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.
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?
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. 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. ————————————————————–
 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. 
 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.
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 to measure the difference in estimation between age group.
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 are mostly elder and more extreme than the ones who vote. 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.
This article is a short introduction into the many names for the same processus: shoveling new clients into the sales funnel of the enterprise and make everybody happy there.
At the end of the article I add my recipe for lead generation. It works.
Rainmakers are salespeople who use techniques not directly visible to outsiders to acquire new customers. Rainmaking and market research go hand in hand.
Who looks for Growth Hackers and who for Rainmakers, and who needs Lead Generation?
Growth Hacking is made for start-ups.
They want exponential growth here. That could double weekly sales. Growth hacking also works for traditional companies that can expand their production fast enough and still grow their market. They only need to find a market.
Growth hackers are people who achieve such sales growth using methods that are not directly visible and easy to conceive for the environment. This gives their work something magical.
Rainmakers are more traditional. They have an approach to sales not used in the enterprise beforehand, sometimes they also change the product line. This looks like magic to the employees in the enterprise. They may do own market research or user the services of professional market research analysts.
Everybody who wants to sell something needs Lead Generation. This is just looking who might be customer, which are cold leads, and stay in contact with the ones who are interested. These are warm leads, and some of them may turn to customers.
Extraordinary skills of the growth hacker
He sees unfulfilled human needs. She or he tests that in niche markets there it is possible to see how well a product is being received. Market research can test new products with well-known techniques, for example group discussion or sending out samples and questioning later.
According to Henry Ford, market research and customer suggestions were not the impetus for the development of the then revolutionary Model T, which was built in huge numbers. Customers would have wanted faster horses instead of cars they had never seen before. Growth Hackers and Market Researchers need to be visionaries to see the new product in his best shape.
Growth hacking is referral marketing
Inexpensive and fast customer acquisition cannot circumvent recommendation marketing. People recommend a product to others if it has helped them or arouses enthusiasm. Or if they can earn money with the recommendation.
Free trial access, distribution of samples, influencers, trial lessons, presence on social media – a lot helps a lot, like Germans like to say.
Outdated business models – leave it behind
People like to think that things who were once successful may be worth reviving or continuing. Market research finds if there is still a market for the product.
Rainmaking, Lead Generation and Market Research work together
Growth hacking is the product of many ideas and numbers. The firm belief in one’s own success is of no use if all market fundamentals oppose it. For example what to do if an analysis of the Totally Accessible Market shows only limited growth opportunities?
The market researcher also knows the penetration and effect of the individual recommendation or influencer channels and can doreliable tests.
Here the Working Recipe
Secret Recipe For Making Leads Rain
Who do I wish to be my client
Why do I wish them do be my clients
Who takes the decisions at my wished client
Who buys the stuff I try to sell already regularly
Who are my competitors
Who takes the decisions at the regular buyers
Where is it easier to sell
Whats in for my buyers
Where can I reach my clients
How do my clients like to be reached
Additional arguments for my clients to buy
Expected costs to find a customer, in percentage of sales
Why doing Market Research in Growth Hacking and Lead Generation?
The article deals with the possible uses of automated product recommendations in sales and customer service. There are buyers who prefer to buy on the internet for there they do not have to talk to human sales representatives. They probably had some experience with vendors staff hiding their cluelessness behind a lot of talk and are trying to sell their products on stockage at anyone who does not get away fast enough.
The problem looks hard to solve. But wait: for a few years now it is possible to extract valuable data points from primarely web activities and connect them with realised sales. The connector are either self-learning models or static traditional models. Models simulate behaviour and results.
The interested customer is looking für information before buying. He or she may ask a specialised salesperson and consultant who has all the relevant answers ready and can think in the customer’s interest. This relieves the customer. Or they, the potential clients, may look for publicly available information and customer review to avoid the advice of an inexperienced and overburdened sales representative.
Many sales representatives for complex products are quickly overwhelmed by the questions and expectations of well-informed prospects. The untrained salesperson feels attacked, the sales talk goes off the rails, the interested party prefers to buy from a supplier without advice.
The web part of customer journey
Depending on the product, between 80% and 90% of the buyers begin their customer journey with an search engine. So it is important for the seller to know what it is reported in the web, take care of the correctness of web information and avoid contradicting information. If there is human sales staff, they are supposed to assist the web customer journey and take care of a seamless process.
The consultant with all the facts in mind and the fully automatic customer advisor are extremes, the practical truth in the company lies in between.
Can the web be the better customer advisor?
Yes and no. The informations in the web and the informations given by a human sales person are supposed to go without contradiction. Nobody likes lies and deception.
Artificial intelligence makes use records of any communication with customers, for example chats, mail, data entries. This is compared with predetermined success goals. The model recommends something and can adjust itself. Something salespeople do intuitively, too, if the selling company lets them. Artificial intelligence therefore means less control for the management. On the other hand, opportunities are used that might otherwise have been overlooked. Where to start now?
Sellers are supposed to make as much information as possible available in a neatly structured way. Many people would like to find out more before making personal contact, but cannot find the information they are looking for. If publicly available documentations saves half an hour of calls per working day for the seller this justifies an expense of €2,000 for an improved sales website or other type of customer information – and that for telephone staff at a standard wage. Informed customers are better customers.
The meaning of human interaction
The ideal salesperson knows the buyers needs and wishes. He ors she build the best possible solution with his expertise. In between, looking up details and availability on the computer is essential for complex inquiries.
Advice form humans or from machines and websites?
Whether or not a human intermediary remains necessary for information collection and transmission depends on the product itself and on the personal preferences of the interested party. Some can evaluate and implement the freely available information, such as product descriptions, customer reviews and more for their own needs, while others cannot.
Good sales people relieve the customers. To do this, they must put the interests of the customer first and not fight for sales at any price. Consulting software in the sales pitch gives standardized information, if the software is capable of extracting the recommendation which work and then the client might be really happy.
Success has many reasons
Incidentally, the screen of the gentleman can turn directly to a prospective customer without the computer interfering. And if he needs information from the cloud or machine, he has it quickly at hand.
The article is about storytelling in village communities still observed in the 21th century, conspiration theories and modern advertising. My experience as longtime villager and suburb dweller helped me.
I am living in the same village I spent my childhood some years ago. There are still many natives, some of them never moved out of their parents and grandparents house. They tell stories about families in the village, what they do, what the members of that families do and their fates.
Wondering why I did not know anybody mentioned in the stories while quite many villagers from associations, businesses and events, I asked the storytellers who these people are. The commone response was how could it happen that I do not know the people from the stories.
The characters are fictional. It is similar to movies many people have seen. Everybody is expected to know the story of the movie, and some words go into common language, like “catfishing” (pretending to be another person online), “gaslighting” (making somebody questioning their own reality) and “weinsteining” (behaviour close to what Harvey Weinstein is imprisoned for).
Village storytelling, repeatedly told by villagers, are the local kind of the bigger stories in movies. The stories connect the community. In the neighbouring village they have other stories the locals do not know.
Modern conspiration theories follow a similar scheme. Insiders know the story, and they feel the knowledge makes them much smarter or at least better informed than others.
What do we do with that information? There are stories others impose on us with the argument “As long you do not know the story you do not belong to us” or they use for gaslighting us. These stories are not worth anything for anybody except for the teller. There are other stories well told and worth remembering and retelling, for example “Good Wife”. Newly elected governments might also suffer the comparison between their results and the stories they told before election.
A buyers persona is a picture of our targeted group. It says persona for the reason we do not need to know everything about that person and there might be different persons in our target group. The second part is about qualitative market research. We use this kind of searching for behavioural characteristics for creating this buyers persona.
A wrong image of our target group costs us money and makes our brand less popular. Simple example is posting on Linkedin without reducing the number of followers. I looked at the list of people I unfollowed. The reason for doing that was that they posted repeatedly content I do not like or is just not true. Most of them are aging german business trainers spreading far-right political statements and sometimes even far-right lies. The other ones are sales newbies who spread too often their pitches aimed at a different buyer persona than me. Is a close group of followers who believe the same political statements as the poster the thing the poster wanted? If there is no reaction to your post does hammering out repeatedly the same sales pitches again and again to the wrong targets a good thing? Or should we appeal to a broader audience?
Why qualitative Market Research
This article is about bringing qualitive and quantitave research together with integrating numbers and representativity into qualitative research. Quantitative research is primarely counting and statistics.
The topic of qualitative market research is understanding. What does the world of my target group look like? What emotional and financial settings do they have? Quantitative research, on the other hand, determines how many people in the target group have which attitudes, how many they are and how their budget looks. A random sample is drawn so that not all members have to be questioned.
The claim of qualitative market research
Qualitative market researchers understand the customers. Its like some newspapers articles, where journalists describe some individuals where they think they are good for the numbers. Quantitave shows the numbers – for example 60% believe that the government does a good job in education. Discussions in focus groups, in-depth interviews, images, social media and much more can be used as data sources for qualitative research.
Also compare here (New Market Research Blog). The qualitative market researcher is an active part of the research process. He or she uses their subjectivity to better understand the phenomenon being studied. You want to understand why people believe in what and see the world a certain way. A qualitative researcher wants to understand the process of selecting a product, for example. A quantitative researcher wants numbers, averages, and more. Manufacturers and dealers can then base their decisions on this.
How qualitative researchers can do representative studies with small numbers
A qualitative researcher makes small samples. Understanding the process and the mindset of the subjects, this researcher assumes some validity. To ensure this this procedure can help:
The result of the small sample are suitable when no changes are to be expected from a larger sample. This can be checked by first evaluating 30 questionnaires. Hopefully there is no significant change with a further 10 questionnaires. If there is, another 10 questionnaires are added. If this does not maintain the hypothesis that the differences in responses are statistically insignificant, the study can be considered representative provided the responding participants represent the target group.
Some subjects suitable for qualitative studies:
Lists of ideas – if the ideas are repeated, the study can be ended. Studies that do not record the subjects’ ideas via structured questions but via free text information fall into this category.
All – or – none results. If every participant in a small study says the same thing—like, “I see a train station in this ad,” or “I prefer the new packaging”—the conclusions are likely to be valid, even with small samples.
Strong hypotheses for the study to support If we have a hunch and it is supported by the small sample, we can be sure that the hunch is correct. All we have to do is verify that the underlying hypothesis is not just speculation. This type of market research is popular with journalists who can get by with just a few interviews.
Understanding instead of measuring – for example customer journey. This analysis helps testing new products.
The problem of representativeness in qualitative research remains.
With small samples, the problem of representativeness remains. There is no statistical way to ensure the representativeness of a non-probable sample. The market researcher’s judgment must be added here: can the result be as the study suggests or are there other, contradictory results? Trusting only statistically proven results without knowing the underlying connections can be misleading.
Pricing affects everybody. Selling your apartment, selling used furniture, doing salary negotiations or finding prices for your products. How to find a price liked by the seller and buyer? In my seller history I found situations with low prices where the customer voluntarely added a gift, and many situations where nobody wanted to buy. The reason for this can be “no market for the product” and “price is prohibitively high”. The latter one means there is a market for the product, but not at the charged price.
The next chapters give structure to this complicated matter.
The classical theory of pricing for optimal is very simplified. It assumes a linear demand function, with only one determinant, the price: demand=f(price) . This means only demand determines the price. What happens if demand has many more determinants?
This can be:
The look of the selling (web-)site
Do the salesmen make a good job?
Do I get good advice from the staff?
Can I expect some value if I pay more?
Does the product make me feel better?
Do I have the budget for luxury?
What are the prices of the competitors?
The price the seller wants
Point 1-4 are the value to the product added by a good sales team. Example: if a retail store buys a pallet with 1000 packets pasta for 600 € and sells the package for 1,2 € the sold package is not the same product as on the pallet. The services of the retail store are added.
The following graph is an example. It may look different for a specific produkt. The graph with the numbers given there works good for used cars and expensive utilities sold on Ebay or webshops.
Example: make advice a valuable good
Many brick-and-mortar retailers complain that their customers do not pay for advice. What they do not tell: how much do their prices differ from common internet sellers without any consultancy? Is there really value added in the store?
In old times, when sales was based on printed catalogs, there was the famous trade costing, with list prices, heavy rebates, and purchase prices. Most retailers ordered their stuff at wholesales, which themselves made their cuts. The result was that end user prices were about 3-10 times as high as the factory price. I understand that many want this time back. The customers had no choice, they had to buy locally. Many wishes and dreams of customers weren’t ever fulfilled for the high costs of maintaining catalogs and storage.
Now many internet retailers order directly at the manufacturers, and they have to look for competing offers worldwide.
There are two approaches for long-term pricing: with interchangeable goods, many manufacturers, in the long run the price will be the production cost plus some earnings. The earnings are the incentive for producing. Examble: a spare part for an historic car. Production cost is 150 €, with this part added the car gains 2000 € value. If the seller knows that and wants a high price, the buyer might look for another source which produces it for 150 € and pays additional 50 € for the favor.
In Services it is a bit different. Production costs is one side. The other is: how much will the buyer profit from my service? If the seller, you, knows that 25-50% of the profit for the seller, the trainer or consultant, are normal.
Is there a formula for prices?
In economic theory the price is equal to marginal use. That means the price equals the benefit the buyer thinks he has from the last unit he or she buys.
If we can measure the benefit in money and realtime, we have the formula.
The benefits are listed at the beginning of the article. The problem is that the customer does so many estimations, that change over time. Also new competitors with low price entrance strategy might show up.
When selling over a website it is quite easy. Look at the ones who buy related to the ones who look at the article. If there are many who look and do not buy then lower the price, if most of the visitors buy then raise. This needs a lot of finetuning. Sometimes visitors look a few times at the articles until they finally buy. When they see that the prices go down, they might wait even longer. So you might want to include the total number of visitors.
How to calculate?
There is a mathematical formula and there are ways to survey it.
We measure the first eight determinants, with due consideration of costs:
how to find and count
The look of the sales (website) position.
Survey of visitors
Are the salespeople doing a good job?
not available everywhere, get feedback
Do I get good advice from the staff?
Can I expect value if I pay more?
Tests and surveys on buying motivation, own samples
Do I feel better because of the product?
Tests and surveys on buying motivation
Do I have the budget for luxury?
What are the prices of the competitors?
The price the seller wants
Look deep inside you
Market research helps with the first six points. Usually it is necessary to repeat the market research every 3-6 months with the well-known market research problem: the target person doesn’t like to answer. They are bored and do not know whatfor they took their time to answer the questions. We must therefore look for other methods. An easy-to-install method is to encourage visitors to comment. You may get 1-5% comments from the customers. This responses add up to valuable data source. It is also possible to conduct studies with paid testers. There are some problems with representation of the correct user group there, additional research helps to circumvent this.
Total addressable market analysis – what does it mean for your plans
To predict the number of future customers we use Total Addressable Market analysis, TAM. The technical term TAM refers to the number of possible customers and the number of products that they will buy as a whole. This is the full market. If you sell cars, everyone can be your customer. There are competitors, and not everyone is going to buy cars from a new producer. So the number of cars you are going to sell is smaller like the market for automobiles. If you want to sell Yoga classes, everyone is a potential customer as well. The vast majority of clients for Yoga classes are women, which decreases the number of clients. People who need to go over 30 kilometers for the workout are unlikely to buy your lessons. For a year now, Covid-19 has been a big problem – either lessons together in a gym are prohibited, or older clients do not want to come due to fear of infection.
The total addressable market narrows down to the servicable accessible market.
These are the customers who may be ever hear from you and suscept you as your potential supplier. You have to be able to sell your products to them. This is much closer to the reality and is one step calculating your future sales.
The third and most important technical term is the “_S_ervicable _O_btainable _M_Market”, SOM. These are the real customers, the ones you can serve and the ones who want to buy your products. They need to know your products and services.
For calculationg, demographic statistics are important. These data indicate the number of people in the target region.
How to find the number of potential customers? The primary source is Wikipedia (really) and the governmental Statistics Office. Their data is normally free. The state administration places great value on correct data. They use this to distribute taxpayer money.
You can find more than population data there. The number of dairy cows is also there, and a lot of the data by economy. So it is possible to predict the number of future clients.
Using google to search for the data you arrive at several websites that are trying to sell industry reports. The normal price for these reports is between 800 and 3700 Euros. On my Fiverr account, a website that acts as an intermediary between freelancers, a lot of requests are aimed at selling these reports for less, say 80 €. The reason the prices are so high is that the authors use the most expensive data, for example at Bloomberg.
Is it necessary to pay more? For small entrepreneurs who offer specialized products, the answer is no. The reason is: it doesn’t say anything which we can use to predict the number of future customers.
Data Quality needed to predict the number of future clients
To estimate the SOM, industry associations offer a lot of data. Perhaps this data is old, but there are no or little fees.
The internet offeres a lot ot data about what is popular with users and what they think an want. That data is hard to find and to be familiar with with web evaluation and web scraping ist helpful. In 2020 I worked on a market study for flower pots. There was a lot of data coming from the associations of the friends of the gardens. They show me that all people love their garden and their flowers more and more. Are the data true and dear to the seller of the flower pots? How much money do they want to spend on the jars? What appearance will they like? Lots of work to do.
How do I find the right story for my product, how does my storytelling become a success?
Products are enhanced with storytelling. I am looking for a 17 mm socket, which of the three available nuts or wrench sockets will I buy:
Simple picture on blue background, price € 5.60, supplier unknown
Same picture as 1., but additional information that it was made in China by a small factory in Shenzen from the best steel for heavy use, price 8.30 €
Again the same picture as 1., and the same manufacturer as 2. The dealer introduces himself as Franz Müller from Rottweil in deep Swabia, who specializes in the sale of quality tools. Price € 9.20.
I learned that Swabian dealers are very quality conscious. Franz Müller connects to my experience, and his story gives me expectation that the tool will run smoothly and that can use it without any problems. I order his stuff despite the higher price.
Storytelling has to appeal the customer’s mental world
Good examples for that:
Promise of getting rich fast with Network Marketing. You are shown the beauty of wealth. The way to do this is very simple: you win a few customers who in turn win new customers.
Anyone who has ever been to partner exchanges surely knows the many supposedly young and good-looking women and men who promise the perfect relationship. Fortunately, they don’t present their price list until late, so you don’t have to pay for the illusion of a perfect relationship due to the lack of a valid contract.
Berlin advertising agencies in particular currently love telling stories from the ideal world of the rurals village or a small city with intact neighborhood “where everybody knows your name. The stories are pinned to every product whose distributor requests it.
Coaches encourage other their coaching clients to tell their history. That is supposed to induce more interest from potential customers. A very common story is that of the poor lady or gentleman, stricken by fate. This person believed him/herself and made it again into great wealth. And there are photos from trips to exotic places or they buy artworks.
How to find your story?
Take care that your story does not disguise the product, unless the story is the product. Example: an email from a coach who promised me a great development as a globally admired speaker and expert in my discipline. I asked myself: “What does he even offer”? It was simply an rhetoric training, as I learned later. By reaching into the rhetorical multi-level marketing language, however, he created unpleasant associations for me. If any kind attention was the target of the action, the coach is successful. If he wants to be a serious personnel developer, less so.
Market Research helps you to find about more about your targeted group.
To the featured image: Thanks to Allie for sharing their work on Unsplash.Photo by Allie on Unsplash