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Web Analytics - Confluence Digital

6 Questions You Should Ask When Developing a Web Analytics Strategy

By | The D-Blog | No Comments

Photo: Sculpture of General Sun Tzu - Confluence Digital Blog“All men can see these tactics whereby I conquer, but what none can see is the strategy out of which victory is evolved.”
Sun Tzu, The Art of War

I’ve got a pet peeve. It’s when I hear someone talk about tactics and claim they are discussing strategy. Or vice versa. It happens time and time again and too often it comes from executives and others who should know better.

Let’s for a moment review the definitions of Vision, Strategy and Tactics:

  • Vision: what you desire the organization to be; your dream.
  • Strategy: what and why you are going to do to achieve your vision.
  • Tactics: how you will achieve your strategy, who will execute and when it will occur.

This rings a bell, right? Whether we remember this from our days in business school or had to double check in Wikipedia, we can hopefully agree on this. So why do we so often confuse strategy and tactics?

One likely reason is the blocking-and-tackling nature of managing a complex business or organization in our increasingly fast-paced world (not to mention the added stress of the current economy). This is particularly apparent in the digital space where “your competitor is but a click away” and where new tactics are invented on a (nearly) daily basis.  As managers of people and processes we are typically responsible for keeping teams moving forward towards multiple goals. And we’re hyper-aware of what needs to get done today to stay on track. It’s easy to get caught up in day-to-day tactics. But to be truly effective, all this effort should be managed within the context of a larger vision and a strategy to support it.

Web analytics strategy vs tactics

To illustrate this point about strategy vs tactics, let’s look at web analytics, since it is critical to effective digital marketing strategy, planning and execution.

The emergence of the web analytics practice has never been driven by organizational vision and strategy (web analytics firms excepted). It’s extremely rare that a data driven culture is part of the over-arching corporate vision. Often the analytics culture evolves from the depths of the organization. Through understanding, embracing and the willingness to push the analytics mindset up through the organization, a hard fought battle is being won with data. But how do you integrate analytics into the culture of an organization? Add to the challenge that Web Analytics as a discipline is still in its nascency.

Take a look at your organization’s regular web analytics collecting activities. Are your efforts aligned with and supporting a web analytics strategy? Some telltale signs that you’re mired in tactics are easy to spot.

Measurement matters

As we enter a new era where data matters up, down and across the organization – and yes marketers, I’m looking at you – we need to embrace analytics strategy and execute supporting tactics. My objective for this post isn’t to school you on defining an analytics strategy but rather to pose questions and get you thinking. You should ask yourself, “do we have a strategy or are we executing a sequence of tactics?” How would you know? To determine whether your efforts are aligned with and supporting of a web analytics strategy, take a look at your organization’s regular web analytics collecting activities. Are you capturing metrics that tie to key performance indicators that help you evaluate progress towards organizational goals? Or are you just measuring everything and producing reports that get shelved at the end of the day? Data collection that does not translate into actionable business intelligence does not follow a strategy.

Who, what, when, where, why and how are your friends

You can rarely go wrong if you start by asking the six important questions we know we should, but often forget to ask related to; who, what, when, where, why and how.

1. Who is the primary customer for analytics?

2. What critical performance variables are we tracking?

3. When does the recipient expect the deliverable?

4. Where will the organization realize the impact of web analytics?

5. Why are business decisions in need of web analytics data support?

6. How will the loop be closed on business decisions that utilize web analytics?

 

Upon answering these questions you should have a clear understanding of what your strategy should be and the tactics required to support the strategy. A very generic strategy statement would look like this:

Our organization uses web analytics data to provide timely business intelligence to enable key decision points to be made based on information that enables a competitive advantage.

The strategic statement developed for your organization should be specific to organizational web analytic needs. When you think you’ve got the answer for each of the questions above, have your team ask, “This is important because…” to critically evaluate the answer and drill down to the core need.

Looking for a little more? Read John Lovett’s Defining A Web Analytics Strategy: A Manifesto.

Have you experienced challenges in getting your organization to adopt and embrace web analytics? Share your experiences with us.

For more on the topic of web analytics, check out the following related posts:

Takeaways From the Seattle WAA Spring Event: Measuring Social Media
Web Analytics as a Business: A Brief History

A Mini Primer on Choosing the Right Metrics for Your Website

By | The D-Blog | No Comments

Photo: Measuring Tape - Confluence Digital BlogYou’ll hear us talk about data, metrics and measurement all the time around the Confluence office. We’re unabashed geeks. One of our favorite quotes is: “If you can’t measure it, you can’t manage it”. It is variously (and credibly) attributed to Peter Drucker, W. Edwards Deming and four or five other notorious individuals.

Even the juiciest web analytics data is just a bunch of numbers, meaningless unless it contributes to key performance indicators (KPIs). KPIs are measures that translate raw data about visitor activity on a site into actionable business intelligence. KPIs function as trackable benchmarks that enable decision makers to evaluate the performance of a site over time as well as define, manage and track progress towards business goals.

So, what’s your favorite KPI?

If you haven’t thought about this, you’re not alone. Many companies collect analytics data about their site without giving it much thought. So, inspired by the excellent online WAA-UBC web analytics course we completed a few months ago, we thought we’d provide some basic guidelines on what constitutes a KPI and how to figure out which measures to pick for your website.

How to identify KPIs for your site

To determine what KPI is most relevant for a website you first need to know what is the main purpose (goal) of the website. This can get tricky since many (most) websites can have more than one goal, for e.g. driving visitors towards online sales (eCommerce) or to fill out a contact form for the purpose of lead generation. Success would be defined in different terms for these two activities. Luckily analytics allows for data segmentation, so it’s a bit more complicated, but not impossible to define KPIs for one part of the site and track them separately from a different part of the site.

In general most websites fulfill one of the following four purposes:

eCommerce

The purpose of these sites is to get visitors to complete an online purchase. ECommerce sites come in all shapes and sizes from mom-and-pop shops with a digital storefront to the 800 lb. Gorilla, Amazon.com. The purpose of an eCommerce site is to generate revenue, pure and simple.

Top KPIs for eCommerce sites include:

(1) Order conversion rate – the ratio of visitors to completed orders.

(2) Checkout conversion rate – the ratio of initiated checkouts to completed orders.

(3) Average order value (AOV) – the amount of the average purchase.

(4) Average visit value – the ratio of orders to site visits.

(5) Customer loyalty – ratio of new to existing customers, and

(6) Search engine referrals – the ratio of referrals from natural and paid search results to the site as benchmarked against industry average(s).

Lead Generation

The primary goal of lead generation sites is to get visitors to call the sales department, submit a sign up form, subscribe to an email list or newsletter or otherwise leave contact information enabling a follow-up call. Businesses that do this typically sell products or services that require careful consideration and represent a significant investment of money or long-term customer commitment. Those types of transactions are information intensive, rely on trust and typically require personal contact with a sales representative for purchase completion. Insurance, investment and mortgage companies are examples of such sites.

Top KPIs for Lead Gen sites include:

(1) Lead conversion rate: the ratio of site visitors to leads generated.

(2) Cost-per-lead (CPL): the ratio of marketing costs to total leads generated.

(3) Single access ratio (bounce rate: the ratio of single page visits, where a visitor only looks at the first page they access (aka landing page or entry page, quite often the home page) to total number of pages visited for the site.

(4) Traffic concentration (page popularity): the ratio of visitors to a page or section of the site to total number of site visitors.

Rich Content

The purpose of content sites is to attract visitors and keep them engaged for as long as possible. They provide interesting, engaging and regularly updated content, often in the form of expert articles or media, which is provided “free of charge” to the public and being supported by advertisers whose ads are displayed to the visitors as they read and browse the site. Some content sites function as corollaries to offline content, for e.g. elaborating on magazine articles or television programming to drive sales and other offline purchases. Examples of content sites include ESPN.com and NewYorkTimes.com. As such content sites rely on fresh, engaging content that has visitors coming back again and again and spending time on the site to attract ad revenue to the site and the offline property as well.

The most relevant KPIs for content sites include:

(1) Depth of visit: the ratio of page views to visits.

(2) Returning visitors: the number of unique visitors who visited the site at least once before. This is a tricky metric since some 30% of people regularly clear their cookies, removing our ability to identify returning visitors as such – they get re-recorded as “first time visitors” again.

(3) New visitor percentage: the ratio of new visitors to unique visitors.

(4) Page/content depth: the ratio of page views to visitors at the page or content level. It is a good idea to measure strategically important (key) content separately to observe changes in engagement over time.

(5) Visit duration: how long visitors stay on a specific page or on the site as a whole.

Customer Service and Support

Customer service and support sites typically have two goals: 1) increase customer satisfaction and 2) reduce cost of in-person customer service at call centers by reducing the need for direct interaction with customers requiring help.  Customer service sites follow a self-service format. They rely on high usability to enable visitors to quickly and efficiently find a solution to their problem or an answer to their question about a product or service.

KPIs for measuring effectiveness of customer service sites include:

(1) Customer satisfaction metrics. Those are typically collected through requests for product ratings or satisfaction surveys.

(2) Time on site. As with content sites, it matters, but whereas content sites want visitors to linger longer, too much time on site in this case may mean that visitors are not finding what they are looking for.

(3) Percentage of visits of fewer than 90 seconds. A subset of the overall time on site metric, if it is high it could be an indicator of bad usability (we will elaborate on the interpretation of the measures in a follow-up post).

(4) Content depth: in contrast to media sites, the goal should be to make it quick and easy for a user to find the information they are looking for or to perform the self-service task.

(5) Top on-site searches: not a traditional KPI but important in identifying areas that may require better navigation or more content to meet customer demands.

Hybrid Sites

Not really their own category, hybrid sites – as their name implies – represent a mix of the four main types I discussed. Two prominent types of hybrid sites that emerged in recent years include social sites like Facebook and wiki sites like About.com. Those types of sites combine elements of support sites with rich content, supported by advertising.  They include crowdsourced or community-created content and they encourage engagement to attract advertising dollars. The appropriate KPIs for such sites would be ones that work for those two site categories. However, it is not as likely for a small business to develop this type of site. It seems that this category firmly belongs to a few big players.

Go Forth and Pick Your KPIs

The bottom line is that the KPIs you pick may represent a combination that makes sense for you based on the nature and goals of your business and the type of site you create to generate revenue for your business. Measurement is only of value if you measure what matters to you.

Stay tuned for our next post in our Mini Primer series in which we will talk about calculating KPIs and elaborate on their interpretation for the purpose of website optimization.

Contact us if you want to chat about metrics, measures, KPIs or have questions about any other aspect of your digital strategy. We’re always happy to answer questions.

virtual office phone

Check out these related posts:

A Mini Primer on Online Advertising Strategy

 

The Evolution of Web Analytics Tech: We’ve Come a Long Way, Baby!

By | The D-Blog | One Comment

I was reading the latest blog post by one of the undisputed authorities in web analytics, Avinash Kaushik. He wrote a post titled: I Wish I’d Known That. [Digital Analytics Edition.] In it Avinash says:

 

Everyone wants the perfect tool that bounces rates in real time while computing multi-channel attributable impact of an email sent to grandma via Facebook based on competitive intelligence gleaned from TV watching behavior of customers with lifetime value greater than $358 and FICO scores of 700 or higher.

Get over it.

I chuckled in agreement. We (digital strategists) have certainly become increasingly greedy for “more and better” web data.  It got me thinking about just how far analytics has come along since it first started. The tools we rely on today are a far cry from their “granddaddy”, 1993’s humble hit counter.

Ancient history: the hit counter

Hit counters, often displayed odometer-style, simply counted and displayed the number of times a web page visitor refreshed a page. Hit counters were introduced circa 1993. They were embedded Perl or C scripts. A hosted hit counter service called Web-Counter started up in 1996. Many others soon followed it. The information gathered by hit counters was eventually displayed in a dedicated page of a web site, rather than as an odometer, usually reflecting a finite period of time. In the late 1990s the concept evolved to a service which embedded web counters, an invisible image made by Javascript, to gather and deliver information about the page and its visitors to clients.

Analyzing web server logs

Search engines were a critical part of analytics before 1998, but web counters could not differentiate whether a visit was by a human or a bot, skewing statistics. WebSideStory, WebTrends, and other developers created web server logs, which could differentiate, human from bot visit, plus report information about user behavior like page views and session lengths.

Page tagging, cookies & packet sniffers

The concern for accuracy of logfile analysis and building analytics as an outsourced service has led to the invention of URL tagging, also known as page tagging. Another analyzer called the packet sniffer intercepted and logged traffic passing over a digital network to improve logfile analysis accuracy. This market grew to include embedded scripts on web pages, which gathered information the web server logs missed and assigned cookies to users to identify them during current and subsequent visits. Cookies are pieces of text assigned to each user that are sent by a web server to a web browser and are sent back by the visitor’s browser each time they visit that site again.

Analytics grows up

Software companies began packaging web services to provide a standardized offering to a growing market in the mid-1990s. What started as the optimization of hit count tracking and web server log analysis, gave rise to a market for sophisticated hybrid analytics services combining page tagging and logfile analysis and the growth of Google Analytics, Omniture, Unica. Coremetrics just to name a few of the household names in this space.

Predictive analytics: impossible or the future (now) of analytics?

Web analytics is about capturing and analyzing past visitor behavior on the site and using it to optimize various aspects of a visitor’s experience in the hope of improving conversions (subscriptions, sales, however we define them). But predictive analytics continues to be the hot topic. That’s because predicting user behavior is every marketer’s (and business owner’s) dream. Imagine using your data to forecast future visitor behavior and market trends, rather than just analyze the past… Ah… Wouldn’t that be grand. But even with our data collection becoming ever more sophisticated, there are so many variables and uncertainties, that it may not be possible at all. Given the history of analytics over the last 17+ years, I prefer to think that it’s simply not possible yet.

Web Analytics as a Business: A Brief History

By | The D-Blog | One Comment

The concept of web analytics, the collection and interpretation of website visitor activity, emerged around 1993. At the time it was a tool for webmasters who needed to understand activity on the site to balance throughput and ensure the site stayed up. Soon after some marketer realized that this information could be used to better target visitors, and web analytics ownership shifted from the IT department to the marketing department.

Two Different Business Models

Initially, two unique approaches to web analytics evolved, leading to the emergence of two types of analytics service providers: analytics software retailers like WebTrends and NetGenesis and Application Service Providers (ASP) including Urchin Software Corporation, Unica, Coremetrics and WebSideStory that provided browser tag based solutions. The “in-house” method made use of installed software on a server configured to analyze data collected in weblogs. The ASP approach involved the use of external hosted services to monitor incoming and outgoing web traffic and other data.

Affiliate Trading and Early Industry Expansion

Affiliate trading sparked a wave of new predictive analytics firms. The idea of site affiliate trading emerged shortly after 1993, mainly on hobby sites. Any given site would host a bar on their page with small banners that linked to their affiliates. These affiliates would typically have something in common with the other sites. Later on, these affiliate programs branched out to include advanced coding that allowed the website to automatically prioritize links based upon the number of visitors they received from their affiliates, giving their affiliates the incentive to place links on their website to trade traffic. Near these banner bars was usually a link to an external analytics service, where affiliates and site visitors could see how many unique visits the site received per day. Growing numbers of sites using this type of affiliate program led to a boom in external website analytics companies, and subscription-based analytics rose gradually afterwards. Subscription services had previously only served corporations and larger business, but expanded to serve small businesses and fan-sites as they became more plentiful with the emergence of ecommerce.

Dot Com Bubble Burst and Consolidation

The dot-com bubble of 1995 began to slow down in 2000 due to rising interest rates. Companies that had previously forged the new frontier of online business began to make less and less off of their new investments. Until that time, companies that simply added a “.com” to the end of their name or a “e-“ prefix to their company name saw their stock increase in value exponentially. Industry consolidation followed as software giants absorbed smaller companies.

Following the dot com bubble burst, the analytics industry started a period of consolidation that continued through 2009. In 2005 Google acquired Urchin and renamed it Google Analytics. In 2007 WebSideStory (NASDAQ: WSSI) re-branded as Visual Sciences. Visual Sciences was purchased by Omniture (NASDAQ: OMTR) in 2008 for a settlement of $394.2 million. Rapid industry consolidation continued to occur when Adobe (NASDAQ: ADBE) acquired Omniture in 2009, re-branding Omniture’s analytics solution as “Adobe Online Marketing Suite”. In early October 2010 IBM acquired Unica, another pioneer in this space.

Looking Ahead in Web Analytics

Monopolization of a fresh market like web market analysis raises the concern of slower innovation. In a short span of time the hegemonic software corporations Google, Adobe and IBM absorbed most of the smaller, nimbler companies that created the analytics space in the first place. On the other hand, the big companies have the resources, both human and cash, to invest into expensive software projects and the size of the market provides incentives for growth and innovation.

The other issue impacting this space is the new concept of web ethics, which brings to question the nature of modern analysis software. All we know for sure is that as the debate about privacy on the web, the use of tags, cookies and data storage heats up, the analytics space is going to be impacted. It will be interesting to see where it goes next.