Top 10 Algorithms Affecting Marketing

How can we relate math and marketing?

Earlier this year io9 listed the "10 Algorithms that Dominate Our World."  These complex math functions are:

• Google Search - at 67% of search traffic, 'nuff said.
• Facebook's News Feed - they pick what you see
• OKCupid Date Matching - more successful than pickup lines because of weighted data
• NSA Data Collection - the method has been redacted
• "You may also enjoy" - from Amazon to Zappos guiding the next choice is rampant
• Google Adwords - figuring out if you'll be satisfied with the results, and then charging more
• High Frequency Stock Trading - leaves humans out of the equation
• MP3 Compression - the pipe is only so big, everything should be compacted
• CRUSH (Criminal Reduction Utilizing Statistical History) - public sector success story
• Auto-Tune - pitch blending for fun and profit, just ask Cher

As a consumer I don't care how they work, I'm just glad that they do.  However, as a marketer I do care since these algorithms sit between my campaigns and my results.   These algorithms are complex, proprietary and changing so fast that understanding the specifics across the board is out of the question.  That said, here are some ideas on how they relate to a marketing point of view:

• Organic search is a popularity contest with one judge and a hidden score card.  Since our content is judged against that from everyone else we need to constantly be looking at the world from the perspective of 'how do we help consumers find what they need'.
• Social news feeds take into account the wisdom of the crowd in determining what to run past you. From this we should be thinking about what content archetypes and forms creates interest and engagement.
• Matching algorithms often work with layers of weighted information across many dimensions. In many respects this follows the same process as branding - reduce the reasons to believe to a promise and ultimately to a single essence that aligns a solution with a need.
• Algorithmic adjudication or determination poses some ethical questions about permitted use. Something anyone dealing with privacy already knows all to well.  Data is not neutral, observation is biased, and all models are based on assumptions and decisions.  
• Recommendation engines are at the heart of personalization and dynamic content.  However, there is a risk of over filtering and missing that fact that decisions are based on emotions rather than facts. "I didn't know I wanted to have...." is tough to program if you've never had....
• Paid search is a combination of two sets of results - theirs and ours.  This is a perfect place to think about and work on the problems of resource allocation and attribution in part because it is down at the intent level of the consumer journey.
• Programmatic marketing, at least in the narrow sense of real time bidding (RTB), evolved from the ability to arbitrage at speed.  With RTB we're still left with two key questions:  What should we pay to reach an audience? and What should we tell them?  Creative optimization is next.
• Compression is an analytic process that removes redundancy and noise in a defined manner.  The best parallel I can think of is the creation of consumer segments - we abstract and reduce the most important details and hope for a lossless solution.
• Data mashups, particularly physical location + digital activity, are a breeding ground for unique insights.  They are also a good way to start to break down traditional channel silos because they look at the world in a new light where everyone can contribute. Adding algorithms makes it better.
• No marketing activity has perfect pitch so comparing data to match a known standard sounds a bit like forecasting.  Since forecasts are always wrong, the interesting bits are "why?" and "what did we learn?"

I'm sure there are numerous other algorithms and parallels - please add yours.