I once went to a strategy meeting where a certain YouTube ad of a well-known bottled water brand was shown as an example of successful marketing according to some Harvard Business Review article. The reason: The video had gotten more than a hundred thousand hits.
After the ad had played, I asked: “Okay, how many people here drink bottled water, show of hands?” In a roomful of about 20 people, almost everybody’s hand went up. “Now, how many of you are going to go out and buy this water after having seen this video?” Two hands went up.
Despite the small sample size, the point was that just because many people watched the video doesn’t necessarily mean they actually bought the product. So, if the esteemed HBR was measuring the effectiveness of a campaign to increase brand awareness, the metric based on number of views is valid. If the measurement of an effective marketing campaign is the extent to which it compels people to buy the product, views alone don’t tell the story.
Which brings me to ask this: Is your measurement strategy measuring the right things? And do your marketing people believe the measurements methodology tells the story correctly?
Demian Brink, Senior Consultant of Data and Ad Technology here at Brand Federation, has an amusing take on how measurement strategy can go wrong. You’ll find some insightful nuggets.
To summarize, here’s where measurement strategy can easily stray:
- Top Funnel Blindness: Focusing on the easily measurable metrics (e.g., clicks) at the expense of harder-to-measure metrics such as changes in brand perception that are better indicators of long-term success.
- Rearview Mirror Driving: While it’s useful to examine what you’ve done in the past — such as regression-based attribution models — that’s not likely to help inform accurate forecasting. If you’re starting a new campaign, toss the old metrics and start fresh with an approach that focuses on those that are more relevant.
- Not My Job-itis: “Hey, we came up with engaging content, don’t judge us by lousy sales results. It’s not our fault you didn’t (fill in the blank, e.g., price it competitively).” Look, if your engaging content isn’t actually engaging, then maybe it actually isn’t. Stop looking for excuses as to why the measurement doesn’t apply; instead, determine what the measurement tells you about how to make corrections.
- Don’t Ask Why: What happens is an important measurement. If site visits are going down, you need to do something. What that “something” is needs an explanation. If you don’t investigate why something is happening (the site isn’t getting high search rankings, so let’s take a look at our SEO strategy) and just react (let’s do an email blast to let customers know about the site so we get more hits), any short-term gains achieved are wiped out by long-term consequences.
So how do you avoid these traps? To begin with, as the Content Marketing Institute recommends, “Just because you can measure just about anything these days, doesn’t mean you should. Metrics can be all-consuming and confusing, so first, determine a few fundamentals you should focus on.”
A good outline includes:
- Determine the marketing campaigns objectives and how those objectives are best tracked in quantifiable and realistic terms, e.g., 15% sales increase over last quarter.
- Define the channels to measure, e.g., web, print, social media, email, paid ads.
- Define Key Performance Indicators (KPIs) for the appropriate channels, e.g., search engine referrals for web, number of mentions on Twitter for social media, etc.
- What combination of search tools to use, e.g., Google Analytics, Kissmetrics Funnel Reporting, RapidMiner data mining, predictive analysis, etc.
It’s not enough to present numbers for the sake of having numbers to present. All your measurements should support your overall business objectives and provide insights into how you can identify and improve performance gaps.
Moreover, as my colleague Demian emphasizes, the measurement strategy must:
- Take into account the customer journey from beginning to end, from discovery through research and shopping to purchase and continued usage.
- Explore why certain behavior occurs, as opposed to just reporting what behaviors occurred.
- Look at collected data from a variety of viewpoints, a combination of behavioral economics, network science and traditional marketing analytics.
- Recognize biases and assumptions.
Perhaps the latter is the most important. It’s easy to get wrapped up in your own preconceptions, particularly if those preconceptions have worked in the past, or (and this is perhaps even more dangerous) worked for others. Consider what your stakeholders think is worth measuring and adjust accordingly. To use his example, just because the data tells you the Poodle is the most popular dog, try selling that idea to a Great Dane owner!
Photo used with permission from Visual Hunt
