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Iterative Path

Marketing Strategy and Pricing

Author

Rags Srinivasan

Just Add Mind

This is the age of abundance. More than that, abundance that is easily accessible anytime, anywhere and using any method.

dataThere is abundance of data – On every possible economic and social activity imaginable. All accessible with tools you already know or with just few lines of code. More data in more quantities on more topics are now available than ever before and it will only increase with time. Granted, sometimes it is easy but not free, but for less than the cost of good bottle of wine you can tap into incredible data sets.

AWS_Simple_Icons_Compute_Amazon_Elastic_MapReduce_HDFS_ClusterThere is abundance of compute – From your own computer to the power to tap into nearly unlimited compute farm in the cloud. All available on demand, when you need it without need for investments or your own IT team. I am talking about individuals with such access to abundance, not just firms.  If what you get for free is not enough, for less than the cost of a bagel (without schmear) you can let loose amazing compute power on that abundance of data you have access to.
moocThere is abundance of knowledge – Do you want to do simple data manipulation with Excel? Or do you want to get data from a source using its API with any programming language? How about unleashing a cloud compute farm on massive data sets? What sounds like a complicated project is broken down to its parts and taught to you in few easy steps.

So there is no scarcity of data, no dearth of tools and no dearth of how to put these together. What is scarce?

mindApplication of mind. There is real and significant lack of critical application of mind. Unless you have a nagging question to ask or a problem that just won’t go away there is no use for the aforementioned abundance. Unless you are insatiable – have bottomless curiosity and persistent in keeping with a problem – there is no value creation. If you willfully suspend your curiosity or don’t feel the constant pain of unresolved problem, there is not much all that data, compute and knowledge can do for you.

What is missing is you. Your mind. A mind that has innumerable questions and feels restless about those questions. Only that can bring together all the abundance to create real value.

Just add mind!

How Product Positioning Helps Set its Price and Define Competition

Does the market really set the price for your product? Do you have any control over price? How can you then succeed with a Gym that charges $500 per month when market decides the rate is $45 a month?

For this we need to start with Segmentation and Product Positioning.

Customers buy products get a job done. And the job to be done varies with customer segments and context. The term ‘job’ really represents the unmet need at hand. It covers both utilitarian as well as hedonistic (and hence needs and wants). It also makes sense to view this as ‘hire’ decision since they can either stick with what they already have or switch when they find better alternative.

Positioning is creating a unique, relevant and differentiated positioning in the minds of the customers for your products. Product positioning is telling customers – specifically, targeted customer segment- what job you want them to hire the product for.

Customer Jobs To Be Done Growth Matrix

. From the jobs perspective it is telling them clearly which job you want them to hire for and why your product is the most suited candidate for that job. While your product could be hired for many jobs you want to go after those where you have sufficient differentiation and also pay well.

Take the case of boutique gyms that charge close to $500 per month when you can sign up for most gyms for $30-$50 a month. In fact you can get an year worth of membership for less than one third of what you for a month at boutique gym. But customers are flocking to the boutique gyms, happily paying far more than what they used to pay (take that reference price). Market research says of the 54 million members of fitness facilities, 42% use boutique gyms paying premium prices. That number is nearly double of what it was the previous year.

Take the more recent example of SoulCycle which filed its S1 for going public. It teaches riding stationary bikes set to music and charges $35 per class and had 2.8 million rides last year from just two states (38 locations).

What happened to market deciding prices? How do you get customers to pay more for what they can get for free or cheap?

It starts with segmentation and ends with product positioning. The target segment clearly has not only willingness to pay but also enough wherewithal to pay. The goal is not market share although that could come later. The goal is give the target customers an excuse to pay their premium prices, willingly.

If fitness is the job to be done the candidates available for this job are very many. Most are free – just put one step in front of other and keep walking. In the organized fitness arena there are many chains that offer equipments and some training. So if you position your new product – the boutique gym – purely for fitness the price you can charge for it is determined by the price of alternatives available to the customer.

On the other hand if you expand the job to be done beyond fitness – more like make fitness as included freebie while you focus on higher order jobs the alternatives shift and hence the price points shift. The boutique gyms focus on different customer job to be done than fitness,

As exercise routines serve more roles in people’s lives—stress relief, psychotherapy, social outlet, even personal identity—the expense of boutiques becomes easier to justify, their devotees say.

Boutique-fitness fans also say they like the fact that most workouts are led by instructors or coaches. Some say they feel a sense of belonging that overrides the fact that they’re spending more for fewer disciplines than what’s available at a health club.

If the job to be done is therapy or a social outlet the alternatives are prices are much higher price point than just gym membership. The visit to gym becomes more than aboring routing, it is an experience that creates sense of belonging. So the boutique gyms get to signal the higher price point and set a price just low enough below the alternatives to get customers to pay.

Take the specific example of SoulCycle which states this in its S1,

SoulCycle isn’t in the business of changing bodies: it’s in the business of changing lives.

What we create: A community for our riders.    SoulCycle is a business built on relationships. It starts with our leadership and extends through our studio teams, instructors and corporate employees.

We build our rider communities by developing relationships with our riders and encouraging them to develop relationships with each other every day. The concept of community and mutual support is reinforced in every single SoulCycle class. We ride to the rhythm of the music, moving on the bikes together as a pack. We are accountable to one another during class, and we celebrate our journey together when class comes to a close. We believe the SoulCycle experience fosters loyal communities of riders whose relationships extend well beyond the doors of our studios.

If I ask you to pay $35 per class for teaching you how to pedal a stationary bike you most likely would laugh at me. If I offer you to build relationship, community and mutual support you will be more than willing to fork over that price.

Market does set prices for alternatives. But you get to choose which alternatives you want to be compared against by positioning your product for the right customer job to be done.

Final note of caution – whether the positioning is sustainable or not is not discussed here. We do not know how defensible is the current positioning of SoulCycle and their ilk. I will discuss the topic of sustainable positioning in coming weeks.

What Data Says on Increasing Retweetability of tweets by adding links

TLDR: Data scientists at IterativePath have analyzed tweets over weeks and break the myth of causality of links in tweets to its retweetability.

Admit it you worry about getting your tweets retweeted.  Hopefully  many times, but you will settle for just once. You wish someone lot more famous will notice and retweet your sparkling thought.You come up with something original, innovative and awesome. After all it is only fair that the world sees it and appreciates it by spreading it.

This applies to individuals or brands (which essentially have interns with fancy social media titles manage tweets).

So you look for ways to increase retweetability – what knobs can you turn.As you do a google search you chance upon this article titles, “scientifically proven ways to increase retweetabilty“. One such proven way from that article is:

Adding links to a tweet increases its retweetability.

That article convinces you to arrive at this conclusion (because you have willfully suspended skepticism) by stating this observed data (mind you, from analyzing 10s of thousands of tweets)

  1. Among the tweets that were Retweeted, 56.7% had a link in them
  2. Among the tweets that were not Retweeted, 19% had a link in them

If you went back and read the article I did on these numbers you will see this does not say anything about retweetability.

I decided to do my own testing. Unlike others who do data dredging I practice data science so I started with the hypotheses

Null hypothesis H0: Any difference found in retweetability of tweets with links and no links is just randomness.

Alternative hypothesis H1: Links do make a difference in retweetability.

Method: I will be non-parametric test — Chi-Square test for test for statistics. This test of statistical significance is non-directional. That is  it is not going to tell us which way the difference favors but only that the difference is statistically significant or not.

Data collection:  Randomly collect a sample of original tweets (that is excluding those “RT @SomeOne …”) and analyze them. I used python-twitter API to collect tweet data and metadata like retweet count, links in it or not etc. I collected about 3500 samples — good enough. In fact too large.

Data Summary:   Here is what the data stands

No Link Link Total
No RT 2248 1072 3320
RT 177 44 221
Total 2425 1116 3541

First observation is just about 6% of any of the tweets got even a single retweet. Vast majority of your original tweets get no love whatsoever.

But that was not the hypothesis. So let us test our hypothesis with data in row 2 by calculating Chi-square value.

Results:

chi-sqr

The difference is indeed statistically significant. That is links do make a difference. But note what I stated about chi-square test being nondirectional. So you need to look at the data and apply mind to see which way is the difference.

You can see that just 20% of retweeted tweets have link in them vs. 80% have no link (row 2 of table above).

More importantly look at column 2. Of those tweets with links, 96% of them have 0 retweets. And just 4% were retweeted.

So links make a difference for the worse, breaking the myth propagated by any previous articles on this.

 

 

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?

 

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?

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