply “this is the future.” And some indicators, such as sales figures, support this theory. So what is genuine, well-founded trend analysis, and what is bullshit? With bullshit, the speaker usually doesn’t understand what he or she is saying, or they are simply lying. Unlike genuine digestive products from male cattle, like the beautiful Charolais bull on the right, Bœuf
Finding trends and bullshit
charolais, taureau, with linguistic bullshit, you can’t tell by smell or appearance whether it’s phrases, inventions, or facts. Those who are intellectually ahead of the linguistic bullshit manufacturer notice contradictions and missing evidence. In this way, well-sounding inventions can be distinguished from reality. Difference between good arguments and facts There are three levels of content in a linguistic expression: the truth, which is understandable at least to a specialist audience, bullshit, and lies. Lies are criminalized and usually lead to the termination of the business relationship. Bullshit was elevated to academic honors by Harry Frankfurt. He, in turn, refers to Socrates: there are two ways to convince people. The first is to overwhelm the audience with pleasant-sounding but not really convincing arguments, thus creating a positive atmosphere. The second approach is clear, well-thought-out, and logical philosophical argumentation. See also here. The bullshit speaker doesn’t lie. He says sentences that, in his own opinion and experience, make a particularly strong impression on the unreflective listener. Many bullshit speakers don’t even notice the lack of connection to reality in their claims.
Use in Sales – Inventing
Trends In software sales and on the internet, new trends are often invented. This creates particularly thick piles of bullshit. I keep reading about the impending complete predictability of human behavior through big data. Anyone can check for themselves whether this is possible using contextual advertising on Google. A second industry susceptible to meaningless messages is health and nutrition. You can have texts tested for the bullshit factor at Blablameter. The result for this one: Your text: 3677 characters, 533 words Bullshit Index: 0.18 Your text shows only slight evidence of “bullshit” German. Another text on this blog about the Cournot point only achieved a bullshit index of 0.19. Texts with a lot of math, which may not be understood by everyone, have a better bullshit index.
Asked for predictions and future trends, younger people and elders forecast a different future. The judgement is based on past experiences, where the elders had a longer time to watch. Now there is research which uses stock market predictions and inflation expectation taken from surveys to measure different estimations of future values in age groups.
Ulrike Malmendier and Jessica Wachter show in their paper how long time experience correlates with investment decisions. People with long experience include old information in their judgments. Do they make better predictions? The study has no answer for that.
Past experiences stick. Did you ever notice the warm emotions showing up when memories to a nice situation long ago appear? Another nice thing of the past is that we know how all ended. And we can connect with others about the long gone events.
For the ones who follow politcs: party members choosing a candidate in primary eclectoins are mostly elder and more extreme than the ones who vote at the general elections. So their candidate might have problems.
Artificial Intelligence has no memory
Humans differ from artificial intelligence in learning. AI connects the data and learns from what was in short sight. Humans take their whole life experience together and build their judgements on that.
Hiding the past helps sometimes
There is a connection between memories memories in market research and in own, personal ways to think and judge. Discovering the role of memories in own judgements and being able to switch it on and off helps to get a better picture of the world of younger people. It is about trying to look at the world while leaving out past memories.
Having a long memory may be helpful in forecasts and judgements. Mathematical evidence from the stock market does not give any proof for that.
Customer journey and past experiences in market research
Along a customer journey the traveller takes a lot of decisions. Will old memories take a big influence on that decision? Research shows it does. Big example are cars. Seniors buy different and bigger cars.
Getting Advice from Groups – are THEY good for that (from: Are Crooks dishonest)
