Predicting What Consumers Want to Buy

iPhone: a great companion at home too

Published January 4, 2010 Apple , iPhone , media 9 Comments

There is general agreement that 2009 was (finally!) the year of mobile. And there are many predictions about how important location awareness is going to be in 2010. Agree with all that and share in the excitement.

But the thing that surprises me about my iPhone is that it has become a great in-home companion for me. It’s pretty much always by my side or in my pocket. Here’s some of the things I do with it at home:

It has replaced my watch, my alarm clock and my kitchen timer. It’s easier to use for all these functions. It automatically remembers to change time zones when I go from Edmonton to Vancouver.
I use it as a remote to control my Macbook connected to our TV to watch programs I’ve downloaded from iTunes.
I use i.TV to look at TV listings. I look up the sad statistics on the Edmonton Oilers. I still check to see when their next game is despite the apparent futility of it all.
I check in on Twitter and Google Reader while having my morning coffee.
I bought my wife a Kindle for Christmas. This lead to me downloading the iPhone Kindle app. Now I read books on it too – surprisingly good at this.
I use Skype to make phone calls. I can wander around the house and make a cup of coffee while on a call. This is the way Skype was meant to be deployed (And not to worry – I refrain from using the washroom.)
I get directions to a place before I go out to it.
I look stuff up. Often. Weather. Stock prices. What something is. Where some place is.
I listen to music on it (through one of those sound system docks) – most often with last.fm.
I send and receive email; update or check my schedule; see what’s happening on Facebook.

Let me know in the comments what else you do with your smart phone at home? I know my son plays games on his while at home. I do that sometimes as well – but to be honest it’s not a primary use case for me.

I’m thinking there is a lot of ‘untapped’ potential in the smart phone as an ‘in-home companion’.

Welcome to 2010.

Real-time Context Targeting

Published October 8, 2009 Improving Local Ad Performance , Predictabuy , advertising , analysis and optimization , context , local , real-time Leave a Comment

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 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:

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.
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.
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

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

Published October 7, 2009 Improving Local Ad Performance , advertising , analysis and optimization , geotargeting , local , publisher , user generated content , yelp 1 Comment

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

These thin slices have implications for both publishers and advertisers:

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.
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.
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

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:

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.
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.
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).
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

Published October 6, 2009 advertising , geotargeting , google , local , pay for placement , small business , strategy , yellow pages Leave a Comment

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:

Claim and verify your Google listing (a good idea any way).
Select your landing page – which defaults to a very functional Place Page provided by Google (so you can almost ignore this step).
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:

How many people saw your ad.
How many clicked on it.
How many got directions to your business.
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

Published October 3, 2009 Apple , augmented reality , local , reviews , user generated content Leave a Comment

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.

Google Patent’s My Invention to Simplify Reviews

Published October 3, 2009 augmented reality , google , mobile , reviews Leave a Comment

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.

Google’s Place Pages are Designed for Optimization

Published September 27, 2009 advertising , analysis and optimization , google , search engine optimization , sem , seo , small business , strategy 12 Comments

Google’s new Place Pages are designed for optimization which potentially makes them great landing pages. Is Google positioning itself to simplify advertising for local businesses?

The downfall of most SEM offers to local merchants is that they deliver lots of clicks but few conversions. That’s because too often nobody is optimizing the landing page (or has even defined what a conversion is). Google has now put themselves in a position to address that by allowing the landing page to be optimized. They could even have merchants use Google Voice if they want to optimize to receive calls.

What Does Designed for Optimization Mean

Recently I wrote in Picking Winners about the use of controlled experiments and A/B testing to optimize website performance. Perhaps the most widely known application of this principle is the optimization of website landing pages using tools like Google Website Optimizer.

The basic idea in landing page optimization is to empirically test the performance of several different design options against some specified conversion goal. For example, if your goal is to get people to ’sign-up’ for something you’d test different page designs and see which one performed best.

If you want to do this easily – and especially if you want to do it using some automated process – you need to adopt a web page design that is amenable to such an approach. Andrew Chen has written a great post on keeping the design consistent during A/B testing. He says that the secret is to create an open design – and gives Amazon’s home page as a classic example.

Well, it turns out that Google’s Place Pages are another excellent example of open design that allows automated optimization. Have a look at one of the example pages Google highlighted:

Google Place Page Showing Block Structure

As shown above, the page is broken in to two columns and the content is organized in to various blocks. This makes it easy for an automated process to vary both the placement and size of each of the blocks and the content shown within each block. What’s more, you can select and optimize the look of the page based on where the traffic is coming from – varying the look and feel of the page based on how the user got there. So, if you arrived at this page as a result of a search for ‘Tartine Bakery reviews’ the ‘review block’ might be much more prominently displayed.

The fact the pages are well suited for optimization doesn’t necessarily mean all that much. Google is well known for being an A/B testing fanatic. So, this may just reflect a desire to be able to more easily optimize how information is presented to users.

But it could also be a first step towards something more…

Could Google Try to Close The Optimization Gap?

Optimizing landing pages is a fairly well understood process. Unfortunately, it’s a process that few smaller businesses have the time and expertise to perform. So, it doesn’t get done. And the end result is that small businesses don’t see the expected results from clicks and become discouraged.

But now Google has designed a landing page that it’s possible for a machine to optimize.

Imagine a tool that allows a local business to set up an Adwords campaign that automatically creates and tests landing pages. The tool might suggest appropriate keyword alternatives along with appropriate landing pages and then start running the alternatives and select the combinations that deliver the best ROI. All with minimal involvement from the business owner. Google certainly has the scale and machine learning expertise to accomplish something like this.

What’s Missing

For one, Google would need local merchant’s to define some sort of ‘conversion event’. This is conceptually as easy as defining a new ‘block type’ that will appear on the landing page and be optimized. For example, a restaurant might view a phone call or an Open Table registration as a conversion event. If it’s a phone call, I imagine the merchant could be encouraged to use Google Voice to provide a closed loop analysis of the conversion event.

Perhaps more likely than having individual merchants doing this (at least in all cases) would be a small army of SEO and SEM experts doing it on the businesses behalf – but within a closed looped system managed by Google. Google could potentially create a whole new eco-system.

Updated (September 28, 2009): Lots of concern around a core issue of whether these pages are being indexed. In fact, Google representatives have weighed in the comments on posts by Erin Schonfeld at Techcrunch, Greg Sterling and Mike Blumenthal. Google is confirming that these new pages won’t be indexed directly, but they may be indexed if they are referred to by other sites.

They probably didn’t want to muddy the issue, but I couldn’t help but notice that they did NOT comment on my thesis about using these pages as landing pages!

Why I Like Adobe’s Purchase of Omniture

Published September 16, 2009 Adobe , Omniture , advertising , analysis and optimization , search engine optimization , sem , seo , social , strategy Leave a Comment

Ok, I know the folks at Adobe (yeah the Photoshop people) and Omniture (web analytics and optimization geeks) have been waiting for me to pronounce on their deal. After mulling it over a bit, I’ve decided that it is good.

Judging from the twitter chatter, some found it perplexing. And apparently the market didn’t much like it either.

I like it because it recognizes that the creation of web content should be done hand in hand with activities like Search Engine Optimization (SEO) and Search Engine Marketing (SEM) and the tools used to the analyze and measure the effectiveness of a web-site. In today’s world, there are those who create websites and those who do SEO and SEM – and they are often blissfully unaware of each other. That’s unfortunate given that the whole point of a website is to engage people and accomplish some commercial goal.

For a long time, the technical hurdles associated with the mechanics of creating a website have dominated the equation. But that continues to get easier – as it should. So rather than focus on the mechanics, you can naturally expect people to start thinking more about how to use a website as a truly effective tool. Which should lead you to think about how you are going to structure and evaluate the content.

So, I can see Adobe creating entire new classes of tools where the very way you think about and create web pages becomes much more oriented towards optimizing and measuring the content. For example, you might have a tool where the first thing you do for a new page is define the ‘objective’ for it (i.e. this page is intended to get people to register for the site). From this objective you would then have tools that would advise you on ‘best practices’ for achieving it. You would design the page from the get go to evaluate several alternatives. The code needed to manage this would just disappear in to the woodwork. The tools required to manage the revision of various content elements are part of the tool-set.

And there is potentially a very nice network effect. The more people use your tools to create pages and analyze them the more data you (can potentially) collect on what works and what doesn’t. This means you can do a better job of pro-actively advising people on what they should and shouldn’t do. This allows the creative people to spend more time exploring new things that might work rather than wasting their time on things that are pretty unlikely to work.

Think of it as ‘objective driven design’. I’m gonna let Adobe use that phrase if they like.

Of course, all that’s easy when you say it fast. And difficult to execute in practice. And I’ve glossed over at least one really important point. What one means by an ‘effective’ web-site is a moving target. Changes like social media and real-time media – not to mention changes in what people expect or want – mean that the very definition of ‘well designed’ is always shifting.

But that just makes it an interesting problem worth tackling. Time will tell.

Picking Winners

Published September 15, 2009 Microsoft , advertising , analysis and optimization , controlled experiments , search engine optimization , sem , seo 1 Comment

Web applications allow us to quickly try out new features, presentations and approaches. But people are terrible at predicting which changes are beneficial and which ones are neutral or even harmful. That’s one reason why a systematic approach to the analysis and optimization of changes through controlled experimentation is important.

At it’s simplest, controlled experimentation is just trying different approaches to a problem (which can be as simple as the color used on a web page) and measuring how user’s respond to these changes.

The paper “Online Experimentation at Microsoft” (PDF) was presented at KDD 2009 and provides a great overview with many concrete examples of actual experiments run at Microsoft. Here’s one example:

The MSN Real Estate site (http://realestate.msn.com) wanted to test different designs for their “Find a home” widget. Visitors to this widget were sent to Microsoft partner sites from which MSN Real estate earns a referral fee. Six different designs, including the incumbent, were tested.
A “contest” was run by Zaaz, the company that built the creative designs, prior to running an experiment with each person guessing which variant will win. Only three out of 21 people guessed the winner…

The winner, Treatment 5, increased revenues from referrals by almost 10% (due to increased clickthrough).

In general, the paper documents that even experienced experts can only pick the winners less than 1/3 of the time. Meaning, the other 2/3 of the time they are recommending changes that are at best neutral or at worst actually harmful.

This is a non-technical paper that provides motivation for taking an experimental approach. They also describe the many cultural barriers they encountered at Microsoft. Overall, a very good read. Highly recommended.

Of course, they recommend a very sophisticated approach. But the same principles apply in a broad range of situations. One common activity that falls in to this category is the optimization of landing pages for SEM and SEO efforts. In these situations you are usually assisted by tools that make it easy to get the statistical analysis right.

The take home message is that successful companies are learning how to fail fast forward rather than getting stuck in endless rounds of paralysis and internal arguments. Real-world experimentation can be the final arbiter.

via Greg Linden

Apple: Purveyor of Remote Controls for Our Lives

Published September 14, 2009 Apple , augmented reality , iPhone , strategy Leave a Comment

Last week Apple marched on announcing price reductions across the iPod line and various new features. The highlight was the addition of a camera and FM radio to the iPod Nano. They high-lighted the importance of the iPod Touch (an iPhone without the phone) as a gaming platform. The technocrati responded with a collective yawn — since there wasn’t anything sensational announced.

overview_hero1_20090909

To me it seemed like a well executed release of incremental products as part of a clear long-term strategy: Apple wants to the the dominant supplier of the remote controls for our lives.

Mobile devices allow us to interact with the virtual world while simultaneously interacting in the real world. In fact, with the coming explosion of Augmented Reality applications – the line between the real world and the virtual one is becoming increasingly blurred. Mobile devices allow us to consume media, produce media and communicate with people.

In Apple’s case, the original iPod focused on the consumption side of the equation – allowing us to consume first music, then videos. The ability to further consume media has been expanded to include games and the internet. What’s more, many applications are creating brand new categories of things for us to consume – like specialized maps and services such as navigation.

Of course, the iPhone also supports the creation of media and communication in many different ways. Some of these – such as voice and text – are traditional mobile features – while others are again enabled by the App eco-system – think Facebook and Skype.

The iPhone is the all-singing, all-dancing remote for our lives, followed closely by the iPod touch. But these devices are expensive — so Apple has to provide less capable devices at other price points – at least for the time being. The thing about technology is that it’s only a matter of time before iPhone capabilities become available at truly mainstream prices.

So, the significance of the new iPod Nano is simply that Apple has expanded this mass market device into one that is now capable of capturing and sharing media as well as consuming it. In some ways, this can be seen as a pragmatic interim step. One which extends Apple’s reach as the purveyor of remote controls while holding firm on a well understood price point.

I sometimes wonder why Apple doesn’t introduce more of an ‘entry-level’ iPhone. I think perhaps the answer is that they don’t have to — they simply have to wait for the economics of silicon to get there instead. When the time comes, people will gladly ditch their cell phones for a shiny new remote control for their lives. In the mean time, they can satisfy themselves with Apple’s partial remotes.

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Predicting What Consumers Want to Buy

iPhone: a great companion at home too