Archive for the 'user generated content' Category

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:

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.

Netflix Prize has lessons for local search

The Netflix Prize seeks to substantially improve the accuracy of predictions about how much someone is going to love a movie based on their movie preferences.

So what does this have to do with local search? Researchers working on this problem have found that you should ignore everything you know about the movies (the genres, the actors, etc.) and base your predictions on how people have rated them.  For local search this means we should base recommendations on people’s preferences – the businesses they like and have used – rather than categorical data about businesses.

Lessons from the Netflix Prize

From a New York Times article on the Netflix Prize:

“You can find things like ‘People who like action movies, but only if there’s a lot of explosions, and not if there’s a lot of blood. And maybe they don’t like profanity,’ ” Volinsky told me when we spoke recently. “Or it’s like ‘I like action movies, but not if they have Keanu Reeves and not if there’s a bus involved.’ ”

So, you can’t base movie recommendations on a simple categorization like ‘action movie’.  Its difficult for most people to articulate what they like or dislike about a movie. So, its better to base predictions on what you know about which movies the person likes or dislikes. In fact, researchers have consistently found that adding in categorical information about the movies doesn’t help with making predictions at all. (And there have been numerous attempts.)

Applying it to Local

The research from the Netflix Prize shows us that you shouldn’t recommend a pizza place to someone just because it’s a pizzeria (i.e. it’s category) and it’s ‘close enough’ to you.  That’s because the best choice can be influenced by many factors:

  • Am I a person who likes my pizza ’straight-up’ or exotic?
  • Am I in a rush and looking for the quickest option?
  • Am I bored and looking for something new?
  • Have I just come back from an exotic location and looking for an old favorite?

Today,  local search engines continue to rank results based primarily on  factors like category and location.  Even sites such as Yelp with extensive reviews rank and present results to users without considering the preferences of the searcher.  This is akin to simply presenting movies ranked by popularity alone.

Research coming out of the Netflix Prize shows the way towards a different way of thinking about local.

RELATED:

Forget search: local is a recommendation problem

Tracked calls are user generated content

How we think about things matters.  I enjoy finding and creating new ways of looking at things because they generate new insights which can eventually lead to disruptive change.  In fact, this blog is about exploring perspectives on mobile, local search and advertising.

And the seed for new perspectives comes from conversation.  Last week I got a new perspective on call tracking as the result of a twitter conversation (with @sebprovencher).

You can think of tracked calls (or clicks or any user action really) as a form of user generated content. In fact, especially for calls resulting from local searches, it’s an extremely valuable form of user generated content:

  1. EVERY user provides this feedback EVERY time they make a call (assuming you are tracking the calls of course); and
  2. It’s a highly structured signal – the user has taken a very explicit action to contact a business.

And, in the case of calls, you can further strengthen the signal by also keeping track of how long the caller stayed on the phone.

Utilizing User Generated Content

To be sure, this implicit user generated content is different from content like ratings and reviews.  You probably don’t want to display something like “300 people have called this person’s ad” (though, thinking about it that might actually be kind of interesting).

However, through appropriate analysis this data can be used to provide users with recommendations based on their situation and preferences.  For example, by looking at this data you can learn what people are looking for on Friday night versus Wednesday morning.  This is the sort of analysis we’re doing at Predictabuy.

And here’s another interesting thing: combining this implicit data with coventional ratings results in better recommendations than you can get by looking at the ratings alone.