Author Archive

26
Jul

Mapping the Future: 3 Essential Terms You Must Redefine Today

Written by Andrew Edwards. Posted in Digital Marketing

Descriptive terms are like the markers on a knowledge map. Maps are so important that Apple recently cashiered the executive who launched its somewhat inaccurate quilt of outsourced online maps (arguably the most-ridiculed Apple blunder since the skateboard-sized Newton).

So we need terms to be accurate and granular enough that we can use them successfully to navigate our study of digital user behavior. We don’t like getting lost – not in Kansas, not in our analytics tools.

Some of the changes in the market have rendered some terms almost meaningless for the purposes of measurement, and are in need of redefinition. Just like what happens when the old road dead-ends at the new overpass, we need the latest information in order to keep from crashing into a wall.

Here are my top 3 suggestions for terms in need of redefinition:

1. Web. Yes, it means too many things now. Simply agreeing it’s dub-dub-dub-and-done doesn’t do it anymore. What are you using it for, and which version draws your audience? Even a foundational industry organization such as the Web Analytics Association is now the Digital Analytics Association.

Consider this: the same “web” content may come in several flavors for different audiences. With HTML5, you can create highly interactive experiences. But users with even relatively new browsers and OS versions may need you to create a four-door sedan version with fewer knobs and smaller woofers. And there may even be a static page version for the later adopters. Then consider the browsing experience and commensurate behavior on an iPad. Or the microscopic (OK, zoomable) text on a smartphone. That’s web-based. But not like the PC version. And what about shared or syndicated media? We could cite more examples. But the term “web” can mean at least these things right now that are especially important to marketers:

  • Web in a browser on a PC with page-to-page navigation
  • Web in a browser on a PC with interactive navigation
  • Web on a large-screen mobile device
  • Web on a tiny-screen mobile device
  • Web coming from somewhere you may not control (syndicated, campaign-related, or from social media like Facebook or LinkedIn)

We have room here only to scratch the surface. Clearly, the browser itself is no longer enough to define what we call “the web.” The town has matured into a city and has quite a number of clearly defined districts. Get to know the neighborhood!

2. Mobile. Measuring “mobile” as a category has become something like saying “New York” without specifying whether you mean the city or the state. I know a guy who grew up in the Catskills and moved to Florida as a teenager. They wanted to know what gang he’d belonged to in New York, as if he’d been haunting the streets of Bushwick.

Recently, Black Friday e-commerce data told us that iOS users converted at drastically higher levels than Android users. Lumping mobile together without specifying platform-type would have left you misinformed. Like Bogie in Casablanca, you’d have come to the desert “for the waters.” Worldwide, many mobile users are “text only,” and sometimes they buy things that way. Many are not even using web protocols. And of course nearly every app is for a mobile audience. Apps behave very differently than websites and require special tools to measure them. Finally, it seems laptops should be considered part of “mobile,” since they are mobile and get used in many environments where other mobile devices also get used. So new definitions of mobile might include:

  • Mobile web iOS
  • Mobile web Android
  • Mobile text-only
  • Mobile app
  • Mobile PC (laptop)

3. Analytics. For the marketer, analytics has come to mean web, or more recently, digital analytics. But each of these terms has become emblematic of “siloed” information. And many organizations are calling ardently for more correlated data. They want to know about their sites, their campaigns, their social, their call center, and every touch point in between. Plus, many are hoping for a predictive capability too. They want to send the right message to the right audience at the right time. Where does the magic happen?

The magic can happen when all the data shows up in one place. Some teams are doing this by hand with spreadsheets and custom algorithms; and now the number of vendors claiming to automate this for the marketer is myriad. It really is a rapidly evolving sector, and the new requirements are obsoleting even the term “digital analytics.” Because when you put everything into one interface, you are converging data.

Therefore, analytics becomes (for the marketer):

Of the three definitions, the last is likely to become the de facto appellation for most analytics going forward, as it most accurately defines both the supply and the demand.

If this column prompts a discussion about granularity and correlation, it will have made an impact. By drawing up a more detailed terminology map, you’ll find yourself taking a much more direct route to Insight Boulevard.

Convergence_Analytics_Report

 

24
Jul

Too Much Data Means Too Much Data

Written by Andrew Edwards. Posted in Big Data

The digital marketing industry is one of the few that talks of a surfeit of resources. This is not only anomalous but cause for real concern. What would you say about a restaurant that had “too much food” or an energy company that had “too much natural gas”?

You might scold them for whining, of course. Or you might take a page from King Ludwig of Bavaria when he told Mozart his opera had “too many notes.”

The surfeit in our case is a problem, not an asset. It is a warning sign, not a harbinger of great achievements to come.

A common definition of “too much” is “more than we need.” And if you’ve seen the slide decks at recent digital marketing events, you know that many presenters see it as a badge of honor that data is piling up at a stunning rate while the ability to use and process this data is lagging farther and farther behind. There is a gleam in the eye of the data-miner, who believes there are more nuggets because there are more mines (and certainly more work for data miners!).

Do We Already Have Enough?

The real news is that we may have already exceeded the useful amount of data being captured. Data mountains are not like Everest: you don’t climb them “because they are there.” You climb a data promontory because you need to see your assets on the field of battle. And if you can do that with a pair of binoculars and a position upon a strategic hill, you really would be wasting everyone’s time and money by hiring Sherpas and pack mules and going atop Everest where you can’t breathe and must carry your own oxygen. In fact, all you might see up there would be other mountains of data!

We certainly can have plenty of data about our target markets. And we certainly have constituencies that love the pileup of data because it allows them to deploy ever-niftier algorithms and ever-more-rapid access to petabytes of information; selling into our fear of falling irredeemably behind the data curve.

Just Another Buzz

But “big data” is really the latest buzz-meme more than it is a goal to conquer. The next most recent buzz-meme, “social media,” has been brought somewhat to earth with the realization that a million “likes” will get you on the bus only if you also have the fare. It’s now clear that social media is a campaign, and that without a tieback to conversion or sale, it’s “a whole lotta nothin” as they might have said in the old vaudeville acts.

It isn’t so different with big data. Big data thrives on the notion that ever more granular information will provide the ability to perform ever better targeting. However, beyond a certain point – a point I believe we have now passed – it becomes an exercise in futility. For instance, how much more targeted can your communications get before you simply exceed the ability of even the most savvy creative genius to craft the perfectly targeted message? And how many microsegments would you care to chase and at what cost toward what benefit? Moreover, how small a segment can you target before you simply freak out your prospect by seeming to know too darn much?

Some might argue for endless data collection with a throttle on its use. But then, the pile of unused data becomes just another bag on the Sherpa’s back as you trudge rather egotistically up the north face.

The answer today is to target not your customer so much as your data collection. Of course, you need to define your marketing goals more carefully than ever. And you’ll need to target your expertise as well, since technology and analysis can get costly. But you want to focus data collection based on need, not ability.

Half a century ago, the automobile reached a performance level that began to exceed the ability of humans to control it. It was possible but impossibly costly and even frightening to put Joe Driver behind the wheel of a 20-foot long heap of hurtling chromium that could make a thoroughbred seem hobbled and lame by comparison. Did it make sense to keep supercharging the engine for even more raw power? Or did it make more sense to refine the mechanics for efficiency and safety? With hardly a V-8 in production today, I think we have our answer.

The Big Data Mythology

Big data is the V-8: mythologized for the burble of its throughput. But beyond fabulously vertical line charts and a feeling of domain mastery, where is the benefit of this mountain of data? My suggestion is that there’s little benefit and a great deal of wasted time, money, and effort.

Never mind the sales pitch from big data specialists. Much of it is today’s equivalent of the kandy-kolored-tangerine-dream with a competition clutch and a big spoiler on the back. You can’t drive this baby anywhere but in circles on a closed track.

But if you keep it simple, you might find yourself leading a cultural revolution. Remember the little bug they called Volkswagen, and what it did to the roaring monsters from Motown?

Collect all the data you need and forget the rest. You won’t miss it. But you will miss the cost. Focus analysis on whether your audience did what you wanted them to do (this is really the heart of the matter and always has been) and don’t bother with trying to sell a different flavor of breadcrumb to every ant under the magnifying glass. They won’t notice the difference, and it will be cheaper for you.

Simplify. Concentrate. Don’t get distracted by what you might get, but focus on what you know you can have. And that is, a targeted data set used against defined goals, properly implemented, and carefully managed to achieve the return on investment that makes beautiful music in anyone’s ear.

Too much data is too much distraction. Keep your compass in hand. The near hills are full of low-hanging fruit, and the picking is good.

Convergence_Analytics_Report

 

18
Jul

Can We Predict Predictive?

Written by Andrew Edwards. Posted in Predictive Analytics

Predictive analytics is gaining momentum and is rapidly becoming a part of every marketer’s lexicon. The allure is obvious: the term itself portends the ability to foretell the future; in our case, the ability to know what the response will be to your content based on comparative analysis of data. But “predictive” today is often not connected directly to action, except for a certain few vendors in the market.

Let’s look into our crystal ball and see what materializes about predictive analytics.

The features of predictive analytics assume the following capabilities (or “stack”):

  • Robust data collection from multiple sources
  • The ability to connect and combine these data sources
  • The ability to display them in a meaningful way

So that:

A marketer can make more accurate “predictions” about successful content
Test that content to see if it matched the prediction

Or:

An action layer in the predictive tool itself can automatically serve stored content to a particular user based on criteria living in a database and determined by the output of a predictive algorithm

As you can see, neither of these actually guarantees anything. So perhaps we should call it “pundit analytics” – but that might be giving pundits too much credit.

That said, the difference between the above two models is substantial.

In the first example: There is combination of data and then it remains up to an analyst, working with a marketer (these days the hybrid skill is rather famously called “data science”) to determine what the content should be, given the comparison metrics. For instance, if your analytics tool can combine data from geographic plus behavioral plus calendar information, you might be able to understand that your customer most likely comes from South Bend on a certain Saturday in September; and you would target her with offers that coincide with her behavioral and geographical patterns. And assuming your effort created an uplift in conversions, you’d then be able to take credit for predicting the outcome.

In the second example: The vendor offering itself contains a “decision engine” or “predictive layer” that automatically takes the same data your marketer would have reviewed and then automatically serves up that content to the South Bend user on a certain Saturday in September. And these companies will live and die by the measure of the uplift they achieve over a non-predictive alternative. That’s because it still isn’t inexpensive to engage a fully automated predictive engine – so it had better work!

Predictive analytics is one of the key features of a capability stack (and industry transformation) I’ve called convergence analytics: where customers are demanding the ability to track and take action upon multiple data streams; and where vendors are rapidly taking up the challenge to track multiple silos of data and perhaps even take action on them.

Convergence analytics assumes the following capabilities:

  • Accurate data collection from multiple sources
  • Combining the data intelligently and flexibly
  • Enabling better connections with customers and prospects

The tea leaves in the bottom of my cup suggest that predictive analytics, a subset of overall convergence analytics, is due for a growth spurt as customers look for more consistent targeting in their hunt for ROI. What predictive analytics really does is act as a better targeting device. It weight-balances the bow so that the arrow flies more true. Sometimes the marketer will be holding the bow. In more advanced systems, the arrow is aimed and sent flying automatically.

The same technologies that power convergence analytics – cloud computing, big data, connectors, algorithms, display layers, and sometimes decision engines – also power predictive tools.

It’s not hard to imagine a growing number of vendors working to differentiate themselves by making claims to predictive capabilities. The challenge for vendors will be to make sure they’re not trying to claim a dashboard is a Ouija board; and if they do manage to get some magic into their algorithms, to allow for easy testing of the results.

Either way, it’s customers driving the pack. Nearly every marketer today is feeling the pressure to see more data at once and do more with it. Predictive is part of their future.

Convergence_Analytics_Report

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