FRUSTRATED ABOUT HOW TO ATTRIBUTE CREDIT FOR SALES AND brand-building success? You're not alone. It's a hot topic given the amount of money spent on digital media and continued media fragmentation.
There's a way to do it called media mix modeling — and it should be at the head of every marketer's list of priorities.
Cases in point: Vonage and XM Radio. Both are advertisers with retail, call center and e-commerce touch points. There's something to be learned from the way they sell their products and interact with their audience over time.
Both sell through retail at establishments like Target, Staples, Best Buy, CompUSA and Circuit City, as well as online. You can buy at these stores or on the Web and then activate your account either by phone or online.
- Offline media mix
Offline, these marketers use a combination of national and local media campaigns together with unique URLs (www.vonage.com/promohere), literal URLs (www.xmradio.com) and 800 numbers on their TV, print, radio, out-of-home and direct mail.
Media planning data for the national buys contains the number of 15- and 30-second spots by station, by day, and the gross rating points attached to it for broadcast. For print, out-of-home and direct mail they will have the ad weeks, days that drops happened, and the period of time ads are in play in different markets.
- Online media mix
Online is where most people struggle in the process. There will be a combination of performance buys, branding ads, affiliate buys, pay-per-click search, paid inclusion search and organic search.
Marketing departments may employ different tactics to try to capture as much spend-to-ROI data as possible through unique and literal URLs, depending on the channel. Most will have some sort of ad-server HREF or unique URL in play. If they're trying to track as much as they can, the only channel with a literal URL is organic search.
Every agency and marketer has a perspective on how to attribute “credit” for driving a customer or building a brand. That's fine — those rules can and should be incorporated into your models, as well as fine-tuned over time.
- IT departments
Another small hurdle can be gaining the cooperation of your IT department. Most companies, like Vonage and XM Radio, have one or more ad agencies in play.
Most clients do not “partner” with their agencies to the extent of providing them enough detailed data and periodic access to it to refine strategies quickly. IT inevitably gets involved, and that's when compromises usually begin to happen.
Let's use the assumption that the agency is perceived as a partner, and IT gives up the goods to get the job done.
- The “choke” point
This is encountered when the agency has to get the online and offline “house” to achieve data integration. Let's say the advertiser is using delivery technologies like Atlas as an ad server, DART for e-mail, a little Point Roll, and Commission Junction for affiliate marketing. Web site analytics companies are not critical to the solution, but can provide some customer behavior data on the site to help with clustering and segmentation.
I count four cookie spaces outside of Web site analytics. In order to get down to business and make sense of it, you must consolidate these at the impression/view level as well as click. Click is easy. Impression is where it all comes together.
I foresee millions of rows of log files that contain rich data for each channel. Stop — I know what you're thinking. You can't do this in Excel.
To get past the first hurdle in consolidation you must synchronize and match code all of these cookie spaces. This is a combination of passing cookie IDs, user IDs, account IDs and global user IDs between the delivery technologies and the clients' CRM/e-commerce/transaction engine.
Once you do that, do not summarize the data. You'll need to maintain an automated process to constantly update and append this match code across all existing and new IDs. This helps overcome cookie deletion and multiple browsers or computers.
Your table in the database will probably be 50 to 100 columns across if you use one table, or you may have a series of smaller relational or star schemas.
- Clustering and modeling
Once you've established a match-code process and it's humming, you're now able to look at a customer acquired or a cluster of customers acquired — say from among clusters A, B, C and D — A being the best, D the worst. Through regression and modeling exercises you can start your media mix modeling.
I worked in the newspaper business 15 years ago. Last time I checked, success did not depend on having the quarter-page ad in one issue in the top left or right corner of the page, but on having that quarter page in the same spot for 18 or 52 weeks. That was said to create the “branding experience”; frequency is essential.
Here is where online advertisers and their agencies have been let down by the delivery technologies. These technologies and click-tracking tools look at the “last click or view” that drove an activity on the client's Web site. This is the exact opposite train of thought leveraged in other areas of marketing.
- Some fruits of the effort
If you've match coded correctly, haven't summarized your data and maintained those match codes, you have the holy grail (or wholly grail, depending on how you look at it).
With this you can look at that cluster of customers and examine all the impression/view and click activities that drove them to the Web site through branding and direct efforts, and which creative, placement, keywords, list, site, etc. they had in common. So for customer segment A you optimize your creative and media mix to drive more of segment A, and you leverage it to optimize out commonalities in segment D to not drive more of them.
Once the online house is in order, you can then fold in the offline media spending and planning data. One of the first opportunities when you integrate these data sources is identifying patterns and correlations in offline marketing activity on the media plan in combination with the impression/view-through activity, click-driven activity, and activity on your Web site where the customer or prospect doesn't appear to have been exposed to online marketing efforts and yet landed on your site. You can then overlay these online and offline data points with the retail sales and call center data that's being captured.
Vonage and XM Radio have an added bonus of data from an administrative panel or online account to use in modeling efforts. The moment users log in to the Vonage admin panel to set their preferences or listen to XM Radio on their laptops, that used ID can then be linked with the match code.
You'll then be able to identify an individual or a cluster of customers that was exposed to a series of online ads over a period of time, by certain offline activities or geography (i.e., that they bought at retail and went online). Bingo — you have a direct tie purely from online branding efforts and conversion.
But you can't just look at one customer and make these sweeping statements. You'll need to consult with your agency and establish the number of customers, sales, etc. needed to make your conclusions statistically valid.
There are a few caveats to the whole process. You can't do this with delivery technologies that don't make 100% of the raw log data available. And remember, some delivery technologies provide richer data sets than others. The price of daily access to all the raw data from delivery technologies runs from free to ridiculous amounts. I tend to go with free since more data for less is always a better option.
Both the online and offline marketing teams will need to come to the table for this effort. That's ultimately what will differentiate one advertiser — and one ad agency — from another: how you look at the data and make those calls.
MARTIN WESLEY is CEO of business analytics systems maker BlackFoot Inc., San Francisco.
