Infopreneur

infopreneur, internet, information, product, laptop, entrepreneur,

What’s up ? This is THE stephane ANDRE !!! I watched an Olivier Roland’s video and I learned some good stuff.

I discovered what it was an infopreneur by reading Tim Ferriss‘s book « 4-Hour Workweek ». It was a book that was recommended in a personal development’s blog and this book has completely changed my way of seeing a « job ». This is definitely a point of view that we don’t learn at school, even at business school.

The profession of infopreneur is a very interesting profession and evolves all the time because of new technologies. In fact, if you don’t know, an infopreneur is a person who earns money on internet with information products. These information products aren’t news but they’re information to help people achieve a goal on a specific area.

What is special about this job is that we enable people to be more effective in things that are important to them and the result is that they’re happier.

Workplace

One of the advantage of this job is geographical freedom. The company is based on internet, which means that you only need a computer and an internet connection to work. There is no obligation to go to a workplace, you can work every day in a different place or in a different country as long as you have access to an internet connection. There are more and more jobs that allow you to have this freedom like programmer, virtual assistant, bookkeeper, data entry, copy writing, translator, travel agent, transcriber, etc.

It’s a profitable business because selling information on internet makes it easier to reach a large number of people with little money.

Distribution

internet company

Imagine how it was to sell a book before the invention of printing. Even if you had written the best book in the world, it was hard to live on it. Imagine, so that 2000 people can read your book, you needed 2000 handwritten copies of your book. Copying books was not a really cool job, so you can imagine how complicated it was. If you offer me a job that consists of copying 2000 books of 300 pages, I’ll answer you : « No, thanks !!! ».

With the invention of printing, authors started earning more mony because more people could read their books and that to increase later with the invention of copyrights.

With digital books (ebooks), it’s possible to sell 100 or 1 000 000 books without the huge costs of printing, distribution, storage and return management. There are still costs but they’re very low for the servers and the support (for example a client didn’t receive the email to download the ebook).

With internet, it give you the opportunity to manage the distribution of your information products yourself. Before internet, you had to have an appointment with a manager of a radio, television to reach as many people as possible. If this manager liked your work, you could have ads on radio, television but if this manager didn’t like your work, no ads.

Many talented people couldn’ have a great exposure to make known their talent because of these « Gatekeepers ». Internet allows you to no longer need to these gatekeepers to reach a large number of people.

If you want to know more, I advise you to read this article that I wrote. Click here .

Subscribe to my newsletter and share this article if you think it can help someone you know. Thank you.

-Steph

P.S. If you’re in Miami and you like Caribbean food, go to my cousin’s bistro to eat Haitian food, click here.

Combine 2 charts

tableau chart compare paralell data mining science

I have just enrolled in a Data Science course on Udemy  and I learned good stuff.

We’ll move to the next level. We’ll work with 2 bar charts in parallel to have a more efficient data mining. In a previous article, we created 2 different bar charts. The 1st was an A/B test (actually, it’s a classification test) that told us in which age range the clients were most likely to leave the bank. The 2nd was a bar chart showing the age distribution of clients in our sample of 10 000 clients.

Let’s go. We’re going to have an A/B test with age range and we’ll add a bar chart of the client distribution below. To add a bar chart, we must start by choosing what we want to keep and what we want to add. In our case, we want to keep the columns because they’re the same in the 2 bar charts.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

And we just want to add a new line so we will add a new variable in « Rows ». As we want to add a bar chart of distribution, we will use the variable which corresponds to the number of observation « Number of Records ».

In « Measures » moves the variable « Number of Records » in « Rows » to the right of « SUM(Number of Records).

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

We have a 2nd bar chart below the 1st bar chart. As you can see, these 2 bar charts are in one column. « Columns » is « Age(bins) ». These 2 bar charts are in 2 different lines which are the lines that correspond to the 2 « SUM(Number of Records) » in « Rows ».

The space on the left has also changed. There is « All » which represents the 2 bar charts at the same time. It means, when your select « All », you make change in the 2 bar charts.

tableau chart compare paralell data mining science

Below this tab « All » we have 2 tabs. The 1st tab represents the 1st bar chart so the 1st « SUM(Number of Records) » in « Rows » and the 2nd tab represents the 2nd bar chart so the 2nd « SUM(Number of Records) » in « Rows ».

tableau chart compare paralell data mining science

Which means that if you want to make changes on the 2 bar charts at the same time, you make the changes in the tab « All ». If you want to make changes only in the first bar chart, you select the first tab below « All ». If you want to make changes only in the 2nd bar chart, you select the second tab below « All ».

So if you change the color in tab « All », our 2 bar charts will be colored by the same color.

Select the « All » tab and click on « Colors ».

tableau chart compare paralell data mining science

Click on « Edit Colors… » and select « Stayed ». Select the green color and click on the « OK » button.

tableau chart compare paralell data mining science

As you can see, the color changed in the 2 bar charts.

tableau chart compare paralell data mining science

Click on the tab of the 2nd bar chart.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

Removes the « Exited » variable from « Colors » to remove colors only in the 2nd bar chart.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

Removes the « SUM(Number of Records) » variable from « Label » to remove the labels only in the 2nd bar chart.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

We will add color on this 2nd bar chart. Click on « Colors », click on « More colors… » and select the blue color. Click on the « OK » button.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

Now, we would like to see the colors vary in intensity depending on the number of observations. Take « SUM(Number of Records) » from the 2nd line in « Rows » and holding « Ctrl » or « Command », move it to « Colors ».

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

Cool ! We will take care of the 1st bar chart. Select the tab of the 1st bar chart.

tableau chart compare paralell data mining science

Click on « Colors ». Click on « Edit Colors… ». Select « Stayed ». Select the brown color and click on the « OK » button.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

For more clarity, we will add labels in 2nd bar chart. Click on the tab of the 2nd bar chart. Take « SUM(Number of Records) » from « Colors » and holding « Ctrl » or « Command » and move it to « Labels ».

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

Perfect. Now we will change the location of the bar chart. We will put the 2nd bar chart instead of the 1st bar chart. According to the logic of « Rows » and « Columns », simply put the 2nd line « SUM(Number of Records) » to the left to pass in 1st line.

tableau chart compare paralell data mining science

tableau chart compare paralell data mining science

BOOM, the bar chart of the age distribution is going over because it’s in the 1st line in « Rows ». With these changes, tabs to change the bar charts have changed order.

Observation

What we can observe with these bar chart is that we see on the 1st bar chart that the majority of bank’s clients are in the age group of 30 to 34 years old and 35 to 39 years old. In these 2 age groups, we see on the 2nd bar chart that client of 30 to 34 years old are less likely to leave the bank than clients between 35 and 39 years old. Look at ages 30 to 34, the rate of clients leaving the bank is 8% while in the 35 to 39 age group, the number of clients leaving the bank is 13%.

In the age group of 40 to 54 years old, we see on the 2nd bar chart that the rate of clients leaving the bank is increasing and is above of the average rate of clients leaving the bank (20%). But we see in the 1st bar chart that the number of clients in the age group of 40 to 54 years old decrease with the age groups.

Do you remember the potential for anomalies in age groups 75, 85 and 90 ? We’ll check it. In the 1st bar chart we can see that there are 11 clients in the age group of 80 to 84 years old, 2 clients in the age group of 85 to 89 years old and 2 clients in the age group of 90 to 94 years old. We can conclude that these observations in age group of 80, 85 and 90 aren’t very significant from a statistical point of view because 2 clients is something negligible in this sample of 10 000 clients.

In the first age group of 15 to 19 years old, we can see that there are 49 clients, which is not very significant.

Compare these 2 bar chart in parallel allows us to have additional insights.

Subscribe to my newsletter and share this article if you think it can help someone you know. Thank you.

-Steph

Create Bins and View Distributions

tableau, bins, bar, chart, distribution, age, data, science

I have just enrolled in a Data Science course on Udemy  and I learned good stuff.

It’s cool, you finished the 1st part. Now we’re going to do more deep Data Mining analysis with this bank’s dataset.

tableau, bins, bar, chart, distribution, age, data, science

To make these analyzes more deep, we’ll create a more statistical approach.

To do that we will create a new tab.

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

For this new tab, we want to understand how client distributed according to their age. Is there a majority of young or old people ?

tableau, bins, bar, chart, distribution, age, data, science

Move the variable « Age » in « Columns ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

As we want to see the distribution of client ages, we need to use the variable « Number of Records » to see the number of observations. Move the variable « Number of Record » to « Rows ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

Boom, we have a chart but there is only one point on the top right. What happened is that Tableau took the sum of the ages of all the bank’s clients and the sum of all the « Number of Records », it means the total number of clients, 10 000 clients.

We’ll find a solution but before we’ll change the format to better see the chart. Right-click in the middle of the chart and select « Format ».

tableau, bins, bar, chart, distribution, age, data, science

For the font’s size, select « 12 ».

tableau, bins, bar, chart, distribution, age, data, science

Here you can see that the total age is 39 218 but that’s not what we’re looking for. What we want to see is the number of clients for each age.

I’ll explain what’s going on. We took the aggregated sums of our variables. Aggregate means that we took the total sum of the variable for each category. We added the ages but in fact we want to see the total number of observations for each age separately.

To have that, just click on the arrow in « SUM(Age) » in « Columns ».

tableau, bins, bar, chart, distribution, age, data, science

Then select « Dimensions »

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

You see, Tableau doesn’t take the aggregated sum of ages but it takes ages separately. We have a curve that shows us the continuous distribution of our clients ages. That is to say, for each age, the curve gives is the number of clients of this age.

We’ll look at the dataset. Right-click on « Churn Modelling » and select « View Data… ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

There is window that appears that shows us the data in detail. If you scroll to the right, you will find the column « Age ».

tableau, bins, bar, chart, distribution, age, data, science

We see that the ages rounded. As all ages rounded, Tableau is able to group clients by age. By positioning the mouse on the curve, we can see that there are 200 clients who are 26 years old.

tableau, bins, bar, chart, distribution, age, data, science

If in the dataset, ages weren’t rounded, you would have seen clients with 26.5 or 26.3 years. It would create a lot of irregularity, there would be plenty of spikes with lots of variations.

Oooooh look, there is a variation that isn’t normal.

tableau, bins, bar, chart, distribution, age, data, science

Let’s analyze it in detail. Around this peak, we see that there are 348 clients who are 29 years old.

tableau, bins, bar, chart, distribution, age, data, science

Here, 404 clients who are 31 years old.

tableau, bins, bar, chart, distribution, age, data, science

And this peak down that shows us that there are 327 clients who are 30 years old.

tableau, bins, bar, chart, distribution, age, data, science

How to explain this irregularity ? It’s possible that many people of 29 years old are about to turn 30 years old and many people of 31 years old who just had 31 years old. It’s chance that make us have inaccuracies. You may have other inaccuracies if you data isn’t precise and rounded. In our case, the ages are rounded but we want to get rid of our small irregularity that we see on our curve.

There is way to see our distribution without our irregularities, it’s « bins ». « Bins » consists of grouping the information into different categories. That is we’re going to regroup our clients in different age groups.

Right-click on « Age » in « Measures ». Select « Create » and select « Bins… ».

tableau, bins, bar, chart, distribution, age, data, science

A window appears. We’ll group our clients in 5-years increments. In « Size of bins », write « 5 » and click on the « OK » button.

tableau, bins, bar, chart, distribution, age, data, science

As you can see, the variable « Age » has remained in « Measures » but there is a new variable in « Dimensions ».This is the variable we created « Age(bins) ».

tableau, bins, bar, chart, distribution, age, data, science

Our « Age(bins) » variable was correctly placed in « Dimensions » because it is a category variable because each category corresponds to a 5-year age group.

For example, one category is 20 to 24 age group. Now we’ll create a new distribution based on « bins ».

To do that, we’ll remove the variable « Age » from « Columns » with a click and drag outside.

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

You move the variable « Age(bins) » from « Dimensions » to « Columns ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

Note

In this case, it’s not possible to directly replace « Age » by « Age(bins) » over « Age » on « Columns ». This is because « Age » is a measure and « Age(bins) is a dimension.

That’s nice distribution, it’s usually the type of distribution (chart) we see in economics or mathematics. The difference with the old chart is that this chart is discrete. This chart is discrete because the clients grouped by age group while the previous chart was continuous.

On this distribution (chart), each bar corresponds to an age range. For example, this bar corresponds to the 25-29 age group.

tableau, bins, bar, chart, distribution, age, data, science

Now, we’ll change the colors.

In « Row », move « SUM(Number of Record) » while holding down the « Ctrl » or « Command » key on your keyboard to « Colors ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

We get our distribution in blue but we’ll change the color to red. Click on « Colors » and click on « Edit Colors »

tableau, bins, bar, chart, distribution, age, data, science

In the window that appears, click on the blue square on the right to display the color pallet.

tableau, bins, bar, chart, distribution, age, data, science

Select the red color and click on the « OK » button.

tableau, bins, bar, chart, distribution, age, data, science

Click on the « OK » button of the « Edit Colors » window.

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

To facilitate the reading of the bar chart, we’ll add the number of clients in each age group. In « Row », move « SUM (Number of Record) » while holding the « Ctrl » or « Command » key on your keyboard to « Label ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

That’s it, we can see how many clients there are in each age group.

We see that the dominant bar is the 35-39 age bracket and the second dominant bar is the 30-34 age bracket. Overall, we can see that most clients are between 25 and 40 years old, which seems consistent.

On our bar chart, we have absolute values. We’ll replace that with percentages. Click in the little arrow in « SUM(Number of Records) » in « Label » and you select « Add Table Calculation… » but I’ll show you another way to do it.

tableau, bins, bar, chart, distribution, age, data, science

Instead of clicking « Add Table Calculation… », click on « Quick Table Calculation » and select « Percent of total ».

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

It’s cool, we have the exact percentage of people in each age bracket. Now, we can see that in the 25 to 40 age group, we have 20 + 23 +17= 60% of clients.

I’ll show you one last thing.You can change the size of the slices easily, just click on « Age(bins) » and select « Edit ».

tableau, bins, bar, chart, distribution, age, data, science

In the windows, you can change the size of the slices (bins). Put « 10 » instead of « 5 » to get 10-years slices. Click on the « OK » button.

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

Now, we have a distibution with fewer slices and the dominant slice is 30 to 39 years old.

Well, it was just to show you how to change the size of bins. To go back to the old distribution with the 5-years slices, click on « Back » button.

tableau, bins, bar, chart, distribution, age, data, science

tableau, bins, bar, chart, distribution, age, data, science

As you can see, the values on bars are in percentages but the values on the axis are in absolutes values. Here is an exercise that I ask you to do : « Put the values of the axis in percentage ». I’ll give you the answer the next article.

Share this article if you think if can help someone you know.Thank you.

-Steph

A Pratical Tip To Validate Your Approach

data science tableau check

I have just enrolled in a Data Science course on Udemy  and I learned good stuff.

How was the A/B test « Number Of Product » ? Easy or difficult ?

Here is the result I found.

data science tableau check bar chart

I think you noticed there was something bizarre. There is an anomaly. We imagine that the more the client has products, the more the client is satisfied with the bank so this type of clients should stay in the bank.

In the first 2 bars we can see that a client who has 1 product is more likely to leave the bank than a client who has 2 products. But when a client has 3 or 4 products, we see a huge rate of clients leaving the bank.

Look, there is a little bizarre detail. In the 2nd bar, we can’t see the « Exited » label. This is because there is no place in the orange part to put the text. To make it simpler, we’ll remove the label « Exited ». Drag and drop on the « Exited » text label to the outside.

data science tableau check bar chart

data science tableau check bar chart

Perfect, we can read the percentages. On the 1st bar, we can see that among the client that have 1 products, 28% left the bank. On the 2nd bar, we can see that among clients who have 2 products, 8% left the bank. This show us that clients who have 1 products are more likely to leave the bank than clients with 2 products.

And for the next bars, we observe an anomaly. On the 3rd bar, we can see that among the clients who have 3 products, 83% left the bank. On the 4th bar, we can see that among clients who have 4 products, 100% left the bank. We clearly see that there is a problem and we need to do a deeper analysis to understand what is going on .

As a Data Scientist, we need to explain what happens in bars 3 and 4. Usually when a client has 3 or 4 banking products, that means he/she is satisfied and is loyal to the bank. But in our case, it’s the opposite because there is a high rate of client who left the bank. This is the time to do deeper analysis.

The first thing to analyze is the quality of the data. There is a very big anomaly and it may be because there is something insignificant in our data that disturbs the statistics. For example, it’s possible that when the bank selected these clients in this sample, there were very few clients with 4 products and all those clients with 4 products left the bank. Sometimes chance can create anomalies and you have to play attention to these effects of chance because they don’t seem important but they can create false interpretations.

To start, we will check the number of clients with 4 products.

In « Measure », move « Number Of Records » (which gives the number of observations) on « Label ».

data science tableau check bar chart

data science tableau check bar chart

We observe on the first 2 bars than many clients with 1 or 2 products selected for our sample. For clients with 3 or 4 products, we can see that there were fewer clients selected for our sample.

There are 220 clients with 3 products and 60 clients with 4 products. These small number of clients probably explain why we observe these anomalies.

In this sample of randomly selected clients, there are very few clients with 4 products and they all left the bank. In this situation, we can confirm that it’s a chance. When thing like that happen, you have to be very careful not to make conclusion too fast and make misinterpretations.

The conclusion is that a lot of clients have been selected for category 1 and 2. For category 3 and 4, there have been few clients selected so we can’t do accurate statistics. We need to do deeper analyze for these categories of clients with 3 and 4 products.

Now, let’s put the percentage back on the bar chart. Click on the « Back » button.

.

data science tableau check bar chart

Or do a click and drag of « SUM(Number of Record) » to outside.

data science tableau check bar chart

data science tableau check bar chart

We saw that there is an anomaly and what is interesting to do is to have a comment to remember to do a more in-depth analysis of columns 3 and 4.

Right-click between the bar chart’s title and the bars. Select « Annotate » then « Areas… ».

data science tableau check bar chart

A window appears. In this window, you write « Low observation in last 2 categories » and click on the « OK » button.

data science tableau check bar chart

data science tableau check bar chart

Click on the comment and move it on bars 3 and 4.

data science tableau check bar chart

data science tableau check bar chart

The next time you work on this bar chart, you will see this comment that will remind you to seriously analyze client who have 3 and 4 products.

Validate our approach

It’s time to show you how to validate an approach and how to validate the data. For this we will create a new A/B test.

Duplicate this worksheet with a right-click on the « NumberOfProducts » tab and select « Duplicate ».

data science tableau check bar chart

And rename the tab « Validation ».

data science tableau check bar chart

For this tab, we will erase the comment. Select the comment and press the « Delete » button on your keyboard.

data science tableau check bar chart

data science tableau check bar chart

Everything is ready, the idea is to find a variable that doesn’t affect our results. That is a variable that has no impact on a client’s decision to leave or stay in the bank.

Take for example, the variable « Customer Id ». Client’s identification number has no influence on the client’s decision to stay or leave the bank.

We’ll do an A/B test with the last digit of the « Customer Id » and we’ill check that there is the same clients proportion who leave the bank in the 10 categories of the last digit of the « Customer Id ». The 10 categories are the numbers 0,1,2,3,4,5,6,7,8,9.

Let’s g.To start, we will create the variable that contains the last digit of the « Customer Id ». To have this variable, we will create a « Calculated Field ».

Right-click on « Customer Id », select « Create » and click on « Calculated Field ».

data science tableau check bar chart

data science tableau check bar chart

Name the calculated field « LastDigitOfCustID ». In the text field, we use the « RIGHT » function with « Customer Id » in parenthesis to select the last character of the « Customer Id ». In our case, the last character of the « Customer Id » is the last digit.

Here is the code to write in the text field : Right ({Customer Id},1)

data science tableau check bar chart

data science tableau check bar chart

Oooops, you see there is a small mistake => The calculation contains errors.

There is an error in the formula because « Customer Id » is a number variable and the « RIGHT » function applies to a variable of type « STRING ».

To use the « RIGHT » function, we will convert « Customer Id » into a string. We will use the « STR » function with « Customer Id » in parenthesis.

Here is the code to write in the text field

And click on the « OK » button : Right (STR({Customer Id}),1).

data science tableau check bar chart

Now, you can see that our calculated field « LastDigitOfCustID » is in « Dimensions ».

Click on « LastDigitOfCustID » and move it on top of « NumOfProducts » in « Columns ».

data science tableau check bar chart

data science tableau check bar chart

Now we have a new bar chart and we see that for every last digit of the « Customer Id » there is about the same proportion of clients leaving the bank. All these proportions don’t correspond exactly to the average of 20% but these slight variations aren’t important.

Seeing this uniform distribution allows us to validate our data because these data are homogenous.

Conculsion

Here’s how you can check the homogeneity of your data. You take a variable that has no impact on the fact that a client leaves or stays in the bank. The example we did with the last digit of the « Customer Id » is excellent. We were able to verify that in each of the categories taken by this variable, if there was the same proportion of clients leaving the bank. As is the case, we can validate our data.

Imagine another result. When we do the test with the last digit of the « Customer Id », we observe that for one of the numbers, the rate of clients who left is really higher than the average. This shows us that there is a problem in our data because it indicates an anomaly.

You can find other ways to verify your data by using other « insignificant variables » to see if the distribution is homogeneous. But be careful when you select an « insignificant variable » because there may be traps.

Here is an example. If you create a variable that takes the first letter of the first name, the distribution will not be homogeneous. The reason is simple, there are many more people who have a name that starts with the letter « M » than with the letter « Y ».

Share this article if you think it can help someone you know. Thank you.

-Steph

Boost Your Marketing Based On Science (Part 3)

tate tougue bitter sour sweet salty

I watched an Olivier Roland’s video and I learned good stuff.

Click if you didn’t read Part 1  and Part 2 .

Taste

Taste is a bit of an amalgam of 5 senses because it’s necessary to use 5 senses to have the full sensation of taste in the brain. It’s enough that it misses 1-2 senses so that the flavor modified. When a brand works to be recognized by several senses, it improves the credibility and the memorization of the brand.

An effective sensory marketing is based on the coherence of the senses to increase the positive evaluation of products, stores and visiting intentions. This multisensory experience increases the probability of creating strong and lasting emotional connection with the consumer.

Cognitive ergonomics, pricing, distribution and sales

cognitive ergonomics

Rules of innovation, presentation product/service and a good selling price increases the turnover and improves a brand’s perceptions. Consumers spend more time in a store when the place is thematized, theatrical or with a sensory experience.

Creating nice design for the brain helps a product to succeed in a market. Certain specific packaging’s elements attract the attention and interest of the brain. Imaging and iconography provike emotional evocation, color awakens somatic makers, writing gives meaning to the product and the brand gives the product qualities linked to its essence.

The preception and the price of a product/service by the brain have important consequences on the amount of purchases. An inapproritate price compared to consumers perception on the price-quality ratio can cancel a sale. A consumer refuses to pay for an expensive product if the quality seems low. With a price too low, a consumer thinks that the quality is bad.

The psychological price, which is the price that a consumer is willing to pay to buy a product/service, is the fundamental basis for the price policy.

Pay creates an impression of pain in the brain and there are several solutions to decrease or avoid this feeling : payment term, credit, deferred payment, product/service presentation like Premium, billing per month rather than per hour, price below a fixed price ($ 299.99 instead of $ 300.00).

When stores are thematized, theatrical or with a sensory experience, it increases the presence time of consumers as well as the interest. The result is an increase in turnover and an improvement in the brand perception of the store.

Subliminal relationship

subliminal messages

Since antiquity, communication has used many subliminal techniques to be more persuasive. Subliminal communication target directly the brain’s subconscious. Ads is still based on Aristote’s rhetoric method to improve credibility and Cicero. Here are Ciceron’s recommendations  : It’s necessary to prove the truth of what you say, it’s Logos. To reconcile the benevolence of the listeners by using the Ethos and to awaken in them all the emotions useful to the cause with Pathos.

  • Logos

    This corresponds to the choice of logical and rational messages. Theses messages show indisputable arguments about the quality of the product/service on an innovation that other brands don’t have.

  • Ethos

    This is to give confidence or seduce consumers.

  • Pathos

    It’s about message based on emotions.

Communications can be based on hedonism to look for inner well-being or joy like luxury or sex. To trigger strong emotions and enter in the memory, messages can be violent as for example advertisements on the road safety.

Subliminal communication target directly the brain’s subconscious, bypassing the barrier of reasoning. Even though subliminal communication is prohibited in some countries, many methods are allowed and used in advertising.

Advertising entertainment is an advertising based on the spectacular using humor or art with the harmony’s rules like Golden Ratio , Vitruvian Man , eroticism, etc.

Brands works with emotions to influence the consumers brain to make purchase. Consumer’s brain is attracted by brands whose history is reminiscent of known myths memorized in the unconscious (copy by somatic markers).

Brands need to create a story, an original epic that can be told in the community and be told for several generations.

Community and social networks

community

Community and social networks influence the consumer’s individual consciousness, which make it a collective consciousness. A consumer has access to a huge amount of information from different sources in real time. This information comes from all over the planet. This access to information allows the consumer to make comparisons before buying. With interactivity, a consumer becomes a neuro-consumer-actor. We’re living the creations of a new behaviororal generation of neuro-consumer multiprogrammed, it means people who consult several media at the same time.

We’re slowly entering a world where zapping replaces logic and reflection. A world where people prefer sequential information based on emotion rather than linear information based on conceptual reasoning.

Several factors influence the purchasing behavior : free economy, uberisation or participative economy of purchases, return of auctions, the wait of the proposal of purchases of the last minute, used to choose from several references as with Amazon (70 millions products), geolocalisation use of different sales channels (social networks, internet, smartphone, etc).

There are methods to meet these new expectations, permission marketing and digital marketing desire.

  • Permission marketing

    Permission marketing allows communication only with prospects who have given their agreements.

  • Desire digital marketing

    The digital marketing of desire is based on quality content and the pratice of « one-to-one ». « One-to-one » is the creation of a personalized relationship with each consumer and to develop this relationship.

Consumers saturated by the advertising of digital marketing gives a very limited confidence to the brands coming from traditional communication. They prefer to seek advice from people who have already tested the brand, product or service.

Use a viral marketing, buzz or word of mouth to spread positive communication on social networks across communities can seriously increase sales. This viral marketing, buzz or word of mouth is based on the oldest media in the world : the rumor. With the development of internet (website, blog and social networks), recommendation becomes more important than the communication.

Share this article if you think it can help someone you know. Thank you.

-Steph

Boost Your Marketing Based On Science (Part 1)

brain

I watched an Olivier Roland’s video and I learned good stuff.

Let’s see what neuroscience found on things that influence people to buy.

The book of Michel Badoc and Anne-Sophie Bayle-Touroulou « Le neuro-consommateur » (in french)  helps us better to understand this.

It’s a book that has been written for other researchers and academics. This is why this book is very interesting for entrepreneurs and consumers.

Here are the elements from this book to boost your marketing.

Until now, marketing and communication are based on the rational purchasing decisions and perceptions of advertising messages by the consumer. But neuroscience shows us that a huge part of our actions come from the subconscious part of our brain.

For A.K Pradeep  and Martin Lindstrom , only 15% of purchasing decision are rational. Current marketing studies limited in the accuracy of customer behavior. What customers say doesn’t always match what they do. Responses collected during a market study can be influenced by context, which disturbs responses. With neuroscience, we can directly communicate with the brain to try to improve marketing.

Here the elements found in neuroscience on the unconscious behavior of consumers.

  • Age et gender

  • Memory

  • Emotions and desire in the decision

  • 5 sens

  • Cognitive ergonomics, pricing, distribution and sales.

  • Subliminals relationships

  • Community and social networks.

Let’s go, we’ll see that in detail. We’ll start with age and gender because these 2 create behaviors and attitudes, sometimes, difficult to understand by a person who doesn’t belong to the same category.

Age

reptilian limbic neocortex brain

Reptilian brain

It’s the center of instincts and the satisfaction of primary needs. This mainly affects young children. They respect the leader who is the mother or the father but also the strongest person who can protect them in case of external danger.

Limbic brain

It’s the center of stress emotions, instinctive behavior and memory. This mainly influences teenagers. They are mainly attracted by new brands / products and original fashions that can distinguish or oppose them to adult fashions.

Teenagers are often interested in causes or subjects with a lot of emotions : social, humanitarian, ecological, fair trade, etc. They prefer emotional communication over rational information.

neocortex,

It’s the center of anticipation and decisions. This mainly affects adults.

With internet, we can see several big differences in the generational behavior of consumers. There are Digital Native and Digital Immigrants and these 2 categories require different approaches.

digital native immigrant

Digital Natives

These are the people who grew up with computers, smartphone and internet. They prefer to have jerky information without verb and without object complement. They can read in parallel information on several different media. They don’t need to structure their thoughts and they can have a random read mode. They feel emotions much more with colors and designs rather than structured text. They want things to go fast.

Digital Immigrants

They prefer a linear processing of information. They like the text’s logic. They wish to receive the information in a slow way with consistency in the structure. They want to keep their privacy and are wary of the information’s distribution on internet. They sometimes want to work alone.

Gender

gender male female

There is a distinct difference between the behavior of female and male consumers.

Female

The left hemisphere of the brain is more developed in women and they’re subject to the hormones influence. We can see more of this phenomenon when a woman becomes a mother. A woman like to communicate more that a man, she likes to talk and be listened to. She needs shares her ideas, feelings and emotions.

She’s very well oriented in time. A woman is less emotional than a ma but she is more sensitive because she has the sens of smell, hearing and touch more developed than a man.

Male

The right hemisphere of the brain is more developed in man and they’re subject to the influence of testosterone. A man is more emotional than a woman, but he expresses less his emotions. He likes action and competition. He’s very well oriented in space, which allows him to find shortcuts. The man’s view is very developed and is eroticized. This explains why the man is attracted by the nude, jewelry, makeup and clothes.

For these reasons, it’s easier to mee a man’s expectations compared to a woman’s expectations.

Differences

difference

Male

As you can see, man primarily uses his view to select a product or service that he can use to show his strength and seductive power. He likes offers that give short-term profits. He prefers simple and direct communication. He prefers images rather than text. Price is more important for the man than for the woman.

Female

A woman is more complex in her expectations. She processes information in a way that is both rational and emotional. A woman is not attracted by nudity. She is attracted by a neat person with harmonious clothes and neat hands. In the case of a salesman, a woman has no preference for a man of a woman. This is influenced by several elements : voice, smell, facial expression, capacity to listen and quality of answers of the salesman.

A women prefers written and documented communication. She likes social media because she can express her ideas and meet people who share her points of view. She filters rational messages through her emotions. She likes positive communications. Before selecting a product/service, she will compare it with competitors and get information with her friends, co-workers and other people with experience.

A woman is less impulsive than a man even if a purchase can serve as an antistress. For a woman, the touch’s quality and the smell can influence a purchase like clothes.

This is the end of the 1st part.

Share this article if you think it can help someone you know. Thank you.

-Steph