spots This post could also have been titled "Tracking Radio Ads" or "Measuring Online Impact of Offline Marketing" or "Success in a Nonline World". It will touch on all of those.

But the title is what it is because the most lovely part of this story was how tracking with a one dimensional mindset (or in a silo) means that you will end up missing so much of the picture. And it is a story about what it means to be a Analyst in this day and age.

[This post is dedicated to my friend Nick: You are a sweetie! Thanks for everything.]

A delightful company, let's call them Market Fire Extinguishers, located at www . marketfireextinguishers . com (there is no such site as of today), would like to grow their business. They have tried lots of nice things online (primarly affiliate marketing). It worked ok.

Then they came upon a radical, for the web, idea: Run radio ads around the country!

This is getting easier to do than ever with many leading companies getting into the space and lowering the barriers to entry. Even I can run a 50 city radio ad for Web Analytics: An Hour A Day.

old radioThey ran campaigns across the US, in 50+ DMAs each that had their own set of cities.

The call to action was primarily driving people online, via a easy to remember vanity url (redirect) www . eztz . com (I am making that up here to protect the client). The audio ad also mentioned a toll free 800 number that listeners could call and purchase the product.

Using the vanity was smart, for tracking purposes (hurray!), keeping it easy to remember was even smarter. Driving people to the site was a business requirement because picking up the phone is expensive.

The first "management" level reporting was extremely simple, and visually appealing (after all pretty sells!). It was an attempt at answering the question: "we have all these ads running in 500 plus cities, which of those are effective at driving traffic to our website?".

Here is the baseline picture, before the campaign. . . .

tracking audio campaigns pre launch

And here is the picture that shows the impact of the radio ads. . . .

tracking audio campaigns geographic impact

Sweet lord that is impact! Get me the champagne!

The nice "chicken pox" geo report is a good visualization in this case because it quickly shows the top 100 cities that sent in the traffic. The before and after makes a nice story in of itself in terms of showing impact of the campaign.

It is also easy, as you can imagine, to dig deeper into the data and analyze all 500 plus cities that sent traffic. You will surely go in and look at the audio ad costs in each city, the number of listeners in each (from a source like Arbitron), the number of resulting Visits and Absolute Unique Visitors etc.

Being a fan of the Trinity Strategy you will surely dive into understanding outcomes (lead forms submitted, orders placed, samples requested and what not). Then you can pick which cities were ROI positive, which were not etc etc.

One of these days it will be easy for you to do much of the above analysis in your web analytics tool.

One graph you will surely create will look like this one. . . .

audio tracking online impact

A correlation of two trends, the brown is the radio ad impressions that you achieved during the course of this campaign. In blue is the resulting traffic to the website.

Nice. Clear correlation between the ad showing up and the traffic spiking on the site, the ebb and flow also match nicely though after every drop in impressions you see residual impact on the site traffic (the blue line drops less dramatically). All very wonderful.

Most people will leave it at this when it comes to measuring success for their campaigns (be they email or audio or tv or SEM/PPC or direct marketing etc). On the web, delightfully I might add, it is rare that a butterfly flutters its wing here and a tree falls in the amazon. (Ok I have been unwell for a few days now and am out of my metaphors juice!)

You take action in one channel / medium and it will surely have a impact in other channels / mediums.

"Unsuspected correlations."

So when you analyze your own campaigns / valiant traffic driving efforts, correlate other sets of data you have to see if there are hidden correlations that could help you understand the story better.

In our story the Analyst Ninja did exactly that and added a line to the graph showing the Traffic from Branded Search for the same time duration. . . .

audio tracking branded search impact


Not only did the radio campaigns drive people to the site using the easy to remember vanity url, eztz . com, but our lovely radio audience, brainwashed as they are like the rest of us, also ended up typing in queries into the search engine and arriving at our site.

Not a flash in the pan, but a consistent trends, mirroring the ebbs and flows of the radio ads.

This was a surprise because that vanity is not that hard to remember yet people used the terms mentioned in the ad (company name, product name, other brand or category terms) and used a search engine to get to the site.

Lesson: People behave in ways that they are used to and many of 'em won't do what you want them to do!

Unsuspected correlation in this case raised the amount of ROI the audio campaign could claim.

But our brave Analyst was not one yet. She tried one more thing. . . .

audio tracking multiple web channel impact

Direct traffic in light blue.

Again a clear trend in the impact of the radio ads on the Direct Traffic (your web analytics tool could be calling this "bookmarks" or "type in" or "none" etc). Technically it is people who have "no referrers" in their session.

In this case it was people who, again this is normal on the web, heard Market Fire Extinguishers and typed in www . marketfireextinguishers .com and got to the site. And that is a hard url to spell!

Another unsuspecting correlation.

In the end the impact of the radio campaigns on this particular website was significantly more than original imagined.

There are interesting implications of the above when it comes to your next media buy and the kind of customer behaviour that will impact it.

Net Net: Next time you are asked to produce a ROI analysis perhaps you'll think of this example and ask yourself if you have correlated enough data streams and looked hard enough to paint the complete picture.

Important Web Analyst Skill Observation:

There is one other facet of this lesson that was important to me. Lots of us get so entrenched in numbers and analysis and Omniture / WebTrends / Google Analytics / IndexTools etc that we often lose touch with the outside world.

The worlds of 1) online marketing (latest trends, happenings, changes, techniques, what not) and 2) online customer behavior (again latest behavioral trends, how people experience the web, likes, dislikes etc).


If you want to be a Magnificent Analyst spend 50% of your time with the above two. Anyone can learn to press buttons or extract data into excel. And thousands are learning that every day. What will set you apart will be your superior knowledge of the marketing and customer experience ecosystem in which we all exist.

Less than 10% of the Analysts I meet are proficient in those two things, almost everyone is proficient in the numerous web analytics tools.

It might seem obvious in hindsight to do the above analysis, I assure you that it was not. The person who did this was less a "web analyst" and more a "online marketer", the 10% I mention above.

Be that.

Important Observation #2: Correlation does not imply causation.

Correlations are a very advanced statistical technique that I am using in perhaps its most humble and lame manner above. (That had to be said!)

sine.correlations 1

More importantly it is important to realize that Correlation does not imply causality.

In our case above we controlled for other externals factors (no other campaigns running, no weird seasonality carp – notice the campaigns ran after Christmas etc). This was to ensure that we were not seeing patterns where none should exist.

It is important to internalize this.

Another example. Here are number of posts I have written in the last few months and the number of RSS feed subscribers in each.

    Oct: 6 posts, 5,542 subscribers.
    Nov: 5 posts, 5,829 subscribers.
    Dec: 4 posts, 6,338 subscribers.
    Jan: 4 posts, 6,898 subscribers.
    Feb: 2 posts, 7,666 subscribers.

The less I write the more subscribers I get (!!).

Those two variables seem to be correlated, but they are (hopefully!!) not causal.

So be careful with causation.

That's it. Lesson over. Hopefully you found it to be of value.

Now its your turn.

Please share your own lessons, perspectives, critique, bouquets and brickbats via comments. What works for you? What does not? Add your voice. Thank you.

[Like this post? For more posts like this please click here, if it might be of interest please check out my book: Web Analytics: An Hour A Day.]

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