4 Reason Why Your Bounce Rate Might Be Wrong

Bounce Rate is generally defined as single page visits. These are the visits that leave the site without going any further than the landing page.  A very common approach to landing page analysis is to start with Bounce Rate and see if it is a problem before diving deeper into the page/site. However, the number that your tool is providing you might be wrong. Relying blindly on the number provide by your Web Analytics tool might lead you into the wrong direction. Below are 4 reasons why your Bounce Rate might be wrong

  1. In Page Actions – If you have certain actions, such as video plays or windows that popup with JavaScript then the chances are that you are not tracking them as valid site interactions. In this case, even though the visitors will take one of the desired actions i.e. watch the video or click on a link to launch the popup window to fill a form etc., your Web Analytics tool will count these visits as single page visit, thus inflating your Bounce Rate.
  2. External Links – If you have a lot of external links on you landing page e.g. “Like Us On Facebook”, “Buy this book on Amazon” etc. then you are purposely taking visitors out of your landing page but you might not be counting these clicks as valid site interactions thus inflating your Bounce Rate, as explained above.
  3. Profile Configuration:  If you build a tracking profile of only select few pages on your site then any views of pages outside those select few are counted as “external links” (see above). For example, if you have 3 pages on your site, home.html, products.html, services.html but your profile only tracks home.html and products.html, then a click to services.html from any of these pages will be counted as an external link and hence counted as bounce if those were the only two page views that happened in the visit.
  4. Spiders and Bots – For a long time Spiders and Bots were not a big issue for JavaScript based Web Analytics solutions, as very few spiders/bots executed JavaScript tags but now more and more of spiders/bots execute JavaScript thus inflating your visits counts. Many of these spiders only execute one page on the site, thus inflating your Bounce Rate as well.  Spiders and Bot seems to be an even bigger problem when major source of your traffic is Banner Advertising (Display Advertising) or Paid Search Ads.  See below for an example of a bot that might be messing up your Bounce Rate.

bot-traffic

 Now that you know that your Bounce Rate might be wrong, do your own calculations and come up with the right number before you start redesigning your Landing Page.  Recently, I came across a situation where all of the above applied. The Web Analytics tool reported that the landing page had over 90% bounce rate, after adjusting for above factors, we ended up in 50%+ range, which is still a little higher than industry average but not as bad as it initially looked (see average bounce rate).  A Bounce Rate of 50% calls for different analysis and actions than a Bounce Rate of 90%.

Read More Bounce Rate Posts

3 Tips for Expanding Tweet Reach and Engagement

Twitter feeds keep flowing with over 9000 tweets every second (Source: http://www.statisticbrain.com/twitter-statistics/). Unless your followers are constantly watching their twitter stream the chances are that your tweets will not be seen by a lot of them.  Below, I have listed three tips that will ensure that your tweets/content gets noticed and reaches most of your followers.

  1. Tweet Same Content Multiple Times in a Day  – There are a lot of people like me who log into twitter few times a day (or after a gap of few days) and then and do a quick scan of timeline (or search).  They go back few hours in their twitter timeline and if your tweet did not happen to be in the timeline or searches at that time then they never see it.  Keeping this in mind, you need to tweet same content multiple times in a day to make sure your tweets are in the timelines of most of your followers when they login to twitter. In my post “Best Time to Tweet”, I suggested following timeline to tweet (this is just a suggestion and you should figure out your own timeline based on your followers and goals)
    1. Tweet at 9:00 AM PST (If all of your follower are in one time zone then tweet at 9:00 AM in your time zone).
    2. Tweet again the same message at 1:00 PST (4:00 EST) – (you might skip this if your followers are local.
    3. Tweet again the same message at 4:00 PST( If all of your follower are in one time zone then tweet at 4:00 PM in your time zone).

    In addition, I suggest adding another one later in the evening.

  2. Tweet Same Content on Different Days – If you have content that is evergreen then it make sense to tweet it again. Just like above, it might take few tweets over few days/months to get your tweets noticed by your followers. Additionally, tweeting your content again after few days/months will put your content in front of your new followers and those who might have missed it previously.  However, going overboard with such strategy can potentially cause issues with some of you long time and ardent followers as they will see the same message over and over again. I use this strategy to tweet my old blog posts, which results in new retweets and followers. (I do it automatically- more on this in future).
  3. Add Images to your Tweets – Late last year, Twitter started showing full images (instead of a link) in the timeline, just like Facebook does. A study (http://blog.bufferapp.com/the-power-of-twitters-new-expanded-images-and-how-to-make-the-most-of-it) showed that tweets with images got 150% more rewteets than tweets without images.  I suggest, you start including images in your tweets, when it make sense, and do your own tests to see how images affect the engagement with your tweets.

Follow me on twitter at @anilbatra

5 Reasons To Use Google Experiments

Google Experiments is an A/B testing tool that is available within Google Analytics interface.  This post is not about what A/B testing is, why you should conduct A/B tests and what other tools are available but really to make a case for using Google Analytics as your testing platform.  I am not getting paid to write this or have any affiliation with Google. This post is in response to a question I received from a reader of my blog.

  1. Free –There is absolutely no cost for the Tool. You can’t beat Free, it is a great way to start with A/B testing and learn about how testing works. I strongly recommend that you try this tool before moving to more sophisticated paid tools. Additionally, if you are just trying to make a case for Testing within your organization then cost does become a barrier and this tools removes that barrier.
  2. Easy To Setup – Easy to use wizard allows you to choose the pages to test and setup test parameters.
  3. Easy Implementation – Once you are done with setting up (point 2 above) the page(s) you want to test, you have to implement some code on your site.  It may sound daunting but that code is very easy to implement. Google provide you the code after your setup is done and all you have to do is stick that on your pages.  Since you already have Google Analytics installed, you are already half way through. Easy setup makes it easy for you to cross the IT/development team barrier.
  4. Setting up Objective– If you have already defined the Goals in Google Analytics, you can use them as the objective of your test. During your setup you can pick a goal that you have already defined in Google Analytics as your desired optimization objective. If you have not defined them already then you can quickly define them while setting up your test.
  5. Segments – Many tools just gives you the final results based on the data of entire population or based on some predefined segments.  In case of Google Experiments, you can pick Segments that you have defined in Google Analytics and see how each variation is performing for each of your segment. Since not all segments behave in similar fashion this kind of analysis helps you drive even more conversion by understanding which variation of your pages(s) work better for which segments.

Keep in mind that no matter how good your conversions are, there is always a room for improvement and A/B testing helps you with it. As Bryan Eisenberg would say, Always Be Testing.

9 Email Marketing Posts That Are Worth Reading Again

Many old post are still relevant after few years but are hard to find on a blog. So I have gone back and found 9 such blog posts on “Email Marketing” from my blog. Below are these 9 posts:

  1. Email Personalization Not Working? Read This
    This posts explains why the email personalization might not work. The bottom line is that you have update your personalization criteria over time and test it.
  2. 3 Techniques for Expanding your Email Reach
    Email marketers are facing a tough time with growing emails remaining unopened and unsubscribes. Acquiring new subscribers using old techniques is expensive. In this post I have listed 3 techniques that you can use to spread the word of your emails/newsletters beyond the email list that you are sending the emails to.
  3. Are You Depleting Your Email List?
    Email marketers, in order to maximize short term conversions, often bombard irrelevant emails in subscribers inbox However this short term mentality results in erosion of long term viability of their email marketing, due to increase in unsubscribes causing depletion of email lists.
  4. 15 Things to Test in your Email Campaign
    This post talks about 15 things you can test today.
  5. Targeting Cart Abandonment by Email
    Targeting Cart Abandonment is a great way to drive conversions however, use incentives/offers cautiously.
  6. Conversion Tip: Making the Most of the Email Confirmation Thank you Page
    Use your Confirmation page effectively, this posts shows an example of a good page and a not so good page.
  7. Number One Email Marketing Mistake
    Number one mistake marketers make with email marketing is to send “Irrelevant” messages to their customers. Find out why this strategy has a far-reaching impact on your email marketing program.
  8. 7 Ways to Create Relevancy in Emails
    7 tried and tested ways of creating relevancy in emails are described in this post.
  9. Relevancy Matters in Email Marketing
    This post shows an example of an email that missed the opportunity to convert.

Email Personalization Not Working? Read This.

I am a big believer in targeting and personalization and have written extensively about it in this blog. However, targeting and personalization is not “set it and forget it” strategy. It needs to be continually tested to make sure that it is working and driving value. Sometimes you have to test and see if “no-personalization” will yield better results than personalization.

To make my point, let’s take an example:
I get a weekly promotional email from a prominent marketing company. They personalize the email subject line (good so far).
Here are few subject lines

  • How [XYZ Company] Should Kickoff New Clients
  •  Ensure that [XYZ Company] delivers great PPC campaign
  • The rules of online branding that [XYZ Company] needs to know

Note: Instead of [XYZ Company] they use the name of the company I used to work with.

However there is an issue with the way they do personalization. Do you see the issue?

The issue is that they use the name of the company I USED TO WORK FOR. I left that company over a year ago and the subject line is so irrelevant to me that I don’t even open the emails. Though rest of the subject line may be relevant, adding personalization (company name) just makes me ignore that email.

This is a perfect example of why personalization fails.

This can be easily avoided by analyzing the data to figure out which subscribers are not responding to these personalized messages and test a different kind of personalization or completely drop the personalization.

Company names, titles, associations, job roles etc. change and your personalization needs to change when that happens. The key is to actively analyze the data and test.

Do you have any other examples of Failed Personalization? If yes, I would love to see those.

 

Also see: 7 Ways to Create Relevancy in Emails

One Tip for Enhancing Anonymous Visitor Data

Let’s face it, Web Analytics data is pretty limited when it comes to visitor analysis.  Many of you might have some data on users who have registered or purchased from you and some of you might be connecting the onsite activity of visitors with the other data you have in your database. However, considering that you have less than 10% conversion rate (or registration rate) there are over 90% of the visitors on your site that you have no information on.

Image Source: imnmarketer.com

This is where 3rd party data provides come to your rescue. These data providers can provide a lot of valuable missing data and bridge the gap. Companies like BlueKai and iBehavior can augment anonymous cookie pool or known customer base with additional attributes that you don’t have.

For example, say you have a segment called “Engaged Users” that is all based on anonymous cookies visiting your site and taking certain actions e.g. downloading a whitepaper.   Since it is all cookie based, all you have is their referring information, onsite behavior and browser/OS but you don’t know the mix of gender, income level, kids/no kids, interests etc. within this segment.  Imagine if you had these other attributes about your anonymous visitors then how rich will your analysis and recommendations be.  If you can see the value in richer analysis then it is time for you to start thinking beyond the data you collect.

 

Also, see 3 Techniques for Expanding your Email Reach


Analytics Jobs


Books that I am reading or have read recently

  1. You Should Test That: Conversion Optimization for More Leads, Sales and Profit or The Art and Science of Optimized Marketing
  2. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
  3. Data Points: Visualization That Means Something

Promotional Codes: Conversion Killers?

Promotional Codes also know as Promo Codes, Discount Codes, Coupon Codes, Offer codes etc, are supposed to drive sales, right? However they can have a reverse action and can actually kill your conversions, if not properly used. Here two examples of discount code implementation that can disrupt customers flow and possibly kill conversion.

  1. Pomo Code Box:  A customer who does not have a discount code but comes across a text box/button that asks for one during the checkout process is likely to stop and think.  If a customer does not have any kind of urgency to buy the product, he/she will likely try to look for a discount code online.  If he/she does not find one then chances are that you might lose that conversion.
  2. Pomo Codes that are not applied automatically: If you advertise a discount code on your site then go ahead and apply it automatically during the checkout process. Do not ask the customer to fill in the discount code manually.  Many customers don’t read the instructions, and if during check out they don’t see what was promised to them then they will leave.  For example see the Nautica.com screenshot below. They advertised 40% off with a discount code.  As you go through the checkout process, they don’t apply the code automatically. Customers have to take an extra step to enter the code manually.  Do you think all the customers will read the instructions (written in red)?

Here are few things that you can do/test to minimize the cart abandonment due to discount codes:

  1. Visible discount Code Box v/s a link to open a discount code box–  Customers who have the discount code might actually find it even though it might not be obvious to other customers (you should test this).
  2. Automatically apply the code so that customer can see the actual price he/she will be paying
  3. If you are going to provide a prominent discount code then provide a bare minimum discount to everybody (and auto fill it). The code could even be for Free Shipping over $75.00 etc.  The point is make sure that everybody feels like they got something (you should test that).

 

Also check out

  1. 5 Things That Could Be Hindering Your Conversions
  2. Are Form Validations Invalidating Your Conversions?

25 Twitter Hashtags Used By Digital Marketing and Analytics Community

Hashtags (keywords that follow #) are a great way to jump to topic and join the conversation on twitter. Below I have complied a list of 25 commonly used hashtags by Digital Marketing and Analytics community that you might want to track as well.

Follow me on @anilbatra

  1. #measure
  2. #msure
  3. #bigdata
  4. #analytics
  5. #insights
  6. #data
  7. #emetrics
  8. #trend
  9. #reports
  10. #segmentation
  11. #segment
  12. #seo
  13. #search
  14. #ppcchat
  15. #cro
  16. #landingpages
  17. #webdesign
  18. #custexp
  19. #mktg
  20. #marketing
  21. #ecom
  22. #ecommerce
  23. #ecomchat
  24. #ux
  25. #smdata

Other tags that were provided by the readers of this blog

#attribution

 

Let me know if I missed any.

Follow me on at @anilbatra


Other Twitter Posts:


Analytics Jobs


Books that I am reading or have read recently

  1. You Should Test That: Conversion Optimization for More Leads, Sales and Profit or The Art and Science of Optimized Marketing
  2. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
  3. Data Points: Visualization That Means Something

4 Data Ownership Questions You Should Ask: Creating a Culture of Analytics

These days most of the marketing solutions are provided as a service. These solutions send emails on your behalf, server ads on your site , serve your ads on other sites/networks, collect your web Analytics data, collect your social media data, collect usage of customer on social media platforms, trade your cookies etc. You get the idea.

As a result, most of your marketing data resides with 3rd party vendors and outside your company’s environment.  In some case you might have an explicit agreement with the company that allows you to have ownership of your data (e.g. Omniture, ExactTarget etc.) while in other cases you might implicitly assume that you have the ownership of data (e.g. Google Analytics, Facebook etc.).  Either way the data resides with someone else.  This lack of direct ownership of your data could potentially pose a threat to your data driven culture.  I am not saying that all of sudden you will lose all your data (though that is also possible) but there is a potential risk.

Source: http://mimiandeunice.com/2011/01/06/ownership/

In order to ensure that you are in control of the situation, you need to carefully evaluate your “Data Ownership” risks and have a well thought out plan to mitigate the risk. Here are few question you need to ask

  1. What if the vendor(s) gets bought by one of your competitors?
  2. What if one of the free tools all of a sudden disables your account because of some violation (perceived or actual) of their policy? (See What I Learned When Facebook Disabled My Account)
  3. What if the vendor has a data breach?
  4. What if you want to move to another vendor?
    1. What will happen to your historical data?
    2. Will you have access to all you historical data? For how long?
    3. Will you be able to port your data into your inhouse system?
    4. Will you be able to port your data into new vendor system?
    5. What will be the cost of porting your data?

3 Techniques for Expanding your Email Reach

“Email-Marketing” image is copyright by ePublicist and made available under a Attribution-NoDerivs 2.0 Generic license. Source: http://www.flickr.com/photos/epublicist/8585152039/sizes/l/in/photostream/

Email marketers are facing a tough time with growing emails remaining unopened and unsubscribes. Acquiring new subscribers using old techniques is expensive. Below I have listed 3 techniques that you can use to spread the word of your emails/newsletters beyond the email list that you are sending the emails to.

  1. Add social sharing in your emails – Let your loyal email subscribers help you. Add easy social sharing links/buttons to help them tweet, share on Facebook, linked in etc. If they like something in your email they will share it with their followers and friends. This will not only spread your messages but also will provide you with new subscribers.
  2. Segment differently – If you are doing segmentation to send emails, it is possible that you might be able grow the list of people in your segment by thinking beyond your current segmentation criteria. For example if you are sending a particular emails to who have listed their title as “Web Analyst”, you can extend the list by looking for subscribers who have not listed themselves as “Web Analyst” but exhibited behavior that looks like “web analysts” e.g. downloaded a whitepaper on tag management.
  3. Find Look Alike Using 3rd Party Data – You can go beyond emails and use 3rd parties to find “Look Alikes” of your loyal subscribers/customers. Find you loyal email subscribers/customers based on email opens/clicks/conversion and use their cookies to find more people like them using 3rd party cookie matching solutions such as BlueKai, i-Behavior etc. (Make sure not to cross the privacy lines when using these services).

I would love to hear about other techniques that you might be using.

Related posts

  1. Are You Depleting Your Email List?
  2. Number One Email Marketing Mistake
  3. 7 Ways to Create Relevancy in Emails

Analytics Jobs

 


Books that I am reading or have read recently

  1. You Should Test That: Conversion Optimization for More Leads, Sales and Profit or The Art and Science of Optimized Marketing
  2. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
  3. Data Points: Visualization That Means Something