Search

Iterative Path

Marketing Strategy and Pricing

Brands Convincing us to Part with our Money

I’m Prithi, the fifth grader, writing this post.

Nestle has pretty good branding skills, especially when it comes to popular products, of course, they take it to extremes sometimes. People want refreshments, but they want to be healthy. The bottled water industry is very popular these days, along with all those diets and health movements.

Nestle cleverly takes advantage of us, as we are more than willing to part with our money, after their advertisements. They want to increase their margin, as Nestle Water provide the least margin of all, out of Nestle’s repertoire.With their smart advertisements, such as this one, they make us feel as if life cannot go on without Nestle Pure Life bottled water.

But do you really know the difference between one bottled water and another, when we pay the premium price?

Simple Price-Value Math from Tesla

Tesla discontinued its $71,000 Model S 60D and replaced it with Model S 70D that comes with all-wheel drive.

Its next higher edition in Model S is Model S 85D that has 85KWh rating (hence longer range) than 70KWh rating of 70D. The 85D however does not come with all-wheel drive, you upgrade by paying $5000 more.

Here is how the price value map looks like among the options

tesla-pic.001Essentially

Additional 15KWh  –  AWD  = $6250

Additional 15KWh + AWD    = $12500

You do the math on how much additional value customers assign to each feature. Clearly Tesla has done the math.

Is the value perception of AWD same for someone decided on 70KWh version vs. one who prefers 80KWh?

 

Nestle’s Marketing Fallacies

Hi, this is Prithi, the fifth grader writing about a wide span of topics, but today my post is on a marketing error.

There is a common noodle soup snack in India called Maggi, made by Nestle. This new advertisement they made has one main messaging error. Watch it carefully, then speculate.

The big revealment:

Take a look at the nutritional content of Maggi. Why would mothers want to feed their children something with so much sodium and fat?

Furthermore, in the advertisement, it shows a girl wanting to be self-reliant, but she should have learned to cook from scratch and eat healthy . To open a pack of processed food, boil it in water, and call that self-reliance is ludicrous and an insult to women in general.

 

 

Data Mistakes Even Children Can Find

Note: I am happy to announce a new  addition to my blog, Prithi Srinivasan, presently a fifth grader. Look for her thoughts and analysis on data, coding, probability and economics.


 

Take this graph on iOS book sales, for example. Look at it carefully.

Can you spot any mistakes, because I sure can: three of them, and I’m only a fifth grader.

The big revealment:

1. The first mistake is that between 0% and 14%, there should be a break, as all the values other than 0% to 14% increase equally.

2. There was one exception to that last statement. Between 15% and 17% there has to be a 16%— not a break.

3. It should be a bar graph! Line graphs are meant to show differences over time–as one big concept. This graph ought to be a bar graph because it is showing the percentage of book sales per day. It shows separate concepts.

If they get paid to create graphs with many errors, then I should get paid for fixing those errors.

A teachable moment in charts, statistics and Data pseudoscience

Here is a chart and the associated assertion on best times to launch iOS apps. This comes to us from data collected and analyzed by SensorTower. (This website and image are linked on April 4th, hopefully they change it all when you read it.)

The claim: Sunday is the best day to promote purchases, period.

Data collection: our Data Science team did a study of all the primary iOS categories to find out which days of the week typically have more estimated downloads and revenue.

I take it what the chart and data is self explanatory. Now can you point out the flagrant flaws in the chart, data and the claim?

If you are not able to get past the beauty of the chart and the boldness of the claim show it to a fifth grader who is not afraid of calling out that the emperor has no clothes.

This is labeled data science which should not impress you or overwhelm you. There is no science here and by any current definition of “data science” this does not even come close. Because you know with data science there is hypotheses, statistics, data cleansing and data validation involved.

Here are the easiest ones you should be able to call out.

  1. Look at this chart and all other similar charts for several categories in that blog post. Look at that nice smooth lines. Then look at the data points again. These are averages per weekday and hence discrete by definition and must only be shown with using bar/column charts.
  2. Look at where the y axis close. It seems standard linear scale. Then what happened to 16? If these are percentages then they should add up to 100%. If I eyeball the data points from the graph I should get within a percentage of 100%. But you get14.9+14.3+14+13.8+13.7+14.8+17.1 = 102.6
    We must conclude that the 17% is really 16%, even there there is error in data.
  3. Take a look at the bold assertion that “Sunday is the best day, period”. To make such a claim, the venerable data scientists should not eye ball it or take the 1% difference as significant – economically or statistically. They must run Chi-squared test to see if the difference is statistically significant. When you do so with help of an online calculator you will find it is not
    chi-square-2When you do you will find the differences are not statistically significant and just part of the randomness. You would think the data scientists would know do to this.
  4. Finally look at all the other charts and the claims about each category of apps. If you were to do another cross cut of these numbers and run another statistical test you will find there is no statistically significant difference across the App categories.

I don’t do data science at least not the way some of these sites do.

Even if the differences are statistically significant one has to ask of it is economically significant. Is the 1-2% difference enough to shift your operating procedures? Just because you shift more of promotion dollars to Sunday will you keep getting more benefits? Don’t forget Fallacy of Composition, if just one marketer takes advantage of Sunday that would work but what if all shifted their launches to Sunday?

Where do you stand?

Decision Making Under Uncertainties – Statistical Modeling

Whether it is filling out March Madness bracket or making investment decisions on a venture it all comes down to scenario analysis taking into account the variabilities. Because it is the variance that kills you.

In past years The Journal used to run Blindfold March Madness Bracket to help us make data driven decisions to fill out the bracket and pick the winner. This year they simplified it for us and yet made it extremely sophisticated. They codified the priorities, added a way to introduce a level of randomness and made a sophisticated statistical modeling for scenario analysis.

In essence we all can be Nate Silver with this model.

Whether it is filling a bracket for entertainment or a key investment decision the process comes down to what WSJ recommends:

  1. Choose a starting point: What is important to you?
  2. Customize your priorities: Assign relative priorities – like is it the people? market? traction?
  3. Account for unknowns
  4. Understand your biases and quantify the level of bias you have – for example how you “feel” about the person
  5. Run statistical simulation

How do you make decisions under uncertainty?

Create a free website or blog at WordPress.com. | The Baskerville Theme.

Up ↑

%d bloggers like this: