5 Reasons Why Your Display Advertising Is Not Working

Image Source: goldspotmedia.com

Image Source: goldspotmedia.com

Are you one of those advertiser, who is struggling to understand why your Display Advertising is not providing the desired results?  If answer is yes, then this post is for you. Below are 4 most common reasons why your display advertising might not be working.

  1. Ad Views: According to a study by Sticky,  77% of ads are never seen by people. Even when the ad is considered viewable, meaning it is within viewing area, only 55% ads are actually viewable. Which results in a very lower click through rate, the average banner CTR is about 0 .1% and declining.
  2. Spider and Bot Ad Clicks:  Spider and bots, instead of humans, make up a significant amount of clicks on the ads. All these spiders do is click on an ad, land on your site and then leave causing millions of dollars in fraudulent clicks.  As a result you will either see a very high bounce rate on your pages and/or mismatch in the clicks reported by ad network and visits reported by your Web Analytics solution.  In 2012, a start-up reported that about 80% of their clicks from Facebook ads were by spiders. Another study found that 20% -90% of clicks on some sites were via spiders. I also showed an example of a bot in my post, 4 Reason Why Your Bounce Rate Might Be Wrong
  3. Fat Finger: Over 35% of the ad clicks on Mobile are by accident, again causing high Bounce Rate.
  4. Mismatched Landing Experience:  Make it a seamless and consistent experience from your banner to conversion. Users don’t have time so make it right the moment they land on your site. For example, If a banner ad promotes “Free Trial” then make sure landing page make it easy for user to sign up for the free trial. Don’t expect the users to click through to your site to find where the “Free Trail” page is.  Mismatched landing page and ad experience leads to High Bounce Rate and Low Conversion Rate.
  5. Site Speed: Slow site speed breaks visitors flow from a display ad to your site. If it takes too long for the page to load then the visitor will be gone before she sees the full page. In this case you will see clicks but not visits and high bounce rate. Fractions of seconds can make a huge difference in the performance of your advertising.

21 Metrics to Measure Online Display Advertising

In this post I am listing the 21 metrics to measure the success of your display advertising.  Most of these are also applicable, with some variation, to other forms of advertising such as Paid Search, Social Media Ads, Print and email. I will cover these other channels and mediums in the future posts.

  1. Impressions – It is the number of times your ad is displayed. The number by itself does not hold much value but it is a metric used to calculate other metrics and KPIs. Keep in mind that an impression does not mean that someone actually saw the ad, it just that the ad was shown on a web page/app.
  2. Reach –This is the number of unique people (generally identified by cookies) that were reached by your ad. This number is always lower than the impressions because your ad is generally shown to same person (cookie) multiple times.
  3. Cost – The total cost of running the ad campaigns.  This is calculated differently by different tools and organizations. Some use actual media cost while other use a fully load number that includes the agency cost, creative cost etc. Whichever number you use, be consistent in your approach. If you are going to do comparisons with CPC models such as Paid Search then I suggest using the actual media cost. Most of the publicly available benchmarks are based on actual media cost and are expressed in CPM (explained later in this list).
  4. Engagement Rate or Interaction Rate– This applies to the Rich Media Ads, where a user can interact with the ad without leaving the Ad unit/widget.  Engagement Rate is the percentage of interactions per impression of the ad unit and is calculated as (Number of Interactions/Total Impressions)*100%.
  5. CPM – This is the cost for 1000 Impressions of the ad unit. Display advertising is generally sold on CPM basis. (For more information on CPM, see  Cost of Advertising: CPM, CPC and eCPM Demystified).
  6. Clicks – Number of clicks on an ad unit that lead to a person leaving the ad unit.  Keep in mind that a click does not mean that a person landed on the intended destination of the banner ad click. There are multiple factors that could lead to a click but not a visit to the destination (I won’t cover those here but am happy to discuss over email or a call).
  7. CTR (Click though rate) – It is the number of Clicks generated per impression of a banner ad. This number is expressed as a percentage. CTR = (click/impressions)*100%
  8. CPC – Cost per Clicks is the cost that you pay for each click.  Generally, display advertising is sold by CMP (see above), you can easily convert the cost in to Cost Per Click to compare it against other channels such as paid search. Cost per click is the effective amount you paid to get a click.  It is calculated by dividing the cost with number of clicks.  CPC = Cost/Clicks. Sometime this number is also referred as eCPC (effective Cost per Click).
  9. Visits – As stated above in the definition of clicks, not every click turns into a person landing on your destination (generally your website). Visits measures the clicks that did end up on your site.  (For more definition of visits, please see Page Views, Visitors, Visits and Hits Demystified)
  10. Visitors – Visitors metric goes one step ahead of the visits and calculates the number of people (as identified by cookies) who ended up on your site as a results of the clicks on the banner ads.
  11. Bounce Rate – Is the percentage of visits that left without taking any actions on your site. It is calculated as Number of Visits with one page view /Total number of visits resulting from the display ads. (Bounce Rate Demystified for further explanation).
  12. Engaged Visit Rate – Generally this is opposite of bounce rate (though you can have your own definitions of engagement).  It measure the quality of the visits arriving from your display advertising. You can calculate Engaged Visits as  (100 – Bounce Rate expressed as percentage).
  13. Cost/Engaged Visit – This is effective cost of each engaged visits. It is calculated as total Cost divided by number of engaged visits.
  14. Page Views/Visit – Page views the number of pages on your site viewed by each visit. With a lot interactions happening on one single page, this metrics is losing its value. However, for now, it is still a valuable metric for ad supported sites.
  15. Cost/Page View – As above, this is valuable metrics for ad supported site to figure out the cost of generating on extra page view.
  16. Conversions – Conversion is defined as the count of action that you want the visitors to take when they arrive from you display ads. Some examples of conversions are – purchase, signup for newsletter, download a whitepaper, sign up for an event, Lead from completions etc.
  17. Conversion Rate  – This is the percentage of visits that resulted in the desired conversion actions.  Conversion Rate = Total conversions/visits*100. If you have more than one conversion actions then you should do this calculation for each one of the action as well for all the actions combined.  In case of Leads, you can take it one step further and calculate not only the “Leads Generation Rate” (Online Conversion Rate) but also Lead Conversion Rate, which is, Leads that convert to a customer divided by total leads generated.
  18. Cost per Conversion – This is the Total Cost divided by the number of conversions achieved from visits coming via display ads.
  19. Revenue – This is total revenue that is directly attributed to the visits coming from display advertising. It is pretty straightforward to calculate in eCommerce but gets a little tricky when you have offline conversions.
  20. Revenue per Visit   – Shows the direct revenue achieved per visit originating from the display advertising. It is calculated as Revenue Generated from Display Ads divided by the total Visits.
  21. Revenue per Page – This is useful for ad supported business models. This is sometimes expressed as RPM (Revenue per thousand impressions of ads) = (Total Ad Revenue/Number of page views) * 1000

Note: In addition to Clicks, you can also looks at View Through and calculate your other related metrics by view through.  View Through is sum of all the cookies that visited a page that showed your ad on it, and then landed on your site. The assumption, in this calculation, is that you landed on the brands site because of that ad exposure.

 Where can you get these metrics from?

  • Impressions, Reach, Cost, Engagement Rate, Clicks, CTR and CPC data is available from your agency or ad server tool.
  • Visits, Visitors, Page Views, Bounce Rate, Engaged Visit Rate, Conversion, and Conversion Rate are available in your Web Analytics tool.
  • Revenue is available in either your Web Analytics tool or other offline sales database.
  • Cost/Conversion, Cost/Engaged Visits, Cost/Page view and Revenue/page are calculated using data from multiple tools.

Questions/Comments?

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