How Are You Monetizing the Analytics You’re Embedding?

February 26, 2019
Kevin Smith's picture
VP, Product Marketing
Kevin Smith is Vice President of Product Marketing for GoodData. Prior to GoodData, Kevin was responsible for delivering consulting services such as analytic product strategy, data monetization, and go-to-market services at NextWave Business Intelligence. He is the author of numerous ebooks, articles, and webinars on embedded analytics and building data products. In addition to NextWave, Kevin has held leadership positions heading analytics teams, designing SaaS products, and performance and managing product teams for both small start-ups and Fortune 500 companies such as SAP, ServiceSource, and Qwest Communications. Kevin holds a B.S. in Finance, as well as an M.B.A. in Quality/Process Management, both from the University of Maryland, College Park.

Before I joined GoodData, when I was building analytic applications for companies, I often wished I could share quantifiable information with executives about the kind of improvements they’d see if they decided to embrace embedding analytics in their applications.

Today, I’m in a role where I’m able to provide the platform to build the analytic applications, instead of doing the actual building myself. This role has also allowed me to work with analyst groups, and I recently had the opportunity to spend time with Aberdeen to go through the research they’ve done to provide hard facts on the true value of embedding analytics into an application.  

I found their study so compelling that we worked together to create a webinar with Aberdeen’s SVP of Research, Michael Locke. During our webinar, I found the figures below to be particularly interesting. Take a look at the orange bars in the graphic below, representing top analytics performers from the survey.

Besides the intangible benefits like self-service capabilities or better security, analytics leaders are also seeing real numbers that prove that embedded analytics have improved their business in tangible ways. They’re seeing an almost 30% growth in year-over-year revenue, with faster sales cycles, larger deal sizes, and cross-sell and upsell opportunities. Those are huge numbers!

Companies with an embedded analytics solution see higher opportunities in data monetization, among other benefits.

These results appear to be at odds with how we hear companies talk about analytics: as a cost to the company versus a profit center. I understand the concern; it’s hard to invest in something new, especially if a company doesn’t have a clear idea of the ROI they’ll achieve. But this continuing research from Aberdeen proves that there are substantial monetary benefits and returns associated with embedding analytics into your business processes and applications. If you have a clear understanding of the problem you’re trying to solve and you tackle each process systematically, you too can recognize tremendous benefits.

So let’s flip the script. Everyone always talks about justifying the cost of embedded business intelligence, but the results from Aberdeen show that embedded analytics are a major revenue center as well. So with that in mind, how can you monetize the analytics you’re embedding?

Charge for the value of analytics

Inevitably, when I’m talking with a company about building an analytic application and developing their go-to-market strategy, someone will ask “should we charge for the analytics?” My answer is always the same when it comes to data monetization: of course. If you deploy analytics properly—structured for specific personas and their pain points, designed to solve business problems—then you’re providing your customer with tremendous value that should be charged for.

What we’re seeing is that companies are going through the effort of building the analytic application, only to give it away because they’re afraid that no one will be willing to pay a higher price for a product with analytics. However, the reality is that there is a justifiable cost for these valuable insights.

Average selling price

Here’s another way that having analytics under the hood can drive value: average selling price. In the graphic above, we see that those best-in-class companies are benefiting from deal sizes that are 27% larger. Why is that? By embedding analytics successfully, these best-in-class companies are able to pique the interest of a wider variety of stakeholders within a customer’s company. This presents the opportunity for larger and broader deployments, ultimately driving up average deal size.

Introduce tiered packages

In addition to the initial selling price, one of the things that companies need to be thinking about is the ability to upsell analytics to existing customers. Ideally, you want to structure your analytics offering into tiered packages that present future sales opportunities. A customer may start off with a more basic tier, but as they move to higher-priced offerings, those with deeper and more customized analytics, your revenue increases. As you’re thinking about monetizing analytics, ask yourself: how can we structure our analytic offering so there’s just enough in each tier to get people to move up to the next one?

Years ago, a company would provide “analytics” in the form of a web-based dashboard for their existing MS Excel reports, then would be shocked that no one wanted to pay extra for this. Companies came to the conclusion that no one wanted to pay for analytics—even though, in reality, no one wanted to pay for a dashboard of Excel reports—and we started to see this mindset of “I can’t charge for analytics” catch on. They didn’t recognize the difference between deep insights that are easy to understand by the average person and a static dashboard. People were used to working in Excel, so there had to be a compelling business reason to ask people to change the way they did business. But what these best-in-class companies show us is that analytics, when embedded seamlessly into a workflow and are easy to understand, have tremendous value in terms of operational efficiency and as a revenue source.

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