Author Archive

08
Nov

Are Multi-Channel Analytics Vendors Failing the Market?

Written by Andrew Edwards. Posted in Analytics, multi-channel attribution

The short answer is “yes,” at least for now.

The most important factor in a successful multi-channel analytics effort is the proper deployment of complex data collection and analysis in an even more complex digital marketing environment. Nearly every organization is currently grappling with the need to understand the success of multiple properties across multiple channels, while vendors are racing to supply them with the means for doing so.

It sounds like a great combination–a dovetail of supply and demand.

However, it isn’t working today and the challenges to success seem steeper than ever.

Many Vendors, Few Solutions

There is no shortage of multi-channel analytics vendors. In fact, as mentioned in the Convergence Analytics 2.0 report, there are probably too many vendors working in this space. Further, too many are making the same claims without sufficient differentiation.

Meanwhile, some of the top vendors (like Adobe) are now creating complete content optimization suites that provide the ability to create, measure and optimize digital properties within the same cloud-based offering.

All this should mean that marketers have lots of choices; oversupply should be a boon.

Except it isn’t working out that way.

andrewedwardsmultichannel

You could say the challenge is related to confusion, but most organizations are not confused about what they need, nor are competing vendor claims getting in the way of deployment.

The main reason for the crisis in analytics today is that the technology is racing ahead of what customers really need and what they can profitably deploy. The gap between vendor capability and customer expertise is widening and may be approaching a stage of critical failure.

We know that many customers continue to struggle with more basic analytics challenges than those suggested by multi-channel, big-data efforts. An enormous and neglected challenge is that of talent deployment (or lack thereof). There’s a premium on technicians, analysts and expertise in multi-channel analytics, in general. Outside of the small vendor communities where experts naturally congregate, there are very few practitioner-experts dealing with digital data and its consequences.

Perhaps the primary barrier to adoption and success is lack of expertise.

Marketers are Not Technologists

Analytics solutions have existed for nearly twenty years, yet today (in some but not all organizations), there remains an only rudimentary understanding of how these tools function and what they deliver.

This suggests a fundamental disconnect between vendors and practitioners.

Vendors are by nature technologists; marketers are not. In a unique marketplace, where technology interacts directly with a constituency that is not technological by nature, the disconnect becomes acute.

Vendors build more and more complexity and “actionability” into their tools because the technology allows them to do so. Too often, though, they neglect to make the tools suitable for deployment by the practitioners to whom they are selling. They are building muscle cars when customers would rather have a fuel-efficient pickup truck.

Marketers look for solutions, but they’re not getting them. Instead, they are offered complex, even arcane toolkits they are asked to navigate and activate as if they were also technologists. Too often, the effort fails. There are too many moving parts, interface and data collection problems; there’s not enough expertise in operation of digital marketing tools or the interpretation of data.

It’s entirely possible that technological capability has begun to outstrip the common ability to comprehend and socialize its ramification.

It’s easy enough to suggest every organization needs to embrace multi-channel analytics and perfect their conversation with their customers based on observable behaviors. But even veteran analytics teams too often “max out” with web analytics; it’s a stretch for them to accommodate even a modest extension like mobile analytics. Add social, CRM, sales and supply-chain, and the most likely scenario is disorganization and dysfunction.

Ground Control to Major Tom

Vendors are floating in low-gravity cyberspace. They can see how technology can help marketers optimize their offerings and to them, it seems it ought to be rather effortless. But the gap between their ability to create technology that can do these things and their ability to create applications marketers can easily use is large and growing.

Meanwhile, marketers are feeling burdened and earthbound. They are struggling with data collection, knowing full well that without good data collection, there is no benefit to analytics. They are struggling with organization, understanding that analytics governance is a dream yet to be achieved. And they’re looking at the sophisticated marketing suites available to them and scratching their heads.

The trend is towards larger and larger disconnects. Marketers are calling out to vendors to come back to earth, to help them solve the basic, rather boring problems of getting the small things right before they try to conquer new worlds. Plain, vanilla web analytics can be hard enough to implement for many marketers. How can vendors expect practitioners to graduate to new analytics challenges before they have seen enough value in tools they’ve been using for years?

Is there a latitude where vendors can meet practitioners and come up with real solutions? Vendors need to come back to earth. Marketers need to make more room for digital expertise in their budgets.

If vendors stay in the stratosphere and marketers remain bogged down, the result is radio silence. We should fully expect multi-channel analytics to fail until the gap between application capability and expertise is narrowed.

 

30
Oct

Data Gaps & Governance: What Was True in the Morning Becomes a Lie Before Noon

Written by Andrew Edwards. Posted in Analytics, Data Integration

You sit at your desk, aware you are supposed to make decisions based on data and knowing that a great deal rides on your choices. Which content goes front and center? Which campaign gets yanked? How can you direct your agency in creating content more effective at converting prospects to customers?

The company has invested millions and management wants to know how the new bag of tricks is doing against the old.

You log in to the digital analytics dashboard and find what you think you need. It’s supposed to show campaign attribution–but it’s only based on the last click. It’s also supposed to show content popularity, but lacks conversion perspective. The paths into and out of the contact page are baffling, to say the least. You call up your analytics admin, who calls the report people, who call the agency that was supposed to do the tagging, to find out if this data is the latest.

In the meantime, the decision is due on your boss’ desk by noon… and it’s 11:53. She asks your opinion, so you assume she wanted your judgment, not the digital beeps and bytes of some machine. Looking everything over, you make some lightning decisions and, feeling smart and well-informed, send an email to your boss at 11:59, to which she simply replies, “Thanks.” Time to grab an in-house latte because that cafe around the corner, while it may be better, is charging seven dollars for a cup of hardcore joe and some cream.

At 12:20, you get a call from your analytics admin, who tells you the agency (who don’t specialize in analytics, but are performing the work as part of their premium support package) never got around to tagging that part of the site, because they were “coding a mobile app that took longer than expected”.

“And what does that mean?” You ask, careful that your tone conveys gentle concern.

What it means is that none of the numbers are relevant or even remotely accurate, because none of the new pages have been measured since they launched a month ago.

You drop the phone but rapidly pick it up and place it back on the receiver. Your boss won’t be back until three. Can the site get tagged and measured before then? That, you know, is a vain hope and shame on you for even thinking about it. Worse, even if it could be tagged in a jiff, you cannot “replay” any data because, without tags, nothing was collected. You’ve got goose-eggs. And goosebumps. Perhaps if you check your Twitter feed, it will all go away.

A text from your boss interrupts your foray into the Twitosphere:

“Data inaccurate? Let’s talk at 12:30.”

Your boss is coming back early, and you’ve had this talk before.

Last time, you had recommended a better process; one where agencies did not own the responsibility of tagging their own creative. Where a centralized data collection team, expert in the art and science of collecting and displaying data, in conjunction with perhaps a tag management solution, would provide both expertise and governance.

Like many recommendations requiring a complex initiative, it was praised but ultimately not implemented.

Maybe this time the talk will be different. It could go one of two ways.

First, you might be blamed for providing inaccurate data and therefore useless insights.

Second, you might be asked to resubmit that governance proposal.

Governance has become easier with the right tools; with properly configured roll-up reporting and an up-to-date tag management tool (plus the essential expertise), this can be solved.

Chaos, as a result of lack of process and lack of expertise, defeats technology and the insights that come from it. The expertise is not impossible to find and the tools are maturing.

It should be easier to make the case this time.

You put down your latte and head for your boss’s office.

A Completely Fictional Story, Right?

Not really. This plays out every day like clockwork, across the country, in companies large and small.

Governance is critical in managing the masses of data available in online business, yet 44 percent of respondents to a recent survey by Rand Secure Archive admit their organization still lacks a data governance policy. Within the next year, 50 percent of respondents plan to take action on data governance.

Whether or not you’re a driver for this necessary change in your own organization depends largely on how you sell the necessity to the boss down the hall.

Have you instigated change in how your organization handles data? Share your tips and experience in the comments.

28
Oct

Convergence Analytics Now: 3 Ways This New Market Changes Everything

Written by Andrew Edwards. Posted in Convergence Analytics

Convergence analytics has been called “multi-channel analytics,” “big data visualization,” “business intelligence for marketers,” “real-time analytics,” “enterprise analytics,” and very recently even “omni-channel analytics.” It’s a safe bet that a hundred different vendors are fielding a tool (or suite of tools) that they will claim, in some way, shape, or form, can “measure data from any source” and “present it to the [target audience] in a visual, interactive interface.”

The most remarkable aspect of the market right now is its relative incoherence as compared to more mature markets like “digital analytics” or “direct marketing.” As the market begins to crystallize, both vendors and practitioners will need a core set of definitions around which to formulate requirements and offerings; hence the need for an overarching concept like convergence analytics.

The convergence analytics market encompasses all of these buzz factors and more. It represents the future of all digital analytics from this day forward. As has been said about other rapidly growing industries, “it’s still early days,” but we’re seeing an explosive growth in offerings, and if you’re not already dealing with CA today, expect that you’ll need to do that in the not-very-distant future.

Convergence analytics, from a technology-centric viewpoint, represents an emerging market that concentrates on the confluence of digital analytics, big data, robust algorithms, and advanced visual presentation. From a marketing standpoint, it’s about the “un-siloing” of data from a variety of places within the organization. Start with the combination of desktop, mobile, and social; then go from there.

How It Changes Everything

1. Web, social, and mobile: not cutting it anymore. When we ran our first CA survey, about half of the respondents said they defined “multi-channel” to include the above three disciplines. However, the other half said it included those plus more channels. The number of organizations needing to combine data from more than just these three sources is growing rapidly. “Web analytics” is already retired as a term in and of itself. “Digital analytics” seems fairly coterminous with the above three areas of interest. But just those three don’t cut it anymore. Organizations now want to look at those plus demographics, campaign data, ad-buy data, e-commerce data, in-store data, call-center data, CRM data, unformatted data from a variety of sources, and so-called “big data,” which is a buzzy way of saying “everything that can be tracked.”

2. Connectors, connectors, connectors. As in real estate, in which “location” completes the three most important aspects of a property, connectors may prove to be the most important asset a convergence analytics vendor possesses. Much of the technology behind connectors (and the rest of CA) has been around for some time, yet now it is put together in new ways (the Wright brothers did this with bicycles, boats, and kites; we ended up calling it an airplane). Now existing technologies are coming together to form convergence analytics.

But the most important unit of CA vendor technology – the one they build their businesses around – are the connectors they build to a variety of data sources. If they don’t work properly, nothing else in the analytics process works either.

In order to master convergence analytics, many practitioners will have to understand some previously data-scientist-only terminology like “ETL” (extract, transform, load) – which is what connectors really do. If you thought marketers had already needed to go a long way toward technology and data, it’s only just begun.

Getting to know the details behind how data integration actually deploys will change the workday of every marketer from now on. And if you’re not doing so already, get ready in the near future to ask your current or prospective analytics vendor about how many channels they connect to – and what they will do if you have a new data source for which they don’t already have a connector.

3. Convergence analytics ownership in flux. Who will own convergence analytics? It’s destined to become the clearing house for all marketing data (and much that isn’t related to marketing); but it represents a much higher technology bar than any breed of tracking tools that have gone before. Will marketers really run convergence analytics the way they have run digital analytics? Will they want to? Will the CMO be responsible for a vendor solution that crosses not just marketing disciplines but also IT, sales, operations, and perhaps even finance? Will she want to? Will newly minted data scientists rule the convergence analytics roost?

All of this is unsettled and up in the air. But it’s going to land soon – probably pretty near your desk. And when CA becomes the norm (as it soon shall), get ready for some major organizational changes. Whomsoever can claim to understand this new type of offering best and most comprehensively will likely carve out a new and very desirable niche for themselves within the organization.

The insights available from the control of an unprecedented amount of previously siloed data is going to be – unprecedented! And the winning organization will be the one that gets out ahead of these major transformations with the right people in the right place at the right time.

 

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