Unlocking the value of Big Data in Travel
In the first part of this three Part Series on ‘Deconstructing Big Data for Travel’, we defined what Big Data means for travel retailers. In the second part, we discussed the three types of Analytics:
- Descriptive Analytics: Analytics based on past behaviours and usually only simple metrics such as averages, totals etc. are used to provide insights.
- Predictive Analytics: Analytics with the primary goal of targeting individual customers based on predictions of their future behaviour.
- Prescriptive Analytics: Analytics using large quantities of data to point to the ideal action for each customer can be automatically taken.
In this final part, we will have a look at what predictive propensity models look like and how can you use propensity models.
Propensity modelling is the key to unlocking the value of Big Data in travel.
This uses sophisticated machine learning algorithms to predict what a customer is likely to do next by exploiting patterns in human behaviour. Propensity predictions support very sophisticated digital marketing campaigns that target a “segment of one” with highly customised offers and super-personalised travel experiences, to optimise conversion rates, online media spend and revenue per visit to your e-commerce site.
Example: Travel Insurance
A traveller could have a high propensity to purchase travel insurance if they are naturally risk averse and purchased insurance in the past. In many cases, a customer may not even have consciously registered that they have a propensity to purchase something, but the machine learning algorithm can predict it.
Propensity is not a new idea, but it is only with the availability of large amounts of high quality data, cloud infrastructure and machine learning that the models are starting to generate predictive signals with reliable accuracy. In particular, some of the more sophisticated retail banks and insurance companies have used this approach with great effect over the past few years. A combination of demographics and personal financial circumstances can play important roles in determining the propensity to purchase a personal loan, mortgage, investment or insurance product.
These same principles apply to travel: propensity in travel is revealed by a combination of a consumer’s demographics, their personality, their transactional history and attitudes. The best marketing and product teams in travel retailing have realised that they must embed these insights and combine these with a Big Data perspective to compete effectively in the digital world.
What do predictive propensity models look like?
Let’s start with the data sources. There are 4 main sources or dimensions to this type of propensity model, all of which relate to “offline” activity – in contrast to the online activity of a consumer that can be tracked by Google Analytics or similar platforms with tag management functionality. These dimensions are:
- Demographic information that tells us “who” this person is based on gender, age etc.;
- Transactional information that tells us “what” a person has purchased in the past as well as their estimated purchase capacity;
- Psychographic information that tells us something about “why” a person purchases things in terms of attitude and opinions. Facebook “likes” are a good example of this;
- Personality information that also tells us something about “why” a person purchases things in terms of their personality biases. This data is usually collected via surveys and can be difficult to acquire.
The data that we use across this universe varies depending on what propensity we want to predict. Generally, transactional data serves as the foundation to everything else in a propensity model. Transactional data is “hard” evidential data that tells us what buying patterns and preferences a consumer has had over time so serves to validate everything else.
Another way of looking at this or another ‘slice through this data’ is 1st party data v 3rd party data:
- 1st Party Data: Information generated from your e-commerce website, social platform and mobile web or apps about your customers. It typically consists of your customer’s personal information (name, email, addresses, phone number), demographic information (gender, age) and limited behavioural data (site interaction, purchase history, interests). It is typically stored in a CRM or web analytics system, and you, as the owner of this data have all of this free – but it can be hard to combine it all.
- 3rd Party Data: Information generated from internet interactions and other websites. This data is used to give you deeper insight about your audience, such as individual demographics (income level, marital status) and household attributes (number of children). It can be used to build consumer segments for more targeted advertising. It’s collected and licensed by third-party providers that have no direct relationship with your customers, and you have to purchase this data to access it.
Until you combine all of this data, you cannot truly claim a customer 360 view in travel.
Key Sources of Data for a Propensity Model.
Demographic, psychographic, transaction and personality data are the sources to ‘train’ a propensity mode for travel. This training process is an iterative process that uses machine learning to “parameterise” a model. These models vary depending on the exact type of propensity model being built, but typically they use logistic regression or k-means clustering for the simpler models, all the way up to support vector machines and neural networks for the more sophisticated models. Yes, advanced Big Data in travel uses some very high-end statistical models!
So what do we do with the propensity models?
There are in fact many ways that propensity models can be deployed and monetised by an e-commerce business. Any data that tells you which segments, which individuals even, are most likely to purchase your products creates a scenario where highly targeted, precision marketing can become a reality. And with this, conversion rates and revenue will improve and marketing costs will fall.
One of the more interesting areas we are working on at OpenJaw is online advertising optimisation. Here, propensity model predictions are supplied to online advertising platforms e.g. Facebook Dynamic Ads, and with this the right content can be delivered to the right person at the right time. In this scenario, we are filling gaps in Facebook’s data arsenal to optimise this advertising channel for the travel retailer. As social channels become more important for e-commerce in travel and sales and service and Chatbots move in to converse with consumers, it is easy to see how knowing more about your customers and their buying preferences will become critical to any successful travel retailer.
Deconstructing Big Data for Travel – Summary
Big Data is THE shift that will completely transform travel. It will change everything, from the way we travel to the way we interact with travel suppliers to our experience at airports. No matter what part of travel you are involved with and no matter what job you work in, Big Data will transform it. Big Data is here to stay – and it’s just starting.
Most travel businesses create a large volume of data and have access to a lot of customer information, but they don’t really know how to leverage it to make good strategic decisions. Without this foundation, adding big data into the mix often adds little value because it lacks actionable information to make effective.
We are seeing amazing advancements in the way we can analyse data: there are now have algorithms that can understand and translate spoken words, and analyse them for content, meaning and sentiment. There are algorithms can now look at photos, identify who is in them and then search the Internet for other pictures of that person. There are even advanced cognitive algorithms that emulates the learning algorithms the natural world.
Today, travel businesses haven’t really arrived at the day when Big Data can reliably tell them why customers behave in a certain way. But travellers have grown more tech savvy, their expectations for travel retail has increased. Customer expectations are soaring, but travel retailers who don’t meet the new shopping experience standard are being forced to play catch up with consumer expectations. At OpenJaw, we believe that part of ‘Big Data’ journey is to know and understand propensity modelling, as this is the key to unlocking the value of Big Data in travel.
With customers changing quickly and expecting retailers to know their needs and habits and provide them with personalised offers and experiences, the question for travel retailers isn’t whether they need to change — it’s ‘Where to start?’ While the concept of a Big Data programme of work may feel overwhelming at first, using the above insights can make it far more manageable and understandable for everybody.