Identifying external data needs for driving conversion optimization

Marc Andreessen famously said ‘software is eating the world’. This appetite for software is fed by the fact that software driven businesses are not only way more effective than traditional businesses; they can also leverage the software to accomplish things unheard of before, such as new business models. Companies such as Google, eBay, and Amazon are well-known examples of businesses that would not exist without software.

What Marc was not saying, is that this shift is largely driven by data. Data is the fuel for these businesses to run on, and as output produce even more data. Sounds like data is a kind of perpetuum mobile. And the more data, the better these businesses run.

Marc Zuckerberg from Facebook is not thinking in terms of what new features to add. Marc thinks about how to tailor the Timeline to keep you engaged. More engagement does not only lead to more advertising revenues; it leads to more engagement resulting in more data.

To get this wheeling spinning enormous amounts of data are needed, coming from different sources. This data needs to be processed in real-time and ‘interpreted’ by emerging technologies such as Artificial Intelligence.

The Facebook Timeline once started as a simple log of activities from your friends. It is now moving towards new frontiers taking into account a wide variety of data, including:

  • User engagement data, e.g. Likes and Posts
  • Messaging, e.g. words used in Facebook Chat and Whatsapp
  • User interactions, e.g. mouse movements
  • Behavior from Friends and People you Follow
  • Real-time News sources (it looks like this is going to be big for Facebook)
  • Context information, e.g. physical location, nearby Events and others inputs coming from mobile device sensors

Another example is the recent introduction of dynamic pricing by AirBnB. It uses machine-learning models (software) to interpret and predict pricing.

These algorithms are fed by data, such as seasonality (for example the date of popular events such as the SxSW conference in Austin), unique features of a listing (such as price, review sentiment, and rating trends), and the performance of other listings in the neighborhood. Without this data, there is no foundation to make dynamic pricing work.

Also Uber is famous for its dynamic pricing (aka surge pricing) to keep demand and supply in balance. Whether it is fair or not that the same ride has a varying price tag, the fact is that such models are impossible without data.

To maximize the number of Uber rides, the algorithms are tweaked in such a way that when demand is at its peak, the price is automatically adjusted, thereby maximizing conversions and revenues.

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Tags: big, conversion, data, external, optimization, rate


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