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:
- 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.
- 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.
- 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.
- 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.
- How much % of the total page, the 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
- 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.
- 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.
- Colour of the product:
Again this is more applicable for lifestyle e-tailers, but significantly important as different color can fetch different pricing.
- 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.
- 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.
- 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.
- 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
- 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
- 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.
- 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.
- 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.
- 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.
- Discount % on the 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.
- 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
- Link Location in the 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
- 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.
- 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.
- 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.
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.
- 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.
- No.of bookmark items in the 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.
- Google Search Results Position:
- 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.
- 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.
- 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.
- 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.
- 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.
- Minutes to add the 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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 🙂
Latest posts by Ravi Pathak (see all)
- How To Design Subscription Pricing Plans To Increase Your Recurring Revenue? - April 30, 2021
- Answering Every Marketer’s Dilemma – Which attribution model to choose? All about Fractional Attribution – Part 2 - September 12, 2019
- Answering Every Marketer’s Dilemma – Which attribution model to choose? All about Fractional Attribution – Part 1 - August 30, 2019