I recently shared an article on how airlines are using information to individually price airfares and extract maximum value from passengers which was thought provoking and insightful for many airline industry and big data professionals.
With all the overwhelming feedback and questions I received, I wanted to dig deeper into the big data world and share with you some insights and a greater understanding on one of my favorite subjects – commercializing big data.
Big data and frequent flyer programs have a symbolic relationship with each other and when combined they represent a powerful illustration of what could be possible by leveraging commercial models within the big data landscape. So rather than get into the mundane world of data modelling, Hadoop, Machine Learning and coding – I want to explore the more practical side on steps that frequent flyer programs can take right now to increase the value offering to members, increase engagement and help generate new revenue for the business.
There are 4 key areas of big data that I believe are important: Application, Modelling, Insights and Analytics. In this article we’ll explore how to commercialize the application of big data to frequent flyer loyalty programs. That is – how to generate new revenues by commercializing your existing data streams.
Without a doubt, frequent flyer programs are collecting information on a massive scale and while your data scientists, analytics and business intelligence folks are crunching numbers , hypothesizing, looking for data trends and burning through your companys cash in the process to hopefully one day strike gold; – I believe there is a faster, easier, more efficient path to new revenues. I call it – Data Technology Commercialization.
Data Technology commercialization – the business of making money
Consider the amount of information you have on your frequent flyer member base – a lot, right?
Now think about all the other companies and brands your customers interact with on a daily basis. How much data do you think they are gathering? It’s likely the data you’re both collecting has very little overlap – and this is where you begin to expand your view of the customers’ spending and engagement profile.
Once we take a step back and look at the top 10 brands your customer interacts with outside of your FFP, we begin to understand what little information the frequent flyer program holds on that customer and it’s clear that a 360 view is simply not possible. Unless you’re the NSA. But since NSA hasn’t opened a commercialization department; we need to create our own mini database to understand all the trigger points of every customer, what drives those trigger points and which partner is best leveraged to engage in the call to action.
The data flow of a frequent flyer program commercializing their ‘big data’ strategy
Many companies make the mistake of thinking they are best positioned to send marketing messages on their own products. While this may be true — in the world of big data, it’s possible to make an instant judgment call on such decisions to know if you or one of your commercial partner is best placed to trigger a marketing message on an individual basis. Who is more likely to convert this customer into one of your products based on the data available today?
Data your partners hold may be the missing link in your marketing chain. Below are some examples of how their data insights can plug into yours for a 3-way mutual benefit between you, partner and the customer:
- Your partner knows which credit cards your customers hold, have held in the past and which cards your customers do not want. Remaining cards in the equation = marketing proposition value. Using your internal data of how receptive this customer is to new card offers/churning and/or a points driven customer; you’re able to calculate – instantaneously which credit card and how likely on a score 1-100 this customer is to apply for the card. From there – you compare NPS scores the customer has given both yours and the other company and which ever has the greatest should be the company who sends the marketing offer to the consumer to increase the overall effectiveness of the message.
- How many miles your customers are crediting to other programs = You know how much business you’re missing and can appropriately adjust individual messages to this customer. You may be receiving 30% of the BIS miles from this passenger, but 100% of premium class revenue tickets and this may be the best possible revenue position, therefore no marketing inducements are triggered. Of course, if you’re receiving 10% of their business, your partners may be able to provide data such as who else is getting their business – which provides opportunity for you to wrap individual pricing/campaigns around their specific engagement with your brand.
- Your customer just bought an event badge for a conference they’re planning to attend in a foreign country. Using data from your partners you could instantly figure out if they have booked flights on a competing airline. Armed with this highly lucrative intel; the event organizer could pre-search flights that have upgrades available and present these as options to your customer, with appropriate routing to match their preferences (does the customer prefer to fly via specific city? Are they status driven for the extra miles? Do they avoid 767s?)
The key here is to leverage your partner brand that has the highest possible chance of turning the data into a sale and for you to recognize that by trying to ‘own’ the customer in every circumstance may be damaging to your relationship.
Remember to be open to the idea of letting someone else do the heavy lifting and share in the success with them. They bring more than just a customer to the table – they potentially bring the difference between you having something and your competitor having everything.
These are only a few examples of how you can leverage the influence of data and commercialize it in a way that makes sense for all parties.
Ultimately your key drivers will be to drive revenue while adding real value to your customer experience, and when you find a balance between cross-sharing data specific data and marketing you’ll ultimately be in a position where new product revenue opportunities naturally occur and you’re able to extract the maximum value from each customer.
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