Ai Editorial: Boosting merchandising strategy via “streaming” data

First Published on 12th April, 2017

Ai Editorial: Can dynamic data, being generated on a continual basis, help in selling an air ancillary? Airlines need to delve deeper to handle such data, processing it on the fly, writes Ai’s Ritesh Gupta

 

Acting on data the moment it is generated isn’t really new, but airlines, as an industry, haven’t seemingly made significant headway in this context.

Let’s first summarize the terminology that is being used in handling dynamic data being generated on a continual basis, be it for human-generated comment, photo etc. or machine-generated data in real-time. Real-time stream processing is all about an incidence or a number of events recorded in a collection of fields, then there is steady flow of data, and eventually the capability to evaluate the same.

Stream processing entails ingesting a series of data, and incrementally bringing up-to-date reports and statistics with arriving data record.

Airlines need to be spot on with their ability to handle real-time data integration and streaming analytics. They need to add context to streaming data - the result could be capitalizing on an up-sell opportunity, for instance, when a shopper is on airline.com. or even handling critical functions like fraud prevention, crisis management etc. Are carriers capable of responding to critical events in time, in-context, be it for improving upon the journey through customer service or even monetizing via selling of an ancillary? Not really.

“Events” - that aren’t mundane 

Seamlessness is what makes the journey easy and enjoyable. And when any organization can understand the intent – be it for a click made on a digital platform or conversation a passenger had via any touchpoint – then only airline would be able to respond, and in doing so, delivering aptly during that moment catapults the performance of the brand. But being data-driven doesn’t end here. More than predictable action (for instance, check-in or conversation at the airport counter), what is equally important is responding to an occurrence that can happen as per the discretion of the passenger, something that is tough to fathom.

Big data and analytics that come into play can be further explained in two components:

In an ideal world, all the customer-oriented systems that airlines operate need to be in sync. This would mean capturing all activities related to a passenger’s journey, right from the moment they made the booking till the point they give their feedback about how all of it went. The story that data can tell about a passenger shouldn’t be missed out on – one system might indicate that a family of three passengers booked their journey (so could capture information related to transaction etc.) plus there could be a repository of data about the same passenger that indicates what this passenger wrote about say on a social platform (a word of praise regarding the in-flight meal) etc.

·          If everything is streamlined, here analytics could be about working out predictive analytical models to discover travelling preferences, new patterns of interest etc., based on chronological/ past data, which can feature data collected from event streams as well as other stored information. So looking beyond the purchase funnel, how about coming up with actionable data related to what a passenger enquired about on the day of travel? Can this enquiry be turned into an offer at the time when the same passenger shops for the next flight?

·          A pertinent facet that can make or break the experience is about in-stream analytics i. e. acting on data as events are happening. For instance, last year, during one of my trips to Europe featuring a connecting flight (via SWISS and Lufthansa), there was a mistake on the part of SWISS when it came to allocation of the seat. Both the airlines acknowledged it via Twitter and ensured the matter would be prioritized at the boarding gate. And the staff at the gate had no clue even after exchanging of tweets with both the airlines spanning over two hours! Clearly airlines tend to miss out on data that is important. What’s the point in having resources meant to serve passengers or core product, air ancillary or non-air ancillary inventory – say a seat on an aircraft or in-flight meal – if that can’t be served or even sold when the passenger is willing to pay for it.

Preparing in an earnest manner

At a time when people, places and things are increasingly getting connected, airlines need to dig deep and focus on preparedness for event stream processing:

What would it take to connect, decode and comprehend streaming data? Enterprises won’t be able to live up to the expectations of travellers if they don’t act on streaming data from transactions, social feed, Internet of Things devices etc.  As Amazon explains, data needs to be processed “sequentially and incrementally on a record-by-record basis or over sliding time windows, and used for a wide variety of analytics including correlations, aggregations, filtering, and sampling”. Also, organizations start with collecting system logs and elementary processing, and eventually perform advanced data analysis such as ones featuring machine learning algorithms.

What’s the benchmark for response time? Airlines need to address issues related to managing massive volume of data and yet responding at lightning speed. If a traveller is indicating that he is willing to pay for access to lounge while being at the airport, but isn’t able to find the way out, then can the airline help him out? What if the traveller fails to reach, and ends up changing his decision?

How to act on apt data? It is imperative that airlines ascertain in real time what data is of value, and filter out the irrelevant data. The value of streaming data needs to be optimized in conjunction with traditional batch data, by combining legacy systems with new streaming platforms. Batch processing can be done to work out arbitrary queries over diverse sets of data, and scrutiny of big data sets. Airlines can assess the efficacy of a hybrid model, working out a real-time layer and a batch layer.

 

Are airlines capitalizing on dynamic data? Gain an insight into intriguing issues at Ai’s 11th edition of Ancillary Merchandising Conference in Spain this year. 

Date: 25 Apr 2017 - 27 Apr 2017; Location: Mallorca, Spain 

For more info, click here

Follow Ai on Twitter: @Ai_Connects_Us

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