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Ten types of innovation can be driven, supported, or measured with analytics. If you’re not using analytics for all ten types, you may not be optimizing your analytical capabilities.
A few weeks ago, I heard an interesting presentation by Larry Keeley of Deloitte Monitor’s Doblin Group, a company that consults on innovation. I had seen Doblin’s “Ten Types of Innovation” before, but hadn’t really paid enough attention to it. Keeley’s presentation reminded me that I thought it was the most complete listing of how companies can be innovative. It also made me wonder how many of the 10 types of innovation might involve analytics in some way.
So I started going through the list, one by one. I didn’t know how many might result in a hit—a link to analytics—when I started. Through the magic of ex-post-facto editing, I now know how many. I won’t spoil the secret, but here’s a hint: This essay is pretty long.
Profit model: Profit model innovation involves new ways to monetize a company’s offerings and assets. There is certainly an analytics spin on this form of innovation, in that many companies in both online and offline businesses are attempting to make profits with new data and analytics-based products and services. GE, Monsanto, and several large banks are among the traditional businesses that are exploring profit model innovation with analytics.
Network: Network-oriented innovations involve new products, services, or processes that are delivered across a business network or ecosystem. In analytics terms, this might involve delivering analytics to suppliers or partners in order to help them make better decisions. In another context, with the Internet of Things, companies almost always need to share sensor data with their ecosystems, and to define standards so that the information can be integrated and analyzed. More about this below.
Structure: To quote the Doblin/Deloitte website, “Structure innovations are focused on organizing company assets—hard, human, or intangible—in unique ways that create value.” In an analytics context, this would most likely mean creating new business units that focus on analytics or using new organizational formats that allow analysts to work with decision makers. Large banks, for example, have formed new business units to analyze customer data. Similarly, other businesses create a centralized group of analysts, and then “embed” many of them with key decision makers in business units and functions. Both of these could qualify as structural innovations.
Process: These types of innovations are, of course, about improvements—small or large—in how organizations go about their operations. Process improvement was perhaps the most common use of analytics in the earliest days—particularly for supply chains and logistical processes. Today, companies use analytics to enable process improvements and innovations in pricing, marketing, sales, and manufacturing. Of course, a firm can never stop innovating with its processes, using analytics or other resources. Otherwise, competitors will adopt the same process innovations and can quickly catch up.
Product performance: Innovations in products have not historically involved analytics. However, this is beginning to change. A variety of devices, from golf clubs to basketballs to health activity trackers, now come with the ability to capture and evaluate physical movements by their user or wearer. Some firms that produce these devices have realized that the ability to generate analytics—largely descriptive at the moment, but potentially more predictive over time—is an important selling point. But as the authors acknowledge, product innovation with analytics—or any other feature—is subject to rapid and widespread copying. Many activity-tracker vendors already offer similar types of analytics, for example.
Product system: Innovations of this type involve broad “ecosystems” of offerings. Analytics can be useful in these contexts after ecosystem players have determined how to integrate and share data. In the Internet of Things (IoT) domain, for example, there are plenty of opportunities to create an “analytics of things” from all the data that sensors create. The big challenge, however, is that no single company can create an IoT initiative on its own; it must collaborate with other firms.
Service: Service innovations can either involve analytics directly, or can be measured by analytics. For complex products that collect and transmit data (computers, network equipment, large and complex vehicles and equipment, jet engines, and so forth), service processes can increasingly be based on analytical calculations about how machines are performing, and when they are likely to need maintenance or servicing. For services involving humans, analytics can be used to create metrics of overall service levels or components of service (one company measures how often service people smile, for example). Analytics can also be used to understand how service improvements yield financial improvements; the “service-profit chain” is a good example.
Channel: Channel innovations involve new approaches to delivering offerings to customers. The key role for analytics here is not to provide a new channel, but to let organizations know how well a new (or old) channel is working. Some channels, such as online, are much better sources of data than others. But today, the enormous challenge for many organizations is to understand customer relationships across all channels and touch points. Even identifying the same customer across channels is often a problem, although analytics can make it much easier.
Brand: This type of innovation involves new approaches to how a company represents its offerings and its general reputation and perception. Analytics may not be terribly useful in creating brand innovations, but they are crucial to knowing how a brand innovation is working. Metrics of brand value, which are somewhat subjective, indicate the overall effectiveness of a brand. Social media analytics can help to assess what people are saying about a brand.
Customer engagement: Innovations in engagement involve approaches to fostering compelling interactions with customers. Doblin says that it requires “understanding customers’ deep-seated aspirations,” and analytics are useful for that—particularly for understanding those aspirations that are revealed by actual behavior. Engagement with online sites and digital environments is particularly easy to measure—and improve, through approaches like A/B testing—with analytics. With analytics, you can know exactly what your customers are looking at, clicking on, and (of course) buying.
I wasn’t sure when I started, but as you have probably counted, I am now quite convinced that all 10 types of innovation can be driven, supported, or measured with analytics. If you’re not using analytics for all 10 types, you may not be optimizing your analytical capabilities.