Mobtown Labs

Guest Post by:  Matt Patton Esq. Director of Optimization and R&D at DoublePositive

Image via Wikipedia

Among the many nuances in the debate about which optimization algorithm represents the best RTB media buying strategy, the most prominent might be the audience retargeting v. audience agnostic approach.  Although exclusive to our business, this dichotomy is conceptually no different than two Wall Street analysts debating about whether a pure qualitative approach is better than a numbers-only quantitative method in purchasing securities.  My engineering training tells me that the only inferences that should ever be drawn as a basis for any decision should originate with the story told by what was actually measured.  But my legal training tells me what I think is the more correct approach, which is, as every law school professor professed so eloquently, “It depends!”

The truth is that measured performance numbers, i.e. impressions, clicks, conversions, and audience data, i.e. categorical audience profiling, both have their own benefits and drawbacks, and will almost always provide you different oscillating returns on your media investment.  On the surface, the only real difference between the two is that one is instinctual while the other is empirical.  With audience data, we infer that a group of users with certain online buying habits will purchase what our clients are selling based on the categorical distance between things previously purchased and what we are currently selling.  With fundamental performance data, we just care about which pieces of inventory have already yielded a statistically significant number of conversions regardless of who actually converted.  Thus it is blatantly clear under the surface that both methods offer the same benefit, which is a calculated a priori probability of a conversion from which we can derive an expected value of an impression, or what my mentor at Advertising.com used to call the “Crown Jewel”, of DR advertising.

The obvious drawback of both approaches is and will always be the cost of learning.  Because of the random nature of Internet advertising, we need to survey the biddable landscape before we can make any type of calculation, qual or quant.  In the quant world, it probably takes more time and thus requires a longer discovery period, while in the audience buying game, those “selective” impressions can get quite expensive when we are still learning which audience represents our sweet spot. Since there is no such thing as a free lunch in this business, both are going to require some upfront cash that will initially skew our calculated CPAs.

Instead of simply accepting this as truth, I want to treat this as an opportunity to thwart the economic black hole of overcoming a campaign’s inertia when starting from rest.  I see opportunity to utilize both approaches as a function of time and campaign maturity in order to get campaigns running more quickly and performing better long term.  I see complex regression analysis and time series plots that will tell us on a campaign by campaign basis of when and how to use audience targeting and when the stats tell us that the audience is worthless.  I see recurring real time cost benefit analysis algorithms telling us that we are paying too much for an audience or that an audience is underpriced.  I see this as a first step towards creating a RTB marketplace where being smarter actually translates to being more successful. For the first time, I see a scientific approach to an age-old art and an artistic approach to centuries-old mathematics.

Will this be accomplished and will it even make a difference? I leave you as a lawyer by saying “it depends”.

Related articles

• The Rise of Media Quants (adweek.com)
• Hey Quants! Hedge Funds And Banks Will Fight For You In Bidding Wars And The Winner Will Be… (businessinsider.com)
• New Survey Reveals Increased Focus on Risk Management in Wake of Financial Crisis (prweb.com)
• Peter Muller’s Quants Find a New Home (blogs.wsj.com)

Linkedin maps data visualization (Photo credit: luc legay)

I am currently working on a advertising and marketing reporting interface that is mostly grid driven and we are adding some data visualization to it because quite frankly the thought is that most users just don’t have that ‘ah-ha’ moment when data is presented to them in a grid.  Even if they are somewhat simple stats grids can be daunting to look at.  That said, a well paginated grid (or better yet infinitely scrolling grid) can be the most powerful tool.

To decide whether to go more complex on a grid or to pop out data visualization you need to consider time spent on that particular part of the application.  You also need to decide whether the visualization of the data is just to show off and be fancy or if it really has a use.

For example when presented with a grid of data from an online advertising campaign, it’s easy to go through and see how much money you spent on a particular campaign for…say 30 days or even 90 days but once you get above about 100 rows you really need a trend chart to see where the spikes and troughs in spend are. 

Another good use of a data visualization are nested trees or what the javascript d3 library calls a Dendogram.  To zero in on, or drill down into data expandable rows in a grid are useful but if you want to come over many layers of nested data to see what is really driving value, a visualization is probably a better choice.  That said, as a rule of thumb, I would always present the grid option so you can see precision in numbers once you have identified your ‘significant’ data points.

Related articles

• Data Visualization Resources (secviz.org)
• Visual Data Tools, Inc. Announces the Mac App Store Release of DataGraph, an Innovative and Powerful Graphing Application for Mac OSX (prweb.com)
• Data Visualization - Is it Important? (mobtownlabs.com)
• Cascades: Rich UI Development for BlackBerry Apps (devblog.blackberry.com)

I recently came across this tweet from the founder of Posterous (sorry I’m a tumblr fan).

Cutting features is hard post.ly/4RfNf

— Sachin Agarwal (@a4agarwal) January 6, 2012

We are in the midst of brining one of our key products out of beta to it’s ready-for-prime-time v1.0 and like any technology project we need to do it profitably, we wish we had more engineers, and we wish we had more time to include or perfect every feature we have dreamt of in the planning process.  That said we need to make cuts.  One recurring debate is whether or not charts and graphs and creative ways to visualize data is a bell/whistle or is it a core component that could propel the product into being a wild success.

Like many other companies that deal with online media we get millions or even hundreds of millions of events and logs to parse through each day and run in massive data warehouses and are rolled up into useable statistics each hour.  There are the standard stats that we need to watch on a regular basis as well as report out to our clients but when we dig into a campaign or try to figure out how to increase performance we are always downloading out to excel or writing a Hive/SQL query to get a deeper look or a different dimension of the data and then graphing it for a nice friendly viewable representation.

And it never fails that the data representation that wins the business, gets the next round of funding, or wins over an executive’s good graces to allow a budget/project/initiative to move forward is inevitably a very simple graphical representation of possibly billions of data points.

So if this is so important, how can it be looked over?  And a better question is why?

(Source: sachin.posterous.com)