Wednesday, February 29, 2012

Move Web Analytics Data Out Of Silo

Web Analytics tools are great for providing a good view of one channel i.e. your website (ok, maybe slightly more than one channel e.g. some email, some social media, some offline). They worked great in silo for first few years of the internet because the only way for customers to interact with your brand online was on your site and websites were not an integral part of the business. Nowadays the story is different, customers interact with your brand in so many way, your website is just one small part of the whole "web" ecosystem and "web" is just one part of the whole "customer" experience and buying cycle ecosystem. Customer’s don’t think and operate in one channel i.e. your website. However, many "web analytics" tools do not even provide you full view of a customer journey and interactions online let alone the offline journey.

To understand today's customer and performance of your marketing efforts, web analytics data has to move out of it's silo and needs to be integrated with other data sources.

Many of you might be already be using 3rd party solutions to pull data from few sources into a dash boarding tool. That is a great start but it still does not provide you a complete view of customer journeys. For example, just because you have social media mentions on the same dashboard as your on-site analytics data does not tell you if those mentions are from your customers or somebody, who is neither a customer nor is your target customer, just blabbering in social media. But I will give you credit for thinking outside the Web Analytics tool.

To understand complete customer journey (i.e. 360 degree view of customer) and to conduct analysis that take you from marginal improvements in conversions to something that has a huge impact on the business you need much more detailed data than a web analytics report or a dash boarding tool can provide. First, you need to collect individual data for each customer in various channels then warehouse the data in one place where you join various sources via common key such as customer id, email address, phone number etc. Only then you can create and run complex cross channel queries to understand try customer behavior and campaign performance.

Many mature organization are already doing it or are working on it. If you are not then it is about time to start thinking about if you want to stay competitive.

Don’t think that just because you are using Google Analytics you can’t have this level of data because you can. You just have to push yourself and start thinking outside what your web analytics tool can provide.

How Can You Do it

Web Analytics tools already anticipated this needs so they have built a way for you to get the data out easily. You can use either of the two methods listed below to get the required data
  1. APIs – Many tools like Google Analytics provide data via APIs. Use those APIs to pull appropriate data into your datamart/datawarehouse.
  2. Data Feeds – Many tools provide data in a flat file that you can use to populate your datamart.
Here are few things to keep in mind before you start putting this data in your datamart
  1. Make sure your tools are configured properly to collect the data in the right format and
  2. Your data transformation process should be able to understand the difference between various custom variables that you have used in the data collection
  3. Various data sources also need proper identifiers (keys) to match them together.
This is not going to be an easy project but this is a critical step in using your web analytics data to stay competitive.

There are few 3rd companies who are already providing tools and service to help you with it. I recommend looking at iJento Datamart solution (Note: I work for iJento). You should also check out Gary Angel’s Blog. Gary has worked and written extensively on this topic.
If you have any question, I will be happy to chat. Email me.


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Wednesday, February 22, 2012

7 Analysis Tips for Improving CTR on Display Advertising

Not all display advertising is created equal, though when you look at your web analytics reports you are most likely not going to find the reasons that makes each campaign and each ad so unique. Web Analytics tools generally start tracking the performance of a display advertising campaign only after the visitors have clicked on an ad and landed on your site. What happens before a visitor clicks resides in an Ad Server or in a spreadsheet on someone’s desktop.

In my last post I wrote 5 tips for Analyzing and Optimizing Display Advertisingand one of those tips was to Improve Click-Through-Rate (CTR). In this post I am focusing on elements you should analyze and optimize to increase the CTR of your display advertising. (Note: I am not saying that you should solely focus on CTR, but assuming conversion rate remains the same, increase CTR on your ads will result in more visitors on top of the funnel causing higher number of conversions. )

7 Things to Analyze for Improving CTR
  1. Publisher - Publisher or the site where your ads are served has a lot to do with how your ads are going to perform. An ad served on MSN is not the same as the ad served on Most of the time such information is absent from the Web Analytics tools and hence never crosses an Analysts mind. Get hold of that data and bring it together with the other data to analyze and optimize your campaigns.
  2. Placement – By placement I mean the area of the site where you ad is shown. Ads shown at certain location on a page are likely to get higher CTR than the other ads. For example, an ad served above the fold is almost guaranteed to be seen by a visitor, while the one served below the fold is up to anyone’s guess. In both cases an ad impression will be counted but both will not generate the same CTR.
  3. Size – Size of the display ads makes a huge difference in its performance. Certain ad sizes tend to get more clicks than the others. When analyzing and optimizing your campaign keep in mind that size does matter.
  4. Day/Time – An ad served at midnight on Friday will have different CTR than the same ad served during lunch time on Wednesday. Analyze you campaigns in light of the day/time when the ads were served, find the best time to launch a campaign.
  5. Creative - Different creative invoke different reactions. Images, colors, fonts etc. are all part of the creative mix. Different combinations will have different CTR. Find out the best mix that drives not only higher CTR but also conversions.
  6. Unique Value Proposition (UVP) – Why should a person click on your banner ad and not another one on the same page? A boring ad just telling about your services will likely have a lower CTR than an ad that provides a unique benefit to the visitor. Factor the messages and UVP when analyzing your campaigns and making recommendations.
  7. Audience – Targeted ads will likely have higher CTR than a general broadcast ad. If you have a remarketing campaign then that is expected to have a higher CTR than just a general broadcast campaign. If your target audience is families with 2 kids under the age of 10 and household income is more than 100K then putting your ad in front of a single guy make making 50K is not going to generate a lot of clicks and conversions. Understanding the goals and objectives is critical before you start analyzing campaigns and making recommendations.
Even though I have listed 7 things to analyze keep in mind that it is not just one thing that will have an impact, you have to analyze all these things together. A blue creative that works better on Yahoo homepage on Wed might not be the best combination for at the same time. Yes, it is not going to be easy to get all this data but that’s why you are getting paid big bucks.
(Note: iJento has a solution that helps you bring such off-site data together with Web Analytics data, email me if you are interested to know more about it).

Always, Go Beyond What Web Analytics Provides

No Comments Here- I am in the process of moving my blog to so I am not allowing any comments here but you can leave comments on my new blog. Also make sure to update your bookmarks.

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Wednesday, February 15, 2012

5 Tips for Analyzing and Optimizing Campaigns – Part III

This is part III of my post on Analyzing and Optimizing Campaigns. In part I I talked about why your campaign analysis is probably is wrong. In part II I showed an example of how obsessing over reducing bounce rate might not get you anywhere.

In this post I am going to provide you 5 tips for analyzing and optimizing your campaign. Here those 5 tips:
  1. Optimize Cost of Advertising
    Cost is dependent on how much you pay per click or pay per 1000 impressions (CPC and CPM). You have control over these cost factors. Those who are running Paid Search campaigns should already be familiar with and should be working hard to reduce the cost (CPC). Those dealing with CPM display ads should know that those rates are highly negotiable. Do you research about pricing etc., play with these numbers and see what will yield the optimal result,. Take your analysis and recommendation to your media buying team.
  2. Improve Click-Through-Rate (CTR )
    CTR depends on several factors such placement, creative, unique value proposition, time of the ad, targeting criteria. Analyzes those factors and see where you are falling short and where are the opportunities for improvement. You can pretty much test all of these and improve them.
  3. Reduce Landing Page Bounce Rate
    We looked at improving the Bounce Rate in the last post. You can reduce the bounce rate by optimizing the messages on your ads, better targeting techniques and optimizing your landing page. If your value proposition and messages are aligned on the ads and the landing pages, you will see a reduction in bounce rate. Conduct A/B and MVT on your landing pages to see what works.
  4. Optimize Conversion Funnel
    Make sure the conversion path steps are optimized and any obstacles are removed. Remove any fields that are not needed e.g. if you don’t have a use for phone number then don’t ask fo it. Streamline the process. Conduct A/B and MVT to improve the conversion funnel. Use personalization, if possible.
  5. Improve Average Order Value (AOV)
    Yes, you can influence the amount a customer pays per transaction. Use on-site recommendations to up-sell and cross-sell to drive up the average basket size and the value. Use customer data to figure out what might interest a particular customer and put those in your recommendations. I have also found that some segment of visitors just won’t convert online, they are not comfortable. If you are able to spot those customers on time and engage them via sales call center then not only the chances of conversion will go up but the AOV will go up as well. In my experience, those customers, who deal with a live person, tend to buy more versus those who complete orders online.

Just optimizing for one of the above variables might not yield the desired improvement; optimize all of them to achieve the maximum ROI.

Comments? Questions?

Follow Me on Twitter: @anilbatra
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Wednesday, February 01, 2012

Bounce Rate Optimization Is Not Always The Cure: Analyzing and Optimizing Campaigns

This is part II of the series on Analyzing and Optimizing Campaigns. I wrote in my previous post that when analyzing campaigns many web analysts just focus on the web analytics data. Some venture to include the cost and impression data of the campaign but they still don’t have a complete view.

In this post I will show you how their lack of complete view results in wrong analysis and wrong conclusions.

Below is the data I used in my last post. This is the type of data most Web Analytics tools provide and hence “Web Analysts” tend to use.

What is missing?

Where is the cost of products and profit margin data? Without that information, you don’t know if this campaign is successful or not. Right?

The sad reality is that many web analysts don’t have access to profit margin data and hence they look at what is available to them and start recommending A/B testing (see my post One Awesome Web Analytics Tip: Think Beyond Web Analytics). And their first target generally is Bounce Rate. Oh… look bounce rate is 50% it is too high, we need to reduce it. Right?

Wait...There is More...

Let’s assume that you are able to get hold of additional data. Now let’s see how the campaign looks if we add that data. Below I have added cost of Goods Sold data (keep in mind there are additional costs in real life).

It is evident now that the campaign is bleeding money. If your business goal is to increase conversions at any cost then you might be ok but if you goal is to increase conversions without losing money then this campaign sucks.

Ok, so what should we do now? If your answer is still bounce rate then you are wrong. Look at the data below, even with a bounce rate of 0% you will never make this a profitable campaign.

So next time get all the data before you jump to the conclusion that all you need to do is reduce “Bounce Rate”. Bounce Rate Optimization looks tempting to tackle but it is not always the cure.

Stay tuned, more coming soon on this subject.

Follow Me on Twitter: @anilbatra
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