Some customers have no idea where exactly they’d like to go when planning their vacation, or their preferences are pretty vague. Since they’re using the e-commerce platform instead of visiting the travel agency’s office, an algorithm, in a way, takes over the agent’s role. Imagine you feel an impulse to book a trip somewhere, but you’re not sure of your preferences. Suggestions that will guide you through the purchase process may come in handy when entering the booking portal.
The more accurate these suggestions are, the better it is both for the client and the vendor. Fact Combiner, allows you to get to know your customers – by understanding their preferences and behaviors. Then, you will find a perfectly tailored offer for them. The first advantage for the client is that they spend less time searching for a perfect trip, and the second, and essential to you, increases the revenue since the conversion rate rises. There is a way to achieve that effect while making the best use of the collected data.
Meet Fact Combiner, the algorithm-based system that suggests products according to individual customer preferences. The system learns customer behavior, so customers find the products they want faster and in an easier way.
Fact Combiner was implemented for Itaka – the largest tour operator in Poland, Central & Eastern Europe. As a travel agency with a global reach, Itaka uses an advanced booking and reservation system – called Resabee (also implemented by Axabee) with many unique features. Since its flow and intuitiveness directly influences the travel sales process, the company continues to introduce additional functionalities that may increase the conversion rate and improve the overall user experience with the platform.
The travel industry is one of the most dynamically changing consumer sectors. In the last two decades, it has undergone a 180 degrees metamorphosis. For example, travel abroad and to other continents has become more accessible and much more affordable – and thus, has gained a significant amount of market acceptance. The number of long-haul trips has increased a great deal. As a result, the market became saturated with travel companies looking to capitalize on this profitable travel segment. At the same time, it became common to book trips online. That, together with rising competition, has generated a demand for seamless booking solutions that fuel sales without the intervention of the travel agent. Ease of consumer interface and accurate recommendations are the keys to it!
Important note: Fact Combiner works perfectly for Itaka, a pioneer in the travel industry in Poland and worldwide. Our algorithm-based system can be used in any industry where customers shop or book online either on web or mobile.
Our project is an add-on to the internal systems of the travel companies based on a set of efficient algorithms. Its primary purposes include:
1. Recommending products to users
2. Arranging the products in the search results in an efficient way
3. Getting familiar with our customers’ behaviors
4. Gathering the data for the automation marketing purposes
5. Creating marketing profiles
6. Creating real-time stats
These aspects might come in handy in optimizing sales funnels and profiling marketing campaigns.
The data is modern gold, regardless of the industry. Yet, even though the companies have access to it, they don’t take advantage of its full potential in many cases. If used thoughtfully, data can become a powerful tool for attracting and retaining clients. Via our data-processing solution, we wanted to help the companies generate better-targeted purchase recommendations and design effective automated marketing campaigns. In the end, it all comes down to an efficient algorithm!
Since the frequency of purchases in the travel sector is relatively low compared to other industries, the recommendation mechanism makes a difference. The clients buy one or two tours per year – thus, it should be accurate enough to take advantage of this scarce opportunity. We chose an algorithm that finds similar users and based recommendations on their searches to make that possible.
Due to the large amounts of processed data, we made the algorithms as efficient as possible. So how did we achieve that? Below, you’ll find our working process explained step-by-step.
For the e-commerce platform, customer-related information is the most precious asset. Fact Combiner enables making the best out of it on various levels. How? Let’s look at all its functionalities and how we developed them.
When entering the booking application, the user follows a particular path. We gather all these paths to make the project as intuitive as possible. In this case, to refine the recommendation system, we’ve researched associations between different actions and the booking. We’ve noticed, for instance, that when the users check the flight hours, they’re relatively close to finalizing the order. This type of information helped us polish the system’s accuracy.
Aside from this algorithm, we also use two others:
1. If we don’t know anything about the user, we recommend them the TOP options
2. If we have a little knowledge about the user, we use the association rules – an algorithm based on comparison which connects the dots and puts the products often browsed together next to each other.
While developing Fact Combiner, our solution needed to be applicable in every industry where customers make online purchases. The basis of our creation was Machine Learning. Our algorithm learns from users’ behavior, can predict future customer actions, and recommends products that match their needs. ML in Fact Combiner provides a timeless solution that learns new trends among customers and standard behavior. It allows business owners to create the right path to purchase for our clients and boost sales.
The results of our recommendation model are seen as the “recommended offers” bar on the main and the product page. It also serves as a support system for mailing and search results.
We’re able to define the number of displayed recommended products. For now, it’s 3 – but it can change if there’s such a necessity. It’s worth mentioning that we generate real-time recommendations for 60K users every day.
80% of users enter the travel platforms to find the trip. While researching the user’s decisions, we’ve noticed that they constitute a relatively common pattern. The first three results get clicks. The rest is ignored, while the last one also gets some attention. Over half of the clicks go to the first and second products in the search results. These conclusions helped polish our solution.
Our Fact Combiner gathers data on users’ preferences, which later perfect the recommendation system. Observing their moves, we can see if they are interested in the products we’ve displayed. Then, knowing that the products on the top receive the majority of clicks, we test the offers, placing them in different positions.
Important note: Part of our users sorts the products by price. Instead of discouraging them, we mark the promoted products that don’t fall under the “sorted” category. We checked that users use the target filter for 60% of searches, browsing popular trips. However, 33% of users choose to sort products by price. If necessary, there’s also an option of adding them to allow lists and blocklists.
Our tool enables gathering detailed data on our users’ actions. Thanks to that, we can measure the efficiency of affiliate campaigns. What kind of data does it collect? Everything that may matter in the marketing context – like, how many people were looking at the particular trip or their geolocation data. It helps us with building the big picture and optimizing processes.
Most users are anonymous, but some log into the system – in this case, we have access to their mail address. It can serve the purposes of marketing automation. Based on the data gathered by Fact Combiner, we’re able to generate and send reminders that encourage users to finalize their purchases. The mail’s structure depends on the path the customer passed through and the stage at which they abandoned their cart (if they did).
All the users’ actions – whether they’re browsing anonymously or logged in – can serve us for creating marketing profiles. These are handy tools for optimizing sales campaigns and many other purposes – but only if based on reliable and meaningful data. With our system, you can access any statistics you need – individual for each accommodation and the aggregated ones. Since data goes straight to the CRM, operating it is much easier.
The profiles can be filled with information such as: – the first browsing session
– the frequency of entries
– the browsed offers
– the booked offers
Currently, we have about 600K behavioral profiles of registered users in the second half of 2021. Plus, over 4M anonymous profiles from the last month. All updated in real-time.
The Fact Combiner algorithm uses real-time data effectively and invisible to the user way to encourage users to finalize their purchases. Seeing that someone else has bought the same products just a few seconds ago, they may be more willing to make their decision right away. Social proof creates a sense of urgency or a ‘let’s do it, why not’ mindset.
Fact Combiner is not dedicated only to the travel industry. It is easily adapted to any business where online consumer decisions are being made. For example, your business can introduce it to an e-commerce platform in the form of real-time stats that report, for instance, how many people saved the offer and when was the last booking. What’s essential Fact Combiner includes offline bookings, too. That provides the highest accuracy of the indications.
The use of Fact Combiner on the e-Commerce platform has provided insights into customer behavior that were previously difficult to learn. For example, since using Fact Combiner, Itaka has personalized trip searches for individual customer preferences. Thanks to that, the implementation of our solution has contributed to increased sales.
Fact Combiner also positively impacts the customer experience when browsing through offers from tour operators. Itaka tracks nearly 120M user actions each month, allowing it to simplify the entire shopping process for users and customize the site for their needs on an ongoing basis.
We also applied Fact Combiner action for one of our main projects – a mobile app for Itaka. This provides the traveler the same experience in searching for tours on a mobile device as on the website. Currently, Fact Combiner action improves the creation of the list of searched tours. Furthermore, based on the customer profile, it allows finding trips in which the customer may be potentially interested.
Fact Combiner is a complex but expandable IT solution for travel that will continue to grow in the upcoming years. Depending on the client’s business model, we can integrate it on different levels.
The effects after implementing our solution are visible in increased revenue. You can see how the path to purchase on your platform has changed and how sales have increased. Thanks to algorithms that find the following products that will be perfectly matched with your customer needs based on customer behavior and preferences.
The implementation of our solution can take up to 5 weeks, depending on the complexity of the client’s system. Note that it requires some interventions in the front-end and back-end. Thus, we may need some support from your development team. However, if they cooperate with us and share their know-how, the whole process will most likely become shorter and much more efficient.
You want to give it a go? Reach us out to talk about the details!
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