People running online business knows many marketing efforts don’t immediately tie to [...]]]> I love segmentation analysis. It gives a deeper understanding about the consumer behaviors and the true value of marketing efforts. It gives a better sense of what’s happening to the outcome rather than the vanity traffic metrics that many marketers or managements love.

People running online business knows many marketing efforts don’t immediately tie to sales. Marketers fuzzily know that one day those consumers who were exposed to the ad have the brand awareness and will one day buy from your site, or else where, or even share the product info to their friends. Let’s try to cut away from that fuzziness.

Let me quickly talk about social gaming… Many social gaming companies track DAU (daily active users) or MAU (monthly active users) as KPI, because overall number of users really don’t matter when they need to make money and growth through active gamers playing their game on their platform. In most cases, those games are usually free, and as users get addicted to playing it, gaming companies would do their best to drive converting those gamers to paying gamers. They pay either through monthly subscriptions, avatars, unlocking stuff, get more stages, remove ads, etc.

In such case, cohort analysis comes into picture in their reporting and analysis practices. A cohort study or panel study is a form of longitudinal study used commonly in medicine, social science, ecology, etc. In this case, it is an analysis of factors and follows a group of people who were exposed to a particular event and tracked across time to see how their behavior change. A cohort is a group of people who share a common characteristic or experience within a defined period (e.g. downloaded the game for the first time, signed up for an email, create their account). Then this cohorts will be followed across time to see if they perform the next desirable action.

Here is an example snap shot from KissMetrics.

In this particular example, it is showing the number of people signed up during that reporting month. Then it follows that group of people in month 1, 2, 3, and so forth measuring “Signed In”. So in month March 2011, there were 1,312 people who signed up for this service, and 30% of them have came back and signed in 1 month after their initial sign up. Seems like that 30% was the all time high when compared to historical. That could indicate some correlations to some marketing activities to drive that conversion.

To me this is a great example of how powerful metrics could be to articulate the effectiveness of marketing or any business efforts to drive the outcome. Here are two ideas I randomly came up with you could do:

• Track the cohorts of Marketing Campaign XYZ on people who had been exposed to the ads for the first time. Follow them and see when they convert, or sign up for newsletter, or share content. See how changing marketing tactics or optimized ads impact the recency of the outcome.
• Track the cohorts of people who have converted to sales on your site for the first time. Then follow that cohorts to see if they come back to re-purchase something else of a higher priced items.

This analysis is pretty hard to do with traditional web anlaytics along as cohorts are defined as particular group of people who you explicitly know. In other words, if people delete their cookies or you loose track of the original cohorts then data becomes pretty dirty after several months. However, if your registrations, logins, sign up events are tied to consumer info, then you should have less challenges in tracking them.

Perhaps you can trying this with your CRM database first, and see how that come out to.

Next… what about some deep dive segmentation in web analytics…

This is not a cohort analysis, but Google Analytics advance segments could give you a pretty good deep analysis of your converting audience. It looks nothing like the cohorts analysis table, but that’s fine. You’re looking for insights and not for pretty graphs/charts.

Taking a look at my own traffic for some random period. I want to compare the new visitors traffic who came from my feeds via feedburner, and see how that compares to returning visitors from the feeds. You could compare the outcomes, and two different segments provide different results, which is great, because I know now that my returning audience through feeds are super precious. Their goal conversion rates, and AdSense response rate and eCPM are much better than my new visitors through feeds. That means I must be doing well bringing back engaged audience and they’re converting.

Now you can look at this return visitors segment’s recency report and see how recent they visit, analyze and get a good sense to see if that recency is shrinking, is it aligned to blog posts recency, etc.

To me this is the important thing that comes out of this is the “story”. There are many marketing tactics that drives spikes, and there are many common metrics that measure that impact for that moment. A lot of times we don’t know how to replicate that… Not so good… Many analysts and marketers focus too much on that. These insights or anlaysis tactics I showed you should tell you what drives the growth of your key audience, or what marketing efforts could be replicated to bring more of these lucrative audience.

Enjoy analyzing!!

Roles and Responsibilities to turn data into an asset

Many companies still think hiring that one web analytics analyst is a great investments. Most of these companies forget that digital [...]]]> My previous blog post talked about treating data as an asset, and would like to focus on the human resource in this post.

Roles and Responsibilities to turn data into an asset

Many companies still think hiring that one web analytics analyst is a great investments. Most of these companies forget that digital analytics discipline is getting more deeper and wider (or complex). I strongly agree with Avinash’s 90-10 rule where 90% should be about people/analyst than tools where it should be 10%. This post is not about that, but focusing on that 90% and really understand why we need smart engineers and analysts behind digital data. Businesses who are new into web analytics need to start thinking about analytics from end to end planning, and NOT just from hiring someone who can look at data coming out of Google Analytics, Webtrends, SiteCatalysts, etc.

What are the common digital analytics disciplines and practices companies should think about (let’s assume these companies are new to this…)

Digital analyst’s disciplines and potential expertises required

• Web Site Analytics (including eCommerce marketing)
• Media planning and buys (including but not limited to SEM, eMail, Banner Ads)
• CRM
• Social Media Monitoring
• Testings: A/B or MVT
• Modeling (marketing mix, forecasting or predictive modeling)
• VOC (voice of customer) or Consumer Insights
• Market analytics or Competitive insights

Imagine the engineering side of these data sources. The engineering side needs following accumen to help turn these data sources into readable data for these analysts.

• Data integration: engineering skills required to bring various data into company’s environment.
• Translation of data: Bad data in, Bad data out. You can still have “Good Data In and Bad Data Out”, if the folks who are working on the logical layer messing up the translation after data comes into database. This a critical piece to make sure analysts and businesses have good data to analyze.
• QA and governance: experts who understand the business needs and making sure the implemented solutions are accurate, not breaking things, and checking to make sure business has good data on hand.
• Expertise in infrastructure: Yes, all these stuff around data needs to be processed, stored, and reported. Where and how? Engineers need to understand the technical environment surrounding the data, and someone needs to own maintaining it.

Even if you’re not thinking about one head count for all of these disciplines, let’s look at where these data come from and ask your self how much level of expertises or resources are needed to “manage” all of these.

Data Sources and potential role name

Web Site Analytics // Web Analytics Implementation Engineer: Data typically comes from analytics platform’s given javascripts embedded on the website, and the analytics service provider would process the received data within a day to aggregate necessary data to report on visitors, sessions, page views, and tie all of these to user behaviors or action events. These could be implemented by web engineers, but in most of the case pure engineers would not understand what tracker means what to business. Dedicated implementation engineers usually requires a strong business acumen so they can connect the dots between javascript to business needs.

Media Planners & Buyers: Web Analytics data captures great post click data, but not on the buy side. There are many companies specialize and are dedicated to the planning, buying, and executing the ads. In digital space, these data are planned in Ad Serve tools that aren’t web analytics (obviously…). In addition, recently, with YouTube gaining momentum replacing TV in gaining eyeballs at low cost, data around these platforms are becoming important as well. YoutTube impressions are replacing the TV GRP’s (j/k).

CRM // CRM manager or analysts or CRM data engineer: In most cases CRM data are stored in a form of database. In some cases those data in house are accessible by 3rd party depending on the company’s contract/relations with the agency. In other cases, the database is hosted and managed by third party service providers (sounds like a bad idea.. but you’ll hear it time to time). Data needs be pushed into the database from some where. These managers should be the expert in the data in/out aspect as well as working with analysts to understand the customer profile by profiling the data. This discipline has two required aspects, both technical side as well as the management side. That is why it is really hard to find one person with both talent, because database engineering and managements are two different skill sets.

Social Media Monitoring // Social Media Analysts or Operations Engineer: Most of the social media monitoring enables the data through paid services or platforms (like Radian 6, CrimsonHexagon, etc.) to collect certain phrases people are talking about and less about tagging. However, from data stand point, you’ll need operations engineer to bring the data into the data warehouse. Otherwise, you’ll be sitting on 3rd party tool collecting and hosting the data for you. Good luck to you when that company goes out of business.

Anyways, Social Data has many text inputs from the users, and to mine those data, it will be challenging and hard to bring in all those data into a data warehouse. But not a problem in an era where Big Data is a commodity right? Well, think about it, you still need data in house if you really want to treat Social Data as your asset.

A/B test analyst // Test Planner // Test Creator: Any web site testing involves planning, and that means you need to have a good planner. Once great plan is locked in or approved, assuming all creative assets are final, then you’ll need to have a technical person who can enable the test by setting it up the event tracker on site. Then test needs to be set up in the Testing Tool as well.

This is a lot of work, and planning and engineering are two different skill sets. If you really want to test multiple times in a month, then business really needs a strong commitment on resources. Learnings from test and the culture built will need to be an asset for the company, so make sure to have those knowledge and learnings stay (perhaps in intranet, or corporate’s wiki site, etc.).

VOC (or Voice of Customer) // Consumer Insights: Quantitative data from analytics alone gives you an insight if you synthesize massive amounts of data to that one learning. However, you can not ignore or argue with what your customer directly tells you. When that VOC data marries with quant analytics, then it is very insightful. That means the data sources would come from these survey tracker implemented on the website, while making sure you have a good designed survey questions to answer critical business questions. Some one needs to be able to plan the end to end aspect of the discipline. One analyst can not do that especially if you’re going to take VOC to a new/next level to best learn your customer’s pain points.

Market Analytics Analysts or Competitive insights: Typically these data sources come from 3rd party paid services like ComScore or Hitwise. You may think, no heavy requirements needed, sounds easy. Yes if you end there then web analyst could take on the rest. However, if you’re in a business like buying data from POS (point of sales) or analyzing market share, then it is a whole another level of man power needed to make sense of it. Web site analyst looks at owned media alone may be missing the big picture when market share of your company is increasing/decreasing by 2x. If you ever seen those data directly from NPD or GFK, and not just Compete.com stuff, then you’re going to say ‘wow’ I need more time to analyze all these data and see how that is impacting consumer behavior online.

My intent of this article is make sure analysts are thinking about the data beyond your one segment of your digital discipline. Data experts are involved with many experts outside of marketers and they are all from data where it starts from some where. Managements who got Web Analytics 1.0 to 2.0, now needs to think about data as an asset, governance, privacy, security, data warehousing, etc. Not to mention growing number of data and sources CMO thinks marketers already have, so take action before business demands it. Start by looking at the resource on hand and the reality of the data eco system.

Should we think of web analytics data as an asset?

Web Analytics data as an asset
Generally speaking, asset is defined as “A resource with economic value that an individual, corporation or country owns or controls with the expectation that it will provide [...]]]> I’d like to get my thoughts together on following question.

Should we think of web analytics data as an asset?

Web Analytics data as an asset
Generally speaking, asset is defined as “A resource with economic value that an individual, corporation or country owns or controls with the expectation that it will provide future benefit.” It certainly sounds like useful data could be an asset. As we all know more and more companies are generating profits and contributing to country’s growth through data business. In my view, this applies to any businesses or industries. Let’s focus on Web Analytics.

Experts in the field of web analytics are well aware of the available quantitative data, highlighting few:
- Traffic to the site and by different acquisition channels
- Traffic to pages
- Actions taken on the site by the visitors
- Custom event parameters tied to user behaviors (i.e. people who came from Organic Search and hit that +1 or Like button)

So any of these worthy of calling it an asset?

Many experts will say, you’ll need to quantify the value of your data. Some will say, go analyze it by seeing what if you have an incorrect data, and review the impact on business. In my view, any data in this erra could be valuable and are worthy of treating like an asset. There would need to be priorities on selecting which data, but recent data storage and infrastructure (even for BIG data) has become a commodity. I believe that good analysts will be able to extract value out of massive amounts of data, but would definitely require good planning around it.

When data is treated like an asset, the first thing that comes to my mind is data warehousing the data. Businesses like to spend money on sales side, but if you look at companies that are making huge dollars from pure digital services, they’re treating user behavior data like the most important thing in their company. Let’s equate that to web data. So why so important??

I’m going to borrow Mark Zuckerberg’s statement in Facebook’s IPO filing –

Simply put: we don’t build services to make money; we make money to build better services. These days I think more and more people want to use services from companies that believe in something beyond simply maximizing profits.

Wow… ok so this is no brainer to me. Building great service means, understanding your consumer/customer, and continue to build better experience by continuing to optimizing the service. Nothing about the sales, but about the upper part of marketing funnel relating to the entire business. These companies championing digital data driving faster revenue growth than any traditional companies are analyzing the crap out of the massive amount of user data, and making investments to drive better engagement, shares, user experience, etc.

They are collecting, processing, and storing non-sales data and ACTING upon it. Great services then yield massive amounts of revenue opportunity. Instead of focusing on sales and review what worked or what didn’t, then try to replicate/apply the learnings, these companies look at consumer’s footprints and other non-sales measures to lead the path to better service or growth.

I can hear people say, “well I got my web analytics data in Google Analytics or in Webtrends or Omniture SiteCatalyst so that’s it right?”.

I say go do your homework and see how long they keep the data, and ask yourself do you really want to keep your company’s asset in someone else’s hands and always have some dependencies around it on third party? Not sure about you, but I’ll say “NO”.

Thankfully, these data companies realize the importance of getting data out of their platform, so most of them provide some form of API interface to query/export data outside of their GUI tool. I love running Webtrends data out of their REST API. Allowing companies to build their own data warehouse gives tremendous amount of leverage to integrate various data and perform valuable analysis beyond “web site” analytics.

Many companies not running a massive .com, but doing simple stuff out of small sites, I would still challenge them to think about the long term big picture, especially if the company is “going” to invest in digital space (could be in a form of online ads, website, CRM, etc.).
A lot of people measure future success by comparing against previous historical data. Some companies plan on historical data and profile data to learn possible opportunities.

These web data could be used in predictive modeling, marketing mix modeling, marketing media planning, CRM/Web Behavior segment targeting, etc.

SO.. web data does seem to be important right?!

3rd-party Analytics Integration (Gigya Documentation)
User profile objects that you can call on custom basis

This object represents [...]]]> So in my previous post I briefly talked about the integration of Social Data with Web Analytics platforms. Here are some more details on the data integration with Gigya.

3rd-party Analytics Integration (Gigya Documentation)
User profile objects that you can call on custom basis

This object represents the current user in the system. The User object may be retrieved with updated information upon a request to a particular method. As you can see, different social platforms provide different kinds of profile info (only when consumers opt-in), so you won’t be getting everything even if users share. Facebook seems to provide the most useful attributes.

What I’ve done is using profile information that are useful and potentially insightful. For example integration some photo thumbnails or photos are less insightful when integrated with Web Analytics. Make sure you plan on what you want to bring in and analyze first.

Some of the interesting things I have in mind to support marketers are…

There are many other application of the data with web analytics, but I’m gonna stop here.

What will be interesting is, if you’re able to export these data into CRM database, and cater your marketing efforts that speaks to the exact audience and their profile, it may lead to a better conversions. Meaning, if consumers give you permission to send email, and they’ve shared their profiel info, you can create email/tweet messages that is customized just for them. That is a very powerful marketing based on data.

Every web analytics tool is unique in how it could be set up to track the events value, so I recommend consulting with your vendor. In Google Analytics Custom Event category and actions could be the part to capture social graph info, but be careful not to pass personal information as it goes against Google’s terms.

On enterprise level analytics platform, it is worth exploring how much additional events you’re expecting to get and the best custom report view to achieve optimal reporting for integrated social graph for insights. As a Webtrends customer, I’m planning to use Webtrends Segments.

Since we can identify unlimited custom events, I’ll apply a standard custom parameters with convention naming that could easily identify the social graph parameters. I would definitely love to hear how other experts were able to leverage social graph and integrate with Web Analytics.

#1) [...]]]> I recently met with an analytics expert in Tokyo and had a discussion about the vendor relationships.
One of the discussions missing from many conferences relating to analytics is how clients build or maintain the relationships, and help web analytics vendors help client’s business to gain more value out of the vendor’s service.

#1) Treat your technical account managers (TAM) like your partner
If you have a dedicated support hours from technical account managers, it is important to have a working relationships that is healthy. Important tracking decisions could be executed well if you communicate with your partner, and explain the details on the business needs.

I’ve heard stories where clients are treating their TAMs like an intern and not leveraging the partner’s expertise and experience/learnings. In most of the cases, TAMs are well experienced and have seen many unique solutions with other clients beside yours. If you treat them like a partner and not an intern, then you can have a healthy dialogue on great solutions that you may have not thought of.

#2) They don’t know your business as much as you do
This was also mentioned at the ‘Accelerate’ conference by Web Analytics Demystified in SF.
Web Analytics vendors are good at what their do, and are the subject matter experts in their solutions. They’re facing hundreds of other clients and your business is not the only one. You should not expect your Web Analytics vendors to know 100% of your internal projects, politics, technical environment, managements, etc.

For example…
You have a fire-drill because the web site went down and important data was lost, and your TAM didn’t pick up the fact that the site was down and he/she can not help you recover data. Don’t expect your TAM to be a super man who can deliver or fix everything. This is totally non-sense.

Another example… Reorganization happend, and the key contact with Web Analytics vendor is gone, new person comes in, don’t expect your web analytics vendor to know everything in between the blank period, and 100% of the previous managements’ projects/tasks.

#3) Set realistic expectations and set priorities
In order to have your web analytics vendors deliver more value and great support, it is important to set priorities straight with your key contacts. 100% of the case, out-of-the-box reports or tag implementations will not deliver to your business expectations or needs. Make sure you have priorities and realistic expectations to have your analytics solution providers to help you get to where you’d like to be with your data infrastructure.

What I do with my TAM is, on a weekly basis we have 30min meeting to go over our task items in Google Docs to collaborate on:
- Priorities (tasks list is numbered in order of priority)
- Status details
- Timeline of expected delivery
- Open questions and items
- Type of support or follow ups
- Business impact

#4) It’s a journey
As I mentioned, out of the box solutions will not cut it in 100% of the cases. Otherwise you can stick with free Google Analytics right? :-)

In addition, it is unrealistic to have your vendor deliver everything from custom reports, tags, segments in one day. It is important to build the road map of the KPIs or metrics to track against the important business questions, tagging implementation, BI integrations, etc.

In fact, digital marketing changes a lot in one year, and expect your data tracking/reporting strategies to change as well. The relationship you have with your partner is an important part of the journey. Some people swap vendors because they’re not building the relationship or managing them right, and probably not even looking at the analytics practice as a journey. Hopefully you don’t fall into that type of management.

#5) Be honest, transparent, and have fun
Same as in marriage or having a girl/boy friend. Honesty is one of the key elements in having a healthy relationship with your partner. I believe it applies to working with any vendors. For web analytics data, it starts from good data planning and collection practice, and it has to go through the process of QA as well as a common launch process. That said, being transparent about your business needs will reflect well on the data collection practice as your vendor may have great ideas or solutions to gain great data.

Lastly, you have to have fun. Bad tensions between you and your support contact is not healthy at all. I’ve learned that having fun in the projects your working on, and sharing the excitements gained through digital metrics will build excitement and confidence in how you and your partners work and grow.

I touched about SoLoMo (Social, Local, Mobile) being the main eco-system on the emerging media and consumer behavior. [...]]]> I covered briefly on mobile analytics in my previous post , but I thought I give it a different view on how we should approach thinking mobile analytics.

I touched about SoLoMo (Social, Local, Mobile) being the main eco-system on the emerging media and consumer behavior. Because successful mobile sites or apps are embracing all these three pilars, it is absolutely important to think deeply about your mobile business’s success factors as well as the environment where the mobile is used.

A lot of us may own a smart phone, so I’m probably preaching to a choir…
Mobile site could in any form of genres and used as:
- Video, music, magazine or any content consumption
- Shopping / eCommerce
- Camera and share photo
- Shop on Amazon.com while in Best Buy
- Check into places and unlock a deal
- Play games
- Cross platform interaction. iPad/iPhone controlled apps on TV

If you think about the data points you could possibly track and tie to different events or data dimensions, it is endless. It definitely has more unique data points or dimensions than the traditional web sites. Depending on the service and number of active users, traditional web site analytics solution may not work. Especially if it is a service requiring real-time data to react to, or provide services that needs to process tera-bytes of data in a day or so.

We could already imagine services like Google, Facebook, News Media companies, or Social Gaming apps may fall into this camp where traditional website anlaytics may not work. They most likely use data managements catered to Big Data.

If you’re a traditional website analytics analyst, and meet some data anlysts who do analytics at (randomly saying…) Zynga, it is very likely that the tools, infrastructures, analytical approach are totally different. That’s not a bad thing, it just means more fun stuff to learn. :)

This difference is already setting the landscape differently in terms of skill sets required to do the job, data tracking environment, reporting practices, etc.

Just keep this in mind, that web analytics is not only about using Omniture, Webtrends, Google Analytics, Unica, Coremetrics, etc.

Shifting gears…

I came across this cool mobile marketing done in South Korea. A number two grocery chain Tesco, wanted to gain share without opening up a new store, became number one in market share by opening up their virtual store front in sub-way station. (I don’t know about now, because I would assume other grocery chain would do the same to compete.)

Source of image: BBC – Shopping by phone at South Korea’s virtual grocery – by Jason Strother

I would imagine the KPIs would not be different from traditional eCommerce. Such as…
- Traffic (traffic after people snap the photo of QR code)
- Transaction
- Revenue
- Average order size

I would imagine, the operational metrics would be very insightful, yet different from traditional website anlaytics.
- Traffic and transactions by Subway (or location)
- Top performing hours by day. My assumption is it works the best during rush hour, but maybe not because people are rushing. hmm
- Customer profile on those who purchased. Is it younger audience? women? men?
- Items performance. Is it the small incremental stuff that people forget to buy at the retail store? or is it the items people would like to carry less?
- Viral effect of promoting such virtual store fronts. I would image, doing some promos or doing such cool digital marketing will yield some buzz. How did that influence the brand?
- Payment preference for mobile transaction.

Wow…

Website analytics have many similar measures and dimension, but what is unique about mobile is the application and the operational aspect of these metrics in online and offline hybrid environment. When that kind of operational metrics are applied different, then obviously that means optimization activities are going to be totally different.

For example…
- Does it work better in non-subway environment? Should they try in air-ports?
- Do bigger graphics on products that work should be the ones they focus on?

This doesn’t mean South Korea is the only country doing cool things with mobile. If we open our eyes, we see a lot of this changes are happening in US, Japan, and many other countries.

Let’s see what you have in mind. Digital mobile marketing is already starting and let’s see how you can apply that website analytics skills to the new era of mobile!!

Not all companies may have mobile optimized site or so called m.site. However, if you get a pretty decent amount of traffic, you should check how fast the mobile segment is growing, perhaps Month-over-Month or Year-over-Year. Traffic [...]]]> What are the key things should we take into consideration when measuring mobile platforms and the user behaviors.

Not all companies may have mobile optimized site or so called m.site. However, if you get a pretty decent amount of traffic, you should check how fast the mobile segment is growing, perhaps Month-over-Month or Year-over-Year. Traffic size may be small, but I bet you anything, most of the case the growth maybe surprising if you haven’t checked it for a long time.

So.. this is where my brain is at and learned from some of the mobile initiatives I came across.

1) Browser based mobile site or Mobile App?
The tagging technology are quite different from mobile website (browser base) Vs. mobile apps like iOS or Android. The metrics and available dimension you want to measure could be different as well.

Typically the objectives and goals for mobile apps differ from the mobile site, and the consumer experience in these two different properties are different as well. While mobile apps tagging may occur by using some type of SDK library in building the app, the mobile browser site are likely to be the traditional javascript tags.

Make sure to involve your engineer and give it a lot of planning.

2) You need to go create the mobile analytics profile for mobile.
What are you waiting for? Digital analytics experts should already be thinking about creating profile to measure the mobile space, and the first easy thing you could do is create a segment of analytics data around mobile browsers or your mobile site (m.site).

3) Identify outcome, and key events
Analytics tools are useless without measuring the site’s contribution to outcome or key events.

The important thing we’ve learned in web site analytics on measuring what worked or not worked against outcomes (downloads, sales, registrations, shares, etc.) need to scale to mobile analytics practice as well.

Events could be tied to some unique mobile interaction events. Could be, but not limited to…
- video view
- check-ins
- sharing (links, image, comments, etc.)
- click-to-call
- screen swype
- promo/coupon redemption at point of sales

4) Prepare for segmentation
As mentioned in my previous post “Measuring ROI using Segmentation” segmentation is the key to getting to an insight and performance analysis.

Mobile Analytics is no different went it comes to segmentation.

The outcomes you defined could the segmentation point. For example, people who registered for newsletter on mobile site Vs. people who didn’t register on the mobile, how are they behaving differently.

Another random example: People who check-in to a store with the app, how is that different behavior different from people who don’t check-in and completed an event X. Was the average order size different?

What else to consider?!

If your site is optimized differently for mobile phone and tablets, then it could be worth looking into separating those two segments out as well.

What are the ways to identify mobile segments?
You can look at the OS or Platform or Screen Size reports and see what makes sense for you to segment the mobile data into the profile in your analytics solution.

If you have a mobile Apps, then it is definitely worth creating a profile for iOS by itself and Android in another profile if you are supporting two different platforms. Make sure you involve the app engineer, because the web analytics vendor may have unique event tags catered to mobile apps.

From reporting stand point…

Even if your objectives and goals mobile/tablets may be the same to your traditional website, there should be unique custom reports catered to mobile/tablets segment. Remember that there are many unique aspects in mobile that traditional website may not offer. (i.e. check-in, screen swype, barcode scan, sharing, using coupons/tickets, etc.)

If you recall the term “SoLoMo”, a term coined by KPCB’s John Doerr. Consumer behavior in the emerging media is revolving around Social, Location, and Mobile.

Recently, Eric Schmidt from Google has mentioned…
“They will change the world. What I’m most excited about is what the next generation of entrepreneurs can do on top of these Cloud platforms. What I do know is that the next generation of these leaders will be something involving mobile, local, social. ” — Eric Schmidt @ 2011 Dreamforce Event

From analytics stand point, we should really pay attention to capturing data and connect the data to build insights around:

• Social: How is mobile platform attributing to social activity that is impacting the business
• Location: Leverage location data to learn and bring insights to build better business strategy around physical location like store, people, services, unique interaction thru mobile, etc.
• Mobile: Champion data around measures that are unique to mobility or mobile platform

Hopefully that gave you some good sense of what to think to get started with mobile analytics. Here some links to popular web anlaytics vendors providing mobile analytics capabilities.

Resources:
Google Analytics for Mobile – Developer’s Guide

Webtrends Mobile Solution

Here is a nice mobile analytics maturity model by Webtrends.

Adobe Omniture Mobile Solution