Tamar Weiss

A Ticket to Ride Last-minute Travel Takes the Lead as a Data-Driven Business David Yitzchaki Head of digital marketing Last minute travel “Moving beyond what we originally had in place opened us up to a higher standard with our analytics. Before, it was more of an estimate of our customer behavior. With Cooladata, we started to know and understand.” The Business Case study How advanced analytics helped drive decisions based on data insights Company Last minute travel lastminutetravel.com Industry Travel, eCommerce Goal Maximize revenue by gaining a deeper understanding of the conversion funnel and the needs of the lastminute traveler Last minute travel (LMT), a part of Travel Holdings Inc., is a leading online travel booking agency delivering the most extensive and thorough travel search for the time-starved traveler, with a full range of offerings from hotels to cruises and charter flights to unique travel clubs. The Challenge As a customer-centric online business, LMT strives to provide the best booking experience on all devices, giving customers an easy way to visually compare offers and easily book the best travel option. Their advanced omni-channel marketing, combined with the high volume of visitors and many customer touchpoints across all apps and sites, made them hit the boundaries of their existing analytics solutions. They realized the need for advanced analytics and BI to answer the more complex business questions and deliver in-depth insights about their travelers' needs. Why Cooladata As marketing technology professionals, LMT wanted the most robust solution to leverage the massive amount of data collected and allow them to ask any business question to best assess the full impact of their marketing efforts across their different channels and customer touchpoints. As a cost-effective alternative to building their own in-house analytics solution, LMT's independent BI team instead chose to quickly implement Cooladata's complete fully managed solution to collect, store and analyze data from all data sources, including data that included far-reaching insights on their conversion funnels. This gave LMT the data-driven edge in the travel industry that they had been seeking. Achievements A more accurate analysis of the various conversion funnels and the micro conversions in between. Identifying the most valuable customers with time-series analytics. Calculate the number of emails that cause an uplift in revenue for each campaign. Deeper Insights into the TimeTravel Towards Booking “What we really needed was a tool that would help us better understand the last-minute traveler. Once we understand the lastminute traveler better, we need to give him what he needs at the right time and in the right way so that he will then say “yes” – whether it’s a click on an email campaign, accepting a special offer, or booking a ticket.” David Yitzchaki Head of digital marketing Last minute travel For LMT, it all boiled down to understanding the needs of the last-minute traveler: What series of actions or behaviors drove visitors to both macroconversions, such as booking a ticket, as well as micro-conversions, such as selecting a hotel room? What types of actions converted visitors to the site into first-time buyers, and which transformed new customers into valued customers? The best way to measure a defined goal in business analytics is to closely examine the conversion funnel, or series of events or steps, leading to that goal over a specific period of time. But what happens when that funnel only measures basic customer actions? Traditionally, funnel analysis is limited to basic steps, such as visiting a particular page, filling out a form, or booking. This results in an inaccurate calculation of the conversion rates. Cooladata’s flexible behavioral funnel analysis can measure any step or action a customer or different customer segments take towards a specific goal over any period of time. This is vital for identifying customer segments that are the most valuable (the ones with the highest conversion) as well as optimizing conversions of - for example - the steps travelers take towards conversion in different 24-hour periods. Another powerful feature of behavioral funnel analysis is its ability to integrate multiple data sources across multiple devices and sessions to answer complex business questions. By integrating data from multiple sources such as Hubspot, or Mailchimp, behavioral funnel analysis offers valuable insights such as determining the number of emails per user that will cause an uplift in the average revenue for that campaign. LMT was starting to understand their last-minute travelers in a whole new way. Identifying the Most Valuable Travelers with Time-Series Analytics David felt that one of the key ways to improve LMT’s digital marketing was to identify their most valuable customers, segment them by behavior to target them with campaigns leading to maximized performance. “Before this, we were segmenting, of course, by geographic region and customers who most recently purchased, but we had no idea which customers were bringing us the most revenue.” Breaking it down by advanced user segments With Cooladata’s behavioral analytics, the analysts could easily drill down from each step of the conversion funnel according to different customer segments. In addition, they could now use the time-series funnels to examine conversion over days, or even hours. That gave them valuable insight into the continuous effect of the marketing activities. One of the most critical business questions LMT sought to answer was which types of customers were the most valuable and demonstrated a higher conversion rate. The ability to compare the conversion funnels of the different customer segments answered that need effortlessly and quickly. With Cooladata, LMT was able to identify, measure and compare other customer types based on their behavior. For example, it could then compare the customers who came through a referring site or campaign that resulted in customer registration, or club membership, with the segment of users who came in from organic search directly to the club homepage. The ability to analyze the granular user level data helped LMT receive those powerful insights and feed its personalized and targeted marketing efforts, yielding higher conversions. 2 Fixing the Funnel to Reveal the True Conversion Rate “Moving beyond what we originally had in place opened us up to a higher standard with our analytics. Before, it was more of an estimate of our customer behavior. With Cooladata, we started to know and understand.” LMT analysts realized that using the standard Google Analytics conversion funnels to calculate the conversion rate of hotel booking resulted in misleading conversion rates. Standard analytics provide very limited and inaccurate options for configuring funnels. It includes both the customers who completed any and all steps to book a hotel - as well as the ones who completed only a few of the steps. In this case, the Google Analytics funnel included the customers who completed the step view_hotel_details and ended up booking a hotel the same as the ones who started with the hotel search prior to view_hotel_ details. This is how Google Analytics does backfilling -- it assumes that all customers who performed a step in the funnel have completed the step prior to it. With the advanced funnel configuration that Cooladata provides, LMT analysts could define funnel conditions to include only those users who complete all funnel steps during 1 day. David Yitzchaki Head of digital marketing Last minute travel Only customers who complete steps 1, 2 and 3 are included in the conversion rate With advanced funnels, LMT could exclude from the conversion funnel all those users who hesitated, exited the site, and booked after receiving an email from a cart abandonment campaign. With Cooladata's advanced funnel supporting configuration wizard, advanced funnel analysis is simple. This results in higher rates of accuracy for KPI's and business goals. When adding the dimension of time to funnels, we take into consideration the time it took to complete the steps. This enables a deeper understanding of customer behavioral patterns towards conversion. 3 Examining Conversion Trends Over Time "With Cooladata, the true conversion rate was revealed, since the conversion rate between the different steps of the funnel became much more accurate. This ability to examine the conversion funnel with a more accurate conversion rate is incredibly valuable to us.” David Yitzchaki Head of digital marketing Last minute travel Optimizing the Time Between Steps in the Conversion Funnel After increasing the accuracy of the conversion funnel by including all steps necessary for booking, LMT can drill down further to examine the conversion rate over time, or trends, for different steps in the funnel. For instance, they can see changes in the rates of conversion from step 1 (search) to step 2 (booking) in comparison with total conversion rates. Hotel conversion trend line Number of total conversions to booking vs. number of searches LMT could then react quickly to these customer behaviors, checking to see if they are delivering relevant or personalized travel offers according to the behavior of each type of user. They might also explore any problems with UX - a button, broken links, or a slow landing page - that are preventing conversions. Beyond discovering trends in conversion, LTM needed to add a dimension of time to the conversion funnel. The ability to see the average time it took users to move from one step to another, made possible with calculations in Cooladata's time-series database, is critical in identifying UX issues that might delay the journey between these steps. How long does it take to complete conversion? The conversion funnel below compares the conversion between users who decided to apply for club membership during different time periods. Once they better understood the differences in conversion, LMT can use this information to improve the step of this simple registration funnel in terms of UX – whether it’s eliminating a form field, ensuring a video can play or a button works correctly – any method of optimizing usability. Membership Upgrade Step Duration (By week) Their ability to examine the funnel between each step and compare them between different time periods gave them great insight into how to best optimize it in the future. 4 Conversion trend - Search hotel to view hotel details Conversion trend - View hotel details to room preferences LMT then took a closer look at the conversion rates (blue line) over time between each step of the funnel. The ability to see the moving average of customer conversions gave LMT insights into the conversion rates at any point in time against the overall average. Once they had a better idea of the daily moving average of conversions, they had a much better idea of exactly how to optimize them. Drilling down into the funnel reveals conversion trends In addition to showing the duration of time it took between steps for different customer segments, LMT examined the funnel more deeply to look at conversions over time, as well as comparing the funnel of different advanced user segments. This gave them a much more accurate view of the conversion and performance of each customer segment. Instead of simply measuring the traditional funnel of users from searching to viewing hotel details to selecting a hotel and booking, LMT took a different approach: They would measure the micro-conversions, or steps between the different conversions. What was the conversion rate of customers that searched and then viewed hotel details? What about those who went from viewing these hotel details to selecting a hotel room? Conversion funnel - Search hotel to room selection Behavioral analytics store these types of customers events, allowing LMT to easily query the raw data using Cooladata's behavioral CoolSQL functions. These simple and agile functions gave them insights on customer conversions over time. 5 Identifying the Magic Number of Emails that Maximize Campaign Performance By integrating data sets from Hubspot and querying email campaign data over a specific time period, LMT was also able to determine the sweet spot - the magic number of emails to send at any given month to maximize both open rates and revenue. This gave them a way to optimize the impact of each email. Impact of monthly emails sent on open rate How many emails sent per user result in an ARPU uplift? LMT then determined that the magic number of emails each user should receive to maximize the open rate is between 7 and 10. The optimal number of emails that result in the most revenue per user are between 36 and 40. 6 Gaining an Edge in the Travel Industry Breaking down the funnel into more granular components, as well as the comparison of advanced user segments, gave LMT a deeper understanding of their customer’s behavior and the steps they take to convert -- whether registration to the site, a visit to view travel deals, or booking a travel ticket. Deeper understanding of their customers’ behavior allows them to continually create and optimize offers that get their customers to return to the site. Cooladata’s behavioal analysis has successfully positioned LMT as a datadriven travel booking agency, enabling them to deliver its travelers an unmatched experience in the industry. David Yitzchaki Head of digital marketing Last minute travel “Behavioral analytics fits in perfectly with our goals for the future: Become a data-driven marketing department, with every decision backed by numbers, which ultimately provides the most personalized experience for each and every user.” Cooladata's BI and behavioral analytics platform lets businesses uncover every user's journey to take action, improve their product and grow their business. It offers end-to-end big data behavioral analytics and the most cost-effective solution on the market. Visit www.cooladata.com.