Sometimes pricing is just wrong

#Pricing Puzzle – All things being equal (brand, cloth, design), should slim-fit shirts be priced same as regular-fit shirts?—
Rags Srinivasan (@rags) March 31, 2013

Take a moment and think about this pricing scenario. What do you think the pricing for slim-fit shirts should be compared to regular-fit shirts of same brand, material and design?

Logical answer would be slim-fit shirts should be priced higher than regular-fit because there exists a smaller set of customers who prefers the look of slim-fit and value it enough to pay more for it. After all, demographically there are not many that would fit (and look) stylish in slim-fit and for those who want to look good with a slim-fit there is value that can be captured as higher price.

This would appear to be a perfect case of second degree price discrimination. Present two different versions at two different price points and let the customers self-select. It is fair too because all customers have the option to choose either one.

Except that is not how shirt makers think about pricing or set pricing. Here is how shirt makers set pricing

First they add up all the costs – including hours spent and fixed cost (overheads) allocation. The use “standard industry markups” to set wholesale price. Finally double it to get retail price. (And mark it down to generate sales)

It is as simple (or simplistic) as that. Cost based pricing with price markups and not based on customer value and willingness to pay.

Hence if you see same pricing for slim-fit and regular-fit it is not just a matter of missing out price discrimination it is a matter of setting the price wrong to begin with.

If you see different pricing it is highly likely that shirt makers chose to allocate different overheads – likely more to slim-fit because of smaller volume – than because they recognized opportunity for price discrimination.

Sometimes things are not as smart as you would like to believe.

Customer Job To Be Done Growth Matrix

There is a very simple way to think about how to grow business. It requires us to think in terms of markets and products.

Markets – Current market segment you play in and new markets you do not serve yet
Products – Your existing products and new products you have not built yet (and are outside of your current product line)

That gives us four ways to grow any business

1. Sell more of what you make now in markets you already play
2. Sell something new – not just product extension, something outside your product line – in markets you already play
3. Enter new markets with your current products
4. Enter new markets with something new - not just product extension, something outside your product line

It is more popularly known as Ansoff Growth Matrix.

The matrix tells us it is easier to do 1 and gets progressively difficult to do steps 2, 3 and 4.

Loyalty proponents believe in staying with 1 and may be add a bit of 2. Product proponents get bored with 1 and want to build new and great (facebook phone). Those who believe buying growth spend more time and resources on 2 and 3 by acquiring businesses that sell in new markets or acquiring companies outside their core (eBay/Microsoft acquiring Skype)

There is a problem with this matrix. It is product driven as opposed to being customer needs (jobs to be done)  driven. When you look through the lens of your current products and new products you end up with approaches like unnecessary M&A and Facebook phone that are not aligned with how customer needs and how those needs are changing.

Let us redraw the matrix but now with Customers (customer segments) and Jobs as the two axes. If you are not aware of the “jobs to be done metaphor“, please see here before reading further.

Briefly, the metaphor asks us to think about customer needs as jobs to be done. Customers hire products among many alternatives to fulfill those jobs.

Now it is not anymore the question of how to sell more of same products or build new products but a question of what are the current jobs we are addressing and what new customers and new jobs provide us opportunities for growth with our core competence.

Here is the recommended strategy for each quadrant

1. Existing Customers and Jobs: Continue product evolution that cements your product as the best candidate for the job.  
2. Existing Customers and New Jobs: The new jobs could arise because of trends impacting customers or simply adjacent jobs you never positioned your product for. Remember positioning is telling customers which job your product is applying for. Instead of going after jobs that are outside your core competence you are better off investing your limited resources on evolving customer jobs and related jobs that can be served by product pivots vs. completely new products (facebook phone)
3. New Customers and Jobs you currently address with Existing Customers: Here the invariant is the jobs – two different segments have the same job to be done but you chose one segment over other and now considering serving the second segment. Understand the reasons why you did not choose that segment in the first place – is it the challenges in reaching them?, is it their willingness to pay? etc.
   Understand that different customer segments have different alternatives for the same job and hence different reference price. Choosing to serve lower willingness to pay segment should not come at the expense of price erosion in higher willingness to pay segment.
   My recommendations are to focus on packaging and pricing innovations that help protect current profits and add net new profits from new segments. It is not revenue growth at the expense of overall profit drop.
4. New Customers and New Jobs: You still have the option of better product positioning to help capture new markets. But most times you are looking at completely new jobs that require product innovations and business model innovations.
   But the advantage is your focus on customer jobs and not on products – your innovations are aligned with customer jobs. While this step once again proves to be most resource intensive with most uncertainty, taking the jobs approach helps you ease into this without taking big risks, pie in the sky product innovation or expensive acquisitions.

There you have it, your recipe for growth derived from customer job to be done.

Thought Leader, Th.D

If you have not watched the original Wizard of Oz, you should do it now. It is fun to watch repeatedly and as bonus we can all superimpose our current day thinking on to the movie dialogs and write blog posts.

Near the end of the movie Oz was about to address Scare Crow’s scarcity of brain,

Why, anybody can have a brain. That’s a very mediocre commodity. Every pusillanimous creature that crawls on the Earth or slinks through slimy seas has a brain. Back where I come from, we have universities, seats of great learning, where men go to become great thinkers. And when they come out, they think deep thoughts and with no more brains than you have! But they have one thing you haven’t got – a diploma. Therefore, by virtue of the authority vested in me by the Universitatus Committeatum E Pluribus Unum, I hereby confer upon you the honorary degree of Th. D…that’s Doctor of Thinkology.

When I see people being described as thought leaders or keynote speakers I am reminded of this great description by the wizard. A similar opinion on thoughts at titles was expressed by Galbraith in his Conventional Wisdom essay.

Let me rewrite wizard’s dialog for the social  media conversation world

Why, anybody can have a brain. That’s a very mediocre commodity. Every pusillanimous creature that crawls on the Earth or slinks through slimy seas has a brain. Back where I come from, we have Startups, Wired, Forbes and Tech Blogs, seats of great learning, where men go to become great thought leaders and keynote speakers. And when they come out, they think deep thoughts and with no more brains than you have! But they have one thing you haven’t got – TED speaker title. Therefore, by virtue of the authority vested in me by the Universitatus Committeatum E Pluribus Unum, I hereby confer upon you the honorary degree of Th. D…that’s Doctor of Thinkology.

 

Big Data predicts people who are promoted often quit anyway – But …

I saw this study from HP that used analytics (okay Big Data analytics, whatever that means here) to predict employee attrition.

HP data scientists believed a companywide implementation of the system could deliver $300 million in potential savings “related to attrition replacement and productivity,

I must say that unlike most data dredging that goes on with selective reporting these data scientists started with a clear goal in mind and a decision to change before diving into data analysis. It is not the usual

“Storage and compute are cheap. Why throw away any data? Why make a decision of what is important and what is not? Why settle for sampling when you can analyze them all? Let us throw in Hadoop and we will find something interesting”

Their work found,

Those employees who had been promoted more times were more likely to quit, unless a more significant pay hike had gone along with the promotion

The problem? Well this is not the hypothesis they developed independent of data and then collected data to test this. That is the prescribed approach to hypothesis driven data analysis. Even with that method one cannot stop when data fits the hypothesis because data can fit any number of plausible hypotheses.

The problem is magnified with Big Data where even tiny correlations get reported because of sheer volume of data.

What does it mean that people who are promoted often quit?

Is it the frequent promotion that is the culprit? Isn’t it likely that those who are driven and high value-add more likely to get promoted often,  likely to want to take on new challenges and also look attractive to other companies?

The study adds, “unless associated with a more significant pay hike”.

Isn’t it more likely that either the company is simply using titles to keep disgruntled employees happy or just making up titles to hold on to high performance employees without really paying them for the value add? In either case, aren’t the employees more like to leave after few namesake promotions that really don’t mean anything?

Let us look at the flip side. Why are people who are not promoted frequently end up staying?  Why do companies give big raises to keep those who were promoted?

Will stopping frequent promotion stop the attrition? Or will frequent promotion with big pay raises stop it? Neither will have an effect.

The study and the analysis fail to ask
Is the business better off paying big raises to keep those who are frequently promoted than letting them leave?

Is the business better off if those who are not promoted often choose to stay?

That is the problem with this study and with Big Data analytics that do not start with a hypothesis developed outside of the very data they use to prove it. It finds the first interesting correlation, “frequent promotions associated with attrition” and declares predictability without getting into alternate explanations and root cause.

Big Data does not mean suspension of mind and eradication of theory. The right flow remains

1. What is the decision you are trying to change?
2. What are the hypotheses about drivers- developed by application of mind and prior knowledge?
3. What is the right data to test?
4. When data fits hypothesis could there be alternate hypotheses that could explain the data?
5. How does the hypotheses change when new data comes in?

How do you put Big Data to work?

When myths triumph over metrics

Did you fill out a March Madness bracket?

I did one and I do not know any of the teams or watch any of the games. I filled out the Blindfold Bracket – one where the match-ups are presented based on offense, defense, size, experience, 3-point shooting, and hot streak metrics. I picked winner of each matchup based on a fixed algorithm I set for my self.

I probably won’t win any bracket pool. What I have is a reproducible system. You present me any match-ups with the aforementioned comparative metrics I will produce the same results repeatedly. It is a system for making decisions based on data.

Of course the data could be wrong – wrongly collected and presented or my algorithm (defense wins games) could be wrong. But those are fixable errors if the outcomes are important to me. Say the decisions are about hiring someone or making product decisions I better have a system that may start out simple but incorporates knowledge from subsequent iterations to get better over time.

Not to mention I should pay more close attention to what data is relevant, how is it collected and its quality before using it for decision making process.

Making decisions based on data does not guarantee success every time. It only gives you a reproducible and refinable system that helps you make better and informed decisions based only on metrics you set forth and devoid of any cognitive biases. The system can be simple or complex. Use as few variables or as many as possible. Could be deterministic or probabilistic. It  is far superior to decisions based on myths.

As it turns out I am not doing so well on my Blindfold Bracket. There were several upsets and prominent one is Harvard that tripped up even those with more sophisticated model to do their Brackets,

One such model is from the expert in this field, John Ezekowitz, who does this for living. His statistical model that uses additional variables like turn-over ratio and rebounding rates predicted less than 5% chance of Harvard pulling a upset and yet it did. Many such upsets happened across NCAA.

There exist a few who so far filled out near perfect bracket. They predicted all the upsets and most likely did not rely on data or models to make their decision. While far too many metrics driven Brackets are wiped out these few stand tall for their success.

What does that mean? Does reliance on metrics drive us down the wrong path? Does gut have upper hand over mind? Are all those who say, “paralysis by analysis” or  ”action over analysis” correct?

Absolutely not.

Recognize the reproducibility of metrics driven approach and our ability to continuously question the data and keep refining it.

Recognize the fact that there were many thousands that filled out the bracket using their own myths and biases and just by sheer luck some are going to be correct. We cannot treat their success of those standing as triumph of gut over mind.

Recognize the fact that March Madness games are sudden-death single matchup games and not even the most sophisticated model can predict the outcome of a single matchup.

You will see similar results in business. You will see spectacular winners who shoot from their hips, went with their gut or didn’t stop to collect data. You will see their success written about in the media and social media or worse their methods touted as the recipe for success.

Go back and re-read this article. Don’t give up on metrics and methods and don’t ever pick myths over metrics.

Be Sufficiently Skeptical – Get Second Opinion on Business Prescriptions

Do you trust your doctor?

Someone you found after several referrals from your friends and social network?

Someone you have been loyal to for several years?

Someone who is over-worked, seeing patient every 10 minutes, double booked, and has no time to catch up on recent advances or practice evidence based medicine?

Someone who gets her/his scoop on latest medical advances from pharma sales reps?

In an article in WSJ, Holly Finn writes about the necessity of getting second medical opinion. What she says about doctors applies to all the management, marketing and business gurus you meet on the social media and the incessant business advice they dole out everyday in their blogs, Forbes articles and books.

Finn writes,

A 2010 Gallup poll found that 70% of Americans are so respectful of their doctor’s advice that they never get a second opinion or do additional research. We apply more scrutiny to choosing bluejeans, buying flat-screen TVs, ordering lunch. We should all get our heads examined.

Looking at the market for pulp-non-fiction books and marketing wisdom we see out there  and their continuing popularity over years I am forced to conclude the same about how we treat our gurus.

We love them. Re-tweet them. Re-blog them. We spread their incorrect theories based on spurious correlations and subject to hundreds of cognitive biases as indisputable business wisdom.

There is marketing lesson from Grateful Dead.

Some urging us to be weird.

There is this one guru who insults you outright by making a diagnosis that you have lizard brain.

Of course don’t forget entrepreneurial wisdom from 10 year olds.

We do not question them, ask what-else, seek contrary evidence or do our own independent research to check the validity. We take comfort in the power, position, pedigree and popularity of the gurus. In essence we are so respectful of our gurus that we suspend our skepticism and genuflect unconditionally in front of their wisdom.

We should all get our heads examined.

Don’t defer to your gurus. Be sufficiently skeptical. Question them. Seek second opinion.

Next time you read yet another indisputable wisdom coming at you from popular gurus, I am happy to provide that second opinion. Write to me before you hit that Retweet button.

At the very least, get a second opinion on the lizard brain. It is most likely not true.

Because multiple options are better than just one – Product Management Series

In my last article on defining and evaluating Influence Skills of product managers  (reminder – Influence Skills was rated as the most important quality in a survey) I mentioned the book Influence by Robert Cialdini. The book, in my opinion, is about influence tactics and not about building a longer lasting working relation based on trust and mutual value in a multi-encounter environment.

The book does present many tactics you can put to use when you are trying to break in or get what you want in some zero-sum games. In my opinion it does not help build an end to end process for win-win in outcomes in situations where you meet the same people over and over.

For instance using asking for a small act and then relying on escalation of commitment to get more and more of what you want does not sound to me like a mutual value-creation and fair value-share arrangement. As I wrote before, Influence is based on trust, mutual value-add and effective communication.

But that is just that, my opinion.

There are two invaluable tactics from the book that I recommend you use without compromising on mutual value and trust.

Because, Because, Because

The Influence book tells us about the effect of using the word ‘because’ in asking for an action from anyone. When asking for a favor/task  from others, a Harvard study found, you will have greater success if you explain the reason for your ask,

People simply like to have reasons for what they do.

For example,

“would you help me get the SKUs created in two weeks because of product launch”

In fact the study went a step further and tested just the use of the word ‘because’ even with illogical reasons and found that it had better effect than giving reason without using ‘because’.

Like saying

“would you help me get the SKUs created in two weeks because I am in a hurry”

I am not going to make a recommendation that you use ‘because’ with illogical reasons but stop with their primary finding about people like to have reasons for what they do and give a valid reason after ‘because’.  In fact this fits perfectly with my recommendation about showing mutual value and effective communication. Using ‘because’  helps us get the value message across effectively.

Options over Ultimatum

The second tactic that helps is giving your peers/customers/bosses multiple options and asking them to pick one over presenting them a single option and making it a ultimatum. Presenting multiple options changes the decision from saying yes or not to a single option to picking the best among the multiple options you present.

Here is a real life case study from the world of politics,

The WSJ article on  President Obama won the Health Care vote describes how he changed the conversation:

Mr. Obama’s most effective move may have been calling for a bipartisan summit on health care, shifting the conversation away from Democratic paralysis. Aides knew there was little chance they would reach a bipartisan agreement, but it forced Republicans to put ideas on the table, framing the choice as between two sets of ideas, rather than simply a referendum on one.

 It is easier for the people you work with to compare the merits of different options vs. deciding merits of picking or not picking the only available path you present.

I recommend you go one step further and present three options and invariably you will get the middle option.

Present multiple options because it turns a yes or no decision into informed choice among multiple options based on relative value.