My name is Ralph Hünermann, and as founder and CEO of odoscope Technologies. With our operational intelligence platform, we want to enable companies to take operational decisions better and faster without the need of data scientists. Today, I want to share a very interesting use case of Operational Intelligence.
The Challenge
A travel agency wants to sell a journey to Majorca, a sunny, warm and popular Mediterranean island. Many people spend their holidays there. Since Majorca offers numerous possibilities to spend one’s time, the island is a place for many different kinds of people: for people who just want to relax and go to the sea, for sportsmen like mountain biker or racing biker, for golfers, for night lovers etc.
All these people are different, they have individual feelings and emotions. Hence, the challenge for the travel agency is to find a way to address each potential customer on a highly individualized level. They have to catch the visitors of their website on their personal interests and particular needs, take them by the hand and lead them to their dream holiday. How can this be done?
The Solution
Supported by an Operational Intelligence platform, they will be able to address different groups of customers individually and in real-time. No matter what type of user visits their online service, he or she will be allocated to the right group of vacationer in order to show him/her perfectly tailored content (pictures, text, graphics, colors,…) at the right time.
Doing so gives the user a highly individualized customer experience and makes him feel comfortable, cared for and understood. This helps the travel agency not only to increase their conversion rate by selling the journey to more people, but also to create brand experiences using memorable, emotional messages and thus catching their visitors on a highly individualized level. Hence, the visitors will buy more, come back and tell their friends about their great experience.
How Can This Be Realized?
The key behind such a platform is Operational Intelligence (OI), a forward-looking technology that draws truly valuable knowledge from big data without any man-power. It automatically incorporates the analytical findings into operational processes, monitors the outcomes and reactions and learns from them for the future. Thus, the OI-platform helps the enterprise to take operational decisions better and faster and to develop perfectly tailored content for any user individually. But how exactly is this possible? In the following, we would like to give you an idea of OI’s technical functioning as well as its added value.
360-Degree Customer View
User- and customer data from all existing internal and external sources become easily combined into one platform. They may come from web analytics, ERP, CRM, public data, social media etc. Through the combination of this incredible amount of data, the travel agency gains a panoramic view of every single visitor. This enables the agency to react faster and more focused on the customers’ behavior.
For example, it may be very useful to find out if the visitor comes from a travel newsletter, a reviewing page or via a click on an ad posted on a price-comparison portal. Furthermore, it is good to know for the agency whether the user has evaluated trips he did with the agency before and if so how he rated them. Let us assume, the visitor is looking for a not too expensive hiking trip on a price-comparison site and finds a matching ad of our travel agency. He clicks it and enters the travel agency’s website where he is looking for a package travel deal he had seen. Our agency immediately presents him exactly the hotel, flight and hiking trip he was looking for on the other site. Additionally, it displays alternatives that might also fit his expectations: e.g. hotels with similar price ranges, but closer to some trails he wants to do, better user reviews or including dinner – depending on his individual expectations. If he is obviously interested in one deal, but hesitates, the website might react by reducing the costs or by offering him airport-transfer for free.
Real-Time Big Data Analysis
Through integrating all the collected data into in-memory databases (IMDBs), the OI-platform is able to analyze the data in real time: Instead of storing the data on the hard disc of PCs, IMDBs save it on the main memory (RAM) where it can be directly processed. Intelligent correlation analyses discover so far unseen insights into the visitors’ digital behavior and derive the demands, wishes and feelings from the aggregated data. Virtually, Big Data serves as the knowledge base and experience of a stationary merchant who is able to get an idea of the personality of his customers and to treat them appropriately. The travel agency may discover if the actual user is an outdoor enthusiast, a beach-lover or a hiker; how much he is willing to pay, if he prefers a campsite, a hotel or maybe a finka (summer cottage) or if he would like to discover everything by himself or rather book a travel guide. In this way, the agency is able to address the customer as he likes it most and to take him on the trip he exactly wishes via storytelling already on their website.
Real-Time Segmenting
After discovering everything possible about the visitor, the platform has to decide which content meets his interests best. Therefore, it uses the historical data and combines it with real-time data: As soon as a person accesses the website, the system detects all historic visitors with similar characteristics (“segment”). Afterwards it retrieves all different content types (e.g. versions of a landing page) that are stored in the CMS and simulates how well they perform for the actual segment. The alternative with the best performance will finally be displayed. This approach offers great advantages compared to more common offline-clustering methods: Here, the users are divided into several, pre-defined “pots” that concentrate on apparent characteristic combinations. As a result, rarer parameter-combinations are neglected and important information get lost. Secondly, because of the limited number of pots the classification is more blurred and less precise.
Intelligent A/B-Testing
Additionally, this approach has another great merit: any created content will not be discarded in contrast to classical A/B-Testing. In this example, 2 versions of landing pages will be created and 50% of the users will be directed to each alternative for a defined period of time. The page with the best conversion rate will win and be used in the long-run, all other versions will be discarded – totally under the motto of “one size fits all”, which is apparently obsolete. However, our intelligent A/B-Testing considers that any single client is different/individual- the taste will differ client-to-client, and so should our landing page. Hence, our approach is also based on at least 2 different page versions, but we test their success with an individual sample based on the actual visitor. Similar to the classical testing, the page that performs best will be delivered – and all other versions will be stored: They will be taken into consideration again for all following visitors. This makes it possible to show a landing page with images of nature offering cheap campsites to one visitor, and a second landing page with timetables of travel packages and storytelling to another visitor.
Perfectly Tailored Content at Any Touchpoint Through a Self-Learning System
In this manner, the travel company is able to meet the customer at his individual needs and emotions– not only by presenting perfectly tailored content to him (landing page, pictures, wording,…), but also by addressing him with the individually matching key stimulus at every touchpoint possible. Structure, content and design will automatically be adjusted, specific for each customer and simultaneously be optimized for his channel, device and context. Thus, for example, one user may see a rating system, the next user an emotional video of the destination and another user detailed information about the flight, the hotel and the support during the journey. The OI-system learns independently which variation offers the best experience for the individual user or customer and automatically adjusts the touchpoint accordingly. As this procedure is then repeated for each object and reaction of each customer, the system constantly learns more and more. Thus, the optimization may be increasingly adapted and improved. As a result, the customer may feel more cared for and understood what makes him more likely to buy. Additionally, he will have a long-lasting memory on his positive brand experience and possibly tell his friends about it. He transformed from a prospect to a client and finally to a brand ambassador – just because the agency understood him best.
Ralph Hünermann is the Founder & CEO of odoscope Technologies. The Operational Intelligence Platform odoscope optimizes any kind of digital touchpoints automatically by using prescriptive big data analyses. Being a SaaS-solution (software as a service) odoscope® is based on a proprietarily developed in-memory-technology for correlation based analyses. Learn more about odoscope at odoscope.com and odoscope.de.
Featured image credit: Funky64 (www.lucarossato.com) / Foter / CC BY-NC-ND
Majorca photo credit: bortescristian / Foter / CC BY
