31
Jul

Campaigns Are Not Conversions: 7 Steps to Unclog the Funnel

Written by Andrew Edwards. Posted in Digital Analytics

The Wall Street Journal reported last year that General Motors pulled $10 million worth of its Facebook ads. And Morningstar said it will be very difficult for Facebook to build an advertising model to justify its lofty offering valuation.

Apparently GM marketers had become unconvinced that their Facebook spend was helping sell cars. Could it be they were deploying the now venerable practice of campaign attribution? In which case the proponents of actionable analytics would claim a victory (or if you are a Facebook shareholder, make that a “victim”).

Hypnotic Effect

Advertising venues – content creators, aggregators, information sleight-of-hand specialists like Google and Facebook – have long held an almost mesmeric power over the advertiser. Looking for sales, the advertiser casts about for audience, or “reach.” Taking Facebook as an example: with near a billion users, that is one heckuva good reach, Zucky.

But audience is not sales delivery. Campaign is not conversion. And the panoply of content creators and ad networks and ad agencies, without conspiring to do so, present an almost united message that prompts us to sharply recall John Wanamaker’s antique dilemma: not knowing which ads worked and which did not.

Too Much at the Top

The big advertising message is that it really is about stuffing the funnel and little else. In fact, they love to crow about how unmanageably complex the ad network business is (you’ve seen the million-box org chart no doubt), and how only mysterious algorithms can deliver your audience to you, and how that alone is so complex and so impenetrable that the notion of trying to simplify it by studying campaign success – basic analytics – is simply not much to discuss. It would be so hard! Unless, of course, you were plugged in to how analytics really works.

In today’s digital marketplace, you can now target ads more carefully than when you called the visor-wearing research guy on the 13th floor. You’ve got exact behavior patterns to study, and customer preferences and surveys and “likes” and referrals and Klout and tweets and mentions. And doesn’t it feel grand when it turns out plenty of folks forwarded your content or shared your YouTube video with a friend? Except not so much in the wallet.

Because campaigns are not conversions. Social media connections are not paying customers. And as GM may have lately discovered, hanging out at the Facebook cocktail party does not make you the bartender. You are spending to be there, and you might meet a new prospect. But how much are you willing to pay for a networking event where they don’t even give you a ticket for a gin and tonic? Meanwhile, as the clock ticks toward midnight, the bar gets more and more crowded. It’s exciting to be part of this! Trouble is, you wake up the next morning in a straw bed full of pumpkins. And not a glass slipper in sight.

UnStuffit

The cure is a fair amount of hard work. The cure is to measure campaign success via independent means. Independent means, means: not the advertising venue, not the advertising creatives, not the ad network.

Instead:

Look to your own humble web analytics implementation and ask for some real answers.

Set up campaign identifiers inside your tool of choice.

Don’t look at who came to the landing page (unless you are using the likes of HubSpot for targeting purposes). Set up a sales scenario. Call it a funnel, a set of desired actions, engagement metrics, or a frozen-banana stand. But make sure it has a beginning (traffic from the campaign); a middle (behavior that indicates readiness to purchase); and an end (that “cha-ching” sound you hear is the conversion – a sale, a lead, a download, for instance).

Track the campaign traffic all the way through to the end. Were there lots of customers reaching the “thank you” page?

Pull the numbers out of your analytics tool and build a spreadsheet for campaign success. For example: campaign name, completion stage limit, number of completed stages, and, if appropriate, revenue. An equally important exercise is to look at revenue against unique visitor amount by campaign. This will tell you which campaign drove the biggest spenders. Tie this all back to how much you spent on the campaign. How much more did you make than you spent?

Get your marketers and creatives and agencies into a room and show them the spreadsheet. Ask them why you should keep spending on the stuff that came up goose-eggs. Reward those who rang the register.

I can’t say for certain about what GM did, and I know some will say I oversimplified it (even if for clarity). But if GM, which had a near-death experience not long ago, finds itself remarkably clear-headed now about where its new dollars are going, then that can be no surprise. All it is saying to the likes of Facebook is: “Instead of believing you, we are going to believe our own two eyes.”

And yes, it can be that simple – with the right conversion data.

 

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

 

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