Archive for October, 2009

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:

Simple Product, Flat Fee, Proven Performance

Simple product, flat fee, proven performance – those are the ingredients for a successful local advertising offering to small and medium sized businesses.  Google’s new Local Listing Ads seem to have the right ingredients.  There are lessons here for all local advertisers.

Simple Product

Small business owners are busy running their business.  They don’t have the time or inclination to figure out complex products.

In Google’s case the offering is simple to set-up and easy to understand.  To set-up up local listing ads, you just have to:

  1. Claim and verify your Google listing (a good idea any way).
  2. Select your landing page – which defaults to a very functional Place Page provided by Google (so you can almost ignore this step).
  3. Select the categories where you want the ad to appear.

Google then creates your ad automatically (based on the information in your listing) and presents it based on the category  a user is searching, the location where they are searching and the location of your business.  All optimization is performed by Google.  There are no controls to tweak and monitor.  The ads automatically include a tracking number (more on this below)

Flat Fee

The business owner pays a flat monthly fee which is apparently based on their location and the categories they’ve targeted.  There is no bidding – it’s no haggle-free pricing.

Oh – and the first month is free.

Proven Performance

Google provides detailed information through Local Business Center that includes:

  1. How many people saw your ad.
  2. How many clicked on it.
  3. How many got directions to your business.
  4. How many people called your business.

And – whenever you receive a call you get a whisper telling you ‘this call brought to you by Google’.

The service is fully transparent.  At the end of the first Free month a business owner will easily be able to assess whether or not the service is providing value to them for the fee they are paying.

Applying the Recipe

All providers of local advertising can follow the same recipe:

Simple Product – This has always been a strength of traditional media like the print yellow pages.  People understand how the product works.  Someone visits you in person to set the product up!  But many digital offerings fall short by failing to ensure a functional landing page is used.  Google has addressed this with their Place Pages which are designed for optimization.  A landing page is an integral part of a complete digital solution – without one there are a lot of wasted clicks.

Flat Fee – I’ve said this many times.  Small businesses want simple pricing – combined with proven, transparent performance.  People too often link the idea of performance driven advertising with variable, performance driven pricing.  This just scares a lot of small business people.

Proven Performance – This is the most important part: you have to deliver the leads to the merchants and PROVE that you’ve delivered those leads.  Google’s service is fully transparent.  As a merchant you don’t control where and when you ad get’s placed, but you do know how well it’s performing and can choose to carry on or not.  As I’ve written before, all forms of advertising should be tracked – including print media.  Imagine a small business owner hearing ‘this call brought to you by the print yellow pages’ every time someone called a number from the book.  That would prove value in the media to them.

More on Local Listing Ads from Mike Blumenthal and Greg Sterling.

Want a review of your local advertising product  strategy?   Contact me at eric AT predictabuy.com.

5 Ways to Simplify Mobile Reviews

You can never have too much data – especially when it comes to local reviews.  So for developers of local, mobile applications its worth looking for ways to simplify the process of capturing reviews.

So, here’s a list of 5 ways mobile application developers can simplify how a user identifies the business they want to review.  Here’s the scenario I’ll explore: I’ve just had a meal at a restaurant and want to quickly identify the business and give it a review.

1. Use a photograph of the menu

Take a picture of the menu and use software to automatically recognize the restaurant based on the picture.  The SnapTell iPhone application which provides ‘visual product search’ is a good example of this principle in action.   Now, just take that technology and apply it to local reviews.

Also uses: geo-location (GPS, cell-towers, wi-fi) as a hint to the image processor.

The challenge: photographing and tagging all those menus.  The crowds can help you out on this.  Restaurant owners might even be motivated.

2. Use a photograph of a code on the menu

Take a picture of a special code (likely a 2 dimensional bar code) somewhere on the menu.  Probably much more reliable.   You also get to engage the restaurant owner as an active participant in the process.  Google recently issued a patent on this idea.

Also uses: probably doesn’t need much help, a 2-D bar code would probably be reliable by itself.  That’s an advantage.

The challenge: getting restaurant owners to re-print their menus with 2 dimensional bar-codes.

3.  Use the restaurant’s wi-fi or blue-tooth signature

The restaurant could be identified by it’s wi-fi or blue-tooth signature.  You could even have the restaurant owner install a device explicitly for the purpose of being identified.

When the user opens the review application, you automatically present them with the restaurant based on the detected signature.  In a dense urban area, you might present them with a few different options on the screen.

Also uses: presents options to the user and gets confirmation/feedback from them.

The challenge: tagging all those signatures.  But others might be doing that anyway.  This might just become part of the general ‘geo-location’ infra-structure.

4.  Use location assisted auto-complete

The review app could use location-assisted auto-complete to quickly pick the restaurant to review.  Location is determined using GPS, cell-tower location, wi-fi or bluetooth signatures.  The user starts typing name of the restaurant and it auto-completes based on knowledge of place.  In most cases, the user will only have to type a few characters.

Also uses: The keyboard for input and a variety of geo-location technologies.

The challenge: geo-location information sometimes isn’t very accurate, so you need to make sure the auto-completion algorithm casts a wide enough net.  You also need geo-references for all the businesses.  But this one feels ready to implement now.

5.  Use augmented reality

Point your video camera at the outside (or possibly the inside) of the restaurant – see the name of the restaurant on the screen – pick it and enter your review.  Augmented reality is a hot-topic right now.  This one has sizzle, but I’m not sure it’s as practical as some of the other approaches.

Also uses: depends on accurate geo-location and a compass.

The challenge: accurate geo-location and tagged photographs of all those places.

More Reading

All of these suggestions are made possible by exploiting the array of new sensors available on mobile phones – which, as I’ve written previously, is turning them in to the Remote Control for Our Lives.

Recently, Tim O’Reilly has been promoting the idea of Web Squared – the evolution of Web 2.0 made possible (in part) by the sensors in phones.  These five suggestions are  an application of these principles to local reviews.

Google Patent’s My Invention to Simplify Reviews

Google has just published a patent for a process to simplify creating reviews using a smart-phone.  Bill Slawski describes the patent on his SEO by the Sea blog.

In simple terms, the idea would be to have UPC codes printed on something like restaurant menus.  Then you use the camera on your phone to photograph the code which automatically identifies the restaurant and lets you link your review to the restaurant.  The use of the code and the camera is intended to be faster and more convenient than having to enter the name of the restaurant manually.  The broad goal is to make it very easy for users to provide feedback.

And as Mike Blumenthal pointed out in a tweet, one nice thing about this process is that you would actively engage local merchants in the process.  Of course, that’s also the biggest hurdle — you have to get all those businesses to use your code.   Fortunately, there are alternative ways to simplify the process.  More on that in a future blog post.

Here’s the funny thing.  I remember discussing this concept with a colleague sitting in an airport in the fall of 2007.  Google filed their patent in March of 2008.  Of course, I didn’t disclose anything and I didn’t file a patent of my own.  So, Google wins.  And my generally ambiguous feeling about the worth of these kinds of patents continues.  I guess I need to either write my own patents or disclose the ideas on my blog in sufficient detail to prevent patents.


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