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March 15th, 2016

Data Rich, Decision Poor: Monetizing Your IoT Investments

This is the first part in our blog series examining the IoT space. Today we look at some of the challenges companies are facing when trying to monetize their IoT investments and where the money is in IoT.

How Do I Compete and Win with IoT Data?

Companies from all over gathered together at the IoT Evolution Conference held in Ft. Lauderdale and Mobile World Congress in Barcelona earlier this year, with IoT dominating most discussions. It’s a wide field, with hopes of driving productivity, asset optimization, cost savings and new revenue across all sectors, from retailers to manufacturers to government. Not only is it cross-functional within an organization, it tends to span multiple players in their value-chain/ecosystem.

A recurring theme among many participants was that their organizations are data rich but decision poor. They are collecting massive amounts of big data, from sensors and applications instrumented to report every click and behavior. They have invested in the software tooling and platforms, but still struggle with how to monetize these investments.

“Now that I’ve made my strategic decisions, implemented the best software, and am the proud owner of massive amounts of IoT data, when do my returns start? How do I compete and win with it?”

Yes, the data is absolutely essential. And it’s not just the IoT data itself. Until it’s augmented (combined with customer, finance, partner, sales data streams), correlated, and mediated with models (ever try looking at raw Fitbit data?), the data itself isn’t going to do it for you.

How do I make better decisions with it, how do I enable my customers to make better decisions with it, how do I improve the customer experience in ways that they will pay for? These all require new applications and decisioning models to support them, often from a mobile-first perspective. As well, they often require changes to the business model to be effective.

Digital Darwinism Is Unkind to Those Who Wait

Some people view these as early days in the evolution of IoT; others see IoT as an immediate threat to their customers and their margins. They are both right. A phrase popularized by Raw Wang in 2014 will be applicable for some time: “Digital Darwinism is unkind to those who wait.”

As one of our customers put it:

“My IoT investments are still just costs until I can generate revenue with them. ‘Strategic’ means I can get away with it for a while, but when our revenues are threatened by new competitors, it’s no longer optional.”

We have been fortunate to work with several customers who are at various stages in their IoT programs. During our sessions at the two conferences, we shared some of their experiences and learnings, especially around their efforts with monetization. Here we’ll look at one.

A $10B+ women’s specialty retailer was making the same offer to all their customers, and we examined whether we could find improvements in this area. This approach is at the heart of many areas of monetization, changing from treating a large, heterogenous population in the same way, to segmenting it based on behaviors derived from big data, and determining offers more appropriate to each segment. Until you do the deep behavioral analysis on the data, you don’t know what’s possible. When you do, the results are often surprising – in this case, a $300+MM net revenue increase when operationalized to provide recommendations in real-time. This was done in a matter of weeks using mostly existing software assets. We have seen similar results with other retailers. We’ll have another post that describes B2B monetization examples later in this blog series.

What we want to point out here is the need to look at the revenue side for monetization, not just the cost savings that many IoT programs are based on. They are both important, and they are both real, but we have found that some customers are reluctant to base their program justifications on the fuzzy potential revenue increases, as they are not well estimated until you get sufficient data and perform the analysis. Over half the people we spoke with at the conferences said that they would not include revenue increases in their IoT justifications. It’s not that some of them didn’t want to, but that their company’s project funding and governance made it too hard. Yet that’s where the money is.

I’ve Got All This Data. What Can I Do with It?

We refer to these types of projects as learning-based projects. Their requirements are often aspirational, and they confound the typical waterfall methodologies and typical funding/governance methodologies. They require an Agile methodology, with the added perspectives of needing and expecting to pivot in terms of their objectives, based on what is found in the data and how their customers respond to new offerings. Okay, most companies are attempting some form of Agile as the market requires it. Agile is certainly a good foundation, and those that are monetizing their efforts are doing so by targeting revenue growth as a constant bias.

The most common question we’ve heard in the past 12 months from CIO’s and CMO’s is:

“I’ve got all this data. What can you help me do with it that moves the needle?”

We are happy to show you how we’ve done it with a number of our customers. Contact us today to learn more.

Upcoming topics will include:

  • B2B examples of monetization
  • Learning-based projects
  • Servitization
  • Capabilities needed for IoT projects
  • Choosing IoT platforms
  • Choosing IoT providers

Thomas Schryver
VP Customer Experience

Tags: big data internet of things IoT Mobile world Congress

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