The Drive-Time Web

“Data Driven Thinking” is a column written by members of the media community and contains fresh ideas on the digital revolution in media.

Today’s column is written by Niel Robertson, CEO of Trada, an online marketplace technology company.

Driving between Boulder and Denver the other day, I did something completely old school – I turned on the radio. It was during rush hour, and I couldn’t help but notice the continued emphasis on the ad-free drive-time show (yes, the irony didn’t escape me they were advertising no ads). I started to think about the pros and cons of ad-free time segments on the radio, and it dawned on me this might apply to the web as well.

One of my predictions for this decade is that content monetization is going to go through a renaissance as content sites have an ever-increasing number of ways to monetize. To date it’s been display ads and a cost-per-impression (CPM) model. Now sites can monetize with affiliate links, real-time content ads, display ads, in-text ads, in-context stores (daily deals on your site), sponsorships and sponsored Twitter feeds. The number of ways content sites can generate money will only continue to expand as the amount of content on the web expands.

I’m going to take a guess that drive-time radio was designed to trade-off ad dollars at the highest trafficked time of the day with the cost of acquiring new listeners at those times. If you hear one hour of uninterrupted great music on your drive, you’ll likely tune into that radio station later and suffer through some ads in off-hours. Websites can (and perhaps should) work exactly the same way.

There are three different participants in the equation when it comes to balancing advertising mix: the publisher, the user and the advertiser. Each of them has distinct optimization vectors. The publisher would like to produce the most revenue as possible from each hour of the day. The user would like the least cluttered experience as possible. The advertiser wants to advertise only in the highest converting times of the day. We’re starting to get excellent data on each of these vectors and could theoretically start to optimize in real-time.

With all this data in hand, content monetization becomes an optimization experts dream. Some companies have been doing this for a long time in the display market. I predict that as more monetization capabilities come into play, companies like these will take control of a larger amount of revenue generation methodologies and optimize across them. Perhaps from 9:00 am to 10:00 am, it’s best to strip out all the ads? Maybe from 2:00 pm to 4:00 pm, a site sees lots of post-lunch shoppers, and there should be special emphasis placed on affiliating as many links as possible. While that means increased link clutter and outclicks, the revenue trade-off may well be sound. Without the data, it’s nearly impossible to make these content changes.

With the data, it becomes simple.

This reminds me of an anecdote my friend David Feinleib (a partner at Mohr Davidow) gave me about his team at a major social gaming company. They intuitively decided to remove a lot of their display inventory on their site. The result: massive usage increases instantly (which they validated with data). While this was done intelligently by the executive team based on intuition – the final results were measurable and such decisions could eventually be testable and optimized down to the user.

While pulling all these pieces together will take a while, I believe we’ll need to head in this direction quickly. Ad fatigue is setting in with display. Performance-based advertisers are moving into display (as cost-per-click starts to rein as king, pure CPM rates and traffic won’t matter anymore). And new monetization formats are appearing daily (such as affiliate links). The content site of the future will be instrumented incredibly well for all kinds of monetization, including no monetization at all, especially when it benefits the user experience more than the money that could be scraped from their presence.

Follow Niel Robertson (@nielr1), Trada (@trada) and (@adexchanger) on Twitter.

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