Author Archive

17
Jul

Real Time Means Right Time

Written by Andrew Edwards. Posted in Digital Analytics

This past spring, I helped conduct a study on the subject of convergence analytics that included a survey. The survey got responses from hundreds of people, and one of the questions we asked was about the popular phrase “real time” and what it means to different people.

The response was surprising: “real time” apparently can mean anything from under one second to a week!

This means that “real time” is probably a stand-in for “right time.” Because what we are actually talking about is utility of information. If the campaign data got to you six weeks after the campaign was over, that’s not real time or right time; that’s nowhere, because you can’t take any action on it.

Bound up in the concept of real time is the utility of the information as it relates to action. Data is actionable only if it comes at the right time. So, depending on what kind of action you need to take, “real time” will mean “soon enough so I could do something with it.”

For media companies, or companies needing to adjust campaigns on the fly, real time means very, very quickly. In fact, some content management systems want to suck in data up to the minute and adjust content during the visit or even during the appearance of a banner. In these cases, real time can mean instantaneous.

For companies that don’t need to react quite so quickly (or that can’t), then real time probably means within a couple of days or within a week. This will allow them to use their analyst skills and their content creation tools to remake their content before and during a certain “freshness” period where the updated content will have relevance for the audience. But it doesn’t necessarily tie to an offer that’s adjustable on the fly.

For companies that are managing large, complex campaigns, especially those that involve apps and interactives, then real time means something a little different. These companies have real development cycles: they’re launching software. Many of the most complex interactive experiences now being measured could not make use of data on the same day or even the same week.

They will collect data over a period of weeks and then review how well the experience performed so that in the next content revision (which may take weeks or months) they can adjust the content based on statistics like which were the most popular parts of the experience, the effectiveness of Facebook referrals, and overall time spent in the app or experience. In these cases, an immediate offer (except perhaps a coupon) is rare and the goal is more about spending time liking the brand than selling directly.

Especially in the case of apps, the fact that they have to recompile the app and submit it to (for instance) Apple means that real-time data is unnecessary for them – a needless luxury. They may not know for weeks whether the changes in their app have resulted in anything meaningful, and as with most software developers, they will have to be OK with that.

“Real time” is a selling feature in many digital analytics offerings today. Some even offer almost cinematic views of behavior on maps in truly real time – within seconds! You can see where and in what intensity behaviors are occurring; especially with social media. This can be a good background in helping make geographical segmentations. But is it something you really need?

It’s long been suggested that vendors put features into their products because they can. Real-time capabilities are one of these. But you need to decide what’s right for your organization – and what “real time” means for you – before buying into the “real time” paradigm.

 

Convergence_Analytics_Report

 

16
Jul

Hello, I Am Analytics. And That’s Not

Written by Andrew Edwards. Posted in Digital Analytics

The other day it was rainier and colder than it should be for the time of year and I pointed my remote at Netflix and found a media relic from the Pre-Cambrian age of television. It happened to be season one of “Saturday Night Live.” It was like stepping back in time – to a place where John Belushi was alive and Chevy Chase was really, really cool.

For those who’ve never seen these video relics, here is the reference: Mr. Chase was so very cool and confident then that he said to the television audience: “Good evening. I’m Chevy Chase. And you’re not.” He’s been eating dinner on that line for about 40 years now.

Closer to our present, it’s important to note that, in an environment characterized by competing visions of digital marketing, some of the lines have become blurred. And because of this, clear expectations about analytics have become somewhat elusive for some marketers.

It’s time for some clarity about what we really mean when we talk about analytics.

So: I Am Analytics. And That’s Not.

I am analytics. I am data collection and reports. I am trend lines. I am historical. I am based either on standard reports or configured reports. I need to be planned in advance and properly tagged and collected. My data is displayed in dashboards. If you are planning on using me to make decisions about the value of your content, you need to perform the following:

  • Understand how to work your way through my interface.
  • Accept “reporting” as my output.
  • Be prepared to interpret the data you see.
  • Remember I am a tracking tool only.
  • Provide data and leave the process of change to real people.

I am analytics. And here is what I am not:

  • I am not an automatic insight engine – I need people to do that, using data I provide.
  • I cannot tell what your business is attempting to accomplish. You need to know this on your own.
  • I cannot change your content. Only you can change your content (after you decide which content worked better based on my reporting).
  • I am not about “asking questions on the fly.” That’s not analytics. That’s “business intelligence.”
  • I do not provide answers to business questions all by myself. I can only supply data on user activity. A real person in the physical world may need to interpret my data and create natural language analysis (and presentations) such that my data makes sense for the business.
  • I am not a process. Process is an important part of the marketer’s optimization exercise, but I do not embody a process.
  • I am not related to artificial intelligence. AI may one day mature and become marketable as a business service – pulling together massive amounts of data, interpreting, making decisions, creating changes in content without much human involvement. But it’s not here yet. And it isn’t “analytics” as we know it today.

Allow me to interrupt.

A pattern has emerged.

Analytics is reporting. Reporting gets the business about halfway toward optimization and is indispensable. It’s currently based on a fairly wide range of tools in the marketplace. But expert humans are needed to make use of the data provided by analytics.

We keep coming back to people: us. We keep coming back to ourselves; and what we need to do for our businesses. Analytics tools are powerful. But putting hype aside, they are really just data collection and display engines. People have to bridge the gap between what analytics is; and what it is not.

Convergence_Analytics_Report

 

15
Jul

The 4 Ps of Digital Analytics: Why They’re in Sequence

Written by Andrew Edwards. Posted in Digital Analytics

Sometimes it’s helpful to have simple reminders about what’s important in a complex process.

The “4 Ps” of digital analytics represent one such reminder.

It helps you understand both the key elements in a successful digital analytics effort as well as the relative importance (and sequence) of each item.

There’s a reason it’s a list and not an array. One comes first. One comes last.

  1. People
  2. Purpose
  3. Process
  4. Platform

That’s the sequence and the relative importance of these critical elements. Notice that “people” comes first, and “platform” comes last.

Many companies fail by focusing on “platform” while ignoring either some or all of the first three elements of success. They fall victim to sales pitches from platform vendors who want to tell them it’s their tool only that will solve the problem, no matter the environment.

Make no mistake: you absolutely need a platform. But you decide what that is after you’ve got the right people, have a purpose to your measurement, and a process wherein you’ll generate marketing success. Then, and only then, should you add the platform.

The best platforms will be able to adapt to your people, purpose, and process; the lesser ones will have restrictions and will need to attempt to impose process, purpose, and sometimes even people; these should be seen as weaknesses in the offering, rather than features.

The best tools are adaptable and powerful, and can be configured by people who have a purpose and process.

People: Who’s going to understand how to do all this? People are. Expertise is the first essential ingredient. Minus this, the rest is for naught.

Purpose: What do you want? You have to know what you want to know and what you would do about it if you knew it, before you decide what to measure and with what platform. Some folks call it KPIs, but that’s really just the half of it. The other half is the “doing something.” It’s equally important.

Process: How are you going to do this over and over? Think about something like this: define, measure, analyze, change, and measure again. It’s a virtuous cycle of improvement. Implemented properly it should look like an upward-charging spiral of better and better marketing.

Platform: Once you know the above, go out into the marketplace and find out which tool matches your requirements best. These days, especially in multi-channel or convergence analytics, there are probably a hundred vendors to look at. They range from all-encompassing suites that want you to marry them and deploy everywhere, all the time; to point-solutions that elegantly solve a single part of the problem (and then you can cobble it with other elegant point solutions).

The most important part is understanding the sequence. First, you need people who know what they are doing. Then, those folks should determine what you want to know, and what you will do about what you find out. After that, your smart people need to settle on a process, the broad outlines of which are easy to discern. Finally, they need to locate a tool or set of tools to accomplish their goals.

And your expert people then need to take responsibility for the entire effort looking much more like an upward-vaulting spiral than a lazy doodle.

Convergence_Analytics_Report

 

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