I’ve been spending a lot of time over the past year or so trying to figure out where the advertising industry is headed and
while I still have many open questions, I’m quite confident that I have one specific area nailed down… and that’s around media optimization.
In context of this post, media optimization is when a media or ad ops team is changing/tweaking a media plan based on the performance of the sites based on a certain schedule. If one site is performing well and other sites are performing poorly, the sites performing poorly would be manually called (yes, via telephone) or emailed and small tweaks would be made by the publisher – and if performance didn’t change, the site would be optimized “out” of the media plan. Lost revenue for publisher.
Please note that in this context, media optimization is not media planning, or the upfront portion of media where the agency/brand selects the sites/audiences they want to use based on different research such as ComScore, Nielsen, QuantCast, etc.
Looking at the future of media optimization at the agency/client side, it’s heading away from human-lead to computer-lead interactions. There are many reasons for this and here are a few of them:
- Humans are humans and are prone to error.
- Most media agencies don’t hire quantitative minded people, and many quantitative minded people don’t end up in advertising.
- Even the smartest human cannot process all of the information necessary to make an optimization quickly (i.e. 6 creatives * 12 sites * 24 hours in a day * 60 minutes in an hour * 13 audience segments * 4 geotargets * etc)
- CRM/databases are plugging into media systems and technologies will need to be bridged
The key note here is #3. As we move into real-time buying for specific audiences on a custom list of sites, technologies are going to lead this. Humans are going to drive the technologies by setting KPIs (key performance indicators) and managing the overall campaign, but once we tell the technologies how much we can bid per impression (how much we can vs. what we should bid is fundamentally different), they will buy/trade and secure placements. Placements that do not work well after a statistically significant sample will be automatically optimized.
The adoption curve of making this happen within an agency or brand is fairly steep, however, there are multiple mid to large scale agencies who are starting to making inroads. The digital media world has been trained for manual optimizations and it took years for our clients to understand this, so changing their mindset could take a bit of time. There is a learning curve not only internally but for our clients as well, and with enough time, we’ll be having algorithms perform these optimizations. I believe that this competency will be necessary as we roll into 2010/11 and exchanges proliferate media planners and strategists. With media folks spending less time now on optimization, they will be able to spend more time on creating big ideas.