First Published on 6th March, 2017
Ai Editorial: Be it for extracting data from airlines’ existing digital assets, all touchpoints or realizing the full potential of IoT, without a competent analytics layer, airlines would continue to struggle to attain customer-centricity, writes Ai’s Ritesh Gupta
With more and more channels, systems and technologies to explore, the role of analytics is evolving in several ways – organizationally and technically. Even from the infrastructure perspective, things aren’t the same. Tracking and measuring results isn’t new, but it is not easy to keep pace with in today’s complicated digital landscape.
Here we explore how airlines can gear up for the new world of analytics:
1. Making the most of analytics dashboard: It is time airlines go for a platform where relevant data is centralized, structured and connected. Such set up should facilitate data integration, the result of which would be enabling all systems work in unison. Data keeps on growing in every organization, if it ends in silos, then one will always struggle to attain customer centricity. Airlines should manage data at an enterprise-wide level. Ultimately, the goal is to capture all guest interactions and preferences. From coping up with the issue of data in silos to getting closer to predictive intelligence, revenue optimization etc. – the vision is to making every touchpoint an asset, deliver precise passenger communication etc. Such analytics platforms provide real-time updates – enterprise-level information, funnel analytics, in-app analytics etc.
Also, it needs to be highlighted that the look of dashboards has evolved from mere analytics around attribution or web and mobile analytics to being a marketing platform. They feature analytics and information about the user, their profile, their experience (tracking devices used by one person, details related to interactions across touchpoints) etc.
Also, aspects related to security, scalability and data ownership of such platforms paving way for an organization to gain control are of paramount importance.
2. Making analytics a part of an enterprise’s culture: Growth hacking is generally associated with start-ups, but it is being asserted that the success garnered from a strategy (that results from expertise that is partly marketing, partly product development, partly engineering) can be integrated into long-term plans, too. With a robust dashboard, various departments can evaluate how one initiative is working across the organization. Metrics can be evaluated at a more granular level, for instance, breaking down a campaign by channel, device etc. This cross-department or cross-functional view is healthy.
Paul Byrne, senior vice president of development at OpenJaw Technologies, as shared in one of our earlier articles, says in order to be data-driven airlines can start with a “lean experimentation approach”. He recommended that airlines can identify some of the low hanging fruits (short term goals). “Digital marketing and operation teams are key contributors at this stage. Identify and outline what you want to know from the data and to do what? Now identify the data sources needed to answer your queries and start aggregating / massaging the data sets,” Byrne suggested. A strong collaboration is needed between strategy, marketing, operations, analytics and IT teams for this to be successful.
From a specialist’s perspective, mixpanel, in its new eBook, “Decentralizing data and the future of analytics”, highlighted that if the first step of building a data for the entire organization is getting everyone to commit to metrics, from there on the focus should be on showcasing how seamless analytics can be and galvanizing data consumers. Some other recommendations – no harm in getting a little messy and analyzing data adjacent to your projects, enable “non-technical employees to own the metrics around their own projects”, and focus on “customer’s experience, and which data sources, however unconventional, can improve it”.
3. Make data-driven decisions based on analytics: Airlines need to run their digital assets, improve upon on them, keep track of revenue resulting from them etc. But all of this can be fruitful if the decision-making is data-driven. For example, by evaluating performance of an app - new vs. returning users, app revenue, user retention, and custom in-app behavior events etc. airlines can assess usage and revenue. If an airline is sending targeted push notifications, how does a traveller respond? How analytics around app usage trends and user behavior increase message relevance and effectiveness?
Companies like Amazon are improving upon their offering for mobile app analytics, and supporting real-time analytics. Such insights are helping organizations to target segments from a variety of different data sources, send targeted notifications with personalized messages etc. Can a passenger be sent a notification at a time defined by the airline? Yes. So for instance, if a user interacts with an airline chatbot, and it turns out that this user has the tendency to ask for what’s going to be served during the flight? Can this user be shown relevant image as a push notification at an appropriate time (on the day of travel or at the airport)? Possibly yes.
4. Approach towards analytics: It is imperative for airlines to work out the best approach – work on analytics internally or buy one. Of course, one of the key aspects is investment that would be required to set up the requisite infrastructure internally. The project timeline, resources required including engineers and data scientists, monetary investment etc. need to be evaluated diligently.
mixpanel, in its study for product analytics solution for an enterprise, shared that the related costs of an internal build can be categorized into 3 parts - an initial cost outlay, maintenance costs (web services required for ingesting and storing user actions, plus the computational cost for each time an organization queries the set of data) and the cost for a visualization tool on top of the infrastructure. The study underlines that considering the 5-year cost of building, running, and maintaining an internal tool, organizations tracking 500 million to 10 billion annual user actions will save between $570K and $5.2M with a robust, customizable product analytics solution from 3rd party specialists.
So airlines need to look at accessibility, cost-effectiveness, speed, and customization before arriving at a decision.
5. Gearing up for now and the future: The pace with which the Internet of Things is evolving means the need to analyze streaming data on an ongoing basis is staring at entities. Also, airlines can benefit from edge computing as of today.
Why this is important for airlines? If we understand from a scenario – say a conversation taking place between passenger at the airport and the airline staff, then how about analyzing the same without any privacy issues? Today, as IBM asserts, technology is in place to “capture the audio of the conversations and run speech-to-text and tone analysis directly on the device, thus completely avoiding sending any sensitive data to the cloud”. Essentially what is being done is “tone analysis” and this can be equated with attributes like happiness, sadness or anger.
Cisco estimates 50 billion devices will be connected to the Internet by 2020 and 500 billion devices by 2030.
Since IoT devices typically send more data than the average technical product, this means such data has to be processed and analyzed in real-time. This is challenging as handling this volume of data is complex. Rather than relying on traditional computing models, the industry has already moved analyzing data at the edge of the network - means analysis and response has moved close to the devices generating the data cut down on latency and reducing the load on the network and the enterprise data centre.
There is a need to assess what sort of infrastructure is required so that it can ingest IoT data. And this includes incidental data that comes along with IoT.
In this context, specialists like mixpanel aptly underline that analytics isn’t just about humans deriving insights. It’s also the competency by which machines will make decisions!
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