{"id":13144,"date":"2022-02-14T18:17:07","date_gmt":"2022-02-14T18:17:07","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2022\/02\/14\/return-on-investment-is-missing-in-action\/"},"modified":"2022-02-14T18:17:07","modified_gmt":"2022-02-14T18:17:07","slug":"return-on-investment-is-missing-in-action","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2022\/02\/14\/return-on-investment-is-missing-in-action\/","title":{"rendered":"Return on investment is missing in action"},"content":{"rendered":"

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The marketing team spent its budget dollars wisely. But management can\u2019t see the results.<\/p>\n

When business spends money on anything, it expects to see a \u201creturn on investment\u201d\u2014 a lift that has some correlation with the spend. This would be fine if marketing was a machine, with fixed, measurable inputs and outputs. Except we are living in \u201cthe Information Age\u201d, so Industrial Age accounting will fail to notice what is really going on.<\/p>\n

Marketing often appears as a cost sink on the balance sheet. It\u2019s relationship to output is not always obvious. Kathleen Schaub, marketing management and organization strategist, and Mark Stouse, chairman of Proof Data Corporation, want to shed some light on this situation. Schaub focuses on how complexity affects marketing, while Stouse takes a deeper look at analytics. These two angles converge on a common point: the old way of measuring marketing ROI misses a lot, providing inaccurate guidance for marketing efforts.<\/p>\n

Schaub goes into further detail in her paper \u201cMarketing Is Not a Vending Machine<\/a>.\u201d \u00a0\u201cMarkets are what science calls complex adaptive systems. The interactions of many independent agents, both individuals and enterprises \u2013 customers, companies, influencers, partners, and governments, produce feedback loops that cause situations to constantly change. Change produces many unknowns.\u201d Schaub wrote.<\/p>\n

Those feedback loops have speeded up, thanks to globalization and digitalization. \u201cThere\u2019s no breathing room,\u201d Schaub told us, so marketers have a hard time keeping up. It is only in the past several decades that data and computers have finally provided a way to see this happening. \u201cNow that things have speeded up, we can see these patterns in the data.\u201d<\/p>\n

\u201cROI for marketing is not there.\u201d Schaub said. \u201cNever has been. Never will be. Not in the old standard sense.\u201d\u00a0<\/p>\n

The only thing certain is uncertainty<\/h2>\n

\u201cThe big shift, which is probably permanent, started with COVID,\u201d Stouse said. \u201cThe feedback loops really matter,\u201d Stouse continued. Now you needed to have a value goal, a route to value, and the ability to track your effort, much like a GPS, he said. This is running concurrently with the disintegration of third-party data.<\/p>\n

To get a more realistic picture, some marketers are returning to an older technique: marketing mix modeling (MMM). This technique is more common among larger Fortune 500 companies, Stouse said. But it was too balky to make operational \u2014 too slow, too manual, too expensive, too hard to scale, and hard for people to understand.<\/p>\n

Today, MMM is getting a second look. \u201cAutomation has eliminated many of these challenges to operationalizing analytics.\u201d Stouse said. \u201cMMM is rooted in multi-variable regression analytics, which is the fundamental underpinning of the scientific method.\u201d And this is mainstream and well understood, he added.<\/p>\n

Any change in analytics will also trigger a change in organization. \u201cYou cannot continue to have the same kind of rigid, command and control, hierarchical, siloed kind of organization and have these very rigid waterfall business processes. They are just too slow, too inhuman, they are barren of information, because information cannot flow to where it is needed,\u201d Schaub said.<\/p>\n

\u201cI kept asking CMOs and CEOS, \u2018can you do ROI yet?\u2019\u201d Schaub recalled. \u201cThe answer was always \u2018no\u2019\u201d. They had workarounds, like attribution, and pushing marketing \u201cdown funnel\u201d to help sales, \u201cbut at the end of the day, people have to give up the fantasy that it [marketing] can ever be completely predictable.\u201d Schaub said.<\/p>\n

People believe in cause-and-effect, that with enough data, the right strategy, the right work processes, or enough efficiency, they could figure things out, Schaub said. COVID crashed that belief system. To rebuild, analytics will help, but organizations will also have to re-align so that they can work better \u201cin conditions that need our response as opposed to our ability to predict.\u201d she said.<\/p>\n

If you can\u2019t predict, project<\/h2>\n

Businesses can still draft scenarios and craft projections, laying down an outline of how things are expected to happen. \u201cThen you have to \u2018fast follow\u2019 as the future becomes the present, with latent, low-level re-calculation, so you can find out where you are relative to that projection.\u201d Stouse explained.<\/p>\n

Here Stouse compared marketing analytics with GPS. The destination is not a fixed ROI point reached by a certain date. Instead, the goal can be reached despite changes in traffic and necessary detours. Just as the GPS recalculates, the decisionmaker can also recalculate the market spend or rescale the project to better accommodate reality.<\/p>\n

Like GPS, you need a driver who can make decisions and change course as needed. Here the marketer is at the wheel, advised by analytics, able to react to circumstances, seize opportunity, or reduce risks as needed, Schaub explained. This is need not be a single person, but will often be an agile, multi-disciplinary team which is quicker, smarter, more innovative, and which can foster human problem solving. \u201cYou build for the long term, but the only time you can act is now,\u201d Schaub said.<\/p>\n

Learning to live with speed<\/h2>\n

The speed of data, and feedback loops has increased tremendously, changing the digital landscape more quickly than a company\u2019s capacity to comprehend and keep up.<\/p>\n

Here the concept of the OODA Loop \u2014 Observe, Orient, Decide, Act \u2014 becomes relevant. The concept was coined by fighter pilot Col. John Boyd, who proved that if you can do this faster than your competitor, he will be stuck reacting to your last action as you execute your next move. \u201cThis is increasingly where business is and increasingly where marketing is,\u201d Stouse said. \u201cYou have all these variables, and these variables are moving at a speed that the unaided human brain can\u2019t track.\u201d<\/p>\n

\u201cBeing able to use software\u2026compresses the OODA loop.\u201d Stouse gave a real life example where his firm is working with a technology company. \u201cThis is a huge B2B enterprise tech company. It\u2019s a long cycle business who has used MMM for six or seven years in a very traditional, conventional way.\u201d he said. \u201c[B]y the time the analytics reaches the marketing people, it\u2019s so out of date that the prediction no longer matters\u2026They don\u2019t need to change the math. They need to change the speed at which it is all computed.\u201d<\/p>\n

Read next: More about leveraging the OODA Loop<\/a><\/em><\/strong><\/p>\n

As Schaub noted in her paper, a marketing department may have to track up to 30 variables to provide the insight needed to understand what present conditions are like. But it may not take as long as one might expect to pull them all together. Data automation can shorten the time it takes to weave insights from data streams.<\/p>\n

\u201cI don\u2019t mean to imply that any of this is easy,\u201d Stouse continued. \u201cBut it has significantly improved.\u201d One can use tool sets like Supermetrics \u00a0or Datorama (both Proof partners) to gather data streams, sanitize, cleanse and harmonize them, and then pipe the data into platforms like Proof or Tableau.<\/p>\n

\u201cWe can go into a customer that has data but doesn\u2019t have any analytical history at all\u2026and the first 10 models can be up and running in less than 60 days.\u201d Stouse said. Go to a traditional consultant doing MMM, and \u201cit would be 10 to 12 months before you saw your first model.\u201d<\/p>\n

In the second part of this two part article learn why predictability and control are out, while perception and reaction are in.<\/em><\/p>\n\n

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About The Author<\/h3>\n
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