How Posadas uses Event Tracking to analyze conversion funnel abandonment

Wednesday, September 19, 2012 | 10:54 AM

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One of the most important features you have access to in Google Analytics is the conversion funnel, which is particularly useful for analyzing your website’s efficiency in converting visitors into customers. Using the conversion funnel, you can easily find optimization points that will help you generate more profit for your business, perhaps without even increasing your advertising budget.

However, conversion funnels show aggregated data of all traffic flowing through them. That is, it does not let you make a differential analysis by product.

Posadas, the largest hotel chain in Mexico, with hotels in Mexico, Brazil, the U.S., Argentina and Chile, decided to adapt their Google Analytics implementation to segment the traffic flowing through the conversion funnel and make a differentiated analysis for each of their more than 100 hotels. In addition to segmenting traffic flow by product, Posadas was able to enrich the abandonment analysis by uncovering the opportunity cost of letting people leave the process before completing a transaction.


The conversion process is simple, it starts with a search for room availability in one of Posadas’ travel destinations. The search results provide a list of rooms for which the visitor can request an instant quote. After selecting the preferred room, the visitor proceeds to make the payment and complete the reservation.

Conversion Funnel:
  • Step 1: Check room availability
  • Step 2: Request an instant quote
  • Step 3: Reservation payment
To get more details about this process, Event Tracking was implemented for step 1 and 2 of the conversion funnel by calling the function _trackEvent() while loading both pages.

The _trackEvent function has 5 parameters:

_trackEvent (category, action, opt_label, opt_value, opt_noninteraction)

However, in this case we decided to use only two parameters for step 1:
  • Category:  "Availability"
  • Action: The unique identifier for the hotel being searched

_trackEvent ('Availability ', Hotel_Id)

and three parameters in step 2:
  • Category: "Quote"
  • Action: The unique identifier for the hotel being quoted
  • Value: the total amount of the quote

_trackEvent ('Quote ', Hotel_Id, '', Quote_Value)

Of course, in the final step of the funnel —the sale— the e-commerce tracking code is implemented, so there’s no need to use Event Tracking as the reserved hotel Id and the final reservation cost are already tracked.

With this customized implementation, Posadas was able to get a data matrix that shows the differences in traffic flow for each of the steps, for each hotel. They were also able to look at the differences between quoted prices and actual revenue. In this matrix, Posadas can look for significant fluctuations in traffic and money amounts and discover potential optimization points with more detail than they might find in the standard conversion funnel report.

Abandonment matrix showing percentage of dropouts and the difference between quoted prices and actual revenue for each hotel

To further improve their analytics, Posadas decided to implement a survey to get feedback from their visitors and find out why they abandon the process in each of the high fluctuation points. After identifying significant fluctuation points and receiving feedback directly from their customers, they are able to make sound decisions when optimizing for particular hotels, perhaps by experimenting with specific sales promotions or pricing policies.

Monica Herrero, eCommerce & Online Marketing Manager at Posadas says:
"Analyzing the behavior of our visitors at this level of detail has many benefits. The first is the ability to track the differences in traffic flow through the conversion process separately for each of our hotels. We realized that there are some hotels that consistently show excellent conversion rates, but these special cases were hiding behind the average showed in the standard funnel report. The second benefit is finding the exact points in the funnel that we must optimize, which are different for each hotel. And the third benefit is discovering the reasons why our visitors abandon the process. 
With these three pieces of information we were able to improve our site and our sales strategies, which led to an 18% increase of our conversion rate on average, and an increase of up to 88% for some specific hotels. 
Understanding why people leave your site before converting and trying to retain them can be a challenging endeavor, but the benefit of doing so is highly worthwhile.”

As always, we invite you to try new ways to use the features Google Analytics provides, and to share those experiences with us.

Published by Enrique Quevedo, Google Analytics Latin America