The problem’s simple to explain but extremely complicated to solve. As a marketer, you spend money on multiple online marketing channels, including online ads, email, affiliate marketing, and (of course) search. Each channel is somehow tied to a mechanism to track return on investment (ROI), usually through a conversion tracking device of some kind, such as a clear pixel tracer. At the end of the month, you can see you sold a specific number of products or had some specific ROI tracking measure fulfilled a specific number of times. Let’s say you sold 1,000 toasters.
Problem is, you get data back from each of your marketing channel partners that doesn’t add up. Say (to keep it simple) we spent money on online ads, email marketing, and keyword buys. When you look at your data, you get a spreadsheet like this:
|Value of conversions||
At face value, it looks great. You didn’t lose money on any of your marketing. You spent $11,628.50 for a $16,518.05 return. You had an ROI of $4,889.65 for this particular marketing effort. But wait. There’s a problem…
You now have 1,000 true conversions in your database. But when you add conversions from your various marketing channels, you end up with 2,449. Something’s not working — you have three disparate marketing channels that are all claiming credit for the conversion.
You sold 1,000 toasters for a net profit of $5,969.60. You’re losing money on your Internet marketing. You need to know which channel drives the conversions. Yet the problem is more complicated than, “Which channel do I credit?” It involves multifaceted frequency and channel overlap.
The above example is actually far too simple to properly emphasize the problem. When you start pulling in other marketing channels, it’s even more complicated. How do you compare effectiveness of all your search efforts (organic site optimization, paid inclusion, and bid optimization) against affiliate programs and advertising across 30 different Web sites?
You may be able to use a back-end analytics solution to resolve such a conflict so long as you’ve gone through the trouble of using unique click-URLs for each marketing channel, and you’ve configured the system properly and followed strict trafficking rules. The industry-accepted counting methodology is to credit the conversion to the most recent channel interaction. The problem is this methodology ignores marketing overlap contributions.
You really want to know what combination of channels was the most effective. If one channel was a unique conversion driver, you can increase spend there and diminish it on other channels. But perhaps you’re missing a magic moment in marketing analytics. Maybe if you increased your spend in two channels that consistently show overlap, you’d achieve higher margins.
Exposure to ads would logically drive brand awareness. That would lead to a search for the offering in a search engine, which in turn would result in the user landing on the Web site and possibly converting. If you credit the conversion to search, you miss the driver of the user behavior. They would never have searched had they not seen the ads.
The problem’s immensely complex. It requires more than a little effort to resolve. The good news is tools for analyzing this kind of data and getting at the information you need are beginning to emerge (at prices that won’t blow your budget). Over coming months, I’ll dig into techniques and best practices for structuring online marketing campaigns so you can perform these types of analytics and get the most out of your marketing dollars.
If any of my faithful readers are doing this already, feel free to contact me. If you’re willing to act as a case study, I’d be happy to publish the results.
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