The “Death of Marketing” Has Been Announced Prematurely

Has there been a fundamental change in how people think about and buy goods and services? Is old-school marketing dead?
According to the recently-minted orthodoxy of social media, marketing is being transformed, and we are moving from the Age of Push to the Era of the Conversation. The Don Drapers of the world, they say, will now have to play the new way, find new jobs or become hobos. Participation in a conversation is voluntary, per the new theory, meaning marketers can’t unilaterally control conversations and so now must learn to communicate differently with the people in the marketplace.


Marketing Has Always Been A Conversation – Marketers Just Haven’t Recognized It Until Now

There is an important truth here – much of the conversation about brands and products is intra-customer, and doesn’t involve the marketer at all. This has always been the case, but it just has not been as observable until it started happening online.

Audiences Have Always Been Able to Walk Away

Audiences have always asserted more control over the “conversation” than marketing models have admitted. People have been fast-forwarding and channel-surfing through television ads since they figured out which remote control buttons to use. Since the very birth of mass media, admen and their clients have been solving and re-solving the problem of how to entice an audience to listen to their stories instead of wandering off to grab something from the refrigerator.

Propaganda Still Works

Working against this is has always been the fact that (as psychological experiments have shown) repeated messages have an influence on peoples’ perceptions and behavior even if they are skeptical or not paying full attention, and even if they are aware they are being ‘sold’.


The Emergence of Social Media

Thanks to social media lowering the bar for what constitutes a relationship, people are regularly engaging in communication with a wider circle of casual or low-involvement contacts.

Conversations are Observable and Trackable

We can find out what people are saying about brands and our products (at least when they happen in online social media). These conversations can assert a great deal of influence on buying behavior.

Advertisers and Marketers can Participate in the Conversation

Marketers can, when they decide it is to their advantage, participate in these conversations and assert some influence on the the agenda and the messages

Advertisers and Marketers can START the Conversation

Marketers can initiate conversations, and they can provide the platform or venue for these conversations to take place. However, the the attempt can backfire. Conversations can and will change locations if a community gets the sense that there is excessive censoring or propagandizing.


So, things have changed, but not that much. People still need to buy things, they still need to decide what to buy and where to buy it. It’s just that now, people increasingly use social media (among other channels) to get information about products and services they are considering. Social networks have always been influential, but now they are pervasive, instantaneous, and measureable. So you shouldn’t ignore social media, you should listen to and participate in it. That’s all.

The Digital Nervous System

The web can function like a giant extension of the human nervous system. Like a spider at the center of a giant global web, you can collect and observe streams of data coming from all over the digital expanse: searches, tweets, forums, blogs, newspaper and magazine sites, press releases, Facebook and LinkedIn. Each time someone looks for or mentions your company or your product you are alerted, and you can choose in that moment to respond to it, ignore it or wait until you have more information.

Does this sound like anything you are doing now? Someone should be doing this for your company, because marketing has increasingly become an ongoing series of conversations (whether you participate in the conversation or not).


There are several national TV shows that frequently have book authors as guests (the Daily Show, The Colbert Report, The Today Show, Good Morning America). The next time you find yourself in front of one of these shows when an author is on plugging their book, try the following experiment (this will work best with a show with a national audience):

1. Fire up your laptop and go to
2. Search in the Books category for the title of the book the author is plugging on the show you are watching
3. Click to the Amazon page for that book.
4. Scroll down past the synopsis and the reviews to the section labelled Product Details. It should look something like this:

The number I have circled is the book’s current sales rank on Amazon.

5. Every few minutes while the author is on the show and for a while after that (until you bore of this experiment), hit function key f5 to refresh the page and watch what happens to the book’s sales ranking.

The rank should get better – in real time – as you are sitting there. I have done this several times when my brother-in-law has done TV appearances to promote his books, and it is amazing. Once he was on Oprah Winfrey and we saw the sales rank improve precipitously from 20-something into the top 10 while he was being interviewed.

Now imagine all the other analogous information streams there are available on the internet. If you could get the monitoring automated, just think of how quickly you will know exactly what the world thinks of your new site, your new ad campaign, your new product. Just think of what you’d be missing by NOT knowing.


In between rank checks you should do check in on Twitter searches for the author’s name and the book’s name. These should also pop during the author’s TV appearance.


After a day or so you should go to Google Trends and see what happened to searches for the author’s name and the book’s name. These should’ve spiked on the day the author did the TV appearance. Google Trends doesn’t provide much flexibility about getting more granular (in time) data in a more real-time way, and it looks like the beta for Google Insights for Search has a latency of a couple of days.


Take a look at the Google Flutrends project ( and you can see what an amazingly useful datasource this would be with access to the full detail in realtime. It turns out that counting Google searches for flu information is a quicker detector of flu epidemics than CDC reports are.

I believe it would be just as accurate in detecting other kinds of contagion sweeping through the world: fads, emerging trends, scares, rumors, accidents, disasters – this is the kind of information that businesses need to know when it involves their products, their brands, or their markets.

Classic GI=GO Equation Holds True for Web Analytics

Garbage In = Garbage Out. People who spend their working hours analyzing numbers generally come to this realization. It is true for modeling, it is true for forecasting, and it is completely true when it comes to website analytics.

The chain of events looks something like this:

1. Someone visits a website integrated with a web analytics platform like Google Analytics, Webtrends or Omniture.
2. A web page visitor either navigates to a tracked page or performs a tracked action.
3. A script is executed in the browser, sending data to the analytics platform.
4. The data is added to the datastore.
5. The data is summarized and analyzed.

Problems arise when you assume that steps 1 through 4 are happening correctly, and you move right on to looking at reports and data that come out of the process. Oddly, most site developers I have met who are instrumenting a site for web analytics consider their job done and successful if tags fire when they are expected to. They don’t look at the data as it is passed with the tags and they don’t look and see what made it into the web analytics platform’s datastore. Anything you don’t check in software development is frequently going to be wrong. If your data is wrong, then all your analysis of it will be just as wrong as the data. Again, there’s the classic equation describing this relationship:

Garbage In = Garbage Out

How do you prevent your data from being garbage? QA and debug the data, that’s how.

Before you use information coming from a web analytics solution, you should (or someone should) do these two tests:

1. Web Analytics Data Test Number One: Is the data being passed correctly?

Use a header tool of some kind to see what tags are being invoked and what kind of data they are passing to the web analytics platform. I use WASP. It shows you what kind of tag is fired when you click on navigation and site functions, and then it lists the data values the tag passes. The test is this:

Step 1: Navigate to every page in the site. A pageview should be generated for every pageview you generate and it should have the correct page name passed with it. Implementation of this is usually OK for standard HTML sites, but is error-prone for Flash sites.

Step 2: Click every function you are tracking as an action or event. See that an action or event is generated for each one you click, and that it is firing a tag that classifies it correctly – as an event, not a page view, and that the name and category that are assigned to the action are what they should be.

(Steps 3-n): Anything else you have tagged for measurement, like ad placements for an ad server, should also be clicked systematically to see that everything that is supposed to be captured about ad impressions is actually captured and passed when the tag is fired.

2. Web Analytics Data Test Number Two: Is the data making it into the database(s) correctly?

Set up your full site in staging so it will have a recognizable hostname that you can filter by in your reporting tool. Tell everyone else not to play with the version in staging for a while.

Step 1: Navigate to every page in the site in a systematic order. Do this several times. Make sure you keep track of how many times each page is viewed.

Step 2: Click every function you are tracking as an action or event. Do this several times. Make sure you keep track of how many times each action is done.

(Steps 3a-3n): Anything else you have tagged for measurement, like ad placements for an ad server, should also be clicked systematically – count the impressions and count the clicks.

Step 4: Click through every funnel you have set up, several times, all the way to the goal. If you have goals like time on site or number of pages viewed, make sure you stay long enough and look at enough pages to meet these goals.
If there are required pages in your funnels, make sure you pass through them. Again keep updating the tallies of page views and actions as you do all this.

Step 5: Wait until the data is likely to be available for reporting. Latency varies by platform. Pull reports, filtering for your hostname. You should see that the numbers of page views, actions, ad impressions, ad clicks, goals/conversions, and funnel stages matches what you did in steps 1-3. If they do not, you probably either have:
a. a tagging problem (wrong tag, misimplemented tag, redundant tags, etc.)
b. a setup problem (e.g. definitions for goals/conversions, funnels)
c. other users muddying up your data by hitting the site in staging while you are testing.
d. a more exotic and difficult problem

If this all sounds like a pain in the hindquarters, compare it to the pain of realizing that you have been reporting erroneous numbers and making business decisions based on them for months or years. Believe me, they could be so far off that you’d have been better off guessing or making numbers up. Do not trust what you cannot verify with test results, or you will have much pain and sadness in your future.