Tag Archives: technology

The price trap

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The price trap is when a business relies on increasing price to grow revenue and that keeps it from innovating.

The math usually says it makes sense to raise prices because the higher revenue from charging all the remaining demand more is greater than the revenue lost from customers buying less.

Since this grows revenue and earnings more reliably than innovation, leaders come to rely on it and not think too hard about why their approach to innovation and product development isn’t contributing to results.

Price is often used to cover the costs of their flops, too. So, customers end up paying more to help cover for leadership’s ability to add real value.

That’s not how it should work. Innovation winners should pay for the flops. If a company raises prices to cover that, that’s a sure sign it needs to take an honest look at its innovation approach.

But, they won’t. Price is keeping them in the game. Companies stuck in the price trap can get 5-10 years down the road without solid innovations and find their products are suddenly losing relevancy against competitors and substitutes because leaders have not kept the product portfolio evolving with the tastes and preferences of customers, while the rest of the market has been.

Managers and employees will blame high prices for the businesses results, instead of the long period without meaningful innovation and product development, which is the real cause.

The price trap isn’t directly the fault of leaders who don’t know how to innovate. I blame Board of Directors who don’t know how to hire leaders who know how to innovate.

When a company is stuck in a price trap, leadership often changes because the Board does get a sense that there’s something wrong about relying on price to drive business results.

But, the Board doesn’t understand the root problem, so they replace leaders with the same type of leaders who don’t know how to innovate and keep the company stuck in the price trap.

These candidates look good on paper. They have polished resumes, have increasing responsibility in their carreer, happened to be at places that had some notable wins and can talk a good game.

From the Board of Directors point of view, they are low risk hires. If things don’t work out, ah shucks, what could they do? He or she was a solid candidate on paper.

Well, here’s one thing they could have done. The could have asked candidates what their innovation track records are and how they went about getting those results?

As for evaluating their answers, that’s for another post.

Strategy vs Tinkering

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In my view, the main reason Day 2 (Bezos term for mature, bureaucratic and dying) companies have a hard time innovating is they don’t know how to tinker, leave no room to tinker and actively suppress tinkering.

In this interview with Bezos, he said his seasoned manufacturing expert at Amazon told him that he needed to pace the release his ideas at rate the the organization could accept them.

https://www.youtube.com/watch?v=PJcUQDtpkpY

He thought that was profound, even though obvious, and it made him change his approach to incorporate more discipline in prioritizing his ideas.

I think many Day 2 execs will stop there and say, “See! That’s what we do and we are doing it right!”

But, then he flipped the script (at the 2 minute mark) and built an organization that could handle more ideas. “We built a company that is very good at doing more than one thing at a time.”

Bingo.

Though, I wonder…Bezos makes it sound like all the ideas came from him. I’ve read elsewhere that Amazon had an open culture to ideas, so that they didn’t all have to come from leadership.

Great vs mediocre companies

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First, ask what new stuff they have in the pipeline for the next 3-5 years?

Great companies will have tens of things in the next 1-2 years and hundreds or thousands of ideas cooking with a 3-5 year or longer launch horizon.

Mediocre companies will have a few things for 1-2 years and nothing on the drawing board after that.

Next, ask why they feel really good about those things?

Great companies will point to results from their proof of concepts, pilots, scalability and operational tests.

Mediocre companies will tell you they have a really good feeling that their new stuff will be game changers, but are short on actual, real world results to support it.

Finally, ask how of an impact do you think the ideas will have on the company’s performance?

Ask them to put some numbers to it. 1%, 3-5%, 5-10%, 10% or more growth in revenue or earnings?

Great companies will have a reasonable idea because they’ve been learning that through pilots and testing how well it will do.

Mediocre companies will tell you they hope it will be big, but we’ll just have to wait and see. But, they don’t really know because they do not piloted or tested in the real world, yet.

If I was advising a Board on hiring a leadership team, I’d advise them to look for candidates that had answers that sounded like the great companies.

I know it may not seem believable for business leaders to have the mediocre mindset above. But, I have experienced sample of about a couple dozen leadership teams at mature businesses that would give the mediocre answers and none that would give the great answers.

And, based on what I read about other businesses, gather from contacts I’ve made over the years at other places and who have come from other places, that’s pretty common.

Amazon encrapification part 2

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I wrote part 1 in March 2025.

This week Amazon announced it is laying off 16,000 employees, or about 10% of its corporate workforce, and that’s after 14,000 were laid off in October, to streamline operations.

Regular periodic layoffs are a sign of an encrapified company.

Even with the benefits of introducing ads on its Prime video and letting 2 day delivery standards slip while likely still raising its price for Prime service, that isn’t enough.

As a reminder, encrapified companies are ones where organic growth of their core product or service is slowing or halted, so they seek ways to take more of the value that is in those transactions.

Like introducing ads on Prime Video. Viewers of Prime who previously got to watch Prime content ad free now has that value removed, unless they want to pay more.

Or by charging suppliers to get high rankings on product searches. That takes a bit of the value from the suppliers, as it costs more for them to sell their product, and from shoppers, who have a harder time finding the products that they want because they have to swim through paid listings of products they don’t want.

Like how Google used to get you to your search result as quickly as possible, a value prop for Amazon was that it could get you to the product that you wanted quickly, too.

Both of those values have been eroded as both companies have sold out that value prop.

What generally happens in encrapified companies is that new leadership teams come in and promise to take the company to the next level. They fund their initiatives to do so, which means hiring a bunch of people to support things before they find if customers want them. They roll them out to discover that customers do not. Then the next leadership group comes in to streamline out the previous group’s failures and then build their own.

How efficiency at scale eventually kills companies

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In the traditional phases, a business goes from startup, growth, maturity and then decline.

As the business nears maturity, its primary focus becomes on delivering its products efficiently to optimize the bottom line.

This sets the stage for the company’s ultimate demise. Efficiency means supply chain and inventory management systems that ensures its products are always available for customers, to not miss any sales, at the lowest possible cost.

The downside of these efficiency systems is that they make it impossible to experiment on small scales and that is where new things are discovered that lead to more growth and evolution for the company to stay healthy as competitors arise, consumer tastes and preferences evolve and disruptive innovations emerge.

The efficiency systems cuts off the company’s ability to evolve and change with the times.

The folks in charge of these systems do not know this. They believe keeping the company healthy comes from Mao-like five-year plans.

These companies can last for quite awhile, as their core products run their natural cycle of relevancy, and then suddenly get shocked when something emerges out of left field that obsoletes them.

Many times, these disruptions are simple solutions that come from a startup running a small, cheap experiment that the big company’s efficient systems made impossible to run.

Here’s my litmus test to see if your company’s efficiency systems are going to kill it: If suggesting an idea results in people chomping at the bit to tell you why it can’t be tried that sound like “our system can’t do that,” or “that would take a big workaround to the system to make that happen,” then your company has this problem.

Companies should make it imperative to enable small experiments to be cheap and easy, even if they don’t match the efficiency of the products that are already at scale.

A good real world example is the story of how Zappos started. The founder didn’t build the supply chain first to get the delivery of shoes purchased online down to a profitable level.

Rather, he built a cheap website to see if people would buy shoes online. When he got an order, he bought the shoes at retail, shipped those to the customer and lost money on each sale. Once he got enough orders to gain confidence that people would buy shoes online, he started building the supply chain to make it profitable.

The startup world calls this an MVP, or minimally viable product. Folks in efficiency-focused organizations know their word, but their idea of an MVP usually costs in the range of $5 to $10 million to get going, instead of $1,000 to $10,000.

The Zappos type of ‘lose money on every sale’ experiment makes the efficiency-driven folks’ heads explode. They don’t like the idea of losing money on every transaction to find out if customers actually want it and they will come with smart sounding reasons to justify building the supply chain first, like if it does work than we can scale quicker and beat our competitors.

But, they have the origin of good business backwards. They think, first build a profitable way to deliver the product then see if people want it.

They don’t see that the Zappos founder saved millions in investment in the process to focus the most important new learning: will people buy shoes online?

The Zappos founder correctly assumed that if the demand was there, then solving the supply chain to deliver the product profitably would be straightforward. But, why build it until you know if people will actually buy shoes online?

Innovation is a monkey and pedestal problem. Building the supply chain is the pedestal. It’s easy. It has been done millions of times for millions of products. There’s a high chance that’s doable if you find a new product.

Seeing if enough people will buy shoes online is teaching the monkey to recite poetry. That’s the unknown. Up to that point before Zappos, that hadn’t been done. So, seeing if that problem is solvable is more important than building the pedestal for it.

Mature companies approach the same problem by focusing on the pedestal and building the supply chain first. If it fails to get enough sales, they would claim a silver lining success because, at least, the supply chain worked.

That’s accepted in these companies because the efficiency-minded folks see their job as delivering efficiency, not finding new stuff.

They are missing the voice of reason who asks the obvious, why are we celebrating building a supply chain that successfully delivers a product nobody wants? That’s the easy part.

We should be embarrassed that we couldn’t figure out ways to find out if anybody wants it without first having to build the whole supply chain for it.

What is AI?

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I was discussing this with a friend. He asked why AI can’t provide a definitive answer to a question like, “Who was the greatest soccer player of all time?”

He said, it will give you an answer, but the answer might be different based on who is asking it or how it is asked, so what good is AI, anyway, if it can’t give the definitive, consistent answer?

I said, I feel one shortcoming of AI is that it doesn’t do a good enough job of letting you know when the problem is the question you are asking. It has gotten better at it. It might preface an answer with, ‘it depends with what your definition of the greatest soccer player is and there is no widespread agreement, so it’s a moot point.”

But, then it usually goes on to answer it anyway.

As we were discussing this, I realized a better way to describe AI.

I explained that all AI is a much better synthesizer of existing knowledge than we’ve had before, a good synthesizer of crowd sourced data. In some cases, it’s about 10x better. In other cases, it might 100x or 1,000x better.

One example is a good illustration something that has just popped up, where people have been uploading their blood work to AI and AI has done a better job of diagnosing problems than the doctors.

That’s because the doctors are one node in a vast network of medical knowledge and while they have absorbed 100x more data than the average person to be able to interpret blood work, they aren’t capable of absorbing and keeping up with all of it. AI can absorb all of it.

So, if you have something wrong that has only occurred 100 times before, most doctors might not know about it. The issue hasn’t reached the level to enter what the typical doctor knows, because they are busy absorbing the new things that have happened thousands or millions of times before, because they are more likely to see those cases.

But, AI can find the things that have happened 100 time and synthesize that, too.

It amazes me what companies get wrong about innovation #4: they don’t know how important innovation is

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Here’s a recap of what amazes me about what companies get wrong about innovation, so far:

#1: They think they can predict winners and losers

#2: They aren’t aware of the odds of success

#3: They don’t notice that their approach to innovation isn’t working

I found it tough to write about these as separate ideas, because they are so intertwined. For example, they think they can predict winners and losers (#1) partially because they don’t have a full appreciation of the odds of success (#2).

They are highly related ideas, but I felt they were also distinct enough to explore each one separately, because thinking you can pick winners is slightly different than not being aware of the odds of success, because you can be aware of the odds and think that you can beat them.

A ran into more of that as I wrote #3, because it #4 is entangled with it: They don’t realize how important innovation is (#4), so they don’t pay enough attention to it to notice it isn’t working (#3).

They pay lip service to innovation. “Of course innovation is important,” they will say. “We have an innovation group, don’t we? We try new things.” And, they believe it, too.

But, their actions speak louder than their words. They treat innovation as second fiddle to their strategic initiatives. Worse, they often constrain innovation to only explore ‘spaces’ they deem fit with their strategic vision.

I have seen this play out at a few mature companies time after time.

A new leadership team comes in. They set their sights on big improvements to the existing business through their strategic initiatives. They are very confident their strategy will result in great things for the business. They hype their plans up and rally the organization behind them. Failure is not an option!

Then they roll out that out and thud. Nothing happens. If you graph lines of the company’s revenue and earnings for the past 10 years, you will see no change in trajectory when their strategy started.

Then the Board turns on them, clears them out and brings in a new set of leaders.

Usually the Board brings in a new set of folks cut from the same cloth as the previous set, folks who believe their strategic initiatives will drive the company forward.

Rinse and repeat.

While all this goes on, the innovation group also keeps putting out duds and nobody notices because in the whole scheme of things nobody truly thinks innovation is as important as the leaders’ strategy.

But, there is no other quote that says it better than: Innovate or Die.

That’s how important innovation is and should be. It is most important for the future.

Instead of trying to fit innovation to their strategic visions, they should try giving innovation some rope and fitting their strategic visions to what innovation discovers.

Jeff Bezos said his version of “Innovate or Die” well in his distinction between Day 1 and Day 2 culture. He wants to keep Amazon in Day 1 startup culture because Day 2 is “stasis. Followed by irrelevance. Followed by death.”

“To be sure, this type of decline would happen in extreme slow motion. An established company might harvest Day 2 for decades, but the final results will still come.”

Many of the folks in these companies, leaders included, aren’t aware that their companies are heading toward death because it is happening in extreme slow motion, as Bezos says, as they harvest the company’s past successes and every day they don’t realize how important innovation is one day closer to the company’s death.

Macy’s is a great example of this. Featured about a year ago on the Freakonomics podcast, I wrote about how that podcast and how they were implementing their Bold New Strategy.

The Bold New Strategy was the current leadership’s strategic initiatives to get the best products in stores, which is just the latest in the long line of similar strategies that successive waves their predecessors have tried to employ as the company has been dying in extreme slow motion over the past few decades.

If I were to guess, Macy’s innovation efforts over the past few decades have likely been structured around these strategies to save the business and make Macy’s the best department store possible, instead of just innovating.

Macy’s sort of won. It’s one of the last remaining department stores. It outlasted many others that went poof over the last few decades. But it’s a pyrrhic victory. Macy’s is worth $5 billion. Amazon is worth $2.4 trillion. That’s 500x more than Macy’s.

Sears started off as a mail order catalog and successfully transitioned to a department store only to die when it couldn’t successfully transition back its own roots when the internet came along. My Mom once said, “I don’t understand. Isn’t Amazon just doing on the internet what Sears used to do by mail? Why couldn’t Sears do that?”

Until it died, Sears was trying strategies like Macy’s Bold New Strategy to turn around the dying department store model.

The answer to my Mom’s question is this post. After decades as a department store business, Sears became department store business and forgot how important innovation was.

It amazes me what companies get wrong about innovation #3: they don’t notice that their approach to innovation isn’t working

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How long has it been since your company has had an innovation win?

For many companies, the answer is a long time.

Yet, it amazes me that nobody in the company seems to notice or wonder why.

In one company I worked in, each year the innovation group would put forth a big new project, promise it would help the company grow, roll it out with a lot of fanfare and then it would flop.

This went on for years and it never occurred to anyone to ask, is there something wrong with our approach to innovation? Should we expect a win every so often? How often? Why do we believe their current project will work when they last five have not?

It even took me awhile to catch on. It helped that I was in a unique spot to notice. I worked in the company’s pricing group. Every time their big idea missed, leadership asked us to raise prices so they could still hit their financial plan.

After a few years of this, employees started blaming prices for the company’s lackluster performance and saw the pricing group as the evil villains, even though we were just following orders.

Employees in the innovation group were also our critics and asked me to present the case for pricing to them because they wanted to better understand why it had increased so much.

It was in preparing for that presentation where it dawned on me that the lack of innovation success was a primary cause of the rising prices, because price was used to cover those.

I ended up showing a chart of the company’s revenue, earnings and number of HQ staff for the most recent year compared to 10 years before.

I made this point:

Just ten years ago the company’s revenue was much less than it is now and the HQ staff was much smaller because the company could not afford to pay all of these employees on that revenue. Many of the folks who work here now, including you all, are here because revenue has increased enough in the past ten years to afford them.

Unfortunately, the only thing that has increased revenue and enabled the company to hire all of us is that its prices are higher. There has been no growth from increasing market share or new products.

Your jobs exist to find ways to grow from increasing market share and new products. Since you haven’t done that, your jobs are being paid for by the price increases that you are concerned about instead of growth from your initiatives.

Just over the past few years your projects were supposed to deliver X in revenue. When they didn’t deliver that, leadership still wanted to meet their numbers, so they told us to raise prices to cover.

I expected a defensive backlash, but instead, a lot of folks nodded their heads and thought that was a great point.

But, it didn’t change anything. They kept promising big wins, failing and nobody questioned if anything was wrong with their approach.

It did change something for me. I learned that over reliance on price increases is a tell-tale sign that there’s something wrong with a company’s approach to innovation and nobody notices.

Silver bullet strategy vs exploration and discovery & exploration

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A conversation with colleagues about innovation often starts with a question like, “What one thing would you do to help the company grow?”

These folks want to hear silver bullet idea. They also believe they can judge whether that strategy is good or not just by how it sounds.

I don’t believe in silver bullet strategies. Mainly because I’ve been around for awhile and haven’t seen such things. And, if such things existed, I think we could Google them or ask ChatGPT what they are. Try it.

Grok gave a decent answer: “There’s no true ‘silver bullet’ for a mature company to spark growth, as success depends on context — industry, market and internal capabilities.”

Good start. But then it veers off into business buzzword gunk:

“However, one high impact strategy is reinvention through digital transformation. This involves leveraging data, AI and emerging technologies to optimize operations, enhance customer experiences, and create new revenue streams.”

Yes! Give that a try, whatever that means.

Though wade through that BS and I agree with the #4 recommendation: “Test and scale: Pilot new models, measure ROI, and expand what works.”

Which is exactly how I answer when someone asks me what I would do.

When I start to explain that the company should cast a wide net on new ideas and build the capabilities to pilot them on small scales and learn their way to success, they shut down. That doesn’t fit their mental model. That’s not what they want to hear.

They’ll try again, “But, what one thing would you do?”

I won’t take the bait and the conversation usually ends.

I’ll even point out that it’s not just what I think, but this is how highly innovative companies operate. This is how the startup world woks. This is how science and medicine works. This is also how finding jobs and dating works and in those domains we all naturally learn strategies that apply directly to the innovation world without realizing it. Strategies like network, apply for a lot of jobs and don’t get bummed out about a low rate of success.

Now I can say this is what AI thinks, too.

That doesn’t seem to matter. They still want your silver bullet answer.

I think part of why they want your one idea is they want to shoot it down.

Shooting down new ideas at mature companies is a common game that earns you street cred.

And that’s part of the problem with innovation at mature companies. Because of that game, new ideas are choked out before they are ever tried. Companies have to try a lot of stuff to find a few winners. This game is part of what keeps that from happening.

Which is better? Insight vs Use Case Based Innovation

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I don’t know, but it might be interesting to study.

I’m not even sure these are quite the right words to use to describe what I’m talking about. But, I’ll give it a try.

Use case based innovation starts with a problem (the use case) and finds ways to solve it. This is often how founders start a business. They make something to solve a problem they have faced. If they find a good solution, others that face the same problem are willing to pay for it.

Insight based innovation starts with the solution based on some great sounding insight and hope it solves a problem for someone somewhere. I think this is more like how large companies traditionally approach innovation. They ‘sift the data’ and do market research looking for smart-sounding insights to influence new products or product strategies.

I’m not sure which one is better, in terms of producing a higher success, but my hunch is use case is.

This idea crystalized for me when reading a post from a Y Combinator founder who credited Michael Seibel for asking a question, ‘who’s going to buy this?’ That pulled the founder’s mind from the more abstract insight based frame of building something for someone to the more concrete use case based frame of building something specifically to solve a problem for real people.

I’ve asked similar questions. In one case, a company was working on one new product variation. I asked folks in the room how many of the current variations of that consumer good they bought in a year? About 20.

Then I asked them to imagine how many of those might be this new variation when it becomes available? Maybe 1 or 2.

Then I asked, with this new variation, would that increase how many they bought a year to 21 or 22 or would this new variation just cannibalize 1 or 2 that they currently purchase? Mostly cannibalize.

Unlike Michael Siebel’s case, however, my line of questioning was promptly dismissed by the highest paid person present as proving nothing because it wasn’t properly weighted market research. “We’ll just have to wait and see,” instead of asking the folks in the room, ‘how can we make this so that you would buy more?”

As time would tell, my unscientific poll was a good predictor that this new product variation would not be the hit everyone was hoping.

In the same company, I heard a few folks in the organization claim that they know someone who looked for a product in the company’s product set, but couldn’t find anything suitable, so they didn’t buy one.

That is a use case!

I suggested to the people who shared this that they go to the product team and work with them to come up with variations of the product that they would buy for this particular use.

They declined because that’s just not how the company develops products. Why? Because it’s anecdotal. It doesn’t come from ‘the data’ or the ‘research.’

They talked themselves out of even considering how they could solve the problem they faced as a customer because they didn’t think it would be a good enough sounding insight for the company to take action on. It would be based too much on common sense and not enough on ‘strategic business analysis.’