Commemities (4): Analyzing Trends in an Innovation Economy
In this series I present a proposal for narrative-centered analytics that may automatically adapt to the unpredictable changes that drive the innovation economy, and that might be a formula for zero-assumption analytics. It builds on the idea that people connect memes and memes connect people. A commemity is defined as {community + shared memes} or, equivalently, memes and people holding each other together in a cluster
In this series:
1. Narrative-Centric Analytics
2. Defining Narrative-Centric Innovation
3. Benefits of Defining Innovation as ‘Introduction of New Narrative’
4. Analyzing Trends in an Innovation Economy
to be continued…..
Challenge: Innovation-Confusion Makes Strategists Compare Apples and Oranges
Let me invite you to make a thought experiment with me. Imagine that we are strategy consultants in the early 2000s and that you and I have started a new business. We measure the trends in IT-usage, draw smart economic conclusions and then sell the report to business leaders. The core of our product is a survey where we ask people if they a) have a mobile phone, b) an mp3 player or c) a PDA (if you don’t remember, this was a hand computer for managing contacts, calendars and to-do-lists). We make good business, and in 2007 the introduction of the iPhone comes but what do we care? We are strategy consultants, after all, not tech-geeks. Then, in 2012, we publish our (last) yearly report delivering our trend analysis to industry leaders, presenting three strong trends: 1. People are talking more then ever before, as shown by the accelerating spread of mobile phones. 2. Music is on its way out, concluded from the dwindling amount of mp3 players in peoples possession, and — our ‘shocker’ trend — 3. People are losing interest in organizing their time, as we can show in the rapid drop in the usage of PDAs. All of our predictions are completely wrong, of course, and we are soon out of business.
As you and I both know, what actually happened was that the iPhone had changed the narrative. The MP3 player and the PDA had both merged into the phone. The gadgets were gone but the purposes they had been used for remained, now assisted by the smartphone, instead. If we, instead of asking people if they had these three gadgets, would have measured the shift in narratives, instead, our research method would have delivered relevant results and we would have stayed in business. In our trend report we would have shown how the words that used to be associated with the mp3 player (like ‘music’) or with the PDA (like ‘calendar’) were now being associated with the phone instead. We could even have slept through 2007 and been happily oblivious about the iPhone, or even be totally ignorant about what a ‘smartphone’ was — we would still be able to deliver the correct trend analysis. We would simply say “here is the cluster of words associated with the mobile phone” and “strongly trending words are: ‘music’, ‘calendar’… (etc)”. This example will, I hope, awaken you to the thought that measuring the narratives and definitions of keywords is an attractive avenue for analytics in the innovation economy.
In my most previous post I have gone through more reasons how and why thinking the narrative-centric way is a better match for the innovation economy, which is, arguably, more narrative-centric than the old economy (Benefits of Defining Innovation as ‘Introduction of New Narrative).
We need analytics that can adapt to how things change their meaning through innovation, like the phone, that can handle the introduction of new words and narratives, discover them, analyze how they affect systems, to make informed decisions in the innovation economy. The analytics should assist in designing functional narratives, changing how we relate.
Here is a suggestion how, which puts the ideas in the previous three posts into use, as analytics.
Commemities: An Idea for Innovation-Friendly Analytics
‘Commemities’’ is a general method and a class of analytics applications for analyzing data that contains social and semantic elements. I am not claiming that techniques like this don’t already exist. In the last decade a lot has happened in the field. ‘Topic modeling’ techniques have been around for quite a while, such as NMF (Non-Negative Matrix Factorization) and LDA (Latent Dirichlet Allocation). But I believe the package of ideas and suggested techniques is an innovation, and techniques like NMF and LDA offer ways of implementing the method. I started drafting the idea a bit more than ten years ago, and over time I started to find mathematical methods. Much of the content in this series of posts is from work done in 2015.
General definition:
A Commemity is the union of a community and it’s shared language
Example of Usage: Analyzing Business Strategies
First, a recap: In my first commemities post I present the idea that every concept needs a name + definition + narrative, and a ‘custodian’ of the narrative. I argued that, from a narrative-centric point of view, strategy is about finding the answers to three questions: 1)” How will my narrative become their narrative?” 2) “How does it remain mine once it's theirs?” and 3) “How can my narrative become the incumbent narrative?” An example was presented, a mysterious invention my friend Bob had created — the ‘Blixflup’ — and I showed how the definition and narrative changed as the invention was turned into a marketable innovation, and how the custody of the narrative shifted along the way.
In real life nothing stands alone, everything is a part of a system, in business its an ecosystem of products and services, organizations, technologies, business models and so on. Every word of any value has custodians seeing to that it keeps its value, protecting it from being joyridden or hijacked. The words build on each other, they become each others contexts and shape each others definitions and narratives — they form ontologies. The analysis I am about to suggest takes the basic structure of my first post to the next level, placing it in the ecosystem. If it looks strange, take a step back, read the first post, and then come back. Here goes.
Leaders need tools to identify competition, assess it and select strategies. For building such analytics tools, we can use the fact that every narrative is centered around a key concept (which can be a product, service, a market or something else that plays an important role). It must have a name (for reference), a definition (to understand what it is), and a narrative (so that people can relate to it). This we connect to value that we care about, typically the market value that the key concept is associated to.
For example, a ‘lawnmower’ (name) is ‘a machine that uses a revolving blade or blades to cut a lawn at an even length’ (definition). ‘The smallest types, pushed by a human, are suitable for small residential lawns, while larger ride-on mowers are suitable for large lawns’ (narrative).
The lawnmower is a part of the wider concept: ‘power lawn and garden equipment’ (an ontology of concepts) attaining ~$15 billion in product sales per year. The lawn mower is part of it, as are other concepts, such as ‘turf and grounds equipment,’ ‘trimmers’ and ‘edgers.’ The ontology (in this case, power lawn and garden equipment market) is in itself a concept with a name, definition and narrative, but at a higher level. In the same way, the lawn mower is an ontology, from the perspective of all the small parts it consist of.
Back to garden equipment. Their definitions fit the ontology — the context (gardening) — which give their narratives value — the most obvious case of such value is the price we are willing to pay for buying the equipment. We are, in fact, buying (into) their narrative. When I buy a lawn mower, I pay for cutting my lawn in a better way, like I have seen and heard from the neighbors who have one. I am not paying for getting my foot stuck in it, which can happen. The manufacturers’ must do everything to own their own narrative. If they lose it, anyone can say anything about their lawn mower (like ‘you should buy that lawn mower only if you hate your feet’) and nobody will be the wiser as to what goes.
The ontology is the context of the lawn mower, but also the common language of a community of actors (manufacturers, purchasers and other stakeholders in garden equipment). And, as I have defined above, the community and its common language is a cluster of memes and actors — a commemity — that hold each other together, where actors connect memes and memes connect actors. The commemity is a convenient idea because of its self-containment. Actors will come and go (with people and companies that live and die), as will memes (who buys horse-manure anymore?) but nevertheless, the commemity goes on existing — people making, buying and selling garden equipment.
All innovators will want their concept to become an important part of the ontology, and some innovators hope their concept will disrupt the ontology, like a Trojan horse, replacing it with a new one that they control, like Steve Jobs tried to disrupt the mobile phone ontology and replace the ‘smartphone’ with the ‘iPhone’. Innovators are introducing a new product or service and must control their own narratives, or someone else will. The most dangerous consequences arise from having the answer to “how will my narrative become their narrative?” but not having an answer for “how does it remain mine once it's theirs?” Such innovators often end up ‘dead in the ditch’, because as soon as people saw it was a good innovation, other players will compete for being the custodians of the narrative and the original innovator will therefore become the main target for elimination.
Incumbents in a market segment want to be the ones who decide what that market is about, ie be custudians of the entire ontology of the market. They, too, are as well-served by narrative-centric analytics as the innovators are.
Narrative-Centric Market Research Tools
Power and narrative are closely related, because power is about how people relate and people relate according to a narrative. Being the custodian of a narrative means power.
Every leader, therefore, strives to be the custodian of concepts — controlling names, definitions and narratives of brands, technologies, policies, whatever. Since, in an ontology, every definition of a concept builds on other concepts, there is always a hierarchy, and therefore a hierarchy of power.
Every narrative tells the story about interaction between concepts and needs, conveying value. The story of a concept also touches the stories of other concepts. It will always have allies and rivals, depending on how the story affects other stories. Some people want all the stories that touch their stories to become their stories, for example.
A successful leader must understand the playing field: Who are the key players? Which are the key concepts? What’s in it for whom? Who are allies? Who are rivals? What’s the gameplay? How should WE play the game?
A ‘commemities method’ should be mixing social-network analysis with language analysis. It is a case of graph theory, with actors (people or organizations) as vertices and memes (keywords, definitions, narratives) as edges.
Case: Who Owns the Smartphone Narrative?
Here is an example of thinking in terms of narratives, with support of available analytics.
Before the iPhone, RIM, Palm and Nokia essentially controlled what the ‘smartphone’ was and the stories around it. Then Apple introduced iPhone and Google released Android. They stole the show and now people look at them to see what a smartphone is about. The smartphone is expected to be the key concept of mobile communication. Which analytics can spotlight the fight for the story of mobile?
In 2010, Symbian was bigger than iPhone and Android together. Still, Symbian was clearly no longer the lead raconteur of the smartphone story. In Q2 2011 Android had 43% market share, Symbian had 22% and Apple had 18%. Android was by far the fastest growing system. Still, a Google News search on Sep 19 2011 showed 28,400 hits for ‘iPhone’ and 22,300 hits for ‘Android.’ Judged only by the crude number of hits, it would seem that Apple was a larger custodian than Google of the smartphone story in the news. Google trends analytics can add information, such as regional interest, or which keywords were searched most often in conjunction. But we may be missing new concepts that we have not realized are playing a part in the story.
I have supposed that the smartphone story is today about the operating system. I have not included concepts such as Skype or gaming, which could converge with smartphone ontology and engage more stakeholders, memes, custodians and narratives that juxtapose the concepts. Regional data reveal sales in regions, but is that the most relevant partitioning of the data? Android may be selling more in one country than another, but there are many more ways of partitioning users and stakeholders. Gender could be a key divider, or age, or any other factor. Finding out which keywords and users form the most important clusters is crucially relevant.
So how can we get the full picture of how much each key player is in custody of the smartphone concept, and through which concept, without presupposing the players or concepts? So the players shape the concepts and the concepts shape the players, in a never-ending interaction between players-players, players-concepts and concepts-concepts. It should be useful to have analytics tools that can map the players and concepts without presupposing any of them, where the data themselves offer their own first-, second- or third-order trends by virtue of their innate characteristics.
Next post: MAPPING ECOLOGIES OF PLAYERS AND CONCEPTS
