Archive for the 'Improving Local Ad Performance' 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:

The Challenge of Fine Segmentation in Local Advertising

This post is the first in a series on Improving Local Ad Performance.  My perspective is primarily that of local information publishers and application providers.  In this post I lay out one of the fundamental challenges of local advertising: the need to finely segment it by geography and business type.  I’m going to focus on local searches, but the principles apply to other types of local information and advertising as well.

Local Searches: Sliced Very Thinly

Local searches are highly targeted.  This makes  makes them both an advertiser’s biggest fantasy and their greatest nightmare.  It presents a number of unique challenges.

As a minimum, local searches are distributed across many different geographies and types of businesses — and this is only the tip of the iceberg since you can also take in to account the user’s context, behavior and preferences.

Local Searches are Thinly Sliced

Local Searches are Thinly Sliced

These thin slices have implications for both publishers and advertisers:

  1. Each category/locality combination receives a small percentage of all traffic.  So publishers need large volumes at a national level or they have to specialize in particular geographies, vertical categories or demographics.
  2. These highly targeted searches are often most meaningful to small and medium sized businesses serving the niche but acquiring the necessary mass of these smaller advertisers is extremely challenging.
  3. National or even regional advertisers have to find ways to make campaigns truly meaningful at a hyper-targeted level.

Like for Like Targeting Alone Won’t Get You There

Most people approach targeting of local advertising by having the advertiser define the category and location they want to target.  Then, the advertisement is presented when a user performs a search in that category and location.  This frequently takes the form of offering the user alternatives to their request.  I’m going to call this ‘like for like’ targeting.

While easy to understand, this approach has a number of lmitations.

In high value categories, demand exceeds supply.  Businesses in categories like Locksmiths or Attorneys are often willing to pay a large fee for a lead.  Unfortunately, searches in these categories are rare, so while the inventory is very valuable and sells quickly and at a premium price – you just don’t have that much of it!  In fact, as the diagram below illustrates – the value of a category (from an advertising perspective) has no relationship to the volume of searches it experiences!

Search Volumes and Value by Category

Search Volumes and Value by Category

Unless you have a huge number of advertisers, for the (vast?) majority of local searches you won’t have a like for like match on the basis of category and location.  At least not one that’s truly relevant.  Providing a user with alternatives that are too far away or always providing them with the same small number of national advertisers undermines the credibility of advertising suggestions.

And the flip-side of the above, is that for many truly local advertisers you won’t have enough traffic to give them a meaningful set of leads.

Finally, in many local search use cases, users aren’t open to substitution.  A true category search – where a user is  open to suggestion and recommendation (and thus relevant advertising) is a relatively small – albeit very valuable – part of a publisher’s search inventory.  Instead, the most frequent use cases result from a user trying to complete a transaction with a business they’ve already selected.  They are most often looking for a phone number or directions.  In these cases it can be better to provide them with an ad that complements their current choice and context rather than trying to get them to substitute their choice.

Tackling the Like for Like Challenge

There are several possible – largely complementary – ways to approach this problem.  I’ll be exploring these options in some detail in future blog posts as part of the Improving Local Ad Performance Series.  Follow me on Twitter, subscribe with an RSS reader or subscribe by email so you don’t miss any of the series.  A quick summary of some of the approaches:

  1. In addition to like for like targeting, target local advertising based on context, behavior and preferences.  With appropriate analysis and targeting models it is possible to deliver relevant and complementary advertising in a way that results in a better match between available inventory and available advertising.
  2. Focus your efforts on being the ‘go to’ destination for the higher value ‘category’ or ‘research’ type searches – either broadly or within verticals.  Yelp is an example of a company that has done this by focusing on creating a community of reviewers making it a destination for people seeking opinions.  The advertising Yelp provides is primarily of the ‘like for like’ type – which is appropriate given that most people viewing review pages are in fact open to suggestion.
  3. Participate in some sort of exchange or market where you can buy traffic (i.e. by using AdWords for example) or gain access to advertisers (i.e. by working with a Yellow Page publisher for example).
  4. Focus your resources from both a publication and advertising perspective on specific verticals.

The Challenge Becomes Even More Acute in Mobile

Increasingly, local searches are occurring on mobile devices.  On the one hand, mobile devices offer the promise of even richer context information (where you are right now).  On the other hand, the more limited screen real-estate means that providing the most meaningful suggestions (or advertisements) becomes even more critical.

This post is part of a series on Improving Local Ad Performance.  To receive future installments you can:


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