Airlines are embracing APIs to reduce distribution costs. It means travel distributors such as aggregators can get direct access to inventory and reservation systems to search for availability and make bookings. Airlines have unquestionable expertise in brand.com analytics, but even LCCs, masters of driving down costs, are missing a trick by not analysing their APIs for greater visibility into their indirect channels.
When not selling through GDSs and TMCs, airlines are keen to sell their inventory directly via their own brand websites. It wasn’t that long ago that even the very presence of alternate booking sites populated with data sourced via website scrapping led to Court cases. Views have changed over recent years and many of the major LCCs in Europe have been busy forging new indirect distributions arrangements with Amadeus, Sabre and Travelport. The impetus has been to further growth into the business sector and to reach the price sensitive but still lucrative business travellers booking through their travel management processes. Despite the opportunity, the LCCs still have commercial challenges here because of the need to avoid raising distribution costs when the LCC brand.com will be viewed as essentially free.
Today, all airlines want to enrich and personalise what they can offer, not only on their own sites but also via indirect sales. To do this they need to know more about their customers in order to craft the offer and there is a need to deliver more content. The result is a whole new customer journey, where the customer is the heart of determining the price – not the seat. NDC is the IATA led innovation to make it happen.
This is in tandem, to the LCC shift towards indirect business. But this isn’t ‘wholesale’ – by that I mean it isn’t wide open and it generally doesn’t include the wider wholesale distribution of tickets via OTAs or other intermediaries. It does include some true metasearch relationships e.g. Skyscanner but bookings are generally still taken on the airline site. As an aside this is quite clunky because a normal round trip can actually involve two separate bookings.
So the challenge to minimise distribution costs remains the key driver for the LCC. With any API, the trick is to optimise the conversion rates. That means access to some decent analytics. It is highly likely that most if not all of the LCCs have excellent website data collection tools and a digital marketing team to analyse every interaction to eke the last conversion out of the shopping flow. Much of this effort will be based around A/B testing of pricing, page design and other website concerns. This is mostly about what works best across all customers and only a small part of this effort can be viewed as customer centric, i.e. what does this customer want? Broadly this approach works and will measurably drive incremental revenue. In the API world, there are no web page designs to consider only prices and details on flights, products and services to feedback as a cold blocks of data. The trick is how can the LCC optimise revenue via its APIs?
If the LCC go-to-market strategies are succeeding, one can expect the shopping traffic at brand.com to be predominately leisure whereas the shopping traffic at the API is likely to be predominately business. Success in the leisure market for the LCC will be largely driven around price. That is not necessarily the case for the business market where convenience and efficiency can command a premium. This is B2B shopping now where virtually all of the API traffic will be anonymous too, so the analytics needs to focus on the details contained in the shopping requests to make an educated guess as to the intent of travel. What we are really talking about here is using off-line predictive analytics to set on-line booking platform rules that will provide the real-time segmentation of flight searches to respond with hopefully the best available, most relevant offers. Detailed data is required to feed the analytics and detailed data is required again to measure the outcome of the rules. In fact you need buckets of it and you need it quickly and appropriately processed. Done correctly, this analysis, set rules and test sequence forms a cyclic feedback loop whose objective is continuous improvement. In the near future, it also likely that the slow manual rules setting process of today will be replaced by the rapid decision making AI technology that is already available.
At Triometric, we see the formulating and testing of the differential offer process as the perfect end game to a set of conversion optimising approaches that can be taken. This is the first blog in a three part discussion. The next two blogs will talk about the Four Pillars of Conversion that form manageable steps in this journey. We will also talk about the type of data needed and how it can be collected and processed.