In a previous post introduced the concept of Insight as a Service and described some of the issues that will need to be addressed for such services to be possible. Insight as a Service refers to action-oriented, analytic-driven solutions that operate on data generated by SaaS applications, proprietary corporate data, as well as syndicated and open source data and are delivered over the cloud. This definition is meant to differentiate Insight as a Service, which I associate with action, from Analytics as a Service, which I associate with data science, and Data as a Service which I associate with the cloud-based delivery of syndicated and open source data. For example, a cloud-based solution that analyzes data to create a model that predicts customer attrition and then uses it to score a company’s customer base in order to establish their propensity to churn is an Analytics as a Service solution. On the other hand, a cloud-based solution which, in addition to establishing each customer’s attrition score, automatically identifies the customers to focus on, recommends the attrition-prevention actions to apply on each target customer and determines the portion of the marketing budget that must be allocated to each set of related actions, is an Insight as a Service solution.
The survey data presented in Pacific Crest’s SaaS workshop pointed to the need for a variety of data analytic services. These services can be offered under the term Insight-as-a-Service. They can range from business benchmarking, e.g., compare one business to its peers’ that are also customers of the same SaaS vendor, to business process improvement recommendations based on a SaaS application’s usage, e.g., reduce the amount spent on search keywords by using the SEM application’s keyword optimization module, to improving business practices by integrating syndicated data with a client’s own data, e.g., reduce the response time to customer service requests by crowdsourcing responses. Today I wanted to explore Insight-as-a-Service as I think it can be the next layer in the cloud stack and can prove the real differentiator between the existing and next-generation SaaS applications (see also here, and Salesforce’s acquisition of Jigsaw).
I have been trying to reconcile two trends I’m seeing. First, large companies are acquiring venture-backed startups to accelerate their innovation efforts. Even as the R&D budgets and associated efforts of large corporations are increasing, they have not been keeping up with the accelerating pace of technology and business model innovation. These acquisitions fall in two categories. First, acquisitions as a means of jump-starting corporate innovation efforts and getting corporations into the “innovation flow.” Good examples of such venture-backed company acquisitions include Avis’ acquisition of Zipcar, Walmart’s acquisition of Kosmix and of Small Society, Wellpoint’s acquisition of Resolution Health, and Home Depot’s recent acquisition of Black Locus. These acquisitions are less about the technology being acquired and more about the innovations the startup employees will be able to create once they are part of the acquiring company. Second, acquisitions as a means of staying in the forefront of innovation. Companies in this category are acquire frequently in order to enter a new sector or grow a sector they are already working on. Good examples include VMWare’s acquisition of Nicira, and Facebook’s acquisition of Instagram. Finally, a growing number of corporations from American Express to P&G, from BMW to GE, and Walmart to Best Buy establishing operations in innovation centers, such as the Silicon Valley, in order to tap into the startup and innovation flow.
Second, while the number of seed-stage companies is increasing dramatically because their founders see opportunities for a quick exit based on the first observation, the number of companies that can receive expansion rounds and make viable acquisition candidates remains small. This is because
- Many of the seed-stage startups that number in the thousands and are funded primarily by non-institutional investors, i.e., entrepreneurs themselves, angels, super-angels, friends and families, are not innovating, don’t have no product roadmap, hypotheses of viable business models, or even ideas of how to acquire and retain customers.
- The number of management teams that can be backed by institutional VCs for scale, give the “escape velocity” and make it a viable candidate for an exit that provides high returns to a venture investor has remained small. As shown below, the number of companies that receive additional rounds of funding by institutional investors has remained largely unchanged in the past 2-3 years.
- The number of institutional VCs who can fund and materially help these early stage companies is getting smaller. Fewer of these institutional venture firms are able to raise new pools of capital particularly capital that can be used for earlier stage investments. The Limited Partners (LPs) that provide the capital to the venture firms want to take on less risk with the capital they provide and they want returns faster. The thinking is that investing in later stage companies shortens the time to liquidity while reducing the risk of the investment. Because of the overall venture industry’s returns have been low over the past 10-12 years, the allocations LPs are making to venture funds have decreased and are now about 25% of their peak in 2000. LPs want to invest in only a few venture funds that they consider as having the right deal flow of early stage companies that have higher probability for meaningful exits. So we are moving from an industry with a broad investor base to an industry of specialists (SaaS specialists, biotech specialists, consumer internet specialists, etc.).
Therefore, because the number of the desirable startup acquisition candidates will remain small, large corporations will need to find ways to foster innovation from within. Corporations must also become better at selecting which companies to acquire. In this way will be able to identify companies that can provide the desired innovation in the short term but also have the teams that will stay with the acquiring company thus providing long-term benefits. The capacity of institutional VCs to invest in seed-stage startups will not increase. In fact, it may continue to decrease further. Rather than creating as many seed-stage startups with weak teams, dubious innovations and no long-term prospects, entrepreneurs must seek to form strong teams that can innovate and build large and enduring companies.
In my last blog I tried to define the concept of insight. In this post I discuss insight generation. Insights are generated by systematically and exhaustively examining a) the output of various analytic models (including predictive, benchmarking, outlier-detection models, etc.) generated from a body of data, and b) the content and structure of the models themselves. Insight generation is a process that takes place together with model generation, but is separate from the decisioning process during which the generated models, as well as the insights and their associated action plans are applied on new data.
A little over two years ago I wrote a series of blogs introducing Insight-as-a-Service. My idea on how companies can provide insight as a service started by observing my SaaS portfolio companies. In addition to each customer’s operational data used by their SaaS applications, like all SaaS companies, these companies collect and store application usage data. As a result, they have the capacity to benchmark the performance of their customers and help them improve their corporate and application performance. I had then determined that insight delivered as a service can be applied not only for benchmarking but to other analytic- and data-driven systems. Over the intervening time I came across several companies that started developing products and services that were building upon the idea of insight generation and providing insight as a service. However, the more I thought about insight-as-a-service, the more I came to understand that we didn’t really have a good enough understanding of what constitutes insight. In today’s environment where corporate marketing overhypes everything associated with big data and analytics, the word “insight” is being used very loosely, most of the times in order to indicate any type of data analysis or prediction. For this reason, I felt it was important to attempt defining the concept of insight. Once we define it we can then determine if we can deliver it as a service. During the past several months I have been interacting with colleagues such as Nikos Anerousis of IBM, Bill Mark of SRI, Ashok Srivastava of Verizon and Ben Lorica of O’Reilly in an effort to try to define “insight.”