Web Analytics Key Performance Indicators (KPI’s) are critical for breaking through the dataglut spewing forth from your web analytics tool.   I mean there’s a just a ton of data in web analytics, and the majority of it tends not to be very useful or applicable for improving your business performance.  While it’s wonderful to have a tool that lets you cut, cross, and slice loads of data every which way but loose, its can be a real challenge to frame the data or put it in context in a way that helps your business optimize the web site.   That’s why I like KPI’s - they identify meaningful, business-focused relationships in your analytics data.  By understanding KPI drivers, setting expectations for KPI performance, and analyzing your KPI’s toward defined goals for those KPI’s, you increase understanding of data, alleviate data confusion, and provide focus for the usage of your web analytics tool.

For those of you who don’t have a KPI strategy or who are just getting into analytics, an easy way to understand a KPI is to consider the example of when you are driving somewhere and trying to get there within a certain period of time.  If your goals is drive 60 miles (kilometers, my European friends) in exactly 60 minutes, you know that you need to drive 60 miles per hour (or KPH).  If you go faster, you will arrive early, if you go slower you won’t meet your goal and will arrive past your deadline.   So as you travel along the road, you measure the KPI of your speed. That’s what is important to measure on your trip.  Of course you may measure other KPI’s like the amount of fuel left or the miles you’ve traveled… those certainly may be KPI’s you measure.  But you definitely don’t need to measure you compression ratio or oil pressure even though it’s available data from your car.  In the same way, when you are looking at web analytics data, you don’t want to track everything, only those things that are important to your business performance toward goals. 

Several activities can assist the creation of KPI’s.  Here are a few of them:

• Determine the Business Strategy.  Why is the company funding and developing an online mission?  What is the strategy?  KPI’s can help you figure out if it’s working.  To find the KPI’s that will help, the web analyst should be asking the question how can web analytics be used to formulate, implement and evaluate cross-functional decisions that will enable an organization to achieve objectives? How will web analytics be used in the process of specifying the organization’s objectives, developing policies and plans to achieve these objectives, and allocating resources to implement the policies and plans to achieve the organization’s objectives?
• Define the Site’s Goals and why the Site Exists.  I covered this in a post a few months ago.  A understanding of why your site exists enables you to effectively use online metrics.  You need to define the purpose of your site in order to create effective KPI’s.  Once you’ve defined your site’s purpose, you are positioned to examine Web data in way that helps you determine whether your site delivers on its purpose — does it exist effectively?   Create your KPI’s, identify goals for your KPI’s, and track your performance against those goals.
• Recognize Value Drivers.  How does the business make money on the site? Monetization, in cases where profitability is important, influences what you should be measuring.  If you run a media site, you probably make money from content consumption (the recency and frequency of content consumption), conversation (social media, such as contributions or comments), and conversion (the rate at which people complete certain value driving actions, like signing up for newsletters, rss feed, webcasts, print subscriptions, or downloading certain content types, like white papers).  So you create goals for and measure KPI performance around those value drivers.
• Map Organizational Roles.  Classify your organization into audiences for your KPI’s based what they do on your web site.  You may create KPI’s around function or action of the actors who receive your KPI reports.  Function defines the group that KPI’s are focused for, such as product development or editorial.  Action defines what those people do on the site to make it successful.  By understanding function and action of key actors on your sites, you gain insight into the type of data needed in KPI’s and the number of different KPI reports you may need to roll out.
• Understand the Customer.  KPI’s purely focused on internal function and actions are important, they need to be customer focused.   If you think measuring conversion is important, while your customers tend to come to your site for informational or non-transactional purposes and then go elsewhere to convert, you may be disconnected from the reality of why your site exists.   Learn customer goals from VOC (voice of customer) data and by examining historic behavioral data of key segments.  Make sure you don’t create KPI’s that are vain or inane.  Instead create KPI’s that help you guide action internally so that your business meets the needs of your customers.

Framing your KPI development around the five bullet points I listed above will help you create KPI’s that assist your team in guiding business performance toward goals - while not forgetting to consider some of the core elements of online business: business strategy, site performance goals, value drivers, the human organization, and the customer. 

Now segment, segment, segment your KPI’s!

The infoglut in web analytics is enormous.  So much data.  Companies report that 69% of all people who consume the data don’t understand it.  How does a business go about making sense of it all?  Formulating a comprehensive KPI (Key Performance Indicator) strategy is a big part of differentiating signal from noise and directing appropriate tool usage.  We’ve all heard about KPI’s before.  They are ratios or derivatives of metrics that pinpoint critical, business relevant web performance.   My good friend, Eric, even wrote a book (a BIG one) about it. 

The process of moving an organization through KPI Change Management starts with a well formulated plan for doing so.  Here are some tips for formulating your KPI plan: 

• Educate senior management and get managerial buy-in.  Education and buy-in can take shape via a number of methods.  Maybe you publish and circulate an internal-only white paper about the importance of KPI’s measurement.  Maybe you leave Eric’s book on the chair of your C-level executives.  Perhaps you hold a meeting and present the web site optimization process and how measurement via KPI’s provides the foundational informational on which to make site optimization decisions.  Perhaps you take your boss out to lunch and explain that basic reporting and tool access is helpful, but “Web analytics is hard” and that KPI’s give context to the data to staff that’s otherwise somewhat confused about what they pull for the tool.  You explain that KPI’s provide a focal point for centering analysis around business goals.  Whatever the method, the goal is managerial approval that “yes, you can do KPI’s.”
• Determine the audience for the KPI’s and train them.The importance of KPI’s will vary by stakeholder, and your KPI strategy needs to take that into account. Different segments of stakeholders will be interested in specific KPI’s, and you must accommodate that need.  As an analyst, you should identify the functional roles and job responsibilities of the people who are going to receive KPI reports.   Everyone may not be the right choice (though it could be), and it may make sense to concentrate a KPI rollout on the needs of the few or it may make sense to “go broad.”  Follow up with comprehensive training about your KPI project and how KPI’s can most effectively be used.
• Start with simple, well-qualified, highly relevant KPI’s.  While some folks with want to throw a “kitchen sink” strategy at KPI’s.  That’s a mistake.  If you report more than 5 to 10 KPI’s (imho) per stakeholding group you may end up with a set of unworkable, confusing, and neglected reports.  It’s better to report just a few, well qualified, highly relevant KPI’s.  How do you qualify them? By mapping KPI’s to important business objectives.  How do you know they are highly-relevant? Because you’ve compelled management to buy-in and to agree that they are critical indicators of site success. 
• Elicit the business goals for the KPI’s, compare KPI’s to goals, and report associated variances (i.e. deviations). Make sure you have determined business performance goals for KPI’s.  Goals give context for performance. It’s that simple.  Without goals, you have no context for determining what’s good and what’s bad.  If your conversion rate KPI is 5%.  Great!  So what though?  If you know your goal is 3%.  Awesome job.  If you know your goal is 10%.  Stop reading now, and get back to work - you have much work cut out for you. 
• Identify the frequency and format for reporting.  You need to determine a frequency that is timely and sustainable, and the format in which you present KPI reports needs to common enough that people can easily examine the data. Perhaps you deliver the reporting in Excel, make it available directly in your tool, use Xcellius, or create reports using a BI tool. 
• Automate the delivery of the reporting.Without automation, you may put on the Report Monkey suit and enter Excel hell.  Critical to the successful rollout of any KPI reporting is an automation plan.  Do you email reports, put them in a shared directory, create a set of reports in the tool and provide access, or deliver them in weekly presentations?  The best choice is the option that gets people to use them, listen, and understand what you are trying to do with KPI’s.
• Following the reporting up with analysis and guidance.  Depending on the size and scale or your organization and the resources you have to work with, it may not be possible to provide every stakeholder with detailed analysis.  But you need to do your best to follow up KPI reporting with true analysis and guidance.  Why are KPI’s going up or down?  What are the drivers of the changes? 
• Segment, segment, segment. Site level KPI’s are helpful in understanding overall audience and customer behavior, but they hide important details.  When you slice a KPI by a specific segment, you will realize insights that help you conclude what action to take next.  Overall site repeat visit rate is 37%, but the repeat visit rate for customers who use your “product lookup tool” is 96%.  What does that data indicate about how you market the site, or about why people are coming to the site? 
• Test, test, test.  As you measure > report > analyze > guide based on KPI’s you will undoubtedly determine actions to take on the site.  You should be testing the hypothesis behind these actions via controlled experimentation.   

There’s obviously a lot more to talk about here - from what constitutes a good KPI, to what types of KPI’s different stakeholders should examine, to what are the best KPI’s for particular site types and more.  I guess there’s more blog posts for that, but in the meantime I hope you’ve found this blogviation useful.  Let me know if you have any thoughts to share.

Web analytics tools can be outgrown by companies, just like pants can be outgrown by people.  Over time, an analytics tool may no longer fit organizational needs or be well suited to deliver on complex organizational requirements for site optimization and multichannel integration (among other things). 

This topic led me Silicon Valley this week thanks to an invitation from Unica I headed over to Webex headquarters to record a preso in their new LiveStream studios.  A few other folks also participated in the production.  In attendance was Fireclick founder and all around cool guy, Steve O’Brien (also VP Internet Marketing at Unica), my pal and fellow blogger, Avinash Kaushik (of ZQ Insights and MarketMotive), the genial Elana Anderson (founder of NxtERA marketing and former VP Marketing Research at Forrester), the excellent bloke and all around nice guy from across the pond, Dr Alan Hall (Director of Analytics at SCL Analytics), forthcoming author and savvy multichannel marketer, Akin Arikan (Analytics Evangelist at Unica), and the jet-setting smarty Karen Hudgins (marketer at Unica).   We all had a blast getting down to business at Webex studios.  And eating at places like Burk’s and Parcel 104 - both excellent restaurants.

The title of my preso was “Symptoms you’ve Outgrown your Web Analytics Tool,” such as:

• Inadequate segmentation
• Poor visualization
• No custom reporting
• Limited Integration (covered in Part 2)
• Cost (covered in Part 2)

While I did manuscript the speech (hey I was being recorded!), it’s way too long to post, so I figured you all might enjoy me paraphrasing my own content.  So as I sit here on a Jetblue redeye, here it goes:

• Inadequate Segmentation.  Segmentation in web analytics describes the activity of categorizing and dividing your online audience and customers by their various attributes.  For example, you might choose to segment your audience based on their demographic location information to determine if a visitors from a certain geography have a higher conversion rate or behave differently on your site than visitors from another geography.  Or you may choose to use your web analytics tool to define a segment that you want to track such as visitors who clicked on a paid search term and did not convert, but came back to the site within one week.  Sounds easy, right? 

But not all web analytics tools can segment data.  The proprietary tool you run in-house may not be able to segment data.  Your expensive vendor solution may not be able to segment data easily.  Many tools only provide simple reports.  Yet basic reporting is insufficient for web analytics.  In order to understand new data relationships and the effectiveness of marketing campaigns to your massive online audience, you need to a web analytics tool that can segment data.

The idea being to do what I describe in this post on web analytics segmentation:

• Define a segment
• Identify expected segment behavior. 
• Measure current segment behavior. 
• Create “optimization hypotheses.” 
• Optimize content, offerings, user experience, and other site elements. 

How does your web analytics tool fit into the process of segementation that I described?  Does it?  Can your tool assist you in this process?  If not, you may have a nice IT tool that reports web metrics, not a marketing tool that enables you to optimize your site and landing pages to offer the best possible messages to known online segments.

• Poor Visualization.  Pictures are worth a thousand words.  Your stakeholders are already overwhelmed with data before ever presenting reports with a whole bunch of numbers.  Not everyone is quantitative.  Some stakeholders just want to be able to quickly digest data, and they prefer an aesthetically pleasing visualization instead of a spreadsheet. 

Data visualization helps stakeholders interpret important data at a glance.  Visualization helps reporting comprehension.  Good visualizations are important when you want to:

• Highlight key trends in the data
• Compare counts of things
• Identify multidimensional relationships using cube visualizations

If your tool can’t visualize your web analytics data, and you need that visualized data to assist comprehension, act as sales tool in a presentation, or as marketing collateral in a report, you have outgrown your web analytics tool.

• No Custom Reporting.  An acute inability to deliver customized reporting that meets the needs a diverse group of stakeholders is one of the signs you’ve outgrown your current web analytics tool.  The problem manifests itself in sheer frustration because people can’t get the data they need. Over time this will cause people to lose faith in web analytics because the data isn’t relevant to their jobs.  Some of the symptoms include:
  • Problems creating KPI’s.  To manage online performance, you need to be able to define Key Performance Indicators.  For example, you may want to define a view:visit ratio, that is the number of page views generated per visit.  You need to define this equation in your web analytics tool.  If you can’t define such simple KPI’s, you are limiting your success at web analytics.
  • You may not have reporting that identifies conversion rates and allows you to define custom metrics for channels like RSS, Newsletters, and Internal and External Search.  A powerful web analytics tool will be able to build custom reporting for conversion rates and other KPI’s by online channel.
  • You may have a limited ability to build reports with filtered data, such as viewing reporting of top pages on a particular day or combinations of days, or filtering data by referrer, geography, or time.
  • No ability to add core web analytics dimensions to your reports, such as creating and saving a report that shows all referrers, their keywords, conversion and bounce rates for each city in the United States.
  • Quite simply, you may only have one set of reports and you can’t build new ones at all.

In the real world practice of web analytics, you need a web analytics tool that has the ability to build as many custom reports as you want when doing analysis, to filter, add metrics, to use dimensions, do AB tests, and save all that stuff until your heart is content so you can meet business goals.  You can’t be restrained by the inability of your current tool to create custom reports. If you are you may have outgrown your web analytics tool.

I’ll post part 2 this weekend as I recover from the jet lag…     Part 2 is up, click here to view it! 

Targeting refers to the process of identifying characteristics of a segment so that relevant content may be matched to it and delivered at a time when the segment is most open to the message. The idea is the right content to the right visitor at the right time (optimally in real time). 

For example, you may visit a site, and see some type of ad unit calling out at you to “meet singles in <insert_your_city>.” When browsing real estate you may see ad units for realtors and mortgage companies.  After entering a keyword such as “car prices” and clickingthrough the SERP, you may see an ad for a local car dealer.   That’s targeting in a nutshell.  It’s simple: 

1. Visitor X has these attributes. 
2. We have content that we think will appeal to Vistor X’s attributes. 
3. Let’s show that content. 

While targeting has helped to increase ad clickthrough rates, it’s far from an ideal science.  Current methods for targeting have inefficiencies.  What if Visitor X just bought a new car after his recent marriage?  Unless the targeting engine is made aware of the visitor’s current state, the targeting may be off and not yield desired results. 

Even with limitations around “current awareness” targeting is perceived in the Internet industry as a crucial activity for maximizing the effectiveness of advertising and content.  Targeting is the next stage after A/B and multivariate testing.  Once you determine the preference of segments based on testing, you identify content to target. 

In new media, targeting is something associated with paid search campaigning, ad serving, and content optimization.  It’s not uncommon for targeting activities to be based on:

• Category and sub-category.  Conceptual constructs like “categories” of topics on a media web site or products on an ecommerce site can be targeted to include certain types of ads or messages.  The notion of a “zone” fits in here as well.  The idea is that if visitors are browsing in your category for “hardware floors” you could offer them an ad or content specific to “flooring installation services.” 
• Geography.  Country, region, city, state, DMA are all targetable constructs.  You may choose target people surfing from 02141 (Cambridge, MA) an ad for pre-sale Red Sox tix or content about Mike Lowell’s recent contract.
• Browsing environment such as the connection speed, type of browser, operating system, user software, domain, and ISP.  An ad network serves an ad for Verizon DSL to a modem-based surfer by detecting the visitor’s browsing environment.
• Time.  The idea of only showing content during specific periods of time is called “parting.”  Common types include day-parting and season-parting.  For example, a B2B site only choosing to show ads for a particular manufacturers product during business hours – the site’s busiest time of day – would be an example of day parting.
• Keyword.  There are many different types of keyword targeting.  Google does fantastic things with targeting ads based on the keywords in queries.  Content Management Systems can target content based on on-site search keywords or referring keywords.  “Keywords” may be associated as metadata with site sections or pages, similar to a zone or a category targeting on an ad server.  Once a page is associated with “keyword” metadata, you can tell your server to target that keyword (and all pages where it exists as metadata).  If two categories each with different content share a targetable keyword, I can target ads across both categories to pages tagged with that specific keyword.
• Language.  When a language is set, you can target ads to visitors with that setting. Think Google.  Keep in mind that when you target by language, the creative copy is not translated. 
• Demographics. If the ad server is aware of a segment’s demographics, such as age, gender, income, title, purchasing power, and so on, an ad can be targeted on that basis.  Sometimes this is called “profile targeting.”
• Context.  Think of Google AdSense and how it matches ads based on the semantics in site content.  Now you understand content targeting based on context.
• Profile.  Targeting is possible based on conclusions drawn and rules created from the known attributes (such as purchasing propensity) about and individual or segment.

Enter one of the holy grails of online advertising and new media: “behavioral targeting” – an advanced form of targeting. Behavioral targeting refers to the process in which content is shown to a visitor based on the web sites they visit (or have visited) and the actions they take on those sites.  

Behavioral targeting involves:

1. Knowing where a visitor “comes from” and what they’ve done in the past. 
2. Determining the context of the visitor on the site. 
3. Detecting the visitor’s current behavior.
4. Serving relevant content and/or ads matched to the behavior.

By understanding the visitor’s past history, current state, and most recent behavior the marketer can target content in order to influence some point in the customer buying cycle- often at the stages of awareness and consideration.

So where does web analytics come in?  You would think web analytics data from “web analytics” technology would provide the seed data for enabling “targeting.”  It can be but in most cases, targeting is a function provided by the ad server or network or another technology called the “behavioral targeting platform,” not the analytics tool… the data does not come directly from the web analytics tool.  I’d love to hear how well (or if at all) Omniture TouchClarity is integrated with Omniture Discover or other offerings. 

In order to make web analytics data useful for targeting (if you can at all), you will need to use your web analytics data to:

1. Define segments to target (hard to export from web analytics tools)
2. Feed those segments and associated behavioral data to another tool (achievable if you own your data and run a tool in-house.  Harder and more costly if not).
3. Report on segment performance after targeting (that requires employing the right people and enabling them with the right tools)..
4. Analyze segment performance after targeting (again employ the right people and enable them with the appropriate tools and resources).

While I’ve only covered a very little bit about “targeting” and even less about “behavioral targeting” in the context of web analytics, I hope that my simple description of current methods for targeting and some thinking about “what is BT” will help you understand the emerging ecosystem in which analytics tool are interoperating now and will interoperate in the future.

Market segmentation existed long before web analytics.  It’s a method for dividing a population into specific groups (segments) that share one or more characteristics.  The goal of segmentation is to maximize future value of that segment by optimizing your marketing mix.

Segment analysis will tell you different things about your audience than you will realize from studying overall population metrics.  In traditional market research, segments are created from demographics (such as age), psychographics (such as attitude), geography (such as zip code), behavior (such as usage patterns), and value (revenue earned and cost).

Using a web analytics tool to segment your online audience requires a bit of upfront thinking and requirements gathering before getting down to business.  Like all things web analytics, segmentation requires process.  Here are some tips that may help you create a process for web analytics segmentation:

• Determine your business objectives.  Like everything in web analytics, you can’t optimize what you haven’t defined as a goal.  A business objective driving segmentation might be to “increase conversion rate (over historical numbers)” or “to improve frequency” by offering something valuable to that segment.
• Define segments. Basic dimensions for segmentation in web analytics include: new visitors, repeat visitors, geography, time, referrer, keyword, and campaign type.  Many more dimensions and attributes can be used for segmentation too.
• Identify expected segment behavior.  By reconciling goals, historic performance data, and VOC research, you should be able to identify the expected behavior of the segment.  If your business objective is to “increase conversion rate,” your expected segment behavior might be to “complete the form” or “click on a link.”
• Measure current segment behavior. Sounds easy, right, but it will take system configuration and the right tool.  Pages may need to be (re)instrumented, tracking codes may need to be applied, query string parameters may need to be parsed, and in the worse case dimensions you want to segment or the metrics you may want to measure against may not be available in your web analytics tool.  For example, how would you use your tool identify the “conversion rate” for a segment of repeat visitors from newsletter X who came from Tokyo and previously downloaded a whitepaper?
• Create “optimization hypotheses.”  Once you’ve measured current behavior, create a hypothesis or hypotheses to test in order to optimize the behavior.  You may want to perform controlled experimentation whether a simple AB test (i.e. champion/challenger), multivariate test, or experience test.  For example, I may have detected that repeat visitors from Newsletter X responded better to Y offer after being exposed to a certain element than those visitors in the same segment who did were not exposed.  That element could have been a content theme, offer, call to action, creative, and so on.  Thus, I might create a hypothesis to test that combines elements of the user experience that I feel are key to persuading the behavior and thus fulfilling the business objective.
• Optimize content, offerings, user experience, and other site elements.  Based on your hypothesis, make subsequent changes to the elements that you think will drive the desired segment behavior.  For example, you may split traffic to two landing pages each with a completely different offer, creative, and call to action.  Or you may choose to switch out specific elements on one landing page (such as an image or call to action) using multivariate methods just to get Visitor X to “complete that form” or “click that link” to improve your “conversion rate.”
• Analyze segment behavior against hypothesis.  How did the segment perform against expected behavior and business objectives based on testing your hypotheses?  Tools that provide drill-down/drill-up and cross-dimensional capability allow to analyze segments and answer such questions. The tools I’m talking about are advanced and powerful, such as Unica NetInsight, Visual Sciences Visual Site, Omniture Discover, and WebTrends Marketing Warehouse.  Capabilities for segmentation analytics vary by tool, so make sure to dig deep into the offerings because not all tools with let you correlate metrics like “conversion rate” with dimensions like “keyword,” let alone build complex multi-dimensional segments.  In fact, some free web analytics don’t allow you to segment data at all (just filter it)!
• Go with what works.  Web analytics segmentation analysis will let you know what appeals to and works for a segment.  Success with web analytics segmentation means that you met your business goals and improved key performance of that segment.  Rinse, lather, and repeat the segmentation analysis and optimization process so your campaign outperforms and margins soar!

As a result of well-executed web analytics segment analysis and hypothesis testing you can realize new value in your customers and new opportunities in your campaigns.