The 4 Horsemen of the Digital Analytics Apocalypse

Written by Andrew Edwards. Posted in Digital Analytics

four-horsement-analytics-185x114-1While digital analytics can uncover unparalleled insights for marketers, too often something goes wrong and sometimes terribly wrong. Throughout history, the abovementioned “four horsemen of the apocalypse” have been identified as Conquest, War, Famine, and Death. Here we will look at how each, in a modern incarnation, can destroy digital analytics effectiveness, and how to battle them.

Horseman 1: Conquest

If you’ve been asked to abandon your healthy skepticism and buy into a single-vendor solution that “solves every problem” in analytics; if this also includes all “services” to be performed by the vendor; and worse, if you have allowed yourself to be convinced by your creative agency that yes, they also do analytics (in other words, they agree to umpire their own balls and strikes); then you have been a victim of conquest. You no longer control your own analytics destiny but have put it entirely in the hands of providers with an agenda that does not necessarily include your getting the most insight out of your analytics.

How to battle it:

Choose only best-of-breed solutions for each purpose. Worry less about interoperability. The dirty secret is that most branded solutions are made up of portals that don’t work together all that well anyway — they just have the same logo. Choose your own consulting company, make sure they know the tool, and let them deliver what they can. In many cases this will be superior to what the vendor can deliver. Finally, don’t let your creative team measure their own success. This is why baseball has umpires and why football has referees.

Horseman 2: War

Internal battles between IT and marketing can be ruinous. Endless turf-wars, truth-blockage, “not-invented-here” attitudes, and lack of clear chains of command make for heavy losses on all sides. And with the stakes higher and the “weapons” more powerful, the battle becomes bloodier. Analytics today is visible, but it’s tough to find someone who can really own it. In too many cases it’s easier to blame someone else — even if the only problem is that the trendline is headed south this month.

How to battle it:

Be an informed peacemaker. Understand the tools and technologies that powerfully affect your business and work to make sure how each division plays its role. Unlike today at most enterprises, the person “in charge of analytics” in fact needs to be in charge of making sure measurement is done correctly — and not at the whim of a developer reluctant to partake. Analytics is not an option. Clear leadership wins the battle.

Horseman 3: Famine

In a digitally driven media environment, lack of accurate information about usership is the equivalent of famine. No matter if you have spent enough to send rockets into space. If your tools are not instrumented properly (a far more common problem than most would want to admit), then your data is going to be inaccurate. Some might say this is nitpicky (yes, I have heard that) as long as the trends are well understood. But we are not talking about a few percentage points — more like the actual raw numbers of visitors, page views, and more. Data famine leads to blindness and eventually (see below).

Horseman 4: Death

You cannot fight death. If your analytics efforts are lying off in a ditch showing little signs of life, then your gong has been rung. But fortunately, in this world, you can reincarnate. Pull the wagon out of the mud. Choose new vendors. Strip out the old, junky code and have it replaced with something that will pass a basic QA test. There is nothing as bracing as a new start.

Remember that throughout history, in the real world, people have cheated every one of these horsemen many times over. In analytics it is no different. Next time we will talk about the legendary spider that failed seven times to make a web but succeeded on the eighth.




Can Digital Analytics Save Itself?

Written by Andrew Edwards. Posted in Digital Analytics

digital_analyticsDigital analytics today is burdened by disillusionment and disappointment. Not that there are no success stories with digital analytics. There certainly are. But they are comparatively rare. Much more common are legions of valiant but frustrated marketers continuing to struggle with the basics:

Is data collection accurate? Once we learn what the data tells us about our business, are we in a position to do something about it? What happens when our agency tells us they’ve taken care of measurement and, behold, the campaigns are “all good” (or at least not a total waste)? What does change really look like, and can we make it happen in time to matter? How do we do that without automation? And where are the successful predictive models that drive automated responses?

The unanswered questions don’t stop there, but for the sake of brevity we shall.

No one suggests that organizations go without analytics. And many businesses do get to a place where they are comfortable measuring with accuracy and understanding. Many fewer end up being able to fix any but the most egregious “disconnects” between themselves and their customers. The vast majority settle for knowing what happened, with a moderately strong determination to do something about it “in the next release.”

How Digital Can Deliver for Marketers
Many of the most dire threats to success in digital marketing can be overcome by adhering to a process. The process is not very mysterious, and, in fact, can, with some alteration, be applied to almost any endeavor requiring rigor and results.

The process looks something like this:

1. Determine key performance indicators
2. Implement data collection and reporting
3. Review and analyze reports
4. Make content changes
5. Measure again to prove success

Following these will go a long way to avoiding disappointment and marketing paralysis, but often it proves devilish hard to get through the process.

Saved by Automation?
The toughest parts of the above process are numbers three and four.

It’s easy enough to figure out your basic metrics and get the data collected properly as long as you have a team of analytics experts. We’re pretty much overrun these days with analysts, but often it’s tough to turn what they say into recommendations. Then, the most difficult part is getting changes made. Figuring out what changes to make, and how to get them made, typically slows the process nearly to a halt.

Automation will be key in changing this from a roadblock to a starting block.

With Tealium’s AudienceStream, you can build in rules and thresholds that send out directives to content delivery systems that let you know it’s time to contact the customer with an offer (for instance). The key to its success is its timeliness and the certainty of its execution. It becomes automatic.

Conductrics deploys Artificial Intelligence to create a system of learning and action based on data. For marketers, this means that Conductrics will facilitate the creation of an “agent” that seeks out challenges and then tackles them (for example, it looks for meaningful patterns and then can direct content to be distributed as needed). Conductrics has likened their agent to a Roomba for digital analytics. It learns its environment and then focuses on doing one task very, very well automatically.

XplusOne [x + 1] markets a product called Origin. According to the firm, “Origin harnesses data to drive real-time, one-to-one interactions across all your digital channels, so every prospect and customer interaction is more relevant.” They also deploy a Data Management Platform that controls numerous customer touchpoints automatically.

These products help conquer the challenges of what many today call omni-channel marketing. They help address how customers can be reached in various “states,” as Rand Schulman has pointed out.

Automation is moving ahead rapidly. It may save analytics by embedding it into an automated process — which probably is where it belongs.


3 Reasons Why Analytics Disappoints and How Not to Be Disappointed

Written by Andrew Edwards. Posted in Digital Analytics

analytics-disappointingMany analytics experts will probably agree they very often come across organizations where analytics is not only “unadvanced” but disappointing. Where analytics isn’t being adopted by executives who need to make decisions based on performance data. Where, for one reason or another, analytics isn’t delivering much value at all.

Rare is the case where analytics is entirely tossed out (that would be an admission of failure!). Much more common is the “back burner” syndrome. Analytics chugs along quietly, ineffectively, inaccurately, unnoticed.

Here are three major reasons why analytics may seem a disappointment (and remedies for same):

No. 1: Business Misalignment

Ask the question “why do you have a website?” and sometimes the response is a deafening silence. No one wants to say “because everybody has one” because they know that can’t be a good enough reason and they don’t want to say “to sell stuff” because they know that’s too broad, and after that you’re liable to find five different answers from five different constituents.

Dumb as the question sounds, it’s the one that, when answered incompletely or carelessly, generates the most frustration with analytics.

If you don’t know why you have a site, it means you don’t know what matters to you about user behavior. If you don’t know what matters to you about user behavior, you don’t know what kind of behaviors to measure. If you don’t know what to measure, you just measure everything or nothing, and the result is much the same either way: meaninglessness.

How to Get Better Aligned

Convene a meeting of your business leaders and your web content stakeholders. Facilitate a discussion of what business outcome each part of the site is attempting to create. Broadly, there will be only a few possibilities and within these there may be drilldowns. But the broad business objectives for sites are generally as follows: e-commerce (sell stuff online); content/branding (spend time on the site to view messaging from your organization or your advertisers); lead generation (lead nurturing, list-building, informative downloads, sales-call triggers); and self-service (think of intranets, insurance portals, problem resolution databases). Nearly every site will fit either entirely inside one of these large buckets or may have a presence in a couple of them at once. But whatever the site seeks in terms of business outcomes will then determine the specific type of reporting you’ll want out of analytics.

By customizing your analytics tool to answer the business questions generated by the abovementioned goals, you will avoid misalignment – and one of the most common and destructive failures in analytics.

No. 2: Poor Implementation

Many frustrated analysts have fallen victim to a sub-par or simply incomplete implementation. Much of the work in getting analytics to answer relevant business questions turns out to be non-obvious and not easy for the occasional user to implement correctly. This persistent complexity, when combined with architecture issues, multiple developers, and simple lack of tool-familiarity, results in what some call a “broken” implementation.

In a broken implementation, analysts will note that data seems incorrect or impossible to reconcile; calls to the tool vendor refer one back to whomever did the implementation (for example, the tagging and report building); developers often lack the understanding to even know what they did wrong; and the analyst is left without any way to do a good job for the company.

Just to give you an example of how typical this can be, imagine a fairly sizable organization deploying a Flash or Ajax module inside their digital offering. With two or three teams of developers involved in creating pages, putting tags on pages, and making pages go live, the communication is already prone to misunderstanding and error. Compounding this is a lack of understanding of how “tagging” actually works. The result is repeated failure to get the module to send tracking information to the analytics engine, even long after launch.

Result: the analyst, and the organization, is left blind as to the success of its campaign – in this case a rather expensive one.

How to Improve Implementation

Remember that page-tagging, tool implementation, and custom code development require specific expertise. Sometimes it’s difficult to see that, with all the web talent in the organization, this remains a significant gap. And yet in many organizations it is enormous. Some companies look to contractors and this can be of great help assuming you get a contractor that is knowledgeable and reliable. Others hire specialized agencies to take on this task and enjoy success, though at a cost in dollars. Training for existing in-house talent is often a stopgap; but mostly, training is geared to helping folks use analytics once it already has been properly set up.

Finally, some organizations, attempting to think ahead more than others, will hire an individual employee or an internal team that will focus solely on analytics deployment. This can also be a great solution, though it has its own significant costs. And there’s always the risk that the knowledge requirements shift away from the specific skill set of the hired specialist(s).

Whichever way you choose to fix a poor implementation, make sure it includes dedicated expertise – deploying deep expertise (both in business and technology) will result in a much more robust and effective analytics platform. Try not to rely on users with only a shallow understanding of how analytics tools, tags, and interactive architecture must work together in order to deliver meaningful insight.

No. 3: Company Politics

OK, lots of things fail because of company politics – not just digital analytics.

But there are particular ways that politics gets in the way of digital analytics, chiefly related to misunderstandings or misinterpretation of proper roles and responsibilities as relates to technology and content.

Put simply, the “measuring” should never be managed by the “measured.” This is because no one wants to be forced to be objective about the success or failure of their own efforts. And when put in that position, they may sometimes behave in what might be called an “obstructionist” manner, even if they are otherwise very helpful and above-boards. Of course this is not universal. But it is a noticeable tendency.

In practice, this means that the agency or marketing team responsible for putting up content (especially if they are third party) should be told that the measurement of the content is going to be handled by someone else. They will also need to be told they need to cooperate as a condition of engagement.

Too often, analytics goes down a rabbit hole and never reappears once a third-party creative shop gets involved in performing analytics. And often enough it’s because of a lack of throughput on the third-party agency side: either because they don’t know how to tag and implement in an expert-enough manner, or they put it on their “later” pile because they have no upside in doing otherwise.

How to Get Past Company Politics

The best way to handle this problem is to think of analytics as a discrete project that needs to be assigned to a particular group that specializes in that and has a clear upside in making sure it’s done properly. Almost universally, this will result in a far better state of analytics than leaving it in the hands of folks who might not have a vested interest in the success of analytics.

Aligning expertise with a properly identified business need – in this case, analytics expertise with a need for accuracy and objectivity – will drive your analytics effort away from the whirlpool of competing interests.

Adoption Is Key to Web Optimization

You’ll probably find that more targeted, more accurate, more objective, more constantly reliable analytics data results in higher adoption rates. This means that people who need to look at the data will look at the data. And then they can make decisions based on what they see. But if the landscape is littered with meaningless reports, inaccuracy, and tardiness, expect low adoption and low impact. And in the end, low impact for analytics can leave your organization at a distinct disadvantage – because the competition may have figured out last week how they might stop being disappointed in their analytics.


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