March 23, 2015 | Written by: Ryan Begley
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The first time I got on the Internet, I was in high school. It was 1995, and it was via a dial-up modem. Not really knowing what to do on the Internet, I went web surfing, but in ‘95 there wasn’t much content, the waves weren’t big, so I lost interest for a couple years.
One thing I’ll always remember about those early Internet days were the ads. Banner ads, pop ups later on, but all of them felt like billboards on the side of the freeway. There was no data, no ability to establish causation in relation top line growth.
In 1999, while in college, a professor told us to google a book written by Goethe to get clarification on what something meant. I think I applied for a job with Google that night after seeing what better search results it delivered compared to Yahoo. A few years later a good friend of mine got a job at Google, and it was then that he explained how google made its money. On the phone he told me to click an ad on the right. I did. “See, you just made google money,” he said. Genius, I thought.
Since then, new channels have entered the digital marketing landscape – with banner ads and search already existing, next came video, mobile then social. Currently marketers have significant variety in channels and platforms to get their messages out.
Today, significant money is spent on digital advertising, and while estimates vary, it’s possible that in 2015 $120B will be spent on digital advertising. That money is spent because marketers can tell it’s effective, they just can’t tell how effective. Let’s imagine an organization is budgeted to spend $1,000 a year on digital ads. For simplicity’s sake that company earns revenues of $2000. So, in theory, you could say marketing produced 100% ROI on their fiscal year marketing budget, and that would be great. But what if they only spent half that, would they only earn $1,000 in revenues?
This is a basic example but illustrates my thesis – the most sophisticated customer and marketing analytic platforms struggle to establish true causality. Many come very close, but marketing spend will always be a correlation to sales, not the reverse. As marketing pioneer John Wanamaker once joked, “half the money I spend on advertising is wasted, the trouble is I do not know which half”.
Today’s marketers see strong correlations to Google ad buys and increased revenues. That’s why Google is such a powerful company and its revenues continue to increase after 15+ years of being in the Search business. Google has data on your search, your YouTube views, your email content, your GPS data, and much more. Google can segment its user base, using highly advanced analytical tools that correlate preferences, context, and location of an individual, and deliver relevant engaging offers. Yet there’s still room for improvement.
Improvement is needed because even though an individual may have clicked a text display ad or a search link, or a video banner ad, there’s no implicit assurance how that ad impacted a sale. If the ad hadn’t been there, would the sale have happened regardless? With no insight into the how or the if, marketers are still left unsure how much top-line growth they’re contributing to the company, they’re still unsure of the cost of revenue.
With banner ads, and, for that matter, billboards and television ads, the goal is to simply reach an audience. But, in today’s world, companies are trying to promote specific behaviors that represent value to a marketing organization. Facebook likes, shares, tweets, registrations, yelp reviews all represent valuable data to the marketing department and so metrics like cost per impression are less valuable. Metrics more closely aligned with revenue, like pay per call or cost per order and cost per lead take a large step forward in articulating correlation and, perhaps in some cases, causality. But there’s still room to improve. Marketing organizations of the future, after solving the “how and the if” challenge, should be able to measure themselves by a simple cost of revenue metric, as in “what does it cost, to buy more revenue.”.
The Internet of Things (IoT) will enable marketers to measure “cost of revenue” because the data created from connected devices will solve the how and if. But how?
In the future, and to some extent today, IoT connected devices are connecting themselves to other devices and exchanging data. Your wearable, which knows how long you sleep, and when you wake up in the morning can communicate that data to your coffee machine. When you wake up, that can trigger your coffee to start brewing and one coffee unit will be subtracted from your personal coffee inventory. Your wearable also knows when you’re away on business, so if you’re not sleeping in your bed, coffee won’t get made. Your wife’s wearable also communicates with your coffee maker, but knows she only drinks decaf on Sundays while reading the New York Times on her tablet, so on Sundays two coffee types are brewed, and two units are subtracted from your personal coffee inventory. Coffee deliveries typically take five days to arrive, so 10 days before you run out of coffee, you’ll get a message from your coffee maker with an ad, and that ad will say “Buy.” No more emails reminding you to repurchase, no more alerts or cookie based banner ads hoping to grab your attention. No more risk that you may forget to click a link and make selections. There will certainly be cross sell and up sell communications, additional offers, but sizable segments of the marketing budgets will be hyper targeted offers that fuse sales and marketing into an unbroken continuum of ecommerce.
Now that offer may come from someone else, perhaps your food and coffee supplier, and the message may come on a tablet, a wearable, your car, or some other surface. It may come in email form, text, phone call, instant message or some other communication channel. You may have set this event up to pre order coffee at the six day left period, in which case you may not even get a message at all, just a receipt.
The point is, marketing will have the data and the ability to actually determine the cost of revenue. Now imagine that message comes from a competing brand, with an offer to try their coffee, with access to the data, maybe offers to switch to tea. The more connected the devices in his or her lives are, the more precise the offers will be, and it seems the precision may be limitless.
The IoT future is one where the point of sale is everywhere and anywhere. By blending sales, marketing, advertising into a omni-channel, platform agnostic ecosystem that captures sales conversion metrics and links them to purchase intent data, establishing causality. Matt Ackley, CMO at Marin Software calls this “Audience based marketing,” Kevin Cain calls this targeted content, but I think this Forbes blog post comes closest to what I’m saying, by labeling it “integrated marketing.”
Essentially they’re all saying the same thing. Using data and analytics, market and advertise by delivering offers to highly defined, hyper-segmented audiences. Make those advertisements and offers as personal and contextual as possible. The huge advance that isn’t discussed is what IoT enables, the true holy grail of marketing, is the data-driven identification of marketing to sales causality, or figuring out true cost of revenue.
I can’t say it enough, now is the time to invest in enterprise marketing management. A tsunami of data is crashing down upon the enterprise, and only those who have plans in place to capitalize will make it out the other side.
Feel free to tell me I’m wrong – I often am! Ciao! @peter_ryans or reach me here! goo.gl/XYB1xb