Working to prove the ROI of data analytics in and of itself is like assessing the ROI of keeping the lights on in the office. Sure, a company can spend time and resources conducting a carefully-controlled experiment to determine the extent to which the usage of light bulbs (vs. no light bulbs) in an office setting improves employee productivity — but at the end of the day, they’ll just be wasting time sitting in the dark.

And while there’s no longer any doubt as to whether data analytics is valuable for businesses (it is), many brands struggle to pinpoint precisely how and where data analytics is improving business outcomes. To be fair, isolating big data’s ROI can be a complex process — primarily because, even in 2018, the ROI of data analytics is not a straightforward one-to-one calculation. Instead, companies must think about the ROI of data analytics in two veins: the optimization and transformation of business practices.

To gauge optimization —  initiatives focused on bottom line growth — companies should be asking: how has data analytics helped our company do business differently? What new efficiencies have data analytics enabled?

To evaluate transformation — initiatives focused on top line growth — companies should consider the ways in which data analytics has fundamentally changed the way they do business. How has it informed the development of new lines of business empowered by analytics?

The true ROI from data and analytics is a direct result of an organization’s ability to integrate data analytics into core business processes, redesign those processes to effectively leverage data analytics, and build out new capabilities and processes because of what data empowers them to do. With this in mind, let’s take a look at how companies can reframe the way they think about — and determine the value of — data analytics.

Using Analytics to Drive Optimization of Existing Processes

An investment in data analytics is about more than just saving money. It’s about getting more mileage out of — or optimizing — each dollar your company is already spending. Let’s use digital analytics as a simple example. There’s little debate as to whether or not it’s wise for a company to build a website in 2017. It’s an essential business cost that enables brands to attract, inform, and convert digital consumers — one that produces real-time data about the way in which consumers are discovering and engaging with your products. Digital analytics don’t represent a separate value-add for which brands must isolate an ROI; they’re inextricably linked with the brand’s motivations for creating its website in the first place, and serve to both support and optimize the investment in web design and development.

The same can be said for ad targeting. Targeting and personalization of content, powered by analytics, enables brands to increase impact per dollar of a given ad spend. The ROI calculation is inextricably linked to improvements and optimizations in the media buy — not the targeting model itself. It’s not a matter of “should we use data and analytics to improve performance?” but of “to what extent have we optimized our investment, leveraging data and analytics?

Driving Business Transformation to Create New Value: Using Micro-Level Insights to Support Transformational Thinking

On the other hand, analytics can drive the transformation of processes and the development  of new ones. ROI comes from the new value that’s been developed: what new products have been developed as a result of leveraging analytics? How much revenue have those products driven? How much organic revenue was spurred by the addition of this new value?

This type of transformative ROI must be considered in the context of a much longer investment cycle. That said, executives will want to extract micro-level insights that provide tangible proof of specific impact along the way to show impact. To that end, it’s helpful to focus on a hyper-specific analytics use case that can function as a proof of concept. Simply supporting vague claims that tie data analytics to increases in revenue is unrealistic, and isn’t a meaningful representation of what big data can do for decision makers.

Instead, stakeholders should select a single example of impactful data and analytics operations and dig into the cause and extent of that success. This could be the targeting of a specific consumer profile, the rollout of a newly-identified line extension, or the first weeks of a newly minted campaign. This will likely involve making a comparison to a similar operation performed without the use of data analytics (or using data analytics to a lesser extent) and drawing out how the data-driven iteration achieved better results.

Let’s get more specific to the healthcare setting: the National Institutes of Health reports that over 80 percent of clinical trials fail because of enrollment roadblocks and difficulties with patient retention. Clinical trial recruitment has long been an Achilles’ heel for pharmaceutical companies, but data analytics is helping companies pinpoint high-quality, interested patients. If a particular clinical trial is able to recruit an unusually good patient group with the help of data analytics, this success needs to be made clear by juxtaposing the product’s time-to-market, FDA-approval timeline, and other indicators of an effective R&D cycle with those of previous, less efficient trials. The role data plays in improving these kinds of metrics may be obvious to the analytics team, but it likely won’t be so clear to the high-level decision-makers to whom ROI is most important.

A Future-Focused Framework

Data is most valuable when it’s interwoven into everything that a company does, but this means that the benefits data provides are simultaneously everywhere and difficult to isolate. That’s why it’s arguably more valuable for marketers to zoom out from the bottom line and consider both the specific and big-picture impact of data analytics on the health of their business.  

Because at the end of the day, data analytics should be just as important to a business as keeping the lights on.  

Written by Kevin Troyanos

I lead the Analytics & Data Science practice at Saatchi & Saatchi Wellness. I have focused my career within the healthcare marketing analytics space, empowering healthcare marketers with data-driven strategic guidance while developing innovative solutions to healthcare marketing problems through the power of data science. I’ve worked to measure, predict, and optimize marketing and business outcomes across personal, non-personal, digital, and social channels. I’ve led engagements with brands that span all stages of the product lifecycle, with a particular focus on established brands. My role is to guide the departmental vision and lead innovation initiatives, effectively positioning marketing analytics as a competitive differentiator and organic growth driver for the agency at large.

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