CONSUMERISM KILLS...EVENTUALLY!

(Life Coaching Series)

Last week, I was doing some quick errands to buy some pending grocery and sweets to cross my home-shopping checklist. I just happened to be on my toes for hours with constant updates over my mobile which included targeted ads across various apps and services that I regularly use. Let me tell you that I prefer walking out to buy my grocery rather than order it online, even in the post-covid era. Throughout my shopping spree, I was bombarded with “relevant” ads, some of which are listed below along with my take on them:    

 

For starters, water is made potable by several methods that include filtration, distillation, reverse osmosis, ultraviolet radiation, etc. Each of these processes along with cleaning the water depletes the mineral content in it. Consumption of such water form bonds with the minerals inside our body and is passed out through urine and sweat; thereby depleting our mineral intake. Firstly, they deplete the minerals from water to make it potable and then they add minerals to make it healthy. I would rather drink directly from a natural and clean pond than falling for this gimmick.

In the last decade, it has become the norm in our nation to gift chocolates on auspicious occasions and festivals. Thanks to this excellent ad that has carefully and consciously manipulated our thought process. We are a nation that celebrates at least three festivals per month (refer any Hindu Panchang i.e. Ephemeris) where each festival has an associated culinary diet that is most suited to the season and region. For example, “thandai” (natural home-made cold drink that cools the body) at Holi (around March / April) which is the onset of summer or “tilgud” (natural home-made sweet rich in oil content) at Makar Sankranti (around mid of January) during autumn. Why do we need to replace such a rich and healthy tradition with fermented and preservative infused food?

As far as I know, cold-pressed oil (still available in rural India) poses the least risk to our health since it is free of man-made chemicals and good cholesterol is preserved. In the modern refinery, filtration of oil takes place in the presence of CMR (Carcinogenic-Mutagenic-Reprotoxic) substances. No matter, what the brands might claim the end product is certainly a risky proposition. Any given day, I would bet on unrefined / unfiltered cold-pressed oil to keep my heart beating normally.  

With the advent of shopping malls and supermarkets, we have unwittingly started consuming processed vegetables instead of fresh vegetables. Most of us are unaware that the preservatives, artificial colors and artificial flavoring used for processed food often contain CMR substances and are never listed on the label. Is it just our ignorance or is it our fast-paced lifestyle that makes us too lazy to work with fresh vegetables?

Each of these ads are an example of brilliant consumerism and are probably used as case studies at B-schools. But the moot point is – where do we draw the line? This kind of consumerism is like a slow poison that kills both the consumer and the society in a gradual manner.

I’m neither a nutritionist or a medical doctor; but as a systems / process auditor I have had the opportunity to audit entire food chains across India & Africa. What I am presenting here is out of direct experience from years of observation, both as an auditor and a consumer. Just like you, I’m a consumer first and an auditor later. So, it does concern me when I find that I’m unaware of what goes inside my body willingly.

Remember that each time you eat, you are either fighting disease or feeding it!

Follow this simple formula to stay fit – OPPRESS:

Oil – Reduce your intake of oil and fried food

Processed – Avoid anything that is labelled as “processed” since it is mostly less natural than you think

Packaged – Avoid anything that is labelled as “packaged” since it is mostly less natural than you think

Refined – Avoid anything that is labelled as “refined” since it is mostly less natural than you think

Excess – Avoid any type of overindulgence including food for thought viz. digital exposure  

Sugar – Reduce the intake of sugar and sugar-based foods

Salt – Reduce consumption of salt and salt-based foods

This way you can OPPRESS any disease with ease and stay fit for a longer period. And, don’t take my advice with a pinch of salt – Just try it out and you will feel the difference!

Von Westendorp Approach Using Python

 

Von Westendorp Approach Using Python

(Artificial Intelligence Series)

Strategic pricing is a critical process of Product Management, when it comes to launching a new product. There are many methods to pricing your product and one of the most popular ones is the Von Westendorp approach. The beauty of this approach is that it provides a glimpse in the form of a “Price Sensitivity Meter”. Once the graph is plot, we get the following price points at the intersections:

a.       OPP (Optimal Price Point) – The ideal price where the consumer is most willing to pay.

b.      IPP (Indifference Price Point) – The price where the consumer is least willing to pay.

c.       PMC (Point of Marginal Cheapness) – The price beyond which the consumer will consider the product to be too cheap and might not consider buying.

d.      PME (Point of Marginal Expensiveness) – The price beyond which the consumer will consider the product to be too expensive and might not consider buying.

From the above four price points, we get the RAI (Range of Acceptable Index); which can be used to price our product. All said and done, let’s get our hands dirty now!

A survey is conducted to assess the likely price points with questions like:

·         At what price would you consider the product to be too expensive and out of reach? (Too Expensive)

·         At what price would you consider the product to be expensive but still worth buying? (Fleecing)

·         At what price would you consider the product to be a cheap and worth buying? (Bargain)

·         At what price would you consider the product to be too cheap that quality would be doubtful? (Too Cheap)

All the responses are collected and the dataset is arranged in the following manner:

Please note that the first two columns are sorted in descending order while the last two are sorted in the ascending manner. Using this dataset, one can plot the Von Westendorp graph in excel itself. While this might seem quite easy and does not require any other dependencies like a Python IDE; I personally detest doing it in excel. I will explain the reasons at the end of this article.

I’ve used Miniconda CLI, Jupyter notebook and .csv data format for this project. You may choose other IDE and tools that you are comfortable with like Spyder, NoSQL, etc.

Launch a fresh notebook and import the necessary libraries. Now, load the data and check the shape as shown below:

Next step is to remove the intransitive price preferences* and compute the cumulative frequencies, as shown below:

Once the above is done, we need to define a table where all the array values can be tabulated.

 

Now, we need to compute the results and get the values, as shown below:

The last step is to plot the graph:

The output graph will look like this:

So, that’s it, pretty simple, isn’t it? Well, no. It wasn’t that easy when I did it for the first time. Let me share my learnings with you. Prior to plotting it in Python, I did it with excel. I’m presenting a comparison table of the values that I got:

One can easily note that the values arrived at via Python are higher than those from excel. The reason being the following:

·         In Excel, it will accept all price points from the dataset as it is, in order to plot the graph while in Python, I have the freedom to “remove intransitive price preferences*”. If I had skipped this step, values from Excel would match those from Python.

·         It is imperative to remove the intransitive price preferences as not doing so would present a skewed graph, as well as, generate incorrect values.

·         As a result, my data shape dropped to 16 from 39; meaning entire 23 rows were invalidated.

The obvious question that would arise in the minds of data analysts and machine learning enthusiasts is – Why not scale or normalize the dataset?

The answer is plain simple here – applying normalization to the dataset would replace the suspect values (intransitive, duplicate, etc.) with the average or mean. Since, this is akin to “changing the respondent answers”, it would amount to gross manipulation of data and render the purpose of survey useless.

Thus, the key takeaways from this exercise are:

1.       Statistical inferences are better accomplished with Python than with Excel

2.       Respondents are prone to error and one might need to drop off the incorrect answers

3.       Adequate sample size is a must to ensure that there are enough data points to suffice for the inference

4.       Scaling or normalization is not a ‘one size fits all’ solution and must be applied with sufficient caution

5.       Intent needs to be preserved even if it is at the cost of the content

Note: *intransitive price preferences – the price points that are not linear but are concurrent. This can happen where a respondent might choose the same price point for more than one question. For e.g. Rs. 8000 selected as ‘bargain value’ as well as ‘too cheap value’.

P.S. You can contact the author to know more about this article. The sample python code is available at https://github.com/southpau79/humintonline .

For those lazy to read & have scrolled to find the end of page, here's the video:

OPINION MINING & JUDGEMENT ERRORS

OPINION MINING & JUDGEMENT ERRORS

(Artificial Intelligence Series)

Video link for this article:

Machine learning is a great tool for developing predictive models. One of the most interesting applications is its use in opinion mining. Opinion mining too is a broad field with numerous use cases ranging from product reviews to personality assessment. It is often argued by advocates of AI that human judgment is always biased and machines can perform better in this area. However, the bone of contention here is this – Who is training those machines? Well, without getting into the complexities involved let me bring your attention towards the psychological / technical basis of judgment and the error-prone systems that we face each day.

Products

How do we judge any given product? Without understanding this, training a machine to analyze huge chunks of data and generate results – be it a Recommender System or a UX dashboard; the value to effort ratio remains below unity. We have tried everything from the basic “queuing theory” to “gamification” and even the “hyper-game theory”, but still struggle to find the golden ratio. In an ideal scenario, the following equation will hold good:

When emotions overtake the intellect fueling our experience for the cause or reason-to-believe that goes beyond the expectation of the product itself, we make the decision to buy. Whether it is a need or a want, whether it is justified or not, whether it comes with a positive or negative feeling (e.g. vaccination is received out of fear), people buy something if and only if the perceived value is greater than the perceived effort. Great marketers and advertisers know this too well and they constantly exploit the emotions to evoke the decision to buy. 

Just look at the film industry and compare the movie reviews from the pre-covid and post-covid eras. Until two years ago, when the public could watch their choice of movie inside a theatre, their review would mostly largely positive where even mediocre movies would gain 3.5-4 stars on a scale of 5. Post-covid, when people are able to watch movies mostly on the OTT platform, their reviews have become more critical where everyone is seeking for quality content amidst binge-watching. Many starrer movies are being rated below 3 stars and seemingly underdog movies are being rated higher. The reason for this shift is that the pandemic has bitten into the overall “movie-going” experience, thereby letting the watchers use their intellect more rather than being swayed by their emotions that were previously being fueled with anticipation via teasers, trailers, promotions, campaigns and advertisements.    

Thus, no amount of “sentiment analysis” of movie review data can predict anything with accuracy; without understanding the basic premise or context. 

Processes

What happens before any output is generated? If we do not understand the process behind a given outcome, we are playing a “trial-and-error” game. Obviously one can’t play a game where the rules, the stakes and the quitting time remain unknown. This is one of the main reasons that domain knowledge is an absolute pre-requisite for delivering any kind of technological solution viz. machine learning in the current context. Understanding a process involves going beyond the process itself, as shown below:

With the kind of technological advancement we have today, one can create a robotic bartender and automate the process of serving drinks at the local pub. But, without considering a “process approach” that includes the process, the environment, the human factor and other variables, one will end up with nothing more than a “talking vending machine”. 

Training a machine simply by mimicking what a human being does, does not add any value to the process per se. If your algorithm can either augment or compliment what a human being or an existing machine can, then it is definitely worth it to take the leap forward. This is the main reason as to why the adoption of “Conversational AI” like chat-bots, digital assistant, etc. has not picked up on a massive scale. I know that many AI enthusiasts and experts will disagree with me and probably refer to several hypotheses and modern theories like AGI, ASI, Swarm Intelligence, etc.; but the harsh truth is that we are centuries far from it.            

People 

Why do people perceive something the way they do? The world we are living in today is primarily system-driven, be it the rule of democracy or online shopping. That’s what most of us are made to believe. But, if you do a reality check at the grassroots level, everything depends on people. End of the day, it is people who affect both processes and products, either directly or indirectly. Organizations with the best processes and products in place can fail if it is not support by the right kind of people. In terms of leadership, people factor is of prime importance. 

For the sake of our understanding, if we ignore the Fortune 500 list; all other companies including startups and SMEs heavily and solely depend on their leadership structure for their success, growth and / or survival. So, when it comes to selection of leaders or rewarding top performers, people make huge judgmental errors. Similar to management of products and processes, there are numerous theories for people management like BEI (Behavioral Event Interviewing), Psychometrics, Adam’s Equity Theory, Hertzberg’s Motivation Theory, Jay Hall Conflict Management Theory, etc. 

Most of these judgmental errors can be attributed to the misunderstanding of two most basic theories: 

Dunning-Kruger Effect – A hypothetical cognitive bias stating people with low ability at a task overestimate their ability. 

Imposter Syndrome – A psychological pattern of perceived fraudulence, where an individual doubts his / her skills, talents, or accomplishments despite having external evidence of competence.

It is simply illogical to compare Guy Martin (famous pit stop wheel changer) with Michael Schumacher (famous F1 racer). If one were to get into their minds, Guy would be found suffering from the Imposter Syndrome and Michael Schumacher facing the Dunning-Kruger Effect. Now, if one were to reverse their roles, what would be the effect? While this may seem to be an extreme example; such scenarios occur often in our day-to-day routines. An example that everyone can relate to is your appraisals. More than the actual ability and past performance, more weightage is given to attitude. Those who rate themselves high during self-appraisal are the ones who usually get the highest rating in the final appraisal. The fact remains that the lot who hold themselves in high esteem and exuberate confidence are actually part of the Dunning-Kruger Effect, while those who are self-doubtful lie somewhere in the Imposter Syndrome region. 

Thus when it comes to people analytics or consumer research, even the best of experts can go wrong when the primary data is based on opinions prone to judgment errors.  

The following quote sums up my point too well:

Summarizing the whole article, I can say that the following factors need to be accounted for before embarking on a decision-making journey based on analytics:

• Value created or projected (product-centric)

• Variables both known and unknown (process-based)

• Viewpoint of everyone involved (people-centric)

I help improve people, processes and products.. Reach out to me to know more. 🙂