The Simplest Sales Secret: Why “Eating Your Own Dog Food” Beats Every Sophisticated Strategy.

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 selling during 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 TypeStatus of EYODCustomer’s Filter
ManufacturingOperational Necessity (Must use their own equipment to produce/operate)Does the product work?
SaaS/IntangiblesStrategic 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:

  1. Which teams must use which features to fully replicate a customer journey?
  2. How is the feedback quantified and categorized to inform strategic pricing and development sprints?
  3. 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.


From 1,000s of Views to 0 Sales:

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.”

But my service isn’t for Dreamers. It’s for ‘Doers’—founders, entrepreneurs, and managers who need to make a decision now. It is about finding the target group.

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.

Searching the Marketing Mistake
Measuring water depths does not necessarely help with marketing mistakes.

Recommendation Network or Artificial Intelligence in Sales – which one has warmer Leads?

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.

How AI took my side job – Artificial Intelligence Business Plan

Until Summer 2024 I offered creating simple business plans for startups. I did that on my website and on Fiverr for about 120 € each. I stopped that for the lack of orders and a lot of weird questions regarding my gig which never ended in a purchase. The prospects were obviously looking for excuses for not ordering my service. Looking at other work about 50% of my personalized offers generate a contract. The gig was meant as entry offer for learning to know each other and sell more services later. I used a template and filled it with data from the client, then I added some treated and interpreted market data to make an earnings prediction. The origin of the data was publicly available sources.

Artificial Intelligince (AI) is really good at working with data and templates from the web. Actually I am using the AI behind Google Gemini. For testing I asked it to write a business plan for a dog grooming business, later I asked it for market data for dog grooming in Germany.

The result was a really nice plan – you can download it here. It was a working example for a plan with estimations done by AI.

For first orientation the plan is really good, and it looks nice. I even think it is possible to apply for a loan at the bank.

I checked the numbers: the market data is quite generic, the assumed sales price is not confirmed by market research, and there are no scenarios. What to expect for free? My former prospects group, who did not want to pay more than 120 € for a business plan, is obviously happy with the very cheap plans delivered by AI.

So Artificial Intelligence took my side job. Maybe it is similar to what they did to map suppliers with their free Google Maps. I use Google Maps frequently and consider it to be technological progress. Google Gemini as one example of an AI chatbot offer requires a subscription for full functionality.

Measuring the qualitiy of artificial intellgence business plan
Measuring the qualitiy of artificial intellgence business plan

Why finding trends using only observation often leads to false conclusions

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
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.

Why artificial intelligence will not replace qualitative interviews done by humans

This article is about the benefits of qualitative interviews and how to make a profit from a small amount of interviews. Qualitative interviews means there are less than 50 interviews, and the answers are not limited to yes/no or a 10-point scale.

Our goal is to improve customer relationship by asking our customers what they want and if there are secretive needs and wishes which are easy to fullfill for us or need only a small change in our product and sales strategy.

Artificial Intelligence relies on text already existing for training the model. So the answers given by AI are always a rephrasing and recombination of something already existing, and it is up to the reader to interpret if the results are valid or not.

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.

Qualitative Interviews are expensive, about 300 € for each interview. This includes finding the right interviewees. Interviewing the correct respondents has a marketing effect: the respondents get aware that there is a supplier interested in them. If they are already customers the effect goes in the same direction.

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.

How to find investment opportunities that combine getting rich and being a good person?

This article is about finding good investment opportunities, getting at least a little rich and being benefactor for society. Reading this article the spirit of Adam Smith, the author of Wealth of the Nations 250 years ago, may be with us.

An investment opportunity is supposed to give us gains and make us happy this way. Posession can satisfy: we are proud of having a part of this enterprise or owning some of these krypto-currencies. If it is possible to make the buyer also happy the world is becoming a better place.

Example: I buy an house for one million Euro. Two years later I sell it for 1.5 million Euro. I enjoy the gain, my buyer enjoys the beautiful he got for his money. Everbody is happy.

I calculated risks and gains and put money on real investment chances, which are a net worth for society. The worth of housing in general did elevate, and my gains are part of that. The house I sold has increased in value, the reason being the higher price the buyer paid,

Another way is to become active, found a business and calculate the perceived earnings per customer

Becoming millionaire with 10 € starting investment in 17 years

Many people got rich with less. For example, starting with 10 € which double themselves every year creates 1.31 million € after only 17 years.

No organism grows continuously over an indefinite time. So the growth will stop after some time. Or the investor looses interest, takes the money and moves to a hurricane-ridden Caribic island to satisfy other human needs and wishes there.

Benefitting society and becoming a little rich is possible

Is there anything in the world I can improve and I get a share of the money created from the use of the thing? This applies even for the ones who make us happy with their videos on tiktok or YouTube. Marc Zuckerberg created Facebook and got rich with that. He forced  competitors out of business. His solution worked better and therefore he had more users and more advertising.

Let’s talk about a investor called Robert. He likes to sell and buy real estate or shares on real estate. Also he wants to contribute to modernization of energy supply. At this moment he has about 50.000 € available. Also he is thinking about selling natural dogfood online. He read that this can be a nice sidejob with little investment.

At first Robert is confused of the many good and bad investment opportunities. What does need to free his entrepreneurial spirit?

From an economical view, getting rich also means to benefit society. People buy things which are useful for them. The entrepreneur provides these goods and services and keeps a part of the revenue.

To find an easy way full of fun to success Robert has to consider the following:

1. What he likes to do

2. What others like him to do

3. What he think he likes to do ( notice the difference to #1 – this is about self-reception)

4. What Robert believes others want to buy from or the enterprise he bought a share

5. The limits of his environment – the world is bigger than the area he can take notice of

6. The hard numbers – is there really a big market for the products?

7. Where he can really improve the perceived life situation of others.

What is the next step?, Ok, consulting me or another business coach/consultant looks good. Some can discuss the matters with mentors and friends, who are good in business, others do better with paid assistance.

After having a free mind without barriers from culture and family background, there is a chance to see the next big thing, for you and society.

The real important steps are:

#7 is the most important thing: this is your working next best thing.

For researching #4 and #6 I am a good fit. I have a lot of data for calculating your market and possible success.

Robert looks for a good way to invest his money and having control about his money

Separating good investments from bad investments is similar to start an own business. Robert has to look at point 4 to 7 of the previous paragraph and do the analysis. If all goes well, he wants to buy. Thats it – buy after best possible analysis and wait for earnings. The investments have to be checked regularly and maybe sold at the right time.

Artificial Intelligence: danger to humanity, productivity increase or just boring?

Artificial Intelligence is based on statistical learning. We all learn with statistics, even if we do not know it. Our brain is good at things we do often, remembers words we frequently use and puts others aside. When we touched an electrical fence we learn it hurts. Animals make a similar experience. Also we look at other humans to learn how they handle situations. This may improve our own lifes. We look at the experience of others, how they do things, and if we think its good we imitate it.

The challenge of Artificial Intelligence is to put learning into a statistical model. With more and more computational power more is possible. Impressive for me is the look at colored 100+ years old silent movies in colors . The machine learned from actual pictures how the old colours were when the original was filmed and put that knowledge in the colorized version. With human painters this process would have taken years of work. Another impressive example of AI is a comparison between the Donald Trump impressions of Alec Baldwin (actor) and Scaredketchup (computer artist working with AI)

What AI and all the models do is to analyze all available text and give the resulting language model the ability to create new text out the old text . This is all based on mathematical and statistical calculations. As a result the new text is really close to existing text found somewhere on publicly available sources. AI can pretend to invent something, just by combining results or something the readers did not find already themselves. Remember: it is not really new, the machine found it somewhere.

A personal view on the benefits of Artificial Intelligence

AI-generated text is good when I have a question and the machine prepares its findings for me. There it can use text I did not know before, and probably I am happy. AI can ease pressure on customer service – the bot can answer many frequent questions without human interaction.

How AI-generated Text creates boredom

The inherent lack of invention by AI may create boredom. Also Artificial Intelligence is actually not well trained to look into human emotions. This may change over time.

Human brains have a capability to recombine things which never put together before. This is called human genius or creativity. Machines cannot do that actually. Computers lack the capability to fast apply the invention on human minds and viewers and so see if it works. Scaredketchup combines his own creativity with AI tools for creating pictures.

For me interesting is to look at how music is created: the musician has some inspirations and tests it before live audience. His mind tells him what the listeners like and what not. 

More Success in Price Negotiations With Decision Tree

Going into price negotiations without a lot of preliminary information mostly ends up in suboptimal results. This post is about structuring in the information in a decision tree stuffed with probabilities for the events. These probabilities are the result of market research.

The party with the most information has advantages in negotiations. They know the negotiation scope of the other side and th bargaining power do both sides. Other questios are: What do I want? How to avoid getting lost in bargaining? After looking into auctions, where for psychological reasons it is difficult to make rational decisions, I look at the decision tree with estimated earnings and probabilities.

Auctions – the risc of following the group

Auctions are popular for antiques, used items and real estate. In the bidding war the offers may run away while the auctionator counts highet and higher. This war requires at least two bidders. Participants see and hear competitors bid higher and higher and infect one another. The bidding continues, so the race goes on for not having a defeat. Giving up means the object is gone. Due to the short decision-making period and the group, spontaneous decisions are made, which be confirmed experimentally. As soon as the bid is accepted, the bidder goes back to his rational normal and recalculates: the high purchase price makes the investment unprofitable. The buyer tries to get out of the contract. This phenomenon occurs less often if a security deposit to be presented limits the bids.

How a decision tree helps

We try to look at every possible scenario and calculate the probability of that scenario. The example is about a freelance teacher, who applies for teaching a class. How much money will she get?

decision tree usable for price negotiation – probabiliy and earnings

Sandra is applying to be a lecturer at the Montgomery Training Center. She has no experience and is pretty much broke right now, so she urgently needs paid work, her lower limit is 20 euros an hour. But she wants to achieve the maximum possible hourly rate in order to look good in front of her friends and colleagues and to earn money.

Sandra negotiantes with the dean, Ms. Dr. Teufel. She wants more earnings for the school and save money spent on teacher salaries. If teachers are scarce or one has special qualifications, she can pay up to 40 euros per hour. She tells the applicants that there are lecturers who work almost on a voluntary basis. So it tests their price scope downwards. That the training center also hires expensive lecturers is not for the appliants ears.

Doing background research helps

Sandra asks herself how much she could ask. She estimates that the school can spend up to 50% of the participiants fees for lecturers salaries[2] . The difficult question about the income of the training center can be answered with the help of price lists of the company, sales figures from the Federal Gazette (corporations have to publish there), inquired or estimated numbers of participants. Sandra does the math and comes to a maximum of 40 € per hour.

Sandra analyzes her competition. What is the likelihood that another qualified person will do it cheaper? This is very high when teaching in university cities. Public statistics on wage levels can help, or a survey among friends. Industry associations often have fee statistics. She heard from friends that they often only pay 25 euros per lesson. So what to do in the price negotiations?

Price Negotiations and Background Research: Add Probabilities to the decision tree and play with scenarios

The probability that a sufficiently qualified applicant appears charging only charges 25 euros per hour is 50%. Let’s figure out whether Sandra should play it safe and demand 25 euros per hour or whether it is worth asking 40 euros per hour. The contract lasts for 200 hours, i.e. 5000 euros at 25 euros per hour and 8000 euros at 40 euros per hour.

Sandra realizes that she still doesn’t know enough about the lecturer market. What is the probability that in the case of a rejection by Dr. Teufel, a new job of the same type appears that brings in at least € 40 an hour? It is not possible to ask the competition, they will hardly tell the truth. Miss Dr. The devil is also talking about volunteering, so no wages at all.

Looking at the decision tree: if demanding 25 € per hour Sandra has the job safe and gets 5000 Euros. Demanding 40 € there is a 50% chance that she will earn 8000 Euro and 50% that she needs to go cleaning houses for 12,50 Euro each hour, that makes 2500 €. In addition and weighted by probability this goes to 5250 €, slightly more than talking about the low price.

External Factors – Prestige and Feeling Safe

Sandra possibly thinks she definitely needs the teaching job. She no longer wants to clean and needs references. Then she plays safe and only charges 25 € per hour. Demanding high prices is only worthwhile to a limited extent, as the previous analysis shows. The opportunity costs in the form of stressed nerves can be an argument for low claims.[3]
————————————————————–

  1. [2] To know this average value, information about the business model of the schools is necessary. The basis is the commercial calculation with cost price, handling costs – and profit surcharge and sales price. The lecturer’s fee is the cost price, the remuneration paid by the participants per lesson is the sales price. The trading surcharge includes rooms, advertising, administration and risk. [3]
  2. [3] Between 2008 to 2010 I did some tests with groups of 30 participants about that subject. The results were that very young people tend to ask too little money, older people tend to ask too much in price negotiations for work.

Forecasting and Storytelling – our personal history and decision taking

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.

The influence of past experiences in market resarch
Getting Advice from Groups – are THEY good for that (from: Are Crooks dishonest)