Marketers continue to grapple with a disjointed mix of data, and there is pressure to handle it in the best way possible. The need to work out a meticulously planned data stream, which capitalises on every possible source of data, is becoming more important given that optimisation and personalisation are the holy grail today.
Take for instance the challenge of blending loyalty data and Facebook data. Today, if a travel organisation has 500,000 'likes' on Facebook, there is a need to identify how many of them are already part of a loyalty programme. The industry has been making inroads into such analysis, but it is not straightforward.
As for ascertaining the synergy between Facebook data and loyalty data, marketers must first find ways to tally the overlap. Then they must make the most of user profiles as well as travellers' shopping patterns and preferences to deliver that all important return. From here on, entities can start to get deeper into segmentation, where active travellers, non-active travellers or non-travellers are categorised.
Making the first move
For Suneel Grover, senior solutions architect, SAS, when it comes to data-driven marketing it is important to:
1. Assess key business challenges, and evaluate whether advancing your analytic approaches would improve chances of meeting and/or exceeding business goals. An example of this would be 'digital attribution' helping marketers become more data-driven and analytically-inclined about how to allocate advertising dollars, and attributing relative success across digital channels.
2. Reflect on your organisation's culture, and align your data-driven goals in formidable, realistic chunks with measurable, tempered progress.
3. Ensure you are mitigating your risk of new investments into data and technology by showcasing positive returns, and senior leadership is aware of the difference data-driven marketing and analytics is impacting the organisation's present state and financial future.
4. Beware of being intrusive in the implementation stages and test, test, test.
5. Do it in stages. Develop practical, and more importantly, rationale goals that are achievable given your annual budget constraints, and current work force skill sets.
6. Don't go too far and compromise privacy. Educate yourself on this important issue and avoid falling into these types of situations. Your brand has everything to gain (or lose) in how you leverage this new opportunity.
7. Be open to learning new techniques to solving business problems, and focus on communicating the value to your organisation in a manner that everyone understands (not just the data scientists). Business leaders will not adopt data-driven marketing and analytic strategies if they do not understand what is happening. Interpretation is key.
8. Focus on execution as failure to do so will limit data-driven marketing's potential. For this reason strong recommendations have been surfacing around campaign management automation and operations technology. The propensity for human error will otherwise increase to unstable levels and create negative customer experiences.
On being exploratory
Right now some travel organisations are relying on digital data and analytics to understand which offers, pricing, content, and creative messages are working and they are doing this through statistical testing and experiments. The opportunity to see why people click on a particular offer package is one benefit.
However, Grover points out that many organisations overlook the value of learning why people don't click on other packages, and there is tremendous business value being ignored.
"For those who have already employed an offer testing culture with success, I would now ask you to consider exploratory data mining with digital/omni-channel data to improve your understanding of why these consumer behaviour decisions occur," he says. According to Grover, testing and experiments only reveal which marketing treatment is working better, relative to competing offers, whereas exploratory data mining takes the learning to a new level, and strategically can alter how to view optimised marketing through digital channels.
Consider the following use cases for digital intelligence:
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Outbound marketing – Utilising digital behavioural data, business rules, and predictive analytics to prescribe which marketing offer to use in your email, display, or remarketing campaign.
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Inbound marketing – Utilising digital behavioural data, business rules, and real-time predictive analytics to prescribe what type of personalised visitor experience should be facilitated when a prospect returns to your website for the 3rd time this week.
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Integrated marketing – Utilising digital behavioural data, business rules, and real-time predictive analytics to prescribe a call centre rep how to up-sell a repeat guest with the most probable (i.e. relevant) offer when he/she calls to make their next vacation reservation.
In Glover's view organisations should definitely focus on owning the digital behaviour stream – rather than the web analytics vendor hosting it in a silo. They should also have the ability to feed this digital behaviour data in real-time into a data-driven, analytical marketing process.
To join the debate around the case and to find out more about how you can make data and analytics work for your business join us at Smart Travel Analytics show in New York (February 11-12: eyefortravel.com/analytics).
Source: Eye for Travel
