In Part 1, I laid out some theory for rate of learning and adaptation as a concept. And how it fits into various levels of strategy (tactics, digital operating basics, digital marathon and maybe competitive advantage).

That’s fun. And it’s an argument for why you should focus on learning and adaptation.

This article is about how to do that in practice.

• What do you build?
• And what are the steps?

And fortunately, Martin Reeves (of BCG) has a pretty good framework for this. It’s in a book called the Adaptive Advantage: Winning Strategies for Uncertain Times. He is pretty much the best thinker on this subject. A lot of the thinking in this article is from that book.

BCG has argued that being faster in learning and adaption is a prerequisite to any sort of real competitive advantage. You need to increase the clock speed for the rest of your strategy to work. Speed and adaptiveness are foundational for competitive advantage. It’s a basic digital operating requirement.

In the book, the authors lay out several capabilities for rate of learning and adaption. These are the 4 that I agree with.

1. Signal Capability
2. Experimentation Capability
3. Organization Capability
4. Systems Capability

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1. Signal Capability

This is the ability to capture, interpret, and act upon signals from an increasingly data-rich environment.

The amount of data is exploding.

And there has been a big decline in most of its shelf life (due to rapid change).

So, it’s about pulling the signal from the noise. The distinction between data and “signal” is also helpful.

And the competitive bar for this has been raised for companies pursuing a signal advantage. So, a key capability for an organization is its ability to rapidly interpret and act on signals.

It’s not about possessing information. Proprietary data has some power. But in most cases, possessing data is dwindling in its power. It’s about acquiring it, analyzing it and adapting with it.

An example BCG uses for signal capability are the Japanese 7-11 convenience stores. They use point-of-sale data to optimize pricing, product assortment, and even store layout. And they do this multiple times a week.

In my frameworks, I put most of this signal capability in Digital Operating Basics #2 and #3. The digital core (DOB3) processes information. And this is used to improve the customer experience (DOB2). You want a tight link between data analysis and operational activities that increase the value to customers. BCG describes companies that create this type of tight data feedback loop as “self-tuning organizations”. “Self-tuning” is a good phrase because it also implies adaptation that is continuous and incremental. With a small size and high frequency.

To build this as a capability, BCG breaks it into 5 steps:

Step 1: Get the relevant data

The 7-11 Japan example is useful here. They use point of sale activity. But they combine it with weather information, customer demographics and other information. Convenience stores can draw on some pretty interesting data.

Step 2: Hunt for patterns in the data

This is where I like to spend my time. It’s easy to see that customer churn is happening in a business. But what are the causes? You can’t really decide your customer retention strategies until you understand why it is happening.

Step 3. Make operational changes in real time

BCG example of Progressive Auto Insurance is helpful here. They use both real-time and historical data to adjust their insurance prices and their customer segmentation.

Step 4: Continuously reinvent the business model

BCG’s example of Tesco UK is good for this. Over time, the supermarket chain added financial products to its retail offerings. That’s a really interesting evolution for their business model. And one that follows directly from their customer data and insights.

Step 5: Shape the information landscape

BCG’s uses Google as the example here. It operates across industries and shaping the information flows globally. That’s not something anyone can copy. But lots of companies are building data ecosystems between themselves and a handful of partners. Or maybe just within their industry. This is a good idea for virtually everyone company. In my frameworks, I put that under DOB4.

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Signal is an important capability. It’s definitely in the Digital Operating Basics (2 3, 4). And it can sometimes rise to the level of an operating advantage. That is the L in SMILE.

2. Experimentation Capability

I once met a former McKinsey partner who was then running a leading digital business in China. I asked him about his training in strategy and analysis. And he basically said he never uses it. They just try lots of stuff and see what works.

That’s the problem with Signal as a capability. The faster things change, the harder they are to predict. And a big percentage of success is now discovered accidentally. You try stuff and see if it works.

Experimentation is crucial. And you need to do it systematically. Every day. It’s needs to be a capability.

And even when you have good signal and insights, you also need to test those. Even the best theories need to be tested. Reality is usually more complicated than our frameworks.

So, the goal here is to build a capability that enables you to experiment frequently, rapidly and economically.

This can be for products and services. Marketing moves and other tactics. And even for business models.

There are 3 steps for this:

Step 1: Generate ideas

That sounds simple. Obvious.

But how can you generate ideas faster and cheaper than rivals?

That’s a pretty interesting question. Certainly, generative AI is pretty great for this. But I like think collaborating with employees and customers is the “go to” strategy.

Keep in mind, we are trying to do this better than our rivals. So, speed and cost matter.

Step 2: Test ideas

Ok. You got some ideas. How can you test them faster and cheaper than rivals?

Digital tools are pretty great for testing ideas. For physical products, you can 3D print prototypes. Or you can digitize them and test them in virtual worlds. You can even post them on ecommerce sites and check traction. Who is clicking on them and who isn’t?

But what you really need is customer feedback.

And this can be tricky. Sending weak products to customers can impact them brand. It can be annoying. Even when it’s your best customers (your loyalty members).

You really need to decrease the cost of failed ideas.

You want to test with as friendly an audience as possible (loyalty members?). Or do anonymous online testing. Alibaba’s Tmall Innovation Center is actually a great approach for this. They help brands by doing data mining for them on the Tmall platform. They come up with the most compelling ideas. And then test these ideas by putting them up on Tmall and seeing the reactions. You can test new product ideas in weeks for little money. And with low risk.

Step 3: Scale Up the Successes

Ok. You’ve got a couple of ideas that are testing well. How do you roll them out? How do you get to bigger volume? Otherwise, what was the point?

And how do you do this process faster, cheaper and with less risk than rivals?

This is part of DOB 2. But this is where I start to think about learning and adaptation as a digital marathon where you can create an operating advantage. Definitely, in Rate of Learning and Adaptation. And also, in Innovation. There are real advantages in having superior scale in experimentation.

BCG says an experimentation advantage is the ability:

• To learn through iteration – and
• To achieve superior economics in experimentation

I like that. Some businesses can generate, test, and deploy a larger number of innovative ideas more quickly, at lower cost, and with less risk than rivals. That’s when I start thinking operating advantage.

And keep in mind, this is not just experimenting rapidly, frequently, and economically with products and services. We are also thinking about business models, processes, and strategies.

Finally, keep in mind that an experimentation capability depends a lot on culture.

This is an activity where you may have problems with management and culture. Few companies are really that comfortable with frequent failure (which most experiments do). That’s not usually how you rise up the ranks as an executive. So, this is often a lot of DOB5 and DOB6 in this.

3. Organization Capability

This refers to the flexibility and skill to manage the organizational trade-offs between adaptability and efficiency.

To keep pace with constant change, companies need an adaptive ability in their organization. It needs to be able to change. But this is not what hierarchical structures do. They are optimized for efficiency. And usually have detailed procedures to achieve this.

In contrast, adaptable organization use decentralized decision-making and delegated authority. Such as with:

• Modular organizational units with plug and play interfaces.
• Agile teams
• The free flow of information
• Limiting strict operating procedures
• Encouraging adaptive values – such as experimentation, accepting failure, cognitive diversity and productive dissidence.

You want to introduce variations in products, processes, and routines. Then you select the most promising variations, and amplify or embed the successes through resource allocation, internal competition, and specialization.

All of this is a problem for a lot of businesses. Most of which are used to hierarchical structures optimized for efficiency in stable conditions. They are used to delegation and specialization in a hierarchy.

Here is an approach for an organization capability:

Step 1: Introduce variations into products and internal routines

Step 2: Test

This means pilots, changing portfolios, and then full tests.

Step 3: Amplify and embed the successes

As mentioned, this means resource allocation, internal or external competition and specialization.

4. Systems Capability

Ok. Last capability.

This is where we try to tap into the power of multi-company systems.

We try to get diverse players to interact to achieve mutual goals.

There are lots of structures for this. We can have:

• Production systems orchestrated by a central player, such as the iPod/iTunes ecosystem.
• Collaborative production communities, such as Wikipedia and Linux.
• Innovation networks
• Marketplace platforms, such as eBay.

This type of systems approach is particularly useful in situations with:

• High product complexity
• Customer demand for large variety
• High uncertainty
• Rapid technological / customer change

In this situation, a single company will struggle to adapt. It’s too difficult. You want an ecosystem working together to find a solution. It can be quite powerful.

However, managing such a system is complex and means less control over individual players.

I put this under DOB4, which is basically the idea that you need to increasingly connect and coordinate with other businesses. In a connected world, business is becoming a team sport. More and more you really need a gang.

Two examples of this I have talked about are consumption and production ecosystems, which any business can do. Data sharing is an easy way to start these types of ecosystems. After data sharing, you can expand to coordination in implementation and innovation.

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Ok. That’s a solid framework for Adaptation Capabilities. Here are a couple of other questions.

Question 1: How Adaptive Should My Business Be?

A little? A lot?

Fortunately, BCG has another 2×2 matrix for this. They ask two questions:

• What is the level of uncertainty and turbulence in my business? High unpredictability requires more experimentation. Analytics will not be as effective.
• What is the degree of change required in my business? Can we just do a narrow range of adaptation? Maybe just a business unit? Do we need the whole company involved?

From this you can get 4 types of situations: Sprinter, Experimenter, Voyager, and Migrator.

What we’re really trying to do is balance specialization and efficiency with flexibility. Flexibility and adaptability can be real benefits but they also have a cost. They are messy and inefficient. There is a reason why rigid hierarchical structures are so effective. Ideally we want specialization without too much rigidity.

Question 2: What is the Cost of Being Wrong?

A couple of years ago, the business strategists were all talking about resilience. And survivability. BCG uses the term harshness.

If your terrain is rapidly changing, maybe our goal is just to adapt enough to survive? Forget winning. Let’s just ride out the storm.

I don’t really buy this. But you’ll hear it a lot. I think trying to win is the only strategy. A good offense is the best defense.

But it is helpful to think about the cost of being wrong. What if we do nothing? What is the cost? What if we aggressively adapt and we are wrong? Is it reversible?

Question 3: Why is Alibaba the Champion at Rapid Learning and Adaptation in Changing Environments?

Ok. Not really a question.

But they are my #1 model to copy.

For twenty years, Alibaba has been continually adapting in the highly dynamic Chinese market. They don’t just do occasional adaptation; it’s a continuous process happening at all levels of the business. From product and platform to business model and even their overall vision. Learning and the processes for learning are baked into the fabric of the company rather than being managed as deliberate, episodic events.

Alibaba definitely focuses on business models that have competitive advantages, such as platforms with network effects, scale in R&D, and linked business models.

But their operating activities are also heavily focused on rate of learning and innovation. They improve the user experience every single year (for both merchants and customers). They are a mix of bottom-up, emergent and continuous adaptation plus top-down and intention-driven strategy.

Most of my personalization strategy is a copy of Alibaba’s overall strategy.

Final Question: Where are the Structural Advantage in Learning and Adaptation?

I think about this question a lot.

While many aspects of rate of learning and adaptation (like being data-driven and fast) are becoming basic operational requirements for digital businesses, they can, in rare situations, elevate to a sustainable competitive advantage.

Either as an operational advantage (i.e., a digital marathon) or a structural advantage (i.e., a moat).

This happens when a business’s ability to gather data, analyze it, make decisions and make changes is sustained at a speed, frequency, and effectiveness that keeps it ahead of competitors in activities that truly matter.

For rate of learning and adaptation to become an advantage – a marathon or a moat – it needs to make a difference to customers. And it needs to be very difficult to replicate.

Difficult to replicate usually means reproduction has significant cost, timing or difficulty. Which can result from:

• Process Complexity and Opacity: As seen in companies like Toyota (in manufacturing) and Amazon (in digital operations), the advantage comes from thousands of little adjustments made over time that result in incredibly fast and efficient processes. It helps when they are not written down or easily transferable. The organizational knowledge is tacit and embedded within the culture, systems, and daily activities, making it difficult to copy. Amazon’s complex logistics and rapid market adjustments are cited as an example of a “rate of learning moat” based on opaque, complex processes.
• Organizational Culture and Structure: Product-focused rate of learning, like Tencent’s ability to rapidly roll out and iterate on new digital products (e.g., QQ, WeChat, WeChat Pay, etc.), is seen as stemming significantly from organization and culture. Adaptive organizations often exhibit characteristics such as modular units enabling rapid variation at low cost and risk, the free flow of knowledge and decentralized decision-making to detect and respond quickly to changes, and reliance on a limited number of guiding principles instead of rigid standard operating procedures.
• Algorithmic Sophistication: As previously mentioned, a company whose software and algorithms can learn and adapt at a level beyond competitors could gain a distinct advantage.
• Data: This is what a lot of people are thinking about. Does proprietary data give you a structural advantage? Usually, the answer is no. But it can give you an operational flywheel. It can give you a head start in a digital marathon. And maybe proprietary data can create an advantage. Maybe.

Anyways, I’m struggling with this last question. I have some theory about it but haven’t got many actual companies to point to. I’m watching for generative AI to make this happen in a major way. We’ll see.

Ok. That’s it for today.

Cheers, Jeff

• The Adaptive Edge: A Primer on Rate of Learning and Adaptation (1 of 2) (Tech Strategy)

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Related articles:

• AutoGPT and Other Tech I Am Super Excited About (Tech Strategy – Podcast 162)
• The Winners and Losers in ChatGPT (Tech Strategy – Daily Article)
• Why ChatGPT and Generative AI Are a Mortal Threat to Disney, Netflix and Most Hollywood Studios (Tech Strategy – Podcast 150)

From the Concept Library, concepts for this article are:

• Rate of Learning and Adaptation
• 4 Capabilities for Rate of learning
• DOB2: Never Ending Improvements
• DOB3: Digital Core
• DOB4: Connectedness and Interoperability
• SMILE: Rate of Learning and Adaptation

From the Company Library, companies for this article are:

• n/a

Photo by Tim Mossholder on Unsplash

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I write, speak and consult about how to win (and not lose) in digital strategy and transformation.

I am the founder of TechMoat Consulting, a boutique consulting firm that helps retailers, brands, and technology companies exploit digital change to grow faster, innovate better and build digital moats. Get in touch here.

My book series Moats and Marathons is one-of-a-kind framework for building and measuring competitive advantages in digital businesses.

This content (articles, podcasts, website info) is not investment, legal or tax advice. The information and opinions from me and any guests may be incorrect. The numbers and information may be wrong. The views expressed may no longer be relevant or accurate. This is not investment advice. Investing is risky. Do your own research.