Archive for Google Analytics

How Associations Are Successfully Using Artificial Intelligence

With AI no longer science fiction, associations are using advanced technologies to convert mountains of data into actionable insights.

At the recent EMERGENT event, hosted by Association Trends, we had the opportunity to jointly present case studies with ASAE’s Senior Director of Business Analytics, Christin Berry.

These success stories include how ASAE has:

Combined artificial intelligence and text analytics to enhance customer engagement, understand evolving trends, and improve product offerings

DOUBLED online engagement with unique open and the click-to-open rates using AI to personalize newsletters

Reduced the need for surveys, identified what’s trending, and measured through Community Exploration

Leveraged Expertise Search and Matching to better identify experts and bring people with similar interests together

I’m Matt Lesnak, VP of Product Development & Technology at Association Analytics and I hope to demystify these emerging technologies to jumpstart your endeavors in association innovation.

Text and Other Analytics

Associations turn to analytics and visual discovery for answers to common questions including:

  • How many members to we have for each member type?
  • How many weeks are people registering in advance of the annual meeting?
  • How much are sales this year for the top products?

Questions about text content can be very different, and less specific.  For example:

  • What is it about?
  • What are the key terms?
  • How can I categorize the content?
  • Who and where is it about?
  • How is it like other content?
  • How is the writer feeling?

It is widely estimated that 70% of analytics effort is spent on data wrangling.

This high proportion is no different for text analytics but can be well worth the effort. Text analytics involves unique challenges including:

  • Term ambiguity: Bank of a river vs. a bank with money vs. an airplane movement
  • Equivalent terms: Eat vs. ate, run vs. running
  • High volume: Rapidly growing social data
  • Different structure: Doesn’t really have rows, columns, and measure
  • Significant data wrangling: Must be transformed into usable format

Like the ever-growing data from association source systems that might flow to data warehouse, text content of interest might include community discussions, articles or other publications/books, session/speaker proposals, journal submissions, and voice calls or messages.

Possible uses include enhancing your content strategy, providing customized resources, extracting trending topics for CEOs, and identifying region-specific challenges.

Learn More

 

Personalized Newsletter

ASAE is working with rasa.io to automatically identify topics of newsletter content as part of a pilot that significantly improved user engagement.  ASAE and rasa.io first tracked newsletters interactions over time to understand individual preferences and trending topics.  Individuals then received personalized newsletters based on demonstrated preferences.

The effort had been very successful, as unique open and the click-to-open rates have more than doubled for the personalized newsletters.

Underlying technology includes Google, IBM Watson, and Amazon Web Services; combined with other machine learning tools developed by rasa.io.


Community Exploration

ASAE leverages a near-real-time integration with over 10 million community data points combined with enterprise data warehouse to analyze over 50,000 pieces of discussion content and over 50,000 site searches.  The integration is offered as part of the Association Analytics Acumen product through a partnership with Higher Logic.

Information extracted includes named entities, key phrases, term relevancy, and sentiment analysis.  This capability provides several impactful benefits.

Quick wins:

  • Visualize search terms
  • What’s trending
  • Staff and volunteer use
  • Reduce need for surveys

Longer-term opportunities:

  • Aboutness of posts as content strategy
  • Identifying key expertise areas
  • Connecting like-minded individuals

Underlying technology includes AWS Comprehend, Python, and Hadoop with Mahout.

Learn More


Expertise Search and Matching

Another application of text analytics that we’ve implemented involves enabling associations to better identify experts and bring together people with similar interests.  In addition to structured data from multiple sources, text from content including meeting abstracts and paper manuscripts provides insights into potential individual interests and expertise.

This incorporates data extracted from content using approaches including content similarity, term relevancy, validation of selected tags, and identifying potential collaborators.

Underlying technology includes Python and Hadoop with Mahout.


Approaches and Technology

We’re written extensively about the importance of transforming data into a format optimized for analytics, such as a dimensional data model implemented as a date warehouse.

Thinking back to the common association questions involving membership, event registration, and product sales; these are based on discrete data such as member type, event, and day.

Text data is structured for analysis using a different approach, but fundamentally similar as each term is a field instead of, for example, a member type table field.

Picture a matrix with each document as a row and each term as a column.

This is referred to as “vector space representation”.  With thousands of commonly used words in the English language, that can be a big matrix.  Fortunately, we have ways to reduce this size and complexity.

First, some basic text preparation:

  • Tokenization – splitting into words and sentences
  • Stop Word Removal – removing words such as “a”, “and”, “the”
  • Stemming – reduction to root word
  • Lemmatization – morphological analysis to reduce words
  • Spelling Correction – like common spell-checkers

Another classic approach is known as “Term Frequency–Inverse Document Frequency (TF-IDF)”.  We use TF-IDF to reduce the data to include the most important terms using the calculated scores.  TF-IDF is different from many other techniques as it considers the entire population of potential content as opposed to isolated individual instances.

It is widely estimated that 70% of analytics effort is spent on data wrangling.  This high proportion is no different for text analytics but can be well worth the effort.

Other key foundational processing:

  • Part-of-Speech Tagging: Noun, verb, adjective
  • Named Entity Recognition: Person, place, organization
  • Structure Parsing: Sentence component relationships
  • Synonym Assignment: Discrete list of synonyms
  • Word Embedding: Words converted to numbers

The use of Word Embedding, also referred to as Word Vectors is particularly interesting.  For example, the word embedding similarity of “question” and “answer” is over 0.93.  This isn’t necessarily intuitive and it is not feasible to manually maintain rules for different term combinations.

A team of researchers at good created a group of models known as Word2vec that is implemented in development languages including Python, Java, and C.

Here are common analysis techniques:

  • Text Classification: Assignment to pre-defined groups, that generally requires a set of classified content
  • Topic Modeling: Derives topics from text content
  • Text Clustering: Separating content into similar groups
  • Sentiment Analysis: Categorizing opinions with measures for positive, negative, and neutral


Finding and Measuring Results

With traditional data queries and interactive visualizations, we generally specify the data we want by selecting values, numeric ranges, or portions of strings.  This is very binary – either the data matches the criteria, or it does not.

We filter and curate text using similarity measures that estimate “distance” between text content.  Examples include point-based Euclidean Distance, Vector-based Cosine Distance, and set-based Jaccard Similarity.

Once we identify desired content, how do we measure overall results?  This is referred as relevance and is made up of measures known as precision and recall.  Precision is the fraction of relevant instances among the retrieved instances, and recall is the fraction of relevant instances that have been retrieved over the total amount of relevant instances.  The balance between these measured is based on a tradeoff between ensuring all content is included and only including content of interest.  This should be driven by the business scenario.

This overall approach to text analytics is like that used for recommendation engines based on collaborative filtering driven by preferences of “similar” users and “similar” products.


APIs to the Rescue

Fortunately, there are web-based Application Programming Interfaces (APIs) that we’ve used to help you get started.  Here are online instances from Amazon and IBM for interactive experimenting:

This is a lot of information, but the takeaways are they there are big opportunities for associations to mine their trove of text data and it is easy to get started using web-based APIs to rapidly provide valuable insights.

Learn More

 

Matt Lesnak, VP of Product Development & Technology
Association Analytics

Get the Most Out of Your Website Analytics

An association website provides invaluable data and information on what your customers (i.e., members, prospective members, nonmembers, and the public) care about, what they don’t care about, and how they find information. By analyzing this data with website analytics, you can help your association provide valuable, engaging customer experiences.
There are a number of tools available for website analytics. Google Analytics is probably the most well-known. Regardless of what tool you use, there are some basic steps that will help you get the most out of your website analytics:

  1. Define business goals and important conversions. This should be based off the association’s strategic plan. A conversion might include someone joining membership, subscribing to a publication, registering for an event, etc. When listing your key conversions, consider what actions your optimal customer would take. What are the customer actions that have the greatest impact on your association’s ability to achieve its goals and objectives?
  2. Select meaningful key performance indicators and goals. Website KPI’s could include:
    • Visits (Sessions), Unique Visitors (Users) and New vs. Returning Visitors
    • Traffic Sources
    • Bounce Rate and Average Session Duration by Channel
    • Conversions and Conversion Rate by Channel
    • Cost per Conversion, Profit and ROI (Return on Investment) by Channel
  3. Determine audience for reports. Before developing reports or visualizations, consider the audience. Design reports that will be meaningful to the intended audience. Think about the goals of your audience and strive to exclude extraneous information.
  4. Analyze data. Investigate things that surprise you or dive deeper to find relationships.
  5. Take Action. Take action from what you learn and make modifications based on what you learn.

Take Action: Using Web Analytics for Marketing Automation

This week we’re wrapping up work with a client that focused on using data analytics to facilitate marketing automation based on web traffic. For example, the client wanted to message only to a group of members who visited a particular page or series of pages on their website. By visiting the page, they showed implicit interest in the topic, which means they might be interesting a related publication or event.
To do this, we took web analytics data from the data mart and put it back into the Association Management System (AMS).  The goal was to enable a particular set of data to be used in a more automated fashion for marketing.
This is a slightly unconventional approach, but here’s why we did it.
In order for the data set to be used in automated marketing it needed to be accessible to the marketing engine, in this case, RealMagnet. If you’re familiar with RealMagnet, you likely know that data can be brought into it manually or via an integration with your AMS.  The client wanted to automate the process so we had to duplicate the Google Analytics data into the AMS so it was accessible to RealMagnet.
The flow of the solution ended up being like this:
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As you can see, the data we put into the AMS originated from Google Analytics.
It’s important to note that Google Analytics’s API has daily limits. The data set we brought isn’t currently close to the limit, but we did experience large spikes. Plus, other sets of Google Analytics data is extracted nightly, which count against the daily limit. However, if we were to duplicate the extract directly to the AMS, we’d be closer to the imposed limit which could limit future growth.  Another downside to that approach would have been implementing the Google Analytics extract on the AMS server which means you would have to maintain it in two places on two separate VPNs.
Instead of going directly from Google Analytics to the AMS, we opted to use the API of the AMS to insert the data to a custom table once it was extracted nightly from the Google Analytics API. Aside from being a more straight-froward approach, this enables us to clean the data prior to insert. We removed any entries that weren’t authenticated with a logged in user in which the customer key was tracked in a custom dimension in Google Analytics.  We also performed aggregation to sum total page views per user, per day loading back to the AMS.
Once the data is in the AMS, it is a simple join to the individual record to get the email address and perform marketing automation by writing queries and flowing data into the RealMagnet engine.
At the beginning of this post I used the word “unconventional.” On the surface talking about putting data in an automated fashion from the data mart into the AMS seems so, but once you get into the details it becomes clear it really is the optimal solution for associations who want to take action on their data.

Looking Back and Forward with Association Analytics

Goodbye-2014-image-and-hello-2015-photo
The end of the year is such a natural time to reflect on the prior year and make predictions for the upcoming year.  I can’t resist sharing my 2014 data highlights and predictions about data trends for 2015.

2014

  1. Google Analytics Universal Analytics Format: the updated format officially transitioned out of beta and is now the de facto standard.  Associations with the old tracking code will soon be required to update or risk losing Google Analytics tracking data.  The new version provides benefits such as simpler and more accurate cross domain tracking, more accessible configuration options in the admin panel, new reporting features, and custom dimensions and custom metrics.  And of course a custom dimension is your gateway to user tracking that allows you to blend with other data sources for more powerful insights.
  2. Microsoft SQL Server 2014 – data collection is still growing rapidly and this new version aims to address that with new features for speedier queries and tighter integration with the cloud.  Associations ready to build a data mart will find SQL Server 2014 to be an excellent foundation for doing so.
  3. ASAE Technology Conference & Expo – the recently completed event went out with a bang where ASAE’s Chief Information Officer, Reggie Henry, highlighted 3D printers, drones, and Bluetooth beacons.  And that’s not all – he also asked our CEO, Debbie King, “What do all these things have in common?” The answer of course is DATA!! Connecting data and understanding the story it is telling will become one of the most important skills in the coming decade.

2015

  1. Adoption – more associations than ever before will be implementing a data analytics initiative to convert their organization into one that is data guided
  2. Advanced Analytics – Associations that have been using analytics for a year or more will take the next steps and start initiatives that utilize predictive analytics and big data
  3. Real time Analytics – I expect a breakthrough that makes it easier and faster to have up to the minute data for analysis. Scheduled ETL data refreshes have worked ok so far, but the world is moving too fast for us to stay satisfied with that standard implementation for long.

There you have it, my reflections on the past year and hopes for the future one.  Happy New Year!

3 ways Associations use Google Analytics

A short time ago Tableau Software released a whitepaper entitled “5 Tips to Get More from Google Analytics,” and although it was full of valuable information, three tips stood out.  The three in this post require that your Google Analytics data be extracted and analyzed with Tableau.  This is easily done via direct connect using Google Analytics as a Tableau Software data source, or by using the Google Analytics API to extract the data daily and load it in your association’s data mart, which is the approach we use most often.

Tip 1: Advanced Analytics

In analyzing web traffic, it is common to see peaks and valleys.  For instance, a spike after an e-marketing message is sent, or valleys on the weekends and after business hours, or a spike before your association’s annual event.  With Tableau it is easy to right click on the chart and add a trend line.  Trend lines are valuable because they quickly show if web traffic is increasing or decreasing over time despite the common peaks and valleys.
Associations Google Analytics

Tip 2: Previous & Next Page Analytics

The Google Analytics documentation provides an excellent description of flow visualization reports, which in essence are visualizations that allow you to see how users are navigating through your association’s web site content.  For example, you could see the path users are taking from landing on your annual conference microsite all the way through registration payment.  As great as flow reports are, you will gain a deeper understanding in how to optimize your content once you see how the result change when analyzed by geographic region, member type or screen size (platform).  Knowing this you could create a targeted marketing campaign to all the members (or customers) in Oklahoma which directs them to the page where the most common drop off occurs.
Association Analytics

Tip 3: Data Blending

This topic keeps coming up, so obviously it is important.  Data blending is exactly what it sounds like – taking separate data sources and combining them together to reveal new insights and draw new conclusions.  The separate data sources should have a value in common, such as customer id.  By blending your Google Analytics with your CRM data, you can give the web traffic an identity, based on a member type or role for example.  When your association knows the type of information that different identities are seeking, you can then deliver the content that identity wants – that’s the best way to add value to the customer’s experience on your website.
Learning how visitors are accessing your content is an ongoing initiative and improving it is equally important.  This information can inform business strategy and financial goals. It seems that many websites get a redesign every three years or so, and having this data on hand will make your next one amazing!
References:
Images provided by Tableau Software white paper, 5 Tips to Get More from Google Analytics,

Building Your Association’s Data Mart with the Google Analytics API

Google Analytics is a free tracking tool you can integrate with your web site to log the clicks and behavior of visitors on your association’s web sites.  Google Analytics provides many online reporting features for data analysis, but sometimes you want to extract even more information from the data.  The Google Analytics API enables your association to extract the collected data so that you can store it in your association’s data mart and do unlimited analysis.
Here are a few reasons you should consider using the API to extract your association’s data from Google Analytics. The data remains with Google so think of it more as a copy of the data.

  1. Limitations – With millions of sites using Google Analytics, it is necessary to put into place limitations to ensure an equitable distribution of system resources which are provided freely to the tracked sites.  Google lets you request limit increases when using the API, and you can also create an application within the limitations, such as 10 queries per second per IP address.  Also, if you are attempting to connect Tableau Desktop directly to Google Analytics, you may select up to 7 dimensions and 10 measures per connection.  Having all the data you want in your data mart obviously avoids the limitation.
  2. Blending – When analyzing data online in the Google Analytics platform, you are unable to blend in any other data sources from your association, such as member data.  Suppose you have a handful of data points and you configured your site with custom dimensions as previously discussed in  a previous blog post, Engage Your Members with Custom Dimensions in Google Analytics.  Hopefully you put a member ID in a custom dimension and now your association is set to extract and blend all your member data.
  3. History – The web is evolving constantly.  Google Analytics can change too, which you might recall from our blog post, Upgrade Your Association’s Google Analytics Account. Putting your data into a separate data mart that you control will ensure you can continue to perform analysis consistently for years even if the underlying system is changed.  Plus, since Google Analytics is a free service, their policy is to only guarantee the storage of data going back 25 months.  Keeping your own copy allows you to extend analysis past two years.

My favorite free tool for working with the Google Analytics API is the Google Analytics Query Explorer 2.  It lets you quickly select all of your options, such as dimensions and metrics, and returns the results to a table right on the web page as shown in the image below.  The best part is that you can even view the URL based query that was used to generate it, bringing you one step closer to extracting your data.  At DSK we’ve helped many associations populate their data mart with Google Analytics data which allows them to achieve greater insight into the interests of their members and prospects.

Example of using the Google Analytics Query Explorer 2 Tool

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Making the Most of Google Analytics Segmentation for Marketing

marketing analytics associationsSegmenting with Google Analytics

Google Analytics (GA) is a very useful, free tool for tracking website statistics.
Through its segmentation function, it enables us to get a glimpse of who is viewing our website and what they are doing while there. It has many built-in, easy to use advanced segmentation properties. Using its practical interface, you can create from among five types: demographics, technology, behavior, date of first visit, and traffic sources. These are great for displaying traffic comparisons over time and alerting you to anomalies that may be affecting your association.
Also, these advanced segments are retroactive – so starting the advanced segments in your Google Analytics today, you can use them to analyze data from as far back as you have analytics data.
Kristi Hines provides a great step by step look at how to implement this advanced segmentation feature in your website analytics.
Some may argue that segmentation in and of itself is not helpful in developing a web marketing strategy, but knowing who your audience members are is an important first step in identifying their needs and how to best meet them.

Combine and Analyze the Data

After creating advanced segments, you will want to start analyzing the data to see the differences among the segments and the traffic they bring. We often write about the great benefits of analyzing your various sources of data through a visualization application such as Tableau. Overlaying your website metrics with other data sources can provide far clearer insights into your market.
Pulling your GA data into Tableau through its native GA connector, and then connecting data from your other data sources (AMS, CRM, SalesForce, other 3rd party applications, etc.) will enable you to easily spot trends and view patterns in a variety of areas. Forecasting, outlier detection, and ‘what if’ analysis are all standard in Tableau.
Combining your Google Analytics data with other sources of relevant information such as event data and marketing email blasts can provide deeper insight into your audience’s motivation behind their path through your site. In order to refine a marketing strategy, you need surrounding details to improve your audience satisfaction and sustain or increase their motivation to engage with your organization. For example pulling in event data enables you to see if your web strategy made an impact in your campaign. Being able to simultaneously see what else was taking place within your organization is the perfect way to reconcile those traffic spikes with the activities that drove them. Raw data itself can be meaningless for marketing purposes. However, analyzing this data in the context of other organizational information to derive meaning from it is the crucial second step in the personalized marketing process. This means reviewing the data to assess customer behavior and identity in the context of surrounding organizational metrics in order to then be able to create more targeted, powerful personalized campaigns.
Combining your Google Analytics data with other relevant data within a visualization application like Tableau enables you to assess all areas and determine what impact was made and what factors tied together to make this so.  You can take action and improve your campaigns and really see the dynamics of the various strategies and actions in play within your organization. Leveraging all of your data in this way is a quick and effective path to gain actionable insights and inform marketing strategy, event planning, and publishing efforts.

Engage Your Members with Custom Dimensions in Google Analytics

In December we posted a blog about how and why it is recommended to upgrade your association’s Google Analytics account to the Universal Analytics tracking code format.  By now it is expected you have, unless you are using one of the features that prevents your association from doing so.  With Universal Analytics in place, it is time to take a look at adding more value to your analytics by creating custom dimensions and/or metrics.
The definitions for Google Analytics dimensions and metrics are the same as they would be in Tableau or any Business Intelligence Tool.  A dimension describes data and a metric measures data.  An example of a dimension is geographic location and measure value examples for that dimension could be Los Angeles and Orlando.  An example of a metric that would also correlate to geographic location is Population.  A city’s population can be measured the count of all the residents.
Everyone can agree that Google Analytics reports on an enormous amount of data, but you can add data points that are specifically relevant to your association.  For instance, what about tracking the author of a particular article?  With a change in the configuration and the tracking code, it is now possible to add custom dimensions and metrics into Google Analytics.  One last note before you begin, custom dimensions and metrics cannot be deleted once created but they can be marked as inactive.  Remember not to reactivate and reuse them for different purposes later.
Instead of repeating the 8 step detailed instructions for creating a custom dimension or metric, please refer to the instructions provided by Google at this location: https://support.google.com/analytics/answer/2709829.
One of the steps does require the JavaScript tracking code on the site to be updated, so collaborate with your developer so you can be sure the data is collected correctly.  The image below of the admin module will aid you in navigating to the correct location of the admin module to begin the process.
The screenshot shows the author of an article was created as a dimension.  Because it is an attribute (of the article) and it can be assigned different values (Alan Weinstein, Debbie King), it is a dimension and not a metric.  In the reports it can be combined with different metrics to create meaningful reports such as the most popular author across the entire library of articles on the site.  With that information in hand, you might want to have the most popular author post more, or have the least popular author adopt the same writing style as  the most popular author. You can continue to monitor the website visitors’ behavior to determine their pattern of engagement with your website.  Similar steps can be implemented in the areas of events, publishing, certifications and more.
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Your members and customers look to your association for professional guidance. Understanding how they interact with the content on your website and providing relevant information they specifically want will increase their engagement and the value you add to their lives. The best ideas about how to serve your audience come from your customers! Take advantage of your website’s data to build relationships with the people who visit and interact with your pages. After all, it’s human insight that drives your mission to serve your members.

Upgrade Your Association’s Google Analytics Account

The web is always evolving, so naturally there is a time when tracking what happens on the web takes an evolutionary step.  If you’re using standard features within your Google Analytics account then the time to upgrade is now!  After all, when is there a better time than the turn of the New Year to enhance your data collection?
Without getting too technical, the fundamental nature of the upgrade is to provide simplified and more configurable data collection-which means updating the JavaScript on your web site that does the tracking.  As always, Google provides the JavaScript tracking code, so it is simply a matter of locating and replacing what you already have included in your site and transitioning your account with a few button clicks.
The new analytics.js (JavaScript) code provides benefits such as enabling you to track any digital device, simpler and more accurate cross domain tracking, more accessible configuration options in the admin panel, new reporting features, and the big onecustom dimensions and custom metrics.  It is also worth mentioning that the new analytics will continue to receive new features and updates, and the old product will not.  The best part is Google Analytics continues to be a free product which makes it perfect for your association or non-profit.
The transition has been divided by Google into 4 phases and Phase 1 is currently in progress.  Dates have not yet been provided for the remaining phases.  This phase is considered a beta and it’s open to anyone who’s not using the following features: A) Remarketing, B) Google Display Network Impression Reporting,  C) DoubleClick Campaign Manager Reporting,  D) Google Analytics Demographics and Interests Reports.  The second phase will begin the auto-transfer of some properties.  Phase 3 marks the time when the new Universal Analytics will be out of beta, and all new properties must use the new system.  In this phase anyone with any feature can safely upgrade.  For the last phase, Universal Analytics becomes the new operating standard and the old collection methodology will be disabled.
It only takes 30 minutes or less to upgrade your association’s account and apply the new tracking code (not counting the 24-48 hour period the account is being converted by Google).  Here are the steps to upgrade:

  1. Login into your account and navigate to the Admin module.
    ga_upgrade1
  2. Select the property you want to upgrade and click on the “Transfer not started” link to begin.
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  3. You will receive a message with a link to the Universal Analytics Upgrade Center containing helpful information about the features and steps involved.  Click on transfer to continue.
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  4. This is the final confirmation page warning that certain features mentioned earlier are not yet supported.  Note: once the transfer is started it cannot be reverted.
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  5. After the account upgrade is complete, you will receive a notification in the Admin area confirming that the transfer was successfully completed.  Now you can get the new tracking code and put it onto your site(s).  Note: All sites and their administration will differ, however there will typically be one location per web site where the substitution should be made.
    ga_upgrade5
  6. Now, the transfer is complete and collecting data using the new tracking code.  Congratulations!
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In a future blog post we will explore new features in more detail after your association has some time to implement and collect data into the new account.  In the meantime, happy analyzing and Happy New Year!

Understanding Google Analytics Measurements

We recently wrote about the many benefits of Google Analytics as a tool for analyzing the traffic to your association’s website.   It is important to understand the metrics Google Analytics provides so that you can review the statistics and take appropriate steps to improve your site.  In this article we focus on two of the many available measurements:  bounce rate and time on page/site.
Bounce rate is the percentage of visits that are single page visits. As an association you want to attract visitors and keep them browsing your site to learn about upcoming events and the other resources you offer so you want a low bounce rate for your site.  If you have a high bounce rate, we advise the following:

  • Take a look at the links on your pages and make sure these are clearly visible and the site is easy to navigate.
  • Your content and link titles must be relevant to the user in order to capture their interest.
  • Check to see how long it takes to load pages on your site. If you have many high-resolution images, pages may take excessively long to load.
  • Make sure your pages are easy to read by paying attention to contrast and font size, especially since many people now view content on their mobile devices.

It’s great to generate a large number of visitors, but if they aren’t staying long, you’re missing opportunities to engage them further.  Regularly check the bounce rate on your site.  If it is high, dig deeper to see if it is being caused by specific pages – then take action.
Google Analytics will track the overall amount of time a user spends on your site as well as how much time is spent on a per page basis.  The measurement is provided in minutes and seconds.  The time spent on page metric is very important for landing pages or blogs to help you understand if people are actually reading your content.  If, for example, you have a long blog post on a page and most people visit your page for just a few seconds, you might need to reduce the amount of text.  Remember also that relevant content is key to holding a visitor’s attention, so consider rewriting your key content.
Google Analytics will track the time a user spends on each page and on your site as a whole, per visit.  Time on page is actually measured as the difference between the time when the request for a page was made, and the time when the next page is requested.  Because of the way Google Analytics counts time spent on pages, you might not realize that time is counted as zero seconds for the page which directly precedes the user’s exit off of the site.  The diagram below provided by the Google Analytics team visually illustrates the time on page calculations:

time on page diagram

The technical reason for doing so is that the tracking code does not run once the user leaves the site.
Both bounce rate and time on page provide deeper insight into how visitors are reacting to your website.  We encourage you to review these metrics regularly so you can take action to improve your site and better tailor it to meet your audience’s needs.