**3.2 Patterns of system evolution**

As industries make their inexorable transition towards Ideal Final Result 'perfection', the journey involves a succession of discontinuous s-curve jumps. As one customer solution matures and hits its 'stuck' plateau, eventually along will come an innovator with a contradiction-solving solution to start a new s-curve. Almost invariably, the start of this new s-curve will present customers with a solution that is 'inferior' in many ways to the incumbent solution, but offers some form of advantage to certain niche situations. Preferably these niche situations will be high value customers prepared to pay a premium for the privilege of their niche advantage (think about the first mobile phones for an iconic example of this dynamic in action). If innovators are able to find such customers, the early revenue they produce, will pay for further developments of the solution that will make it progressively more attractive to a wider variety of customers. And by this means, the new solution will gradually begin to climb its s-curve, until such times as it too becomes stuck. If the innovator has chosen their new solution well, the ideality of their new solution will be higher than the peak ideality of the previous incumbent solution – **Figure 7**.

Such is the way of the world of discontinuous innovation: things will tend to get worse before they get better. This is another challenge for Operational Excellence dominated industries – where KPIs that acknowledge things may get worse for a period of time are virtually non-existent. The problem is not so big in the e-service sector, because the rate of s-curve jumps tends to be much higher than in most (non-digital) industries), and investors are more accustomed to the s-curve rollercoaster ride.

**Figure 7.** *Evolution trends as roadmaps to the ideal final result.*

#### *Digital Service Platforms*

The relative speed of the e-Service sector jumps, once the noise associated with failed jump attempts is removed so only the successful ones remain, turn out to be one of the best ways to reveal just how clear the road-map to success actually are. **Figure 8** illustrates one of the most vivid and important of these patterns. One that was first revealed in the work of Gilmore & Pine [20], and is now generally know as the 'Customer Expectation' Trend.

Each stage of the Trend in effect represents an s-curve, and the direction of travel occurs from left to right. The e-Service sector in effect emerges thanks to the jump from the second ('Product') stage of the Trend to the third ('Service'). One of the implications of which is that innovators looking for innovation opportunities would do well to look at industries and sectors that are still at the Product stage, and, preferably, are at the mature end of their current s-curve.

This Customer Expectation Trend was one of the first business evolution patterns to be uncovered. To date, the research has now uncovered over thirty other discontinuous evolution Trend patterns [21]. Lack of space here prevents examination of all of them. What follows, however, are what might be thought of as the next four in a 'Top Five' evolution roadmaps for e-Service businesses:

**Figure 9** illustrates the 'Segmentation' Trend. It applies both to the internal structures of a business, but mainly, in the e-service context, to the segmentation of customers. The left-to-right trend trajectory effectively tells a story of customization and personalization of services. By the time a service has evolved to the next-to-last 'Segments of One' stage, the business has recognised that every customer is different to every other one, and is able to tune the service to suit each individual customer need. The final stage of the Trend takes things one step further and sees service providers acknowledging that not only is every customer unique, but that they are also unique as their moods shift dynamically. In many ways, this Trend is the polar opposite of the core Operational Excellence drive for standardisation, a standardised solution being the one that traditionally delivers the best profit margin. This standardised *and* customised contradiction is in many ways one that the shift from the physical to the digital has enabled more than anything else…

…and that in turn has been made possible thanks to the next Trend, 'Reducing Human Involvement', illustrated in **Figure 10**.

**Figure 8.** *'Customer expectation' evolution trend.*

**Figure 9.** *'Segmentation' evolution trend.*

*Systematic e-Service Innovation DOI: http://dx.doi.org/10.5772/intechopen.96463*

**Figure 10.**

*'Reducing Human Involvement' evolution trend.*

The reason customisation of solutions costs providers money is because delivering a customised service means having large numbers of highly capable and therefore expensive, staff. By replacing these staff with intelligent and increasingly emotionally aware digital equivalents, service providers will ultimately achieve the best of both worlds. How quickly this replacement will occur depends to a large extent on how quickly and how effectively the emotion-related first principles described in **Figure 4** can be absorbed into the software. On this front, the immediate good news is that we know what the job to be done is.

As ever, of course, any kind of progress inevitable generates some form of collateral damage. In this case it looks like the collateral damage will come in the form of swathes of service jobs being displaced. A partial answer to this contradiction may be seen in the next Trend. A Trend showing innovators that in addition to the 'things get worse before they get better' characteristic of s-curve jumps things, as a solution evolves along its s-curve there is a clear pattern of increasing-followed-bydecreasing complexity.

During the initial 'increasing-complexity' portion of this curve, the e-service world is likely to see a host of integrated solutions (in which multiple services are combined into 'one-stop-shop', end-to-end offerings) and hybrid solutions in which human service providers are assisted by data-providing digital assistants. It is already established in the insurance industry, for example, that AI algorithms are already capable of making better loss-adjustment decisions and, in a smaller number of cases, fraud-detection decisions than the average employee, but, in order to ensure the emotional needs of claimants are also met, the average employee is still, for the most part, much more capable than even the best 'emotion-equipped' AI.

In the final analysis, however, the decreasing-complexity portion of the **Figure 11** Trend sees the contradictions of these kinds of human-computer hybrid service solutions being solved such that the customer receives all of the benefits they desire from a service without any of the attendant complexity. This is not to say that the complexity has disappeared per se, but rather that it has been subsumed into the algorithms and is therefore hidden from the customer's view.

Fifth in the Top Five e-Service Trends is the Customer Purchase Focus Trend reproduced in **Figure 12**. This Trend works a little differently from the previous four. At least in so far as implications for service providers. The step-changes described in this Trend examine the non-linear shifts in focus of customer attention as their relationship with services evolves. Initially, on the left-hand-side of the Trend, customer purchase decisions are largely based on their need for performance. As these needs become satisfied, performance thresholds will emerge, beyond which, customers will be no happier and no more likely to purchase the service should providers continue to increase them further (many Microsoft solutions crossed these thresholds some time ago – the majority of Word users, for example, do not use 90 + % of the available functionality of the software, and compatibility issues aside, would be quite happy with the capabilities provided in Word 2). When customers perceive they have achieved enough performance, their

**Figure 11.** *'Increasing-decreasing complexity' evolution trend.*

**Figure 12.** *'Customer purchase focus' evolution trend.*

primary purchase attention shifts to reliability. And then, when they have enough of this, their attention shifts again, this time to convenience. Finally – bad news for providers – when customers have enough performance, reliability and convenience, their purchase decisions are made solely on price. Which effectively means that the service offering has become commoditised.

The job of providers when service offerings approach or reach this final stage is to innovate in such a way that they are able to shift customer attention to new measures of performance. One likely candidate in this regard, to return briefly to **Figure 4** one more time, is that 'meaning' will become a generically applicable new performance delivery opportunity. One that the Covid-19 pandemic, again as discussed earlier, seems likely to play a significant role in bringing to the front of many e-service customers' minds.
