What we learned at MITX's "The Science of Marketing" Event
August 07, 2013
With analytics at our core, we’re always on the hunt for new learnings in this arena that we can pass on to our clients and infuse into campaigns we’re working on. Last week MITX held it’s “The Science of Marketing: Using Data & Analytics for Winning” event and we had a front row seat. Here are some key takeaways from the event, according to Bob Deininger, a NorBella Media Director, with more than 20 years in the biz:
1. To build a concrete base in your analytics program, it should be rooted within a strong framework. Jesse Harriott, Ph.D., Constant Contact’s Chief Analytics Officer, described this as “Business Analytics Success Platform.” Consider the following when putting your plan together:
- Business Challenges – address your organizational objectives to ensure that analytic solutions can be aligned to discover holes in strategy or drive business.
- Data Foundation – decide a company-wide definition for your metrics.
- Analytics Implementation – keep end goals in mind to ensure solutions are then implemented.
- Insight – how does this data shape campaigns or actions moving forward?
- Execution and Measurement – make the numbers work for you, decide what actions will be taken and drive this to track results.
- Distributed Knowledge – make sure your insights are easy to digest, from coordinator level folks to CEO’s; data does no good if no one can understand how it impacts them.
- Innovation – analytics must always be one step ahead to be able to give clients what they need, without knowing they need it yet.
2. Programmatic media buying is here to stay. As defined by DataXu’s Mike Baker, “the use of consumer data and algorithms, adoption of real-time bidding platforms, measurement and interaction to consumers at the impression level,” programmatic buying is not only NOT going away, it’s growing and growing.
eMarketer predicts that programmatic will expand its share of total U.S. digital display ad spending to more than one in four ad dollars by 2016, or roughly seven billion dollars!
3. Computer algorithms don’t necessarily understand when we are being sarcastic vs. if we really do love something. Hence, the below example could be counted as a positive sentiment for brand X, when in reality this tweet is a rant! Social media measurement platforms are now evolving to provide appropriate gauges of sentiment. For example, Crimson Hexagon is allowing humans to “teach” the algorithms to better recognize sentiment. (Maybe they can help people get our sarcasm in real life too!)