Archive for the 'real-time' Category

Real-time Context Targeting

Targeting local advertising using the real-time context of local searchers is an effective alternative that overcomes the limitations inherent in simply advertising a local business as a potential alternative based on the category and location of the search.  In this post, I’ll describe what real-time context is, how it can be discovered and how models can be trained to target local advertising using this approach.

This post is part of the Improving Local Ad Performance series.  In The Challenge of Fine Segmentation in Local Advertising I discussed the challenges inherent in relying solely on a ‘like for like’ targeting approach in local search.  In a like for like targeting, local advertising is displayed by matching the category and location of the advertising business to the category and location of a search request.  In today’s post, we are going to discuss using Real-time Context Targeting as a complementary alternative to this approach.

What is Real-Time Context?

Let’s use the example of someone searching for a Taxi to call.  They might be looking up a number for a particular Taxi, looking for any old Taxi or even looking for the nearest available Taxi.  The following diagram illustrates various situations where a user might be looking for a Taxi:

The Real Time Context of Someone Looking for a Taxi

The Real Time Context of Someone Looking for a Taxi

The actual context of the user is hidden from us, but let’s assume for the moment we actually know it (or could at least take an educated guess at it).  In that case, we could advertise based on that context.  So, for example, if they are call a Taxi because they’ve lost their keys, we could advertise the Locksmith they will also need!

Why Not Just Advertise Taxis?

There are several reasons to consider advertising something other than just Taxi services:

  1. Other types of advertisers (a Locksmith for example) might be willing to pay you far more for a lead.  So, even if your response rate ended up lower than for a Taxi ad you might make more money because you’ve delivered more valuable leads.
  2. Some users may be loyal to a particular brand of Taxi or using a service that provides the nearest available Taxi.  As such, they aren’t really open to substitution – but may be more more open to something that meets another need in their context.
  3. You might not have Taxi ads in that locality.

If the advertising is relevant to the real-time context of the caller, they’ll appreciate and perhaps act upon them.

How Do You Determine Context?

Sounds great – but how do we discover the hidden context of our searcher?  We have to infer the hidden context from characteristics (or attributes) of  the search and current events.  Examples include:

  • time and date based attributes (time of day, day of week, weekday/weekend, holiday, seasons, etc.);
  • place attributes (requested location and current location of the searcher if known); and
  • real-time events such as weather, sporting events and cultural events.

In fact, by examining past search history (in a completely anonymous way that protects the privacy of individuals) and the historical event record we can apply machine learning algorithms to build models that recommend the best ads based on current events and the attributes of a local search.  An example of how we do this at Predictabuy is shown below:

Real-time context targeting of local advertising

Real-time context targeting of local advertising

What’s in the Event Stream?

The explosion of social sites and mobile usage exemplified by Twitter and Facebook provides a rich and evolving set of events that a context-driven local targeting engine can exploit.  As such, this approach will just get richer and more effective over time.  If you have a suggestion for events you think will be important in local advertising leave a comment and let me know.  I’d love to hear from you.

Learn More

For a free consultation on how you can use Real-time Context Targeting in your business contact me.

This post is part of an ongoing series on Improving Local Ad Performance.  Upcoming posts will cover the use of consumer behavior and preferences in the effective targeting of local advertisements.  To ensure you don’t miss any of the discussion:

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