First published on 27th January, 2016
Ai Editorial: By being too transaction-centric, overlooking the limitations of big data or not respecting travellers’ privacy, airlines can go wrong with their personalisation-related efforts, writes Ai’s Ritesh Gupta
Amidst all the talk around how personalisation, backed by smarter data capabilities, can elevate the overall brand experience, there are noticeable instances where the whole initiative can prove to be either useless or even annoying.
Yes, personalisation is a journey – moving from demographic segmentation to bringing behavioural and transactional data into the picture to algorithms-driven, automated one-to-one personalisation. But being meaningless during the path is what marketers are trying to avoid. As cautioned by a couple of airline executives in the recent past, when things go wrong, the image of a brand takes a beating.
· “Awkward personalisation can be worse than not personalising at all,” this is what Maria Cardenal, head of product development at Vueling Airlines had to say when we interacted about selling ancillaries and personalisation.
· In another interaction, Frank Bornemann, head of marketing, Loyalty Programs & Provider Management APAC at Lufthansa, highlighted that airlines have so much data available to address individual needs, but yet they usually blast offers to all customers in newsletters, apps, social media etc.
What personalisation shouldn’t be like then?
Ø Irrelevant by being “transaction-centric”: An email addressing users with their “name” and sending 10 emails in a month with all of them enticing them about buying your next flight with a promo code or discount can detest a user from even opening an email. And if users have accrued points or miles, it is even more detrimental to the experience as users neither can unsubscribe (since they would lose out on something relevant to them) nor wouldn’t like to open as most of it seems like “spamming”.
As highlighted by Olson Digital, for most entities operating in the digital space, every interaction with a visitor is not a direct sales opportunity.
Be it for historical data, the recent activity (say keyword search or clicks on the website) etc., anticipate where the user is in the booking funnel. Let’s say a user typically takes two family vacations (in March and in June) and there are other times when the same traveller travels for business. What would the right time to inspire this traveller for family vacations? What sort of content would be shown depending upon historical data, the recent activity (keywords search, sections clicked on email, behavior on airline app/ website etc.), the stage in the booking funnel etc.
Treat every piece of communication as part of the journey, starting from the inspiration phase, and enabling the user to take a decision with relevant messaging, choices, deals etc. Olson Digital sums this up with an analogy: a suit “must fit one’s preferences (cut, color, fabric), the season or occasions during which it is likely to be worn, and ultimately one’s body”.
Ø Missing the mark with big data: A major issue with big data is assumptions arrived at, based on variables. These can be 90% - 95% accurate and yet can end up being irrelevant with the recommendation. VCCP’s co-founder and chairman Charles Vallance made a pertinent point in a blog post few months ago, “the trick with big data is to make it small”, and “distil and compress it until it tells you something concrete, substantial and discriminating”. He underlines that if one would only end up counting on data-driven targeting as personalised and to being personal, and overall a way to forge desirable customer relationships, this may well end up being a mistake. Vallance accentuates that one needs to balance out, and make the most of the blend of human angle, communication and technology, and also states: “technological leaps that big data makes possible are seldom about understanding me better, they are more often about serving me better”.
Airlines and travel organizations can also dig deep into what sort of analytical process failure can result in disappointing results. Look into bias, and their impact, and also learn about true positive, true negative, false positive, and false negative, and ask pertinent question like why same data can result in different interpretations?
According to specialists in this arena, being extremely reliable on the initial dataset, no data cleansing or transformation, or statistics being done wrong are common issues.
Ø For trust, know your limits: We all dread the idea of sharing too much information on any social network or even sharing our email id thinking of being chased or being exposed to unwanted messages. The responsibility is even bigger when a consumer shared some piece of information for a meaningful association. Make the effort to ensure customers can opt out. Retargeting definitely hasn’t evolved it seems – be it for chasing with same creative or showing an ad umpteen times even when a consumer isn’t interested.
As Cardenal explained, even if an organization is ready with data and has managed right interpretation of that data (aligned with the business strategy, as well as the technology to be able to use it effectively), do ensure the same comes into play at the right moment and with the right message. If no, then the same effort can come across as intrusive by the traveller.
Technology is not enough, you need to build your customer’s trust.
Travelers are willing to provide more personal information if it means a better customer experience for them. If you make proper use of their personal information and the message you send is relevant to them, then it will not annoy them, but will develop trust.
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