Isn’t this a lovely map? I call it an Ad-Map, and I sometimes use them to get about a strange town. Businesses pay to be listed, tourists get an orientation.
Tragically, at least for advertisers and tourists, maps such as this are of little use. Advertisers in each category are listed in seemingly random order. Numbers on the map have no order. Ease of use is all for the cartologist. Alphabetical? Too much work. Ordered by location? Ditto.
My top use case is not supported by this map:
I’m hungry, I find an Indian restaurant listed, and now I want to go there.
I stare at every number on every street on the map until I find my goal. It takes several passes, because I am past the age of Where’s Waldo virtuosity.
My second use case is even more poorly supported, because I not only search for the number on the map, I search for the name in the legend:
I want to go to the Police Museum.
Somewhat better supported:
I’m at the corner of Cross and Columbia, and I want the nearest coffee.
In this case, I match the nearby numbers to the legend until I find my goal.
Not very well supported:
I’m a business, and I want tourists to find me.
A little more thought to how the content is used would have produced much more effective advertising. As it stands, the message and the user are both mostly lost.
My weather app offered me this banner this morning:
OK, Google, take me to the nearest hair salon. (Try it now)
I’ve been looking for a new salon. “Nearest” has never been in my criteria. Under 10 miles would be a plus, but “nearest”, nope.
Granted, there are certainly people who LOOK as though that was how they chose. The Unstyled Look might be a feature of Techie Non-Chic. Innate kindness generally prevents me from asking, “Is that how you wanted it to look?” because it is hard to believe the answer will be “yes.”
I can imagine the ad team starting with “OK, Google, take me to the nearest bar” and then deciding it was not PC. Ditto, “take me to the nearest barber.” Let me recommend instead coffee, brew pub, parking lot, lottery seller, sandwich, drugstore, sports store, deli, Dunkin Donuts (or Tim Hortons), Apple store (oops, never mind), fish market.
Statisticians joke (yes, they do!) that half the job is wrestling the data into shape, half is performing useful analyses, and half is explaining the findings. The data wrestling too often expands to consume all my time, leaving me too ennervated to find any findings.
Big Data is what got everyone so excited about data science. But Bigger isn’t necessarily Better. I have interviewed many many people, and every single one is groaning about data quality. As my friend Jonas Dahl (a data scientist at Adobe) says, “Too much data can actually be crippling for an organization because it slows down our analyses. Just because you have a lot of data doesn’t mean that you have a lot of knowledge or insights. Organizations are too often obsessed with recording enormous amounts of data and not obsessed enough about turning this pile of data into actionable knowledge.
“Bottom line, you can’t get too much high-value data — but you can be buried in too much low-value data.”
Inescapable: data science delivers the insights you need in order to be effective with digital marketing.
Short-term avoidable: hiring a data scientist.
Embraceable: Your new data scientist.Creative people and data science people should work together all day, every day, until they can understand each other, with a shared vision and shared vocabulary.
Your data scientist must be everyone’s new best friend — Partnerds forever.
OK, not really a tragedy. But a source of tremendous pain for many: targeted marketing, and personalization, are the antithesis of one size fits all. And this means you need an assortment for every bit of content that personalizes your customer experience. Not one walnut, but 7 or 8 or dozens.
Having lots of content alternates, and using them, means you have:
- A content strategy
- A content plan
- Lots of content
- A system for associating specific content with specific experiences
- A method for tracking and improving the results of applying specific content to specific experiences
Not necessarily a tough nut to crack, but far from simple.
It would be great if we all had data to drive our decisions and processes — and used it. But the obstacles are so daunting that most of us can’t. Here are the top 6:
- Leadership from the top is mostly lacking.
- Data quality issues are omnipresent.
- Data science in marketing is not established (it may exist for production operations).
- Marketing lacks the technology platform for effective lead generation. Among the best prepared, the technology platform is full of holes.
- Operational tasks and organizational responsibilities are not identified and assigned.
- Skills for developing and executing methods to create data, analyze it, and communicate findings are very scarce.
Data science, a nascent skill for most organizations, has so much to offer. But for a marketing person, the big excitement is what it can do for lead generation. And, oh, BTW, customer experience. Because a better customer experience can really boost your lead gen results…
Interviews with two dozen people, mostly marketing managers and executives, on the subject of data-driven decision making, reveal unwavering enthusiasm for applying every tool in the bag to the one thing virtually all marketing departments have in common: the lead gen goal.
That’s fine, but when you’ve achieved competence in applying data science to lead gen, don’t lean back. Sprinkle a little of that science pixie dust on your brand messaging, competitive analysis, and operational optimization.