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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2 Responses to “The Challenge of Fine Segmentation in Local Advertising”


  1. 1 Sunil Kumar January 11, 2010 at 3:51 am

    Hello,

    I read your article. Many valid points. Would it be possible for you to share the list of the top 100 categories used in chart to explain volume versus dollar?

    thx

    Sunil


  1. 1 Real-time Context Targeting « Predicting What Consumers Want to Buy Trackback on October 8, 2009 at 8:52 pm

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