Using A Group For Improving Advice

This post is about using group decisions for personal development and avoiding the trap of getting contradictory advice. A scaled down method from social research is useful to get better advice for personal and business matters.

“Guter Rat ist teuer” (Good advice is expensive) is an old German meme. Jokes transfered that to “Guter Rad ist teuer”, meaning a good bicycle is expensive. This is also true for getting good advice in transition periods, where many of us are vulnerable and insecure.

Some annoying people give unwanted advice. Their motivation is to lift themselves and their self-esteem by influencing somebody to change behaviour. Social and market researchers exclude some participiants for the reason that their opinion is not helpful. For personal and business growth it is important to ignore them too.

We like to ask people we know and we trust. They tell us their opinion looking from their own situation. For giving best advice they are supposed to leave their world and go into the thought system of the one they want to help. That is not easy to do and it is prone to errors. Example: I (the author) am really good at fixing cars and bikes. I am inclined to I think everbody can do that. For many people that skill is not important, so they cant, and I have to consider that if giving advice.

So, what to do, to not fall into despair after the first advice from a friend, collegue or coworker or fall into more despair after getting different advice and statements from different people.

How about asking for example 20 people, and write down or memorize the answers. If the same answer appears more often, there must be some reason behind it. I do not include the statistical proof for that right now.

For me that technique solved a personal problem: I generally do not trust human authorities. I think most of them are somehow crazy, corrupt and stupid. How to find truthful information with this precondition? I want to participate at the wisdom of humanity. My solution is finding opinions and statements which are similar – if many rely in the same facts, there must be some truth in it.

Another way for solving similar problems you might find here: Price Negotiations and Decision Tree. There you make a decision tree and add probabilities to each event.

Village Storytelling And Advertising Today

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.

Predict the number of future clients – howto

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.

The math

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.

Boutique market researchers sell premanufactured reports

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.

Quick and only a little dirty

For very quick results it is possible to do an analysis of reviews on Amazon and other sellers. There is software to do this. Youtube is full of videos that show you this technique. Here are some basics how to analyze that (German) and how I do it.

Price Negotiations and Decision Tree

Going into price negotiations is difficult. What is the negotiation scope of the other side? How much bargaining power do both sides? 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. For that happens there have to be 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 herselfhow 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. Hell, 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.

Growth Hacking und Market Analytics

Who needs Growth Hacking?

Usually start-ups. Survival and growth need exponential growth here. That could double weekly sales. Growth hacking also works for more traditional companies that can expand their production fast enough and still grow their 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.

Traditionally Hacking meant using not necessarely legal methods to gain access to data that is not immediately visible or protected. The hacker searches for loopholes and hidden entrances in computer systems. A growth hacker seeks and finds direct ways into the consciousness and perception of potential customers. This are paths the competitor or late-bloomer in the business says: “If I knew that”. Both Hackers use methods that would require too much technical knowledge and creativity from others.

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.

Market Researchers – are they still needed?

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.