Management&Business: innovation

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пятница, 29 августа 2025 г.

Business Model Innovation

It requires care to identify exactly which kind of innovation you undertake as a business because they create different kinds of value and have different economic consequences.

Identify Your Innovation

Companies are innovating their business models all the time; these can be categorized into:

Deepening – Within box innovations – e.g. new charging systems, new ways of delivering value.

Adding – Without changing the fundamentals of dyadic or triadic – e.g. adding a work-for-hire service to a product business model, that makes a portfolio of business models for the company.

Hybridizations – combining dyadic and triadic business models – e.g. taking a product based video- game and creating a version that is free to user but supported fully by advertising and offering both simultaneously – typically a challenging move because for the free-to-use version the rm needs a new version of the game and needs to nd advertising companies to pay for the free-to-use version. Note the classic “freemium business model” where the free product is not sustained by advertising and so not economically sustainable is NOT a hybridization, but a simple product business model with two offers (free offers to entice payment).

Test the Value of Your Innovation

Innovations can be categorized in terms of impact:

Mimetic – New to the company, but copying others in the same sector or location, so unlikely to be more than “catch up moves”.

Innovative – New to the sector or locality – but typically borrowed from another sector – that can sometimes transform an industry (EasyJet copied Southwest Airlines, Amazon copied Sears Roebuck).

New to the world – Rare, hard to achieve, even harder to make work, but totally game-changing when it works. One of the best examples is Google’s internet search supported by SME advertising which remains one of the most pro table business models ever invented.

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понедельник, 21 июля 2025 г.

Mapping AI-Based Martech Using the STIB Innovation Diffusion Model

This post fleshes out the STIB model of innovation diffusion, tests it by classifying recent customer management product announcements into STIB categories, and draws some insights about the model and customer management trends. As a bonus, it proposes a future of "hyper-personalized, financially-optimized treatment of each customer during each interaction across all channels."

The Substitution-Transformation-Infrastructure-Business Model (STIB) model of innovation diffusion that I presented in my last blog post is intriguing, but does it reflect reality? One measure is whether it provides a useful classification of industry products. To test this, I looked at the past ten weeks of product news in the CDP Institute Daily Newsletter, which came to about 50 items. Results were quite interesting.

But before we get to that, let’s flesh out the model a bit. It describes two core elements:

Innovation: the development whose impact is being measured. This might be electric motors or internal combustion engines. (More formally, innovation is a change in technology, where technology is defined as the tools, methods and knowledge used to perform a task. "Technology" in this sense isn't always "technical": a new marketing methods and financial tools can be important innovations. But I digress.)

Application: the process or product changed by the innovation being studied. This might be manufacturing in factories (a process) or transportation vehicles (a product). The application is assembled from components, which might be physical objects in a product or steps in a process workflow. The same innovation might affect many different applications but it's often more useful to study just one.

The model describes how an application changes in response to an innovation. This change comes in three stages:

Substitution: the innovation is deployed as an exact replacement for one component in the applications, without changing anything else. Electric motors were hooked to turn factory shaftwork instead of water wheels; internal combustion engines replaced horses for powering carriages.

Intermediate States: this is a period of experimentation. Multiple components are changed as the industry explores how to best take advantage of the innovation. The innovation itself continues to mature during this period, opening up additional possibilities.

Transformation: the industry settles on an optimal design incorporating the innovation. This "final" design is fundamentally stable although incremental improvements continue. Factory tools incorporate built-in electric motors; motor vehicles locate the engine and drive train most efficiently.

The model also tracks changes over time to technologies beyond the application itself:

Infrastructure: this includes products, suppliers, and processes that support the transformed application or are changed as a result. Electric motors require generating stations, power grids, and new machine tools, while they result in more efficient factory designs. Parts and materials manufacturers support motor-driven vehicles while auto ownership spawns new roads, filling stations, employment opportunities, legal frameworks, and more.

Business model: this describes business methods such as revenue sources, pricing models, marketing, distribution, customer service, funding, and ownership models. Larger factories lead to scale economies that favor large, national brands. Complex auto technology favors large organizations with deep resources for research, mass production, national advertising, and dealer networks.

The graphic illustrates relationships among these elements.*

In the initial state, the application, infrastructure and business model are all in their baseline (i.e., pre-innovation) configuration.

This is followed by substitution, where the innovation is applied to make a slight change in the application while infrastructure and business model are unaffected.

In the third stage, intermediate states, developers try variety of changes to the application and the infrastructure starts to mature in response to growing demands. Some will be a dead end (direct current electrical systems, steam powered cars). Others may hit on the transformed design before infrastructure is available to support them. Vendors may also fail if they don't change quickly enough to keep up with competition.

In the final stage, developers converge on a transformed configuration and application change comes more slowly. Some companies drop out or merge as the market consolidates. The infrastructure continues to evolve and new business models start to emerge.

The Future of Customer Management

Applying the STIB model requires specifying the application being changed and the innovation that is changing it. This might be tricky in some situations, but it’s pretty simple in the current context: the application is customer management while the innovation is artificial intelligence.

What’s not simple is predicting the form of the transformed application. Hindsight about factory equipment or auto design is easy, but we don’t know how customers will be managed in the future. This prediction isn't strictly necessary: we can simply observe changes as they occur without assessing their relation to the final state. But that assessment helps to organize the analysis and gives buyers and developers decide where to invest their resources.

Predicting the transformed state is a thought experiment: identify the constraints imposed by the current technology and imagine what the ideal design would look like if those constraints were removed (and replaced by whatever constraints the innovation imposes.)

As I see it, the dominant feature of customer management today is the number of separate steps that are performed by different people. There’s a creative team with a multi-step process to develop content, a targeting team with a multi-step process to select audience segments, a media team with a process to buy advertising, and multiple operations teams with multi-step processes to deliver messages via websites, email, social media, connected TV, digital video, games, out-of-home, podcasts, and elsewhere. The reason we have so many teams with so many steps is the limited capability of individual humans: each can be expert in only a narrow area, so many must work together to deliver a complete result. And, because they’re human, each person can only deliver a relatively small number of outputs over time. Thus, each output must apply to many people to deliver messages to everyone.

AI removes this limit. At least in theory, a single AI agent can expertly execute all steps in the messaging process, combining content creation, audience selection, media buying, and delivery. In practice, a single super-agent is less likely than a master agent that calls on specialized subordinate agents. But so long as those agents complete their work almost instantaneously, the process will still function as a single step. Ideally, these agents would view all available data and collaborate with each other: content development would be informed by audience characteristics; media buying would take into consideration other channel opportunities; and so on. All these decisions would be coordinated to produce the highest total value: for a given advertising impression, the system(s) might simulate the results of several different creative treatments for several different offers for several different products, and then select the best creative/offer/product combination – or conclude that even the highest possible value for that impression is too low to justify the investment. This requires a sophisticated value prediction algorithm which would consider long-term as well as immediate results.

In addition to speed and coordination, the process would be completed at near-zero incremental cost.

In short, I see the transformed state as hyper-personalized, financially-optimized treatment of each customer during each interaction across all channels.

This is a radical change. (That's why they call it a transformation.) There are no more campaigns (defined as a predefined sequence of messages), no more audiences (defined as a group of customers who receive the same messages), no more content (defined as a fixed combination of text, images, and offers), and no more media buys (defined as group of impressions purchased together). What we have instead is a master agent orchestrating customer interactions across all channels to optimize use of budgets, data, customer attention, products, staff, and computing power. That agent would call internal and external data to guide its actions and would deliver messages across both internal (owned) and external (paid) media.

In the transformed world, today's creative, analytics, media, and operations departments have largely vanished, apart from a few human(?) experts left behind to monitor the AI. On the other hand, there might be more need for humans to do strategy and product development.

This vision also points to new infrastructure and business models:

Supporting infrastructure would include vendors building the base AI systems, which are too complicated for most companies to build for themselves (although non-technical users might use no/low-code tools to tune them); data tools to prepare, integrate, and expose data from all sources in real time; and analytical tools to measure interaction value.

Surrounding infrastructure would include new media that provide real-time access to their customers; low-friction integration methods to connect marketers with these systems; new billing and analytical methods; and social/legal frameworks to govern customer data collection, sharing and privacy.

New business models would be needed for companies and the systems, data, and media providers that support and surround them. With each interaction managed separately, providers might become on-demand services that charge for value provided in each case rather than billing based on labor, system use, or impressions.

I’m not sure this will happen. At most, I’d bet one of my later-born children on it. I’m laying it here out because the STIB model works best if you measure against a transformed state (and because it’s fun to think about.)

Mapping against the STIB Framework

That said, let’s try mapping recent product news against the STIB framework.

Application

The application we’re analyzing is customer management, which is roughly what we cover in the CDP Institute daily newsletter. This gives us a reasonable collection of announcements to work with, although it’s just a small sample based on items that happened to appear during a relatively brief period.

If the STIB model is correct, we should find clusters of products on a spectrum from business as usual, to substitution of AI within current processes, to complete transformation. Because transformation doesn’t happen all at once, we would expect to find several intermediate stages. Indeed, that’s the case.

Current State: The first cluster would hold products that execute the existing process without any change. AI-powered co-pilots might fit here. But co-pilots are so common that we don’t bother to write about them in the newsletter. Nor do we usually cover products that aren’t doing anything new. So this cluster is empty apart from one item about agentic helpers.

CommerceIQ Offers Agentic Teammates to Ecommerce Marketers

Substitution: The second cluster is simple substitution: products that replace a discrete task with an AI-generated equivalent. We see plenty of these, often offered as collections of (separate) AI tools for multiple tasks. We also see toolkits for users to build their own. So long as each AI tool executes one task separately from the others, this is still substitution. Recent examples are:

Intermediate Products: Now we move into products that change the underlying process but don’t reach the fully transformed state. The news items seem to fall into three clusters. It’s important to note that we didn’t define these in advance: they have emerged from the data itself.

Content Creation: This is a popular task to automate. It’s a single task but more than substitution because most vendors connect their content generator to response data and use this to automatically optimize content over time.

Goal-Driven Workflow: this cluster holds systems that have automated development and execution of a multi-step workflow, such as audience segmentation, journey design, or media buying. Like content creation, these usually let users specify a goal for the workflow and collect data to help measure results.

Customer Management: these products collect data to support optimization, help users to create messages, and select and deliver messages across customer touchpoints. They come the closest to the fully transformed state but don’t support all channels or create hyper-personalized messages in real time.

Transformed Products: This cluster would hold products that deliver the fully transformed process. There’s a good chance that some vendors have this mind, but we haven’t seen any products that deliver it. So the cluster is empty.

Infrastructure

Infrastructure and Business Models won’t take their final shapes until the transformed process is fully deployed, but they do co-evolve with the application changes. This applies especially to supporting infrastructure, parts of which must be in place for some intermediate product to function effectively.

Supporting infrastructures: The key supporting infrastructures for transformed customer management are inputs (data access and quality), internal processes (agent cooperation and analytics), and outputs (media integration). We see announcements in all these areas.

Data Access: most data access announcements describe accessing data in real time without loading it into a separate database. So far, these developments focus primarily on reading data in the company’s internal systems. But remember that the full vision for transformation includes access to third-party data such as compiled customer behaviors and local weather. We do see some of that, although none is in the current sample.

Data Quality: these tools prepare data for AI use. Many data quality vendors have added features to support AI use and have added AI-powered offerings. These apply to customer data but we don’t usually write about them, so this cluster is fairly sparse.

Agent Deployment: this describes technology for building agents and helping agents work together. It’s another field with extensive activity that is largely beyond the scope of the daily newsletter. It will be critically important if the transformed state involves teams of agents that cooperate closely with each other.

Analytics: any goal-seeking agent will need internal analytics to guide its decisions. Still, there may turn out to be a market for independent agents that make their results available as a shared resource for teams of specialist agents. This would apply especially to customer value analytics, which are needed to compare opportunities across different channels.

Hightouch Adds Journey Simulations

Media Integration: this is the output infrastructure that deliver messages created by the hyper-personalization. The tools are steadily encompassing more channels.

Surrounding Infrastructures: changes to the surrounding infrastructure happen after the transformed application is in place. This makes them harder to predict than supporting infrastructure, which develops sooner. We do have some current developments that are likely to become more important as hyper-personalization matures. There will no doubt be others.

Integrated Commerce (Sales Agents): this describes shopping in non-traditional channels, including retail media, search engines, social media, video, connected TV, and mobile apps. These can work without hyper-personalization but are vastly more effective when offers are tailored to the individual and context. In many cases, the interface will be a chat-style, AI-based sales agent that has a real-time dialog with the customer.

AI-Based Search (Buyer Agents): this describes marketing that is targeted at AI agents rather than humans. Today's most common implementation is AI search overviews, which do research for buyers. This changes the goal of search marketing from attracting traffic to appearing in genAI summaries. Other agents are evolving for other types of research and other stages in the sales cycle including making purchases on the customer's behalf.

Interacting with these buyer agents at scale and cost-effectively will require AI-based sales agents. In some ways, it won’t matter whether the sales agent is interacting with a person or a bot: to the sales agent, both are collections of data that must be analyzed and responded to appropriately. That said, the behaviors of humans and bots will be significantly different, so the sales bots will no doubt develop separate approaches to each. Ultimately, marketing systems may give buyer agents direct access to (some of) the product and promotional information they provide to sales agents, bypassing the sales agents altogether.

Because this is a relatively new development, our news coverage includes more research reports than product announcements.

Business Models

Few companies make announcements about changes in their business model, especially when those changes involve firing people. As a result, we have few newsletter items on AI-based business models. One change we do see is movement towards pricing based on value created rather than resources consumed. The transformed process will surely create other opportunities, perhaps including “no staff” companies run almost entirely by AI or “no product” companies that source products in real time as they find interested customers. It's likely the new business models will include ones we haven't even imagined.

What We’ve Learned

The main purpose of this blog post is to determine whether the STIB model provides a useful way to think about technology innovations. Of course I’m biased, but I believe it does. Not only was it reasonably easy to slot products into different categories, but the process generated several helpful insights:

Products that substitute AI for one step within an existing workflow are significantly different from products change the workflow.

It helps to envision the final, fully transformed product so we can assess intermediate products, identify the supporting capabilities and infrastructures it requires, and predict the surrounding infrastructure and business models it is likely to generate.

We should acknowledge that our prediction could be wrong. It may help to build scenarios around alternative outcomes.

Intermediate products appear before the final transformation. These become possible as new capabilities, infrastructures and business models appear. Observing the intermediate products helps to assess whether the changes are moving in the direction we expect or to adjust our prediction of the transformed state..

A minimum set of capabilities, infrastructures, and business models must be in place before the final design can succeed. Companies will fail if they offer the transformed design before supporting infrastructures are in place.

Capabilities, infrastructure, and business models continue to evolve after the (successful) transformed design is introduced. These developments make the product more effective and exploit the opportunities it creates.

Continued evolution creates rapid growth and expands the value of the final design, giving it an increasing advantage over alternatives. This advantage ultimately locks other configurations out of the market, even though some may be technically superior.

Applying the STIB model to customer management offers additional insights. Our sample of product news is enough to show:

Many current products do simple substitution. These are the easiest to deploy and can show immediate improvements in cost and quality. (There are also many products that support the existing workflow without any changes, but those don’t show up in our sample.)

Some intermediate products are already available. Most of these automate a single stream of tasks within the customer management workflow, such as content creation, campaign design, media buying or analytics.

Intermediate products usually work within a single department. This makes them easier to drop into the larger workflow and reduces the number of users whose work is disrupted.

Some products aim to automate an entire workflow, such as campaign design, development, and execution. Doing this with autonomous AI agents is the leading edge of the industry today.

Some companies offer components that support the final vision. These include application capabilities such as content optimization, response simulation, and advanced attribution, as well as infrastructure and business model changes including agent coordination, cross-company data sharing, touchpoint integration, and performance-based pricing.

I haven’t seen any products that promise the predicted final state of “real-time, hyper-personalized, omni-channel messages.” This has surely occurred to many smart people, so I’ll guess they have decided (correctly) that the capabilities, infrastructure and business models aren’t ready yet.

The final state may be delivered by a single integrated product or by multiple agents working in concert. Remember that the final state requires close cooperation between advertisers and media companies and between different departments within the same company. This makes a multi-agent solution more likely and reinforces the needed for data- and process-sharing technology..

Implications

Here are some practical implications of what we’ve discussed.

Substitution products can be valuable, but the benefit won’t last. It’s tempting to argue that substitution is a poor investment because it offers only incremental improvements on today’s current processes and that companies, and software developers should instead focus on more profound solutions. But the plain fact is that substitution creates substantial benefits and is easier to deploy than changes that require process change. Product developers and business users shouldn’t shy away from substitution but they do need to recognize it has a relatively short shelf life. Product developers should also realize that nearly all incumbent vendors will add substitutions to their current products or have already done this. That makes it hard to convince users to change to a new system on the basis of substitution alone. Attracting new clients will require solutions with more substantial advantages, which is what the intermediate product designs can offer.
The goal of "real-time, hyper-personalized, omni-channel messaging" is not yet widely discussed. What leads me to expect this as the transformed state is the growth of direct sales in social media, search results, connected TV, podcasts, and pretty much every other channel. This "instant commerce" collapses the multi-step "awareness, interest, desire, act" cycle into a single moment when the customer is presented with an opportunity to buy. Taking full advantage of this moment requires marketers to connect with all message opportunities, to gather all data so they can assess the potential value of each opportunity, and to deliver the most effective possible message for each opportunity they purchase. Only AI can do this effectively at scale, most likely by presenting intelligent agents to interact with customers who engage.**
The transition can be gradual. Instant commerce can be deployed in one channel at a time, can work with limited data, and doesn’t require advanced message optimization. Companies and vendors can move towards the fully transformed state in stages, building experience, product, infrastructure, and business models along the way.
Instant commerce depends on long-term relationships. That may seem like a paradox but customers will only engage with companies that they trust. There’s no time to build trust during the interaction moment, so trust must be built in advance. The good news is that the importance of trust is widely recognized and the methods for building trust (among humans) are well understood, if not always well executed. The industry may need new lessons in building trust among AI agents.
Infrastructure may offer the greatest opportunities. The biggest gaps between what’s currently available and what’s needed in the transformed state seem to be deep connections to collect external data and interact with touchpoint systems.

Shallow connections already exist, but real-time, hyper-personalized, omni-channel messaging implies real-time queries of external data sources about individual customers to collect up-to-moment information on behaviors. The touchpoints where those behaviors occur must capture, identify, assess, expose, and charge for that data in real time in a privacy-compliant fashion. Bear in mind I’m talking about touchpoints outside the company that wants to use this data. Very little technology exists to do that today. Data clean rooms are a start.
Beyond sharing information, those same touchpoints need to receive ad messages and deliver them to their visitors, again in real time and with feedback on response. Most of the messaging technology will reside with the ad buyer or middlemen, who will need to receive notification of contact opportunities, gather data and assess those opportunities, select the appropriate message, bid on delivering the message, transmit the message on bids they win, and measure results. Again, the existing technology to do all this in real time, at scale, and across many channels is limited at best. (Today’s programmatic ad system is a partial model.) In addition to data movement, this process requires sophisticated evaluation models so marketers can accurately bid on the projected value of each interaction.
Because all this interaction is happening between different companies, the infrastructure technology must be widely shared. This could imply broadly accepted standards implemented by many developers, or, more likely, proprietary technology built and sold by a few major suppliers. Competition to be one of those suppliers will be fierce but the rewards are likely to be huge. The rewards might diminish over time once the process is well enough understood to create open standards that are a viable, cheaper alternative.

Data quality doesn’t get the attention it deserves. Survey after survey shows that data issues are the top roadblocks to marketing, personalization, and AI success. Yet companies rarely make data quality an investment priority. There isn’t much to say about this except that real-time, hyper-personalized, omni-channel messages make data quality more important than ever. This is yet another piece of infrastructure that’s ripe for improvement.
Buyer bots might change everything. Buyers have limited attention, which is why gaining attention has always been the first step in successful marketing. This still applies to real-time, hyper-personalized, omni-channel messages -- so long as they're being sent to humans. But if customers delegate their purchasing activities to bots, the fundamental truth is no longer true: buyer attention will no longer be limited. Developments such as search engine optimization and AI search overviews as early examples of marketing to bots: marketers must target the algorithms, not attract human attention. But search marketing of any kind is still aimed at putting messages in front of eyeballs. Truly automated purchasing will remove humans from the entire process. The change won't happen overnight but it seems plausible to expect a mix of human and bot buyers in the near future. A STIB model with bot buyers as the final transformed state would be quite different from the one I’ve presented here.

Summary

This post has explored whether current developments in customer management technology can be effectively analyzed with the STIB model of innovation diffusion and whether the results provide useful insights into industry trends. I believe the answers are Yes and Yes. I've also presented a specific vision for the industry future, of "instant commerce" delivered through real-time, hyper-personalized, omni-channel messages. I can't promise that this is correct, but it's an interesting starting point for discussion.

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* The graphic is conceptual but could be quantified by counting the number of components in each product that match the final transformed design or that differ from the initial state. Fun!

** Things are admittedly a bit more complicated for non-impulse purchases. But I’d argue you’re still trying to motivate an action in the moment, even if it’s only saving an offer in a wallet for future consideration.

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пятница, 6 июня 2025 г.

Build Innovation Into Your Strategy

By Dan Montgomery & Gail Stout Perry

Innovation can include both paradigm-busting breakthroughs and incremental improvements in existing products or services. Read more to see how an organization can articulate, align, and communicate how innovation fits into its overall strategy.

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воскресенье, 30 марта 2025 г.

Business Models of Non-Practicing Entities

In the discussion of organizations that provide technology or patent rights to others, Non-Practicing Entities or as some of them are called "Patent trolls" are always debated. My objective with this post is to introduce the concepts and explain the frequently disputed business model.

A few words about patents

A patent is an exclusive right to prevent others from making, using, selling or distributing an invention that is considered new, non-obvious and useful or industrially applicable. A patent does not give the proprietor of the patent the right to use the patented invention, should it fall within the scope of an earlier patent. Patents per se has nothing to do with the business model used by the patent holder or the pricing of products; Skype, Google, and other firms known for providing free services to their users, is still developing and filing patents to claim their rights to inventions, primarily to keep competitors away.

A patent is a limited right (often 20 years from the filing date) that the government offers to inventors in exchange for their agreement to share the details of their invention with the public. The patent system incentivizes organizations to invest in research and development, and to disclose instead of keeping inventions secret in exchange for exclusivity. Being able to keep competitors away for a limited time, gives the inventor the chance to recover their up-front investment in making the invention. For a new pharmaceutical drug this investment can be billions of dollars. In contrast to some granted patents covering software or business methods, the investment can in some cases be close to zero excluding the costs of patent filing. Like any other property right, a patent may be sold, licensed, mortgaged, assigned or transferred, given away or abandoned.

Monetizing innovations

A university researcher or single inventor can chose to start a company to manufacture and sell products based on its research outcome, or chose to use the exclusive right status of a patent to become a licensor. This allows the inventor to accumulate capital from licensing the invention so the inventor's time and energy can be spent on pure innovation, allowing others to concentrate on manufacturability and marketing of downstream products. This can be seen as a straight forward application of Adam Smith's division of labor that having a group of people focus exclusively on inventing new things.

There are numbers of research-based companies that develop new technology or pharmaceutical drugs just to license it to other firms to commercialize. The object for transaction can be just the right to exclude someone, or it can be drawings, data, relating non-patented inventions, knowledge in different forms, knowhow etc.

This ability to assign ownership or rights increases the liquidity of patents as property. Third parties can license or acquire patents and the same rights to prevent others from exploiting the inventions, as if they had originally made the inventions themselves. Small companies or individual inventors that don't have the funds to claim their rights against multinational companies can sell their patents to companies willing to enforce them against infringers.

Licensing of patents is nothing new. In 1894, American Bell Telephone Company's R&D department licensed 73 patents from outside inventors, developing only 12 inventions from its own employees.

Cross-licensing of patent rights

It is common for companies engaged in complex technical fields to enter into license agreements associated with the production of a single product. Scott McGregor, President & CEO of Broadcom, has said that "a cell phone these days can have hundreds of devices that are part of it; each one of those can have hundreds of aspects to it. You could literally have a million or more patents that would apply to a single handset". It is therefore common that even competitors license patents to each other under cross-licensing agreements in order to share the benefits of using each other's patented inventions and reduce the risk of being sued by the other party.

When one company sues another (Nokia vs Apple) the other company often countersues the first one (Apple vs Nokia) and the litigation process often results in that the parties come to an agreement, cross license patents, and pay licensing fees to the company with a stronger case. Counter-assertion is an important stabilizing force in many patent disputes.

Non-Practicing Entities - a question of value proposition

A non-practicing entity (NPE) is a patent owner who does not manufacture or use patented inventions but rather than abandoning the rights to exclude, an NPE seeks to license the rights to others. The value proposition is thus not a product or service, but the rights to an invention with or without supporting knowledge and know-how. A single inventor, a university, a research institute, an SME, a multinational company or an investment fund, can all be non-practicing entities, depending on their choice of business model.

The term patent troll is sometimes used for NPEs that enforce its patents against one or more alleged infringers in an aggressive or opportunistic manner. The general idea of these firms is to develop a large patent portfolio and to license these patents to companies that infringe on them or potentially filing lawsuits against these companies if they refuse to take a license. In some cases the NPE analyze popular products on the market to find remote patents that could be infringed, and approach the patent holder to acquire or get a license to sue.

In some cases these firms have as their business model to purchase patents, often cheaply from technology companies forced by bankruptcy to auction its patents, with the sole purpose to sue and enforce it against companies that manufacture or market products, potentially infringing any of the patents. Some of those accused of being patent trolls argue that they are the modern incarnation of Robin Hood helping smaller companies and inventors against large companies who have stolen their ideas.

Legal extortion or value proposition?

To seek to derive income from the enforcement of patent rights is perfectly legal but it sometimes has troubling implications for the makers and sellers of products and services. As the entity is not selling products or services, almost by definition it does not infringe on the patent rights contained by others, thus they are essentially invulnerable to counter-assertion.

This gives NPEs a position to negotiate licensing fees that could be argued to be out of alignment with their contribution to the alleged infringer's product or service. As a result, a patent held by an NPE is often considered more threatening to industry participants than the same patent held by a competitor. As a result of that, there are operating companies that sell their patents to NPEs to assert the patents without the operating company being involved.

The more dubious NPEs can see even a weak patent as a lottery ticket hoping the alleged infringer chose to pay up without completing a lawsuit. If the defendant chooses to litigate then both sides must absorb heavy litigation costs no matter who wins. As even a successful litigant must pay the costs of defending its case, and that the cost can run into the millions, operating companies may chose to pay up even though they probably would win if they take the case all the way through trial, to avoid the time, expense, and uncertainty. This is something that the more dubious NPEs take into consideration when they approach operating companies.

Revenue model of Non-Practicing Entities

NPEs generate revenues through licensing agreements or damages awarded by a court. Licensing agreements can include up-front license fees, milestone payments contingent upon achieving certain goals, and royalty revenue from the commercialization of the licensed technology. Damages are awarded on the basis of how much value the defendant is obtaining as a result of its infringing activity.

Activities performed by Non-Practicing Entities

All NPEs have to manage their patent portfolios, and identifying existing or potential application areas. Independent of being a single inventor, a university or a patent holding company, activities also involve finding potential licensees, negotiating terms and conditions and collecting license fees. Most NPEs also have internal research and development and patent filing and maintenance activities. There is a continuum from organizations that are doing substantial investments in R&D to generate inventions and patents, and organizations focusing on acquiring and aggregating others' patents.

The good guys and the bad guys

The business model of Non-Practicing Entities is widely debated and there are firms with dubious motifs. Companies that do not manufacture or intend to manufacture anything are often seen as profiting from others in a negative way (at least in the media). At the same time companies outsource or move production to low cost countries, leaving the western world to be “the innovators” where the output is innovation and intellectual property.

Good guys can be found in both ends of the scale, providing valuable inventions, knowledge, data, instructions or knowhow to manufacturing companies, or just aggregating and providing the rights reducing the time and cost for manufacturing companies to find all relevant patents covering a technology area. Without the existence of entities willing to buy patents as a last resort there’s no credible threat a single inventor can make towards a large company. A rational defendant will simply carry on knowing the patent can’t be successfully enforced, and inventors at the margins may not undertake their research in the first place.

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пятница, 21 марта 2025 г.

Using Innovation to Grow - Within and Beyond - Your Core

Rich Kohler

While it is well known that innovation is critical to all companies’ growth, research shows that the most successful organizations use it to:

Not only expand their lead within their industries,

But also disrupt new ones, even in uncertain times.

In last week's article, Pathway to Sustainable Growth, I stated that the biggest key to future growth, many experts believe, will come from innovation: operational improvements that make products faster, better, or cheaper, or new devices that save consumers time and money.

And that innovation could also come from investing in talent.

Innovation and Growth are Inherently Linked

Companies that build new businesses and develop new offerings, processes, or business models are better able to capture growth opportunities - as well as and hedge against market and technology disruption in a highly uncertain business environment.

In McKinsey's recent survey of over 1,000 global companies, the largest share (39%) of respondents identified the ability to innovate as the most important strategic factor for generating growth over the coming 12 months.

The next 4 include: Relationships with Customers (38%), Relationships with Business Partners (30%), Talent (28%), Operational /Manufacturing Excellence (27%).

The biggest sources of competitive advantage vary by industry, but innovation is consistently in the top three.

In sectors undergoing significant disruption - energy, for example, where supply disruptions and large investments in sustainability require companies to evolve their businesses - innovation is particularly important.

But even in industries where the evolution of business models is a less urgent need, such as retail, nearly a third of the respondents identified innovation as a top three source of competitive advantage.

What distinguishes top economic performers3 from the broader group, however, is their comprehensive approach to innovation and growth. This applies both within and outside their current industries or geographies.

In McKinsey's survey, top performers cited innovating new offerings as their number one investment priority for accelerating growth over the next 12 months.

They were also more than

63 percent more likely to innovate at scale - by building or acquiring new businesses outside their current industries and

50 percent more likely to expand geographically compared with their lower-performing peers.

Innovation Spurs Growth Within - and Beyond - the Core

On average, 80 percent of corporate growth comes from within a company’s core industry, and innovation is critical to that growth.

While overall industry momentum and commercial levers such as pricing and marketing are critical and cited by 42% of respondents, the next two largest factors, noted by 38 and 34 percent of survey respondents, respectively, are innovation of new offerings within the core business and expanding into new regions.

According to McKinsey, Innovation not only gives companies new revenue streams within their core businesses - but also potentially steepens the entire sector’s growth trajectory.

For example, Taiwan Semiconductor Manufacturing Company’s:

Disruption of the integrated semiconductor industry by supplying manufacturing services to other players,

Combined with its innovations that increase chip computing density,

both raised its revenues by 17 percent annually between 1995 and 2025 and contributed to boosting the sector’s growth.

Similarly, Apple famously helped redefine the music industry by introducing the iPod and its associated apps and created entirely new platforms with the iPad and Apple Watch, all of which bolstered its ascent to the number-two spot among the world’s most profitable companies.

Top-performing companies put as much effort as other firms do into growing the core.

What differentiates them from their peers is their use of innovation to venture beyond their industries.

As technology continues to break down traditional industry barriers, the need to innovate outside the core deepens.

For example, in McKinsey's research, top performers were:

78 percent more likely than their peers to build new businesses in different industries

and 68 percent more likely to acquire one in another sector.

This pattern holds true when one narrows the lens to the top 20 global companies by average five-year economic profit.

Fourteen of them accelerated growth through:

Significant innovation investments within their core businesses or by

Creating entirely new markets outside their core

Sometimes both - underscoring the importance of innovation-led growth.

These moves often occurred over numerous years, even entire economic cycles.

Consider:

What is your approach to Innovation?

How are you using it to accelerate your growth?

And how might it compare with top industry performers?

https://tinyurl.com/5n7vrnef

суббота, 28 декабря 2024 г.

How to Measure Innovation

By Mark Malinoski & Gail Stout Perry

https://tinyurl.com/2s459w7a

понедельник, 26 августа 2024 г.

Lateral Marketing by Kotler

Alexey Cherkasov

The book «Lateral Marketing: New Techniques for Finding Breakthrough Ideas» by Philip Kotler and Fernando Trias de Bes is devoted to a non-standard thinking in marketing. Classic marketing theories continue to play an important role in the market, but nowadays a broader perspective on marketing opportunities is needed.

The authors give many reasons for the fact that existing marketing techniques are no longer so successful, it is connected with the reduction of the product life cycle, and with the revolution made by the transition to digital technologies and with the growth of diversity within the categories of goods and much more. All this only proves that the modern world needs a new approach. Innovation is the key and basis of modern competitive strategies. Innovations can be both from the inside of the market, and from the outside. From the inside of the market, innovations are based on modulation (variation of one of the basic qualities of goods or services, which is to strengthen or reduce this quality), sizing, packaging, design, complements development, effort reduction. But the most effective way, according to the authors, is innovations from outside of the market, such as the creation of a new market or category.

P. Kotler and T. de Bes do not oppose traditional and lateral marketing. They believe that lateral marketing is a complement to traditional marketing. Vertical marketing process is a sequence of steps: identification of needs, definition of the market, segmentation, positioning, development of marketing tools. A vertical marketing process is a logically consistent movement from the general to the particular. Lateral marketing – involves restructuring existing information and moving from the private to the general with a less rigorous thought process – research, risky and creative.

In their book, P. Kotler and T. De Bes attempted to formulate a theory of lateral marketing. They give the following definition of lateral marketing: it is a workflow that receives existing objects (goods or services) at the input and gives innovation goods or services that are targeted to needs, customer groups or ways / situations of use not currently covered ; thus, this process with a high probability leads to the creation of new categories or markets.

The authors propose a scheme for the process of lateral marketing. It consists of three steps and is based on the process of creative thinking:

Choosing a Focus in the Marketing Process
Generating a Marketing Gap
Making Connections

Lateral marketing begins with a product or service. There are two options:

Select the product or service that we are selling.
Choose a product or service with which it is difficult for us to compete.

1 step. Having determined the goods, we must choose the focus in it. For the purposes of lateral marketing, it is necessary to divide all components of vertical marketing into three main levels:

Market definition level (need, target group, mode/situation of use)
Product Level
The level of marketing tools (i.e. the entire marketing mix except for the product).

The second step is to shift the focus, which is located on one of three levels. Here you can select six basic operations:

– Substitution

– Combination

– Inversion

– Exaggeration

– Elimination

– Reordering

Step 3 – establishing a connection or eliminating a gap. For this purpose, an analytical evaluation is performed. There are three ways to assess this: track the purchase process, identify useful properties and find the right situation.

The process of lateral marketing gives three types of results:

The same product, new use
New product, new use
New product, same use

At the present time, when new products are brought to the market with unusual speed, a significant proportion of attempts fail. The book describes a new technique for successfully competing in the market, it allows you to develop new products, find new market niches and eventually make a breakthrough in business. The authors do not reject classical marketing but advise in addition to it to use non-standard ways of thinking.

The book will be useful for those who are going to use lateral marketing in their company, for specialists in marketing and advertising, as well as for those who are interested in unconventional thinking as an ideal way of developing new ideas.

https://tinyurl.com/29xx88vn

пятница, 29 августа 2025 г.

Identify Your Innovation

Test the Value of Your Innovation

понедельник, 21 июля 2025 г.

The Future of Customer Management

Mapping against the STIB Framework

Application

Content Creation: This is a popular task to automate. It’s a single task but more than substitution because most vendors connect their content generator to response data and use this to automatically optimize content over time.

Infrastructure

Media Integration: this is the output infrastructure that deliver messages created by the hyper-personalization. The tools are steadily encompassing more channels.