Recently, we had some of excellent clients who have been demanding a unique feature that reduces time for analysts who manages larger accounts with more profiles.

The request was to have ability to extract multiple profiles data within one query. The use case was:

“I manage about 38 countries dashboard within one google analytics accounting with 38 profiles dedicated for each country. I have to compare visits, time on site, etcs all the time and the only way to do it is via creating many more queries in excel plugin- Is there a better way “

And we said “yes”. We figured that if we allow the country name to be extracted from list of profile names written in excel, we can pass that in our query and have it extracted from Google Analytics.

Here is the quick video representing the same.

In addition to this, we also have worked out similar solution for advance segment. For e.g. if you have 10 advance segments that you would like to compare for their performance against the same metrics, you can do the same as described in the above video for profiles.

As we continue to build more features, we are more committed to listen to ideas that you are looking forward to automate, please feel free to reach out to us if you have any feedback or feature request for Tatvic google analytics excel plug-in.

Note: To use this feature, you’ll need to update your Tatvic Excel plug-in. Here’s how to do it:

1. Before loging-in to the tool, click on “Check for update”.

   

2. Click on the link that appears at the bottom of the tool.

Done, enjoy the feature now.
Thanks again!

Ravi Pathak

Ravi Pathak is a Co-Founder of Tatvic and expert at Managing different web analytics tool. Ravi's actively works on conversion optimization projects to improve conversion rate and test newer hypothesis with e-commerce companies. He regularly tweets via @tatvic or @ravipathak and is easy to have conversation with. Google Plus Profile: Ravi Pathak

Website - Twitter - Facebook - More Posts

Recently, Google released an upgraded version of Google Analytics as “Google Analytics Premium“. We had been following some conversation on twitter about it and have been speaking to clients about important features of premium. One of the key features that users of Google Analytics Premium are going to enjoy is having ability to use 50 custom variables.

This if implemented well, can save ton of hours in analysis, reporting. IMHO, this is probably the most valuable feature of GA premium compared to other features more shiny features. (if you disagree, feel free to shout out) During several conversations, it struck to me that lot of analyst would have hard time figuring out how they can use 50 custom variables and I thought it would be great if I just put down, how would I use it in standard ways for different category of websites. i.e.  E-Commerce, Lead Gen & Content.

Therefore, I am starting of a blog post series to explain how best to use 50 custom variables in Google Analytics for an e-commerce site and in following weeks, would talk about other types of websites.

As we are aware there are three different types of custom variables available:

Visitor Level: Used for tracking interactions of a visitor across multiple session

Session Level: Used for tracking within session activity of a user.

Page Level: Used for tracking page level activities ie. Button clicks etc.

All custom variable key & value are stored in cookie and they expire at different time. For e.g. Page level custom variable value gets expired when user moves to next page, visit level gets expired when visit is ended.

Here is the starter list of custom variables for Page Level:

1. Page type:

Name the type of page. You can track different page types. One way to do it is to use the research that Gary Angel & team’s research on functionalism.

2. Search Results Rank:

For all the e-commerce sites, this becomes vital to understand how the internal site search is performing. There are ways to do it ,but knowing the search ranking of your internal site search would tell you if you need tweak there or not. I also had an old blog post on value of internal site search if you may want to review the importance of it.

3. Wishlist ID:

If you have a wish list, you should capture its id there to help measure which products are being added to wish list. You can have wishlish ID & the SKU as key value pair respectively.

4. Product View - Product SKU:

An extremely important data in e-commerce that gets frequently missed is product conversion rate. i.e. ration of product purchase to product views. The key value pair here could be product-view & SKU or product name.

5. How much % of the total page, window is viewing  :

Lot of time, you need to understand how much scroll users are doing as most of the times users would not be scrolling down and some of the important communications that is in second fold of the page gets ignored. The key & value pair could be oc-<url of the page> & % of page viewed. You can use some of the nice plugins like this(need to find link) to achieve this or use analyticsengine.net to get it done. This should be page level.

Here is the good resource to get started

6. Product Category:

Product category should be tracked to understand the relative popularity of given category and since this is tracked only at a category page, key value pair could be product-cat & category name/category id.

7. Variation/Size of Product:

Often lifestyle stores would have additional items that a visitor needs to select. For example, size in case of clothes/shoes. This should be an onclick type of call which should fire setcustomvar function.

8. Colour of the product:

Again this is more applicable for lifestyle e-tailers, but significantly important as different color can fetch different pricing.

9. Filter Type/Sort By:

Often on category page or similar other page, your e-commerce stores may be displaying products in default way, but you may have functionality to display priceà low to high or something similar. You must track if there is a change in filter. Key Value pair could be filter type-> values in drop down.

10. Internal Promotion ID:

Lot of large websites have multiple sites, multiple divisions owning different web properties. If there are internal promotions that are carried out, you need to track it specifically. Key Value pair here could be IPID & nomenclature that is used for internal promotions.

11. Cart Add -Product SKU:

Brilliant understanding of shopping cart behaviour could be possible when you understand how many & which products are added to shopping cart by users. The key value pair would be cartadd & product sku/product id/product name.

12. Cart Remove –  Product SKU:

Similar to above, it would be great understanding to learn which products are being removed from the carts and what do people to after removing the cart. A key value pair for custom variable could be cartremove & product SKU/product id/product name

13. Cart View – Product SKU:

Another super duper dimension that you can generate with the Cart View to understand how many times the cart is viewed without another product being added. A key value pair here could be cartview & product sku/product id/product name

14. Add to Compare – product SKU:

If your website is having comparison ability , you have to understand which SKUs are getting compared the most & you need to track this information.

15. No.of Search Results:

Google Analytics has great tracking abilities for internal site search; however, one critical thing to understand is # of search results for given search within the site. When you see 0 results or less results for any keywords, that will be great pointer to understand. A key value pair could be SR count & integer displaying no.of search results.

16. Product SKU- Product Ratings:

If you are using services like Bazaarvoice or similar which provides product rating , you should track it to understand if there is any co-relation between higher product rating & increase in revenue of the product which I believe there is. A key value pair would be rating & sku/product id/product name.

17. URL – Action (Print, Share, Email, Favorite ):

This is very subtle, but lot of product pages, has few action that some of the users do perform that are not social per nature , but important to know. They are generally, print, share, email or mark it as Favorite. To track this the key value pair could be URL & action name.

18. Discount % on product page:

If you are offering discount you should track to understand how different discount works. The key value pair here could be product SKU & % discount.

19. Product Brand Name:

It has been very interesting to know the X & Y dimension of any links that being clicked. You can always identify where in a page if link placed has higher likely of getting clicked. The key could be Linklocation & value could be x & y co-ordinates.
If you are selling products of different brands, and you would like to know which brand is more popular, you should have key value pair as brand & values of brand name that exist on the product page

20. Link Location in page:

Most of these are suggested as page level variable but you can have at least some of them used as visit level and have it expire on transaction page as well if you want to. There are benefits to it and disadvantage as well , but I’d talk about them at some other time.

Visit level Custom Variables

21. Coupon used:

If you are a heavy play on coupons , you ought to understand how the coupons are used. Are users just redeeming or also purchasing after redeeming as well. The key-value pair could be coupon – coupon_name+discount.

22. No.of browser Plugins:

If your target market is technology related website visitors, it would be interesting to understand their browser usage via how many plugins they have downloaded. For it with this script it tells me that I have 18 plugins installed, and I am fairly technical so you classify, how many users with strong technical background visited your website.

Key value pair could be no.of plugins & integer value coming out of that script.

23. Local hour of the day:

The hour of the day report in google analytics report at what hour (“from standpoint of your timezone”) how many visits have come or how much leads / Revenue has been generated at a specific day in hour. However, what you need to know is visitors local time. So if you are in New York, its 1 pm, where as at the same time you are in San fransisco, you would have 9 am coffee. You need to understand , whats the local time when people buy your product, irrespective of what timezone they are in. This will provide huge understanding to you about your users buying patterns in terms of when they buy. Key Value pair could be local_hour & integer representing the hour. Further, you can also have value as morning/afternoon/evening/night defined based on different values of hour for further simple analysis.

24. Currency

Often if you have a store which sells to more than one country knowing which currency user bought your product with is more important. You can set it as visit level custom variable to be expired at the end of transaction & start it to be populated when user is seeing a product page.

25. Recommended or normal product view

Lot of e-commerce stores have ability to show product recommendations , Most web shop owners doesnt realize is how much important it is for them. By placing a custom variable to know whether the product view is from recommended or not, helps a lot.

26. No.of bookmark items in browser:

# of bookmarked items provide the information about maturity of user. The more items bookmarked, the more mature the user is & more likely to share your content.

27. Google Search Results Position:

There is a nice little nifty javascript available again by John Doherty & Michael King here which helps you to figure out the actual search results position in case of google. There are surely other ways as well, but important is to get this position figured out quickly.

28. Shipping Method:

Shipping method provides highlight into what kind of shipping method is more likely to be used by users. If you are considering free shipping, you can also revise the work around to get this going.

29. Payment Type:

When users are presented with different mode of payment , it is very likely that these different payment method cost you different charges associated with them, but knowing which payment method brings different volumes would yield , huge insights on what kind of plan to choose for payment type.

30. No.of Cart Items:

At any stage of visitor if you know the # of items present is cart is an interesting fact to know. It also helps to figure out how users add the second product to the cart & how long it takes them to do so. You may have to rewrite some cookies here if you choose this as session variable.

31. Value of Cart Items:

Related to #30, if you know the value of cart items during different visitor activities, it helps you identify, what is the avg. wallet size users are willing to shell out for your products.

32. Minutes to Complete purchase:

It would be interesting to know & to evaluate the effectiveness of webpages leading to product page to understand how long it takes to have user complete the purchase starting from a session. If you see that it  takes less than a minute for users to finish it, you might want to break it down by count of visit to see if these are repeat visitors who are just coming back to buy something that they already have planned.

33. Minutes to add product to cart:

Similar to #32, it would be great help to understand how long does it really take users to add a product to cart. This information measures the effectiveness of category pages & product details pages.

34. Shipping city:

Knowing the shipping state & if broken down by city helps you figure out whether users are buying your products for themselves of they are buying for others.

35. Product Stock Status (instock, outstock)

Quite often when product goes out of stock a webmaster would get notification & it would be taken away. Smarter way is to keep it there and add couple of lines ” notify me when this is available”. When you track such things you are aware of the popularity of some of your products which are out of stock.

36. Name of Product Manager/Brand Manager:

For large corporations, different set of web properties and products/brands are handled by different brand managers. Even the product promotions have different strategies. At the end of the day it boil downs to single person. Brand Manager/Product Manager. To know how effectively web has been used by brand manager & compare it with others, you may want to put Name of product/brand manager in the custom variable of course abbreviated.

37. Product Margin type (high, low , medium):

Different e-commerce products have different margins knowing margin and how users react to products with different margins, would be a huge insight for a web analyst to promote different products. You dont necessarily have to add exact % of margin just keep it at high , medium & low.

38. Day type (weekday vs. weekend):

There are products gets sold on weekday Or few products especially on weekends. Most people do not now about it , when you break this information with what has gotten sold or what’s the revenue proportion between week day or weekend, it would help immensely devising new product launch strategies. You can also append it to have information of whether this day is a holiday or not to understand if your product is bought more when people are at work or whether it is bought when people are at home.

39. Location Type (metro vs. Non- metro):

Geography, when classified appropriately, i.e. metro area vs. non-metro area, it helps to figure the difference in avg. order value. You can change your Adwords strategy based on it.

40. Market Type:

Most large companies have their own defined market types, done in variety of ways which are beyond my imagination. Having market type be placed in Google Analytics would help to measure market type performance within Google Analytics itself.

41. Deal of the Day:

Most e-commerce site these days offer deal of the day, knowing that this product is sold via deal of the day will help you figure out what kind of products to be offered for deal of the day. Same strategy of measurement can be used if you have deal of the week instead of deal of the day.

Visitor type custom variable

42. Visitor ID:

Hugely important if you are doing some custom analysis or prediction for data collected, this having a primary key is utmost important. However as per TOS #7, you can not use it to match it with any personally identifiable information of users.

43. Registered/Non Registered User:

Lots of time , you have buyer who may have registered earlier, but now coming back to buy second time. If you are doing some analysis like, customer life cycle value, categorizing visitors based on register or non-register would be hugely helpful.

44. Password reset:

Sounds very trivial but to me its most important. These days, people don’t bother to remember less than critical password, if lot of users are sending reset password request, this might be alarming to you might want to consider guest check out or may be sign up with facebook.

45. Buyer Type (new buyer vs. returning buyer)

You can always have data about whether this visitor is a repeat visitor or not ,but its seldom same as new buyer vs. returning buyer. New buyer is the one who registers with you to buy the product whereas returning buyer is the one already registered with you to buy the product. You can have more criteria to decide whether this is returning buyer or new buyer , but whatever it they are , important fact is , you have to know whether this transaction is by repeat buyer or First time buyer. What this essentially means is that you ought to have better methodology to identify a repeat buyer than just cookie

Got it , so where does rest of 5 other variables exist ? I thought hard about it and realized that if you have read this far, You would have certainly few ideas that I couldn’t think about, if you do, share it and we can make the rest 5 custom variables together !

Hope you enjoyed ! lets give few more custom variables to the next reader

Google Analytics Blog: Introducing Google Analytics Premium

Ravi Pathak

Ravi Pathak is a Co-Founder of Tatvic and expert at Managing different web analytics tool. Ravi's actively works on conversion optimization projects to improve conversion rate and test newer hypothesis with e-commerce companies. He regularly tweets via @tatvic or @ravipathak and is easy to have conversation with. Google Plus Profile: Ravi Pathak

Website - Twitter - Facebook - More Posts

I am extremely excited to announce the v2 of  Tatvic excel plug-in. We have been working on it for so long and its great feel to see it getting light of real world.

By this blog post, I want to pass on some key features that we’ve built over some time.

More APIs = More data

We are now offering ability to extract data via Adwords API, Keyword traffic estimator on top of Google Analytics data export API. We wanted to remain in direction with the Google Analytics as it firmly integrates with Adwords. You will however need to have Adwords API token and you can only use it for MCC account though.

You can watch an example of how you can extract keywords data via Adwords API below:

How to Extract data from Adwords API?

You can moreover, extract data from keywords estimator tool as well. This is first application in excel which can get you data from Keyword estimator to understand how much traffic that you can get if you go ahead with specific keyword.

How to Extract Data from Adwords Keywords Estimator API?

Save/Share query

With new google adwords excel plugin it’s easy to remember a set of inputs that you provided to pull out specific reports so that you can reproduce it or share it with your colleague. We are also working to add few more APIs e.g. SEOmoz as well but haven’t matured it as yet. If you have an opinion on what other APIs that we should be adding to the excel integration, please feel free to reach out to us. Your opinion is most valuable to us.

You can also review new pricing plan which is monthly pricing without any commitment.

Ravi Pathak

Ravi Pathak is a Co-Founder of Tatvic and expert at Managing different web analytics tool. Ravi's actively works on conversion optimization projects to improve conversion rate and test newer hypothesis with e-commerce companies. He regularly tweets via @tatvic or @ravipathak and is easy to have conversation with. Google Plus Profile: Ravi Pathak

Website - Twitter - Facebook - More Posts

Consumer buying behavior has been fascinating & Google Analytics is becoming superior every day for such purposes. As usual this blog post is one of the efforts trying to find better ways to create interesting advance segments within Google Analytics.

We generally start looking at any web analytics project from the perspective of web analytics framework that we have developed. So obviously we will start with a business question or hypothesis that we can test using google analytics data.

The business question/hypothesis that that we were aiming to check was

“whether those visitors who are more engaged, ends up buying more. If they do, how much more worth they become.”

We used Time on site per visit as primary metric representing engagement and created multiple advance segments for different time ranges as below.

• Visit which lasted between 0 to 100 seconds
• Visit which lasted between 100-199 seconds
• Visit which lasted between 200-299 seconds
• Visit which lasted between 300-300 seconds
• Visit which lasted more than 400 seconds

The image below shows how you can create advance segment for different time range using brand new Google Analytics V5. Each advance segment will become one line item of frequency distribution of website conversion rate and other metrics with frequency as time on site (in seconds) that user is staying on website.

Sample image of advance segment for visits that lasted between 100-199 seconds

(click on image to see larger view)

Similarly you can create different Google Analytics Advance Segments in the same lines. Once you create them you don’t need to go any far to generate actionable analysis.

Once you check all the advance segments & apply to reports it will result into a frequency distribution against several metrics. I recommend you use it with e-commerce conversion rate & Average order value for this purpose to start with. Here is what your e-commerce dashboard in Google analytics would look like.

These are actual results of a client e-commerce application & it is clear that if we can have tools, application information on the website that increases engagement, the likelyhood of conversion increases.

An inference from this could be if my visitor spends more than 200 seconds of time (close to 3 minutes) he is twice (or more) as much likely to convert than other. Moreover, its not just to convert, its likely that he would buy more items resulting in higher revenue per transaction.

Ravi Pathak

Ravi Pathak is a Co-Founder of Tatvic and expert at Managing different web analytics tool. Ravi's actively works on conversion optimization projects to improve conversion rate and test newer hypothesis with e-commerce companies. He regularly tweets via @tatvic or @ravipathak and is easy to have conversation with. Google Plus Profile: Ravi Pathak

Website - Twitter - Facebook - More Posts

Working at a web analytics company we have find an interesting pattern of customers and especially those who are decision makers.

Decision makers are alone. They know inside themselves, that there is no perfect world, online or offline. If you face following questions, read along!

• You fear that you are making decision based on wrong data collected via Google Analytics/Omniture.
• You don’t trust the data as well as your manager, even if analyst makes hardest effort to convince?
• You cannot make decision & delay taking a decision since on your google analytics/Omniture data just doesn’t have what you need?
• You pay for incredibly large amount $$ for Google Analytic/Implementations implementation and don’t know what decision you can take?

I say “Welcome to the show” – You are not alone. Its Act 1.

Web Analytics tool implementation is sub optimal

Over last several (6years), I have been involved in Google Analytics & Omniture Implementation a lot. Quite a bit of the time, it involved cleaning up the Google Analytics/Omniture implementation done either by agency or client on their website/e-commerce store. A huge learning that we recently relayed internally was the fact that, most of the time either implementation was erroneous, either it lack action-ability , or may be even mistaken. This happened in 85% of the cases of implementation when Google Analytics was implemented by non-GA expert.

Problems with Web Analytics Implementation were not known

However, this problem is sub-optimal implementation of Web Analytics tool is both not known, not identified, well enough. Its normal that if you don’t know the problem, you are less likely to know the impact & you are not going to make any effort to solve that problem.

Challenge to Online Marketers:

Lot of people including you, my reader, may feel above two paragraphs are not right & doesn’t represent your situation as Leader in Analytics in your organization. Great!!

Why don’t you do act 2 then. Here is the challenge.

Show the world what actions did you took which helped your online business. You can be discreet & okay not to disclose the actual figures, but come forward & show the world that whatever was the analytics, you took decision & the online organization was benefitted. I am not saying that it is not possible to take good business decisions based on Google Analytics, I am just saying that its rare.

Comments section is yours. Do you second act my friend!

Okay if you cannot! It’s time to roll up your sleeves and carry out the Google Analytics Audit /Omniture Audit. If you need how to do it, sample reports, download free from Audit section.

Looking forward to Act 2.

Ravi Pathak

Ravi Pathak is a Co-Founder of Tatvic and expert at Managing different web analytics tool. Ravi's actively works on conversion optimization projects to improve conversion rate and test newer hypothesis with e-commerce companies. He regularly tweets via @tatvic or @ravipathak and is easy to have conversation with. Google Plus Profile: Ravi Pathak

Website - Twitter - Facebook - More Posts

This post post aims to develop an understanding of how to calculate product specific conversion rate & discuss the potential analysis & insights can be generated from it.

Quite often the business problem or questions that most ecommerce website encounters is very specific to product management as to how much product to stock, which are fast moving ? how can I move some of the product quickly etc.

How to measure how many views different products have received & whether those views are turning into transaction & revenue? What actions can I take based on such data?

What is product conversion rate?

There are many ways to define what we can call the product conversion rate. For example you can check # transaction for any given product divided by # of visits to that specific product. Moreover, you can also use quantity to # of visits ratio to calculate the product conversion rate.

In omniture there is an event called prodview which counts # of pageviews for any given product and then when it is used with revenue or transaction, it gives interesting conversion ratios for different product.

For our understanding we will use ratio of quantity to # of visits for different pr

oduct as a product conversion rate.

Why product conversion rate?

Calculating product conversion rate is very important to understand which product is converting better vs. others? Moreover if you have cost data, you can also find out that whether the profitable products are converting better or not? With such insights, you can modify the e-commerce shop to ensure that the profitable products get more chance to convert etc.

As a normal analytics practice first thing is to build a template of what data we want to calculate the product conversion rate.

For our understanding we will use ratio of quantity to # unique views to product page for different products as a product conversion rate.

The quantity data will be available from the Google analytics interface & we need to get data of quantify & product views for different product.

How to get data from Google Analytic?

There is only small change that is required to calculate the product conversion rate in Google Analytics i.e. by using Google Analytics Events tracking.

Here is the syntax that you can use to generate product views statistic in

event tracking:

pageTracker._trackEvent

(“uniqueproductname”,

“variantname”,

“uniqecategoryname ”,

1)

This needs to fire on all your product pages in Google Analytics.

As a result of this, in your event tracking report you will find product names in event actions and get unique product views as well of total product views of the event via total events and unique event metrics.

You will find product names in event actions and get count of unique views as well of total views of the event via total events and unique event metrics.

By putting together all the data you have here is what you can get as an output for different product. Please note that in last column you can create conversion rate for your different product.

Obviously, some of the product has very high conversion rate as shown in highlighted portion. These are your winners.

Plot this conversion data with the product’s revenue and you know whether high converting products are getting sold or not ? You can also use the advance segment to calculate the product conversion rate for different sources which will provide even better insights to what products works and which doesnt work ?! What more ? You can figure out low converting product and reduce them from your stocks  & save the money from warehouse !

Ravi Pathak

Ravi Pathak is a Co-Founder of Tatvic and expert at Managing different web analytics tool. Ravi's actively works on conversion optimization projects to improve conversion rate and test newer hypothesis with e-commerce companies. He regularly tweets via @tatvic or @ravipathak and is easy to have conversation with. Google Plus Profile: Ravi Pathak

Website - Twitter - Facebook - More Posts

What is Targeting?

My understanding of targeting is to provide content to the user which has high probably to be consumed.  It may be over simplified, however, easier said than done. Now that I am in my 6th year of web analytics industry, I now started realizing what & why probably Jim Sterne has his firm’s name as Target !

Content personalization and targeting are two interchangeably used word that I have come across. However they are separate from behavioral targeting which has more to do with the targeted ads to be shown to the user. There is significant debate getting generated at Anil Batra’s Blog about the definition of behavioral targeting ,however lets keep the focus of this post to content personalization and not behavioral targeting !

Different levels of targeting

I can think of two levels of targeting; Segment level & Individual level. The difference is in the granularity by which you can segment. In other words, this classification is based on the degree of small segments that you can create for your individual users. For example, you can create two segments of your visitor new & repeat and provide them different content. This is pretty straight forward as you create two different design of the page which works for your new & repeat visitor.

Individual level targeting or what I call one-to-one targeting is more granular where you provide exactly the content that your specific visitor would like to consume. For example if you are coming back to Amazon, it provides you specific unique content that would want to consume based on your prior visits to the site.

While Derek has a great content personalization presentation, I plan to expand the concepts that he has laid out. Another nice variable that should be added apart from different levels of targeting is situation in which customer is viewing the site.

In other words, your targeting effort doesn’t stop at just segmenting your traffic, but it should also include the situation in which users are coming to your site. For e.g. if you go to Zaaz on Friday night, you will see great message customized for you for friday night.

More to come on content personalization – What are your thoughts ?

Ravi Pathak

Ravi Pathak is a Co-Founder of Tatvic and expert at Managing different web analytics tool. Ravi's actively works on conversion optimization projects to improve conversion rate and test newer hypothesis with e-commerce companies. He regularly tweets via @tatvic or @ravipathak and is easy to have conversation with. Google Plus Profile: Ravi Pathak

Website - Twitter - Facebook - More Posts

Tracking worldpay transaction with Google Analytics & getting ecommerce data in most accurate manner is amongst one of the challenging project that we executed for one of our clients recently. I wanted this blog post to address what we all did to capture most accurate Google Analytics data possible into worldpay.

Several issues need to be addressed before proper e commerce tracking can be executed.

Here is the design how worldpay works:

Worldpay doesnt allow javascript to be executed on their domains. So you cannot post any Google analytics code on green boxes as describe above. So technically, nothing can be tracked post step no.1 as described above.

To make this tracking work, several things needs to happen.

1. We need a thank you page after worldpay transaction finishes which is on a domain where javascript can be executed. That is, we need to have one more step after (3) occurs which pushes user to move from (3) to a new page. That is the page where we can place Google analytics ecommerce tracking and track the value of transaction. Let’s call it step no.(4)

   

2. More importantly, we need to pass all the Google analytics cookies to this thank you page ,so that the ecommerce code that gets executed on step no.4 attributes the transaction to original cookies that were present on product page or step (1).

So essentially we need to have a page at step (3) that provides a button on Worldpay that says click here to finish the transaction. I will come back later as to what other things that button needs to do.

So till now , by having that button “click now to finish the transaction” ,one problem is solved so that we have one page where we can put GA code & execute the addtrans etc functions of Google Analytics.

Lets focus now on how we can pass on same cookies that existed on step (1) to step (4). Normal google analytics functions of linkbypage will not work as they can only work if the next page can fire Google Analytics.

This is where this gets really innovative.

You can get all the required cookies one by one. But, there is a simple way to get all GA cookies required at once. By using the method, pageTracker._getLinkerUrl of Google Analytics, which will give you all the cookies required along with the URL.

Now, there is a concept of hidden variable in Worldpay. Hidden variables can pass different values like price, currency, order type etc. You can create a new hidden variable and pass the Google Analytics cookies into these hidden variable.

Generally, the RBS World Pay form (where the RBS World Pay button is located and from where the users goes to RBS World Pay site for payment) looks like the following:

<form action="https://select.worldpay.com/wcc/transaction" method="post">
	<input type="hidden" name="cartId" value="Cart ID">
	<input type="hidden" name="desc" value="Product Desc">
	<input type="hidden" name="currency" value="GBP">
	<input type="hidden" name="amount" value="XX.XX">
	<input type="hidden" name="instId" value=137379>
	<input type="hidden" name="lang" value="en">
	<input type="hidden" name="subst" value="yes">
	<input type="submit" value="Order by credit/debit card" name="submit">
</form>

There are some changes that you need to make for this to work:

1. The form needs to be given a name, if one is not present. This can be done by adding name=”worldpay” in the form element.
2. We also need to have one custom variable defined in RBS World Pay system which will have the value of the cookies required, that can be accessed in Thank you page. Here, the defined custom variable is MC_gacookie. In this particular example, we also have one more defined custom variable MC_analytics which will have account id. This is required only when pages with different profile ids leads to same Thank you page.

With these changes, the above code of RBS world pay on product page will look like the following:

<form action="https://select.worldpay.com/wcc/transaction" method="post"> 
	<input name="cartId" type="hidden" value="Cart ID" />
	<input name="desc" type="hidden" value="Product Desc" />
	<input name="currency" type="hidden" value="GBP" />
	<input name="amount" type="hidden" value="XX.XX" />
	<input name="instId" type="hidden" value="”XXXXXX”" />
	<input name="lang" type="hidden" value="en" />
	<input name="subst" type="hidden" value="yes" />
	<input name="MC_analytics" type="hidden" value="XX-XXXXXX-X" />
	<input name="MC_gacookie" type="hidden" />
	<input name="submit" type="submit" value="Order by credit/debit card" />
</form>

Now, let us see what extra statements that we need to add to the normal GA tracking script on product page (or step: 1) that is generally placed at the end of the page.

The modified GA tracking script looks like the following:

<script type="text/javascript">
var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www.");
document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E"));
</script>

<script type="text/javascript">
try {
	var pageTracker = _gat._getTracker("UA-XXXXX-Y");
	pageTracker._setDomainName("none");
	pageTracker._setAllowLinker(true);
	pageTracker._setAllowHash(false);
	pageTracker._trackPageview();
	var save_url = pageTracker._getLinkerUrl("www.abc.com");
	var aPosition = save_url.indexOf("?");
	save_url = save_url.substring(aPosition + 1);
	worldpay.MC_gacookie.value = save_url;
} catch(err) {}
</script>

These additional statements are aimed to accomplish the task of saving all the required cookies as the value of the custom variable MC_gacookie that is created above.

Now, we need to store this value in a custom variable, MC_gacookie, which is specially created for this purpose. This value is accessed in the format <formname>.<variablename>.value and to this, we assign the value of required substring, save_url, obtained above. In the above example, formname is worldpay and variablename is MC_gacookie.

So by doing above, we have been able to pass the cookie values to a specific custom variable to be passed into worldpay without the need of javascript.

What is needed now is what to be done on page 3 (in worldpay’s language its called callback page). In This call back page (step: 3), we get all the variables through post methods. Just to remind that this page (or call back page or step 3) is generally a purchase completion page. Here we are changing to just have a button which says “click here to complete the transaction” button. This call back page resides on website’s server however it gets executed on worldpay.

Now this button needs to pass the values of Google analytics cookies that we had been able to pass it via a custom variable (in our example MC_gacookie) in worldpay. If you use PHP, you can use following code to get that custom variable and form a query string (don’t confuse this with GA custom variable- this is WP custom variable).

<?php $parameter = 'MC_gacookie';
if($_REQUEST[$parameter])
{
    $query="";
    //Get all existing parameters
    foreach($_REQUEST as $k => $v){
     if($k != $parameter) {
     $query=$query.$k."=".$v."&";
        }
        else
		{
        $query=$query.$v."&";
        }
    }
    $query=substr($query, 0, -1);
?>

Here $currentpage is the complete path of the thank you page. So, $currentpage can take value like http://yourwebsite.com/thankyou.html
Note: You will get a warning message when you click on the button above.
So what you have done till now is, you have passed the GA cookies till step 3 and have it as a query parameter when user clicks on the button.
The thank you page will now have the following GA e-commerce code.

<script>
var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www.");
document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E"));

<script>
var pageTracker = _gat._getTracker("UA-XXXXXX-XX"); //This needs to be set if this value is different
pageTracker._setDomainName("none")
pageTracker._setAllowLinker(true);
pageTracker._setAllowHash(false);
pageTracker._trackPageview();
pageTracker._addTrans(
	"", // order ID - required
	"", // affiliation or store name
	"", // total - required
	"", // tax
	"", // shipping
	"", // city
	"", // state or province
	"" // country
);
pageTracker._addItem(
	"", // order ID - required
	"", // SKU code (stock keeping unit)
	">?=$_GET['item_name']?>", // product name
	"", // category or variation
	"", // unit price - required
	"" // quantity - required
);
pageTracker._trackTrans();
</script>

Ravi Pathak

Ravi Pathak is a Co-Founder of Tatvic and expert at Managing different web analytics tool. Ravi's actively works on conversion optimization projects to improve conversion rate and test newer hypothesis with e-commerce companies. He regularly tweets via @tatvic or @ravipathak and is easy to have conversation with. Google Plus Profile: Ravi Pathak

Website - Twitter - Facebook - More Posts

Update : Google Adwords now have released a nice feature detailing the performance of ad unit within adwords reporting it self.

See this blog post for more details

Recently, I read highly respectable Hal Varian’s research at inside adwords about their research on ad position and conversion rate. I was really intrigued by the findings but since their findings are statistical, it is reliable. I still have respectful disagreement to the concept which I’d share here.

The hypothesis is higher positions tend to get more clicks and therefore more conversions in total and higher relative value derived from the top position.

We tried studying the cause and effect relationship between the Ad Slots and revenue. The purpose was to establish a relationship that if we move up our advert on Google Adwords, can we gain extra revenue? If ye how much?

To answer this question we created two advance segments with respect to medium and Ad slot (Top and RHS) as shown below:

Advance segment for Adwords slots

If you are not familiar about Ad slots here is what I am talking about:

Top spots in google adwords

Note that, when you see data for Top slot your ad is shown in first three position and if it is RHS , it could be from 4 to 11 slot no.

So what does that mean now from site usage standpoint!!

Site usage report after applying advance segments in google analytics

Ok I know its not rocket science and probably now you even dont need me to analyze but just to finish the post, I will get it done ! Bear with me. If you are an Analytics Ninja, you are most likely to go off from here and immediately create these segments !

So what does the site usage report suggests to us :

• Visitor engagement is more for Top slot compared to RHS slot as seen from the No. of pages/visit metric which is 9.02 for former and 7.72 for later one.
• Bounce rate in Top slot case is less than 50 % of site’s bounce rate.
• Average time on site is also higher in case of Top slot visits compared to RHS visits.

Essentially the Top slot provides quality traffic to web site. Now lets have a look at what is the revenue impact of your ad slot.

Ecommerce report with adwords position slot

It isn’t hard to find out that, top Slot provides almost double per visit value than the RHS slot.

Now you know that the Top spot per visit value (which is our friend’s favorite metric) which is about 100% more than when ads are shown at right hand side of the google ads. You can now go ahead to your adwords change the budget and position for your important keyword based on the data that you will see after creating such segment.

So in nutshell, I dont think the positioning of have has no relavance with your conversion/revenue. It certainly has. However, to establish better relationship (above is too simplistic) , we need to take into account quality score and ad copy etc.

But can you discount the numbers if you see them as you see in above reports ? Do you have opinion here ? do you think if there is better way to do this ? no ?

Thanks for reading !

Ravi Pathak

Ravi Pathak is a Co-Founder of Tatvic and expert at Managing different web analytics tool. Ravi's actively works on conversion optimization projects to improve conversion rate and test newer hypothesis with e-commerce companies. He regularly tweets via @tatvic or @ravipathak and is easy to have conversation with. Google Plus Profile: Ravi Pathak

Website - Twitter - Facebook - More Posts

While we had overwhelming response to our Google Analytics Excel Plugin, we realized that it might be worth explaining in detail the core features of the plug-in.

So for this post, I have decided to explain how to use our auto refresh feature using cell references from an Excel sheet. Below are the steps that users can take in order to update their dashboards automatically by changing input criteria. You would require to login before you execute these steps.

1.       Insert required from and To dates in excel sheet in two different cells as shown in image below.

2.       Click on Excel Icon in the client next to “From” date

3.       Select the cell where “from” date is inserted(in example it is B3) & Click “OK”. In example below, it is B3.

4.       Similar to #4 select the “To” date by clicking on excel icon next to “To” date

5.       Select the cell within excel where you have “To” date provided. In example below it is B4.

6.       Select required parameters like data granularity (breakdown by)

7.       Select the appropriate metrics & dimension that is required for analysis. In the example below, for the sake of simplicity, I have requested no. of visits by visitor type.

Once, the above request executes you will see the output as shown below.

Above table starting from cell D4 provides you visits by visitor type for the month of July-09.

Now if you need the same data for Aug-09 or for any other dates, you can just change the date in excel sheet (aka B3 & B4) and click on refresh to get updated data in your excel sheet directly.

For .e.g.

As shown below once you change the dates in cell B3 & B4, and click on refresh, the Tatvic Google Analytics Plugin will automatically, extract the data from your Google Analytics account for the updated dates based on value in cell B3 & B4. This will result in updated numbers for visitor type table as shown below. Notice that in July, there were, 22,240 visits by new visitors vs. in August about 20,762 visits by new visitors !

Ravi Pathak

Ravi Pathak is a Co-Founder of Tatvic and expert at Managing different web analytics tool. Ravi's actively works on conversion optimization projects to improve conversion rate and test newer hypothesis with e-commerce companies. He regularly tweets via @tatvic or @ravipathak and is easy to have conversation with. Google Plus Profile: Ravi Pathak

Website - Twitter - Facebook - More Posts