Archive for category filter
The Value Proposition in Business Models
Posted by Tim in aggregate, business models, connect, filter on 28 August 2010
Anders Sundelin wrote a post earlier this week about the evolution of the business model concept. He does a great job of showing the various ways in which this idea has been operationalized – it’s still surprisingly fuzzy. For the state of the art thinking on business model innovation, a special issue of Long Range Planning has twenty articles on the topic (all free to download through September).
One element that is consistent across nearly all of the different ways of thinking about business models is that of the Value Proposition. A central part of building a successful business model is creating value for your customers. Innovation plays a role here in two ways: first, innovation is the process of executing new ideas to create value, so it is a central part of any new value proposition; second, we can innovate in the way that we create value, not just in the products, services or know-how that we offer.
In order to innovate the way we create value, it makes sense to look at how we create value from information. In general, we do this by aggregating, filtering and connecting. This works for big firms like Amazon, and smaller firms like O’Reilly Publishing.
I ran across two more examples of how this can work for smaller firms this week. The first comes from Seth Godin’s description of Gerald Roush and his Ferrari Market Newsletter. Here is the description of the newsletter:
The newsletter, it appears, was not just lucrative, it was a bargain. It chronicled the pricing, whereabouts and details of just about every Ferrari ever made. If you were a buyer or a seller, you subscribed. If you wanted to run an ad, you were required to include the car’s VIN, which added to Roush’s voluminous database.
The Roush effect involves extraordinary domain knowledge, a market small enough to understand and diligently earning the role of data middleman. The players in the market want there to be one clearinghouse, one authority who can connect the data, see the trends and publish the conventional wisdom.
Often when people talk about “aggregators”, they are referring to places like Amazon or Google, who try to catalog everything (or close to it). This is a great example of how you can effectively aggregate on a much smaller scale. The Ferrari Market Newsletter isn’t trying to aggregate everything, it’s just trying to aggregate all available information on Ferraris.
In this case, the aggregating is combined with filtering to create an comprehensive aggregation of information in a specific niche. The connections are made between people that are interested in Ferraris – most importantly, between those that want to sell one and those who wish to buy one.
Note that this is not algorithmic filtering, as we see on the comprehensive sites. It is judgment-based filtering. It often sounds as though algorithms are the only way to go these days, and as this case shows, that is not at all the case. There are still opportunities to build effective business models based on personal judgment.
Here’s another example, though it is more speculative. On Techdirt, Michael Masnick talks about the idea of building affinity-based music groups. Techdirt is a consistently interesting blog, and you should definitely check it out. Here is how he describes these groups:
… Topspin’s CEO, Ian Rogers, penned an open letter to Guy Hands, the head of (struggling) EMI, suggesting that rather than think of itself as a “record label” focused on promotion and distribution (two things that are easier and cheaper than ever before), it could instead focus on being the smart filter for music listeners today, struggling to find the music they love amidst so much musical abundance in the world. The suggestion was to take some of the key, iconic, bands under the EMI roof, and put them under affinity-based “mini-labels” with other less well known bands, that would appeal to people who liked the more well known band. It seemed like a great idea, which, of course, EMI has not done.
Here again, the value is created through filtering. And as with the Ferrari Market Newsletter, this model would then try to aggregate all of the bands that relate to each other in a specific way. This is a model that has worked very effectively for many years for Dischord Records – and like Masnick I think it has great potential.
Creating a novel value proposition is an essential part of generating an effective business model. There are great opportunities to do this in creative ways. If you focus on aggregating, filtering and connecting, you can build a good information-based value proposition.
LikeWhich Part of Your Business Model is Creating Value?
Posted by Tim in business models, filter on 14 August 2010
Andrew Keen posted a fascinating interview with Jeff Jarvis yesterday. All of the interview clips are worth watching – they touch on a number of interesting topics, including the relative benefits of publicness and privacy, the future of news and how to best develop new business models for journalism, why google struggles with social applications, and the changing nature of internet-based business models. The latter is included in this clip:
One of the really interesting points here is that the economics of media has changed dramatically. Jarvis points out that with 30% of internet users creating content now, there is no longer economic value in acting as a gatekeeper – which is the way that many media firms have tended to view themselves. Instead, value is created through curating, which I think of as depending on filtering methods.
This is a critical issue in looking at topics like building a new business model for news. One of the reasons that the established media companies are struggling right now is that they are not coming to grips with these important economic changes and the implications that they have for which business models work and which ones don’t.
The problem here may actually be that the current business models are actually still ok, but that the firms have been protecting the wrong part of them. Mike Masnick made this argument a couple of years ago, saying:
You can actually be succeeding in a market you don’t think you’re in.
When it comes to the entertainment industry, that may be exactly the case. We’ve been arguing that there are plenty of business models that don’t involve actually selling the content, but involve selling other, related products that are made valuable by the content. In fact, that’s what both the music and the movie industry already do. Everyone may think that you’re buying “music” or “movies” but that’s very rarely what you’re actually buying. You’re buying the experience of going to the movies. Or the ability to have the convenience of a DVD. Or the convenience of being able to listen to a song on your iPod. And, in many cases, it’s not just one thing, but a bundle of things: the convenience of being able to hear a song in any CD player, combined with a nice set of liner notes and the opportunity to hear a set of songs the way a band wants you to hear. It can be any number of different “benefits” that people are buying, but it’s not the “movie” or the “music” itself that anyone is buying.
So maybe the impact of the changing economics that Jarvis points out is really on the perception of business models more than on the business models themselves. I think that Masnick has hit on an interesting point – and as I’ve been arguing for a while, I agree with him that the main reason media companies are in trouble is that they haven’t been protecting the right parts of their business.
This is why it is so critical to analyse your business model now, so that you understand why it works (or doesn’t), and so you know which parts are essential, and which can be changed more easily.
Get Out of the Echo Chamber to Improve Innovation
Posted by Tim in connect, filter, innovation on 16 July 2010
Ethan Zuckerman’s great talk from this week’s TED Global conference was just posted – it is well worth watching (the notes for the talk are here on Zuckerman’s blog):
This talk raises an important general point – if we want to be good global citizens, we need to be making more of an effort to discover what’s actually happening around the globe.
This relates to innovation though too. Connecting ideas to each other is the core creative act in innovation. And it is well-documented that we make more creative connections between ideas when we are exposed to a greater diversity of ideas. The problem is that most of us generally interact with people that are quite a bit like us. This greatly limits the diversity of viewpoints and ideas to which we are exposed. Consequently, this constrains our ability to innovate.
Here is how Zuckerman frames the issue:
We tend to use two types of filters to manage the internet – search, which is great at telling us what we want to know, and social, which promises to tell us things that we don’t know we want to know. There’s a lot of people trying to engineer serendipity by taking advantage of the fact that not only are you on the internet, your friends are also on the internet. And if your friends – or just someone with similar interests – finds something that’s interesting, it might be a serendipitous discovery for you as well.
There’s just one problem with this method. Human beings are herd animals. Like birds of a feather, we flock together. And so what you see on a site like Reddit or Digg – or what links you get from your friends on Facebook or Twitter – is what the flock is seeing. The flock might help you find something that’s unexpected and helpful, but it’s not likely to find you something from halfway around the world.
His solution to this problem is to find bridges – people that span multiple communities. These are people that can provide exposure to new ideas. John and I have talked about the importance of bridging in network terms – and Zuckerman provides clear examples of bridges, and the benefits of connecting to them.
He also talks about the importance of non-algorithmic filtering. I agree with him that we have lost sight of the value of this, and that we need to use different forms of filtering to create different forms of value. He talks about expert-based filtering as one specific method for finding bridges and increasing the diversity of ideas that you consider.
Exposing ourselves to a wider diversity of ideas is critical. It is especially important in our roles as citizens, but it is also crucial for improving innovation. We need to hear idea that don’t simply echo our own. The best way to make novel connections between ideas is to hear ideas that are radically different from those we’re used to thinking about.
We need to find bridges, or be a bridge, between diverse ideas. That’s one of the simplest ways to become more innovative.
Different Forms of Filtering Create Different Forms of Value
Posted by Tim in aggregate, business models, connect, filter on 8 July 2010
Ethan Zuckerman wrote a very interesting post today called What if Search Drove Newspapers? He talks about several different initiatives designed to gauge readers’ interest in different news stories, particularly those that are currently under-reported, and then devising methods for reporting stories on these topics. He asserts (correctly, I think) that this is basically search-driven content development. In particular, this is a strategy that will work well with Google.
Zuckerman concludes by making an interesting point (but you should go read the full post):
I’d propose another way in which search-driven content creation might be evil – it’s a step towards news outlet as search engine and away from news outlet as source of serendipity.
The front page of a newspaper is a statement not just about what’s happened in the world in the previous 24 hours, but what the editor believes is important for you to know about. There’s always more that happens in the world that can fit on a paper page – or even a much larger web page – and the editorial decisions made shape a vision of what you need to know as a reader and what you can safely ignore. Smart editors use this ability to engineer serendipity, pushing readers towards topics they might not have known they were interested in, featuring more obscure content that’s got good storytelling and a high likelihood of capturing a (previously uninterested) reader’s interest. (I wrote about this idea at more length in a post called The Architecture of Serendipity.)
The way to create value in digital business models is by creating value through aggregating, filtering and connecting ideas. The thing that I think is interesting about Zuckerman’s piece is that it basically looks at Google-style filtering as the only method for driving search. This method is algorithmic filtering – This is what people often end up talking about when they discuss news aggregators and other search-driven journalism.
However, there are at least five forms of filtering, and using each of them can create value differently. I think that we need to explore these other forms of filtering in trying to create online value – in the news industry as well as in other contexts.
The editor deciding what is important is expert filtering. This still is used in several contexts, such as at politico.com (discussed previously here). The expert network could be a very interesting approach to filtering new as well.
The main point here is that there is definitely still opportunity to take advantage of judgment in filtering and connecting news stories. Mechanical filtering methods (the algorithm-based approaches) appear to be dominating right now, in large part because of Google’s current gigantic footprint on the internet.
This does not mean that this is the only way to go, though. In order to create value with one of the different forms of filtering, you have to think through very carefully how you are going to do each of the aggregate, filter and connect steps. I’ve been arguing for a long time that the money in digital business models comes from filtering well, and that the firms that realise this are the ones that will do well. A business model with mechanical aggregating, and judgment-based filtering and connecting should still work. It might not be all things to all people, but then, very few successful business models are.
Innovation Through Prototyping and Experiments
Posted by Tim in business models, filter, innovation on 23 May 2010
I’ve talked before about the importance of experiments in the innovation process. Experiments are essential for two reasons. First, they allow us to be more confident that our ideas will work. If we run a successful small experiment, that gives us some idea of how the innovation might work as we try to scale it up. Second, they allow us to sort our ideas more effectively. If we can devise a quick and dirty way to test out an idea, it will help us figure out which ones won’t work.
This seems fairly straightforward for testing product ideas, or really anything that is based on physical existence. But how can we experiment with intangible things, like services, or business models?
Diego Rodriguez provides some ideas in a great post that is part of his Innovation Principles series – Anything can by prototyped. You can prototype with anything:
You can prototype with anything. You want to get an answer to your big question using the bare minimum of energy and expense possibly, but not at the expense of the fidelity of the results. It’s not only about aluminum, foamcore, glue, and plywood. A video of the human experience of your proposed design is a prototype. Used correctly, an Excel spreadsheet is a wonderful prototyping tool. GMail started out as an in-market prototype. A temporary pop-up shop is a prototype. Believing that you can prototype with anything is a critical constraint in the design process, because it enables wise action, as opposed to the shots in the dark that arise from skipping to the end solution because zero imagination was applied to figuring out how to run a create a prototype to generate feedback from the world.
This really points out the great flaw in not thinking about innovation as a process. If innovation is simply coming up with great ideas, then you don’t need to prototype, and you don’t need to put any effort into diffusing the ideas. The great ideas will just sell themselves. This, of course is false.
The problem is that often inventive people just want to stop once they have their idea. It takes a lot of work to figure out how to prototype it to see if it will work, and it takes just as much work to develop a business model that will get the idea to spread. However, as Rodriguez points out, we need to invest just as much imagination into prototyping as we put into problem solving. On top of that, I’ll add that we need to invest this much imagination again to building our business model.
Successful innovation actually requires three separate creative acts: one great idea to solve a problem, another idea to test it, and a third idea to get it to spread. We have to be good at all three kinds of creativity to drive innovation. This is one reason that it is a often a collaborative process.
The action point today is clear: the next time you have a great idea, invest some time into figuring out how to prototype it. Once you’ve done this, then you can start working on your business model!
Grassroots Innovation
Veronica Vera pointed me to a great talk by Anil Gupta from TEDIndia. He talks about grassroots innovation, and methods for getting ideas to spread in poorer regions. It’s a fascinating talk:
Innovation in developing countries is a wildly unappreciated phenomenon – there are incredibly interesting things going on in places like India, China and Brazil. Some of them are built around finding innovative ways to provide goods and services to poorer people at much lower costs. Aravind Eye Care and the Tata Nano car are just two good examples of how this works.
Gupta is talking about something different though. He is not approaching poor people as consumers, but as inventors. This is reflected in one of the slogans of the Honey Bee Network – minds on the margin are not marginal minds.
The Honey Bee Network has done some great work in cataloging thousands of inventive ideas that people have developed. Most of them are things that make their own lives better, but many of them also have much wider potential applications. There are several important things that we can learn from this.
- Innovations diffuse through networks – inventions inventoried by the Honey Bee Network have gone through two steps of the innovation process. Someone had a great idea, and they figured out how to make it work. The next step is to get the idea to spread. The HNB takes a network approach to getting people to share ideas. Their objective is the creation of technology commons – ideas are free for people to people use, but a license is required for firms. By cataloging the ideas in one central registry, it is much easier to help people connect up with the ideas. To get the ideas to spread they are creating a network.
- Use a portfolio approach to take advantage of the long tail of innovation – one of the big issues in diffusing these ideas is that many of them are of use to a relatively small number of people. Guptil argues that this should not discourage attempts to get the ideas to spread. By developing a broad network of people interested in grassroots innovation, it is easier to locate the people in the long tail. The central registry of ideas makes it relatively easy to sort through them and find ones that are appropriate to use in particular circumstances.
- Not all good ideas come from where we are – Guptil says this when trying to encourage Indians to be more willing to adopt ideas from China and Brazil. The idea applies more broadly too. It doesn’t matter where you are – there are plenty of great ideas that come from someplace else. It benefits us to be humble enough to realise this and to learn from others.
This is actually a great example of an aggregate, filter and connect value creation strategy. The Honey Bee Network does all three very effectively. They have aggregated over 10,000 great inventive ideas from around the world. By assessing and describing each one, they enable potential adopters to filter through this huge database to find the ideas that will be most useful. And they have created an extensive, strong network that they can leverage to connect ideas to ideas, and ideas to people. This is how they get the great ideas to spread.
There are great ideas everywhere – the key to innovation is developing systems that allow us to test these ideas and get them to spread. The Honey Bee Network is a great example of how to build a platform that enables the process of innovation to take place – even in locations that many people don’t often think of as innovative. There’s a lot we can learn from this.
Here is a full slide show from Anil Gupta that has more detail on a lot of the examples that he uses in his TED talk:
What’s the Best Idea?
Posted by Tim in aggregate, connect, filter, innovation strategy on 16 April 2010
Over the past couple of weeks I’ve been participating in an innovation jam organised by Kate Morrison from Vulture Street Innovation Services – it’s been a fascinating experience. I’ve talked about jams before, but it’s been great to get deeply involved with one. I’ve been thinking about this one through the aggregate, filter and connect lens, and I’ve learned some interesting things about using these kinds of tools to aid innovation.
- The first point is that the idea generation process was clearly tied to strategy. The group sponsoring the jam has specific strategic objectives that they are trying to meet, and the questions that were asked address these goals. These objectives were identified by looking at the needs of the sponsoring organisation and their customers.
- Second, the jam invited specific people with an interest in these objectives to participate. In other words, as I said yesterday, people and process were considered first. In this case, this was part of the filtering process – ideas weren’t solicited from everyone, there was filtering right from the start.
- After people were filtered, then the jam process itself ran over two weeks – this was the aggregation stage. As is usually the case with this kind of exercise, participation followed a power-law distribution that looked roughly like this:
There is almost always a disproportionate contribution from the most prolific contributors. However, as in the example above, the most popular ideas (shown by the stars) came from people from all across the range of participation. This is normal for most idea generation exercises.
- Throughout the jam, people voted on the ideas that they liked and disliked. This was one form of filtering. However, the really interesting part is what this organisation did after the voting closed. Today they held a workshop where they invited all of the participants to come and select the ideas that were the best ones to take forward. About 40% of the people that contributed to the jam came along to today’s event. So the process looks like the one used by linux, and icanhazcheesburger:
This is the way that crowdsourcing initiatives often work. The ideas are gathered from everyone (aggregating), and then a smaller group selects the ones that are most promising (filtering), leading to the final content that is then distributed widely (connecting).
- The selection process today was very interesting to watch. Our first step was to filter out ideas that did not match up with the strategic objectives that were initially outlined. This knocked out a few ideas that had been quite popular in the voting. Some of these were off-topic, and some were too vague to be executed. Once we had done that, we had discussions within small groups about which ideas were best, and each group picked two or three to develop further. This step is critically important – this is where connecting plays a huge role. We were connecting the ideas to the strategic objectives, and in several of the groups we connected up several of the ideas that had been submitted to form more coherent plans around the best ideas.
- Once all the groups had picked the best ideas, we reconvened with everyone and heard about the eleven best ideas (we started with 48 in the morning). These will be developed further, and then brought to the project sponsors for implementation.
One interesting point – for all my talk about ideas being the least important part of the innovation process, I ended up being pretty strongly attached to the idea that I had contributed. It had done well in the voting, and it also did well in the group discussion today. I’m convinced that it is a good idea, and I’m going to carry out myself regardless of how this process ends up. Typical behavior for innovators – you do have to be stubborn about your ideas sometimes. Still, I do think that idea execution is the most important part of innovation.
Overall, I think this was an excellent process. The jam + workshop method is able to combine crowdsourcing with a good level of strategic thinking and judgement. The ideas that had been most popular in the voting didn’t all end up in the final eleven, and several that did had been less popular during the ideation part of the process. At least one idea that I really liked got discarded, and a couple of lousy ones ended up in the final mix, but despite that, I think the process worked really well.
So that’s how aggregate, filter and connect can work on smaller-scale innovation projects. You can see how all three procedures were used to build a system that is effective overall. The biggest lesson from all of this is that idea generation processes must be supported by selection and implementation processes as well.
What’s the best idea? Hold an innovation jam and find out.
Five Forms of Filtering
Posted by Tim in business models, connect, filter on 8 April 2010
We create economic value out of information when we figure out an effective strategy that includes aggregating, filtering and connecting. The three steps interact and reinforce each other – and successful information-based business models have all three. We can undertake business model innovation by changing our methods in these three areas, or by changing where in the value network the processes take place. I’ve run across a few things recently that have gotten me thinking about filtering – and it made me realise that we have another classification issue here. Here is how Clay Shirky frames it:
So, the real question is, how do we design filters that let us find our way through this particular abundance of information? And, you know, my answer to that question has been: the only group that can catalog everything is everybody. One of the reasons you see this enormous move towards social filters, as with Digg, as with del.icio.us, as with Google Reader, in a way, is simply that the scale of the problem has exceeded what professional catalogers can do. But, you know, you never hear twenty-year-olds talking about information overload because they understand the filters they’re given. You only hear, you know, forty- and fifty-year-olds taking about it, sixty-year-olds talking about because we grew up in the world of card catalogs and TV Guide. And now, all the filters we’re used to are broken and we’d like to blame it on the environment instead of admitting that we’re just, you know, we just don’t understand what’s going on.
Filtering is what helps us deal with the vast amount of information available to us. We try to filter information so that we end up with something that is relevant to us – it helps us learn something, it helps us solve a problem, it helps us develop a new hypothesis about the world around us. These are all connections – and this is what really drives value creation. However, we can’t connect without some filtering going on. So filtering is important, and it’s a term that includes several different sub-types. I can think of at least five forms of filtering.
The five forms of filtering break into two categories: judgement-based, or mechanical.
Judgement-based filtering is what people do. At it’s most basic level, we have naive filtering. This is what we do when we don’t know anything about the information that we are trying to filter. This is a fairly complex internal process, and there are plenty of models available for what is happening in this step. It’s basically everything going on in the ‘Sense’ step in this diagram by Harold Jarche:
As we gain skills and knowledge, the amount of information we can process increases. If we invest enough time in learning something, we can reach filter like an expert. I previously explained how this process can work in bird-watching.
However, even experts can’t deal with all of the information available on the subjects that interest them – that’s why they end up specialising. One way to increase the amount of information that gets filtered is by relying on a network. This can be a network of learners, as in the Connectivism course run by George Siemens and Stephen Downes, it can be a group of people with a similar interest, like all of us talking about #innovation on twitter, it can be large groups of otherwise unconnected people as in Shirky’s examples on places like Digg and Delicious. Networks expand our reach enormously.
There can also be expert networks – in some sense that is what the original search engines were, and what mahalo.com is trying now. The problem that the original search engines encountered is that they amount of information available on the web expanded so quickly that it outstripped the ability of the network to keep up with it. This led to the development of google’s search algorithm – an example of one of the versions of mechanical filtering: algorithmic.
Algorithmic filtering is used by most of the filtering tools available on the web. They can cover the entire web, like google does, or sub-sections of it, like an RSS feed does. Howard Rheingold describes many of these sorts of tools in his post and video on Mindful Infotention.
Rheingold also provides a pretty good description of the other form of mechanical filtering, heuristic, in his piece on crap detection. Heuristic filtering is based on a set of rules or routines that people can follow to help them sort through the information available to them.
Why is filtering important? Understanding the variety of filters available explains why there are often arguments about the discrimination process, and over what role a particular filter plays. If someone writes about filtering, and they mean ‘algorithmic filtering’, a reader thinking of filtering in terms of ‘network filtering’ is likely to misunderstand the discussion. Another source of confusion is that some people talk about filtering not as a search for useful information, but as a way to block information that annoys them.
Filtering by itself is important, but it only creates value when you combine it with aggregating and connecting. As Rheingold puts it:
The important part, as I stressed at the beginning, is in your head. It really doesn’t do any good to multiply the amount of information flowing in, and even filtering that information so that only the best gets to you, if you don’t have a mental cognitive and social strategy for how you’re going to deploy your attention. (emphasis added)
Filtering and connecting is what leads to important skills, like pattern recognition (described well by Venessa Miemis).
Finally, we can use these ideas about filtering to help with business model innovation by changing where it takes place in the value network. One of Shirky’s points is that since Gutenberg, the economic logic of publishing required publishers (of books, music, movies) to act as filters in order to maximise their investment. As publishing and filtering has shifted out to human networks, publishers no longer need to fill this role. Someone (or some network) needs to, and since that creates value, it’s something that can perhaps be monetised.
You can see this in investing. You can put money in Berkshire-Hathaway, where investment choices are run through the personal expert filter of Warren Buffett. Or you can invest in individual stocks recommended by a broker- which is filtering through an expert network. Or you can take advantage of DIY investing, where you do your own filtering, probably aided by some heuristic filters as well. Three different investing business models based on three different filtering methods.
People or networks filling the filtering role now are creating significant value – and people trying to come up with innovative business models in these fields should be thinking about how they can create value through filtering. Of course, the filtering needs to be part of an overall aggregate, filter and connect strategy – which is at the core of successful information-based digital business models.
(Thanks to John, Phil Long & Nancy Pachana for talking to me about these ideas- of course, none of this is their fault. Special thanks to Phil for editorial suggestions.)
Innovation Vision
Posted by Tim in aggregate, business models, connect, filter, innovation strategy on 30 March 2010
How do we decide what our innovation strategy should be? Jeffrey Phillips says that we don’t need an innovation strategy at all, we just need a strategy, and it should have innovation embedded within it. That’s pretty consistent with what I’ve said here before as well when I talked about four different ways to integrate innovation and strategy. But given that, what do we do?
A good first step is to figure out where you want to be positioned. Tom Fishburne has some very good advice in this:
I blogged a few months ago that companies can be classified either as Rule Makers, Rule Followers, or Rule Breakers. Most companies duke it out amongst themselves as Followers, trying to gain share against the market leader by playing the rules of the market leader. …
Instead of obsessing about market share, think market creation. Become “the only ones who do what you do”.
This is certainly the way to think if you’re working on radical innovations. In an interview that just came out, Roberto Verganti argues that the best way to do this is to work on innovating the meaning of your products and services:
In the blog I mentioned that companies that are focusing on stripped-down “value” products risk making the mistake of assuming consumers care more about utility and low price than meanings. In the current ‘Great Recession’ meanings are becoming even more important, and companies should not think consumers care less about the emotional and social dimensions of products.
Although it is counter-intuitive, utility is not the only thing that matters to consumers. Even when they are hard pressed financially they don’t want to feel poor.
Yes, they do care about prices and want to spend less. If you have a lot of money, who cares? If you have less money, you care a lot about how you spend the money. Every time you spend your money, it is a very emotional and symbolic act.
Another way to think of this is to find a way to do something that people really believe in, as suggested by Hugh MacLeod:
Of course, this approach can be risky. The chances of failure are non-trivial. On the other hand, the one sure way to fail at innovation is to try to avoid failing. Scott Anthony makes this point nicely in an interview that just came out:
Interview Question: A famous innovation story is about Bank of America, which mandated that 30% of ideas had to fail. Google also had a similar working line with 20% of the employee time being spent on side projects. What’s your take on such strategising?
Scott Anthony: Those are actually two different strategies, and generally I like the Bank of America one more. That metric tells people that it is acceptable to take some amount of risk. If you never tolerate failure what you eventually get are very close to the core, incremental ideas. Those are fine, but won’t produce blockbuster results.
The Google approach, which 3M has done for a long period of time, works well in particular cultures. But it works less well in organisations that are still getting their innovation legs. All things being equal I would rather have three people spending all of their time on innovation than 100 people spending 10% of their time on innovation.Part of the issue with replicating Google’s ‘20%’ system is there aren’t many people who have an end to end approach to innovation that is like Google’s. And if you copy one piece without the surrounding elements, it just won’t work.
So we have to be prepared to fail, at least with a few ideas. The key point here is to make the failures happen as quickly and as cheaply as possible. But we have to do it, even if it’s risky. After all:
The ROI on innovation is survival
— Andrew Howlett, CEO en Rain
That all looks pretty familiar, doesn’t it? Or maybe not. All of the quotes were included in my post yesterday, but there I asked you weave your own story around them. That was the least successful post that I’ve written in over three months, at least according to views, retweets, and every other metric that I normally look at. Why?
I think it says something pretty important about the aggregate, filter and connect idea – that to create value you have to do all three things. Yesterday, I only aggregated and filtered. All of the quotes were things that came through my aggregating tools – primarily the RSS feed and twitter. I filtered through all of that, and found four items that created a theme – at least inside my head. So I put them out there to see if they’d resonate with you in the same way. It doesn’t appear as though they did.
Today’s post might not be much better, but at least there’s a coherent story in it. That’s because in addition to aggregating and filtering, I connected up the ideas. In order to create value that people are interested in, you need all three components.
This also illustrates an interesting point that is currently being discussed. It started with Robert Scoble talking about the tools that are needed for curation. I love the way that he describes curation:
This is a guide for how we can build “info molecules” that have a lot more value than the atomic world we live in now. First, what are info atoms? A tweet is an atom. A photo on Flickr is an atom. A conversation item on Google Buzz is an atom. A Facebook status message is an atom. A YouTube video is an atom.
Thousands of these atoms flow across our screens in tools like Seesmic, Google Reader, Tweetdeck, Tweetie, Simply Tweet, Twitroid, etc.
A curator is an information chemist. He or she mixes atoms together in a way to build an info-molecule. Then adds value to that molecule.
This prompted interesting responses from Joanne McNeil and Erica Glasier. They both have some issues with Scoble’s post. But I think that really, both of them are responding to the more widely-held view of what “curation” is – more of an aggregate-filter process, like yesterday’s post. I think that Scoble is pretty clearly talking about an aggregate-filter-connect process. So maybe we need a new word for what he’s talking about?
In any case, I thought it would be fun to experiment with two different approaches to compiling and presenting related information. Which do you think worked better?
Putting in the Hours
Posted by Tim in filter, innovation, time on 26 March 2010
When I was in university I spent a whole lot of time at the campus radion station. I started out as a trainee DJ in the first semester. I was in the practice studio constantly, spinning records, making segues from one cut to the next, talking on the microphone and taping it so I could try to get better. And I hung out with the experiences DJs while they were on the air – watching how they did things and learning everything I could through osmosis (and asking a lot of questions). Whenever the trainees were asked to volunteer for something, I was there – cleaning up the station, and doing all kinds of little crummy jobs that no one else wanted to do.
At the end of my semester as a trainee, all that work was rewarded in two ways. I was given my own show for second semester (Mondays from 12-3 pm!), and I was given the position of Assistant Music Director. AMD was a lousy job, but it had some good parts to it as well. Every day I had to walk over to the post office to pick up the mail (which mostly consisted of vinyl records – so hauling them back to the station was a pain), and I did any other crummy jobs that we couldn’t find eager trainees to do. I also reviewed 5-10 new records a week. The Program Director and the Music Director had first pick of the really good records to review, so I had to sort through all the stuff that no one had heard of before. There were a few gems in there, but there was an unbelievable amount of crap too. That’s when I first started thinking about the importance of filtering.
I kept working hard (and having a lot of fun), and over time I did a lot of stuff at the station. I was a DJ all the way through, and a few people seemed to enjoy my shows. After my time as AMD, I was Program Director and then Station Manager. After a few years, I really felt like I had accomplished a fair bit at the station.
Then I took a break of a couple of years from university. I worked during that time and saved up money to pay for the last bit when I went back. While I was doing this, I went to see about maybe getting involved with the college radio station in my home town. I hung around a bit, asked about how to get a show. The answer was pretty much the same as it was the first time around – volunteer to do a bunch of the crummy work that no one else wants to do, do that for a while, and then I’d eventually earn my shot to get on the air.
I started doing that – going in once a week to add up stats on how many times the new releases got played. And I hated every minute of it. I just didn’t have the stomach for working my way up the pecking order all over again. I was 20 years old, and thought that I’d earned some respect. Back then, in a lot of ways, I was pretty stupid.
I was reminded of all of this recently because there have been a couple of situations recently where I’ve had to earn respect – where I’ve figuratively had to go back to doing the crummy work that no one else wants to prove that I’m worthy. Since my time in radio, I’ve learned how to do that a little bit better. Social networks have been a useful part of that learning process. Every time I enter a new one, I’ve realised that I’m starting at 0, and have to work my up. It took nine months of writing this blog before it really started to click with people. There were plenty of chances to give it up in that time. But we kept writing, and kept telling one person at a time about the blog. We put in the hours and the blog has grown.
It’s a useful lesson to have learned.
So what does this have to do with innovation? Plenty. The main lesson here is that I think it’s important to never assume that we have already earned respect or someone’s business simply because we’ve already had some success with others. Every time we ask someone to adopt our idea, whether it’s a proposal for a new product, a new service, or a new way of doing things, we are starting at 0.
We can’t assume that they already know how great our idea is, or that the value in it is self-evident. This is a particularly important lesson if we are trying to cross domains. If you are a lab scientist trying to commercialise your great discovery, out in business your reputation starts at 0, no matter how much reknown you’re held in as a scientist.
It’s easy to get caught up in how great our ideas are – but when it comes time to get them to diffuse, we have to win people over one at a time. And with every one of them, we’re starting at 0. A little humility goes a long way when we’re innovating. Every time we start something new, we have to put in the hours.
