A few days ago I posted on Praxis using Handy's sigmoid curves. I hinted then that I planned to combine those with a variation of Moore's Crossing the Chasm and I am going to do that in two stages. Today the background, or build up to a variation of Moore's model; tomorrow the integration and modification of axes labels etc.
This is the basic life cycle curve and you will find it in a lot of text books. The figures as I remember it come from original Proctor and Gamble research and I validated them over a decade ago when I had a marketing role in new technology adoption. The brilliant innovation that we find in Moore's Crossing the Chasm, is to recognise that there is a gap, or chasm between the early adopters and the early majority as shown to the right. The idea (like all good ones) is simple; the early majority are happy to take some risk but not all of it. The will not adopt for its own sake, but when they can see clear benefits. Moore identified the issue for start ups who win the early adopter market and then assume that growth will continue up the curve.
Now I came across Moore in 1991 when it was first published. At the time I was running a start up software business within a larger company. We had a lot of initial success although we skated on the edge of closure several times. I still remember negotiating with EMI knowing that if I did not win the contract I would have to close down the business the next day, and having the guts to refuse to compromise on price. We lived as they say in interesting times. Moore's book not only explained why our initial success had not continued exponentially but also outlined what needed to be done, and gave me ammunition to sustain continued investment from the Board. Probably the most important lesson was to realise that buyer behaviour changes at each state. The innovators and to a degree the early adopters want to know the details, they were more interested in how we did something that the outcome, but for the early majority the behaviour switched. That product, MURCO (which is still alive today with some of my original staff which is something I am proud of) was one of the first stock forecasting and inventory management application software products. We did clever things with different algorithms for different demand types, adjusted buffer stock accordingly and performed both seasonal and weather based adjustments; all a major step forward from two bin replenishment systems. Now in the early days customers would do things like write algorithms on boards with deliberate mistakes to see if we picked it up, and they wanted to deep dive into the mathematics. They also spent a lot of time telling us how to do it better, some of which (and a very small some) was valuable. As we moved out of that phase we carried on telling people how we did things (well it had worked before after all) rather than telling them what it would do for them. The context had shifted and we had not made the switch early enough.
Now the other big model around at the time was the famous Gartner Hype cycle and it still survives. Now this made a lot of money for Gartner! If you could get your product on the right part of the curve, or get the right industry analyst report it could make a huge difference to internal investment and market assessment. In the latter days of DataSciences I put together the Genus Programme, a mix of object orientation, RAD/JAD and legacy system management. It represented a completely new approach to service marketing, mixing the old and new in a coherent whole. The day we got a Gartner report which commended the originality of the integration and forecast success. That was what we needed to commit the organisation and over the next two years the programme came to dominate our sales, came second to Windows 95 in the integrated marketing awards and was one of the factors in making DataSciences attractive to IBM. There are other stories to be told there, including the failure of imagination on execution but I will save that tale for another day. Now the hype cycle has a lot in common with Moore, although it has a different purpose and relates more to total markets than it does to individual products.
Now while at DataSciences I put together a basis synthesis of these models which is shown right. I wanted to make it simpler, and also wanted to argue a different strategy for selling new products. I combined the first two classes of innovator and early adaptor into one described as excitable to focus more on the buying behaviour. The main strategy I advocated was to find ways of selling on the other side of the chasm. The Genus programme did that by combining new technologies with established needs (legacy system management), in effect we used differentiation of late majority offerings with excitable capabilities to create something that could be sold on benefits in the early stages of the market. Sally Bean suggested I buy Managing the Hype Cycle which I have although I have not had a chance to read it yet. However a quick skim says it is a good source of case studies and retrospective coherence (and good stuff) so I plan to integrate some material from that in a future post.
The next stage on is to combine that model with Handy's sigmoids, but that is for tomorrow when I will also elaborate on some use of the synthesis.
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