“Demystifying” the Formula for Social Media ROI (Spoiler: There Isn’t One)
By Tim Wilson on in Analysis, Facebook Insights, Metrics, Social Media, Twitter with 4 Comments
I raved about John Lovett’s new book, Social Media Metrics Secrets in an earlier post, and, while I make my way through Marshall Sponder’s Social Media Analytics book that arrived on bookshelves at almost exactly the same time, I’ve also been working on putting some of Lovett’s ideas into action.
One of the more directly usable sections of the book is in Chapter 5, where Lovett lays out pseudo formulas for KPIs for various possible (probable) social media business objectives. This post started out to be about my experiences drilling down into some of those formulas…but then the content took a turn, and one of Lovett’s partners at Web Analytics Demystified wrote a provocative blog post…so I’ll save the formula exploration for a subsequent post.
Instead…Social Media ROI
Lovett explicitly notes in his book that there is no secret formula for social media ROI. In my mind, there never will be — just as there will never be unicorns, world peace, or delicious chocolate ice cream that is as healthy as a sprig of raw broccoli, no matter how much little girls and boys, rationale adults, or my waistline wish for them.
Yes, the breadth of social media data available is getting better by the day, but, at best, it’s barely keeping pace with the constant changes in consumer behavior and social media platforms. It’s not really gaining ground.
What Lovett proposes, instead of a universally standard social media ROI calculation, is that marketers be very clear as to what their business objectives are – a level down from “increase revenue,” “lower costs,”and “increase customer satisfaction” – and then work to measure against those business objectives.
The way I’ve described this basic approach over the past few years is using the phrase “logical model,” – as in, “You need to build a logical link from the activity you’re doing all the way to ultimate business benefit, even if you’re not able to track those links all the way along that chain. Then…measure progress on the activity.”
Unfortunately, “logical model” is a tricky term, as it already has a very specific meaning in the world of database design. But, if you squint and tilt you’re head just a bit, that’s okay. Just as a database logical model is a representation of how the data is linked and interrelated from a business perspective (as opposed to the “physical model,” which is how the data actually gets structured under the hood), building a logical model of how you expect your brand’s digital/social activities to ladder up to meaningful business outcomes is a perfectly valid way to set up effective performance measurement in a messy, messy digital marketing world.
No Wonder These Guys Work Together
Right along the lines of Lovett’s approach comes one of the other partners at Web Analytics Demystified with, in my mind, highly complementary thinking. Eric Peterson’s post about The Myth of the “Data-Driven Business” postulates that there are pitfalls a-looming if the digital analytics industry continues to espouse “being totally data-driven” as the penultimate goal. He notes:
…I simply have not seen nearly enough evidence that eschewing the type of business acumen, experience, and awareness that is the very heart-and-soul of every successful business in favor of a “by the numbers” approach creates the type of result that the “data-driven” school seems to be evangelizing for.
What I do see in our best clients and those rare, transcendent organizations that truly understand the relationship between people, process, and technology — and are able to leverage that knowledge to inform their overarching business strategy — is a very healthy blend of data and business knowledge, each applied judiciously based on the challenge at hand. Smart business leaders leveraging insights and recommendations made by a trusted analytics organization — not automatons pulling levers based on a hit count, p-value, or conversion rate.
I agree 100% with his post, and he effectively counters the dissenting commenters (partial dissent, generally – no one has chimed in yet fully disagreeing with him). Peterson himself questions whether he is simply making a mountain out of a semantic molehill. He’s not. We’ve painted ourselves into corners semantically before (“web analyst” is too confining a label, anyone…?). The sooner we try to get out of this one, the better — it’s over-promising / over-selling / over-simplifying the realities of what data can do and what it can’t.
Which Gets Back to “Is It Easy?”
Both Lovett’s and Peterson’s ideas ultimately go back to the need for effective analysts to have a healthy blend of data-crunching skills and business acumen. And…storytelling! Let’s not forget that! It means we will have to be communicators and educators — figuring out the sound bites that get at the larger truths about the most effective ways to approach digital and social media measurement and analysis. Here’s my quick list of regularly (in the past…or going forward!) phrases:
- There is no silver bullet for calculating social media ROI — the increasing fragmentation of the consumer experience and the increasing proliferation of communication channels makes it so
- We’re talking about measuring people and their behavior and attitudes — not a manufacturing process; people are much, much messier than widgets on a production line in a controlled environment
- While it’s certainly advisable to use data in business, it’s more about using that data to be “data-informed” rather than aiming to be “data-driven” — experience and smart thinking count!
- Rather than looking to link each marketing activity all the way to the bottom line, focus on working through a logical model that fits each activity into the larger business context, and then find the measurement and analysis points that balance “nearness to the activity” with “nearness to the ultimate business outcome.”
- Measurement and analytics really is a mix of art and science, and whether more “art” is required or more “science” is required varies based on the specific analytics problem you’re trying to solve
There’s my list — cobbled from my own experience and from the words of others!