The four layers of data analytics and examples for travel

Make your insights actionable with four analytics layers to navigate digital transformation and beyond

In the highly competitive travel industry, it’s critical for businesses who aim to improve the traveler experience to benchmark their performance against competitors, in real time. Having the time and resources to manually manage data such as airport, airline, flight, schedules, passenger data and more for competitive analysis, is no longer possible in our speed-of-light world.  Below we review the four layers, and then how they are applied in aircraft cabin design, airline travel management & communications, and, we look at examples beyond our industry too.

According to a Cloud Computing Study by IDG, the average company expects its data volumes will have reached 247.1TB this year.  Analytics are the key to interpreting those massive amounts of data and getting to the answers you’re really looking for. With the right know-how, companies can harness the power of big data analytics to accurately forecast upcoming changes and industry trends.

Advanced data analytics offer an increasingly powerful toolbox to dig deeper into your company’s performance and better prepare for changes in the market. But, the fact is these analytics tools can take many forms, which means even the more experienced industry pros may need a helping hand to figure out the best analytics mix to meet business goals. With so much to choose from, it pays to take your time and create a focused plan detailing the ‘why’ before you buy. In talking with all kinds of customers, we see greater success when we start with defining what exactly are you seeking to achieve.

Just like the problems you’re solving, analytics need layers. Incorporating multi-dimensional insights will get you more value for your business, and highlight where to focus. It’s time your teams take action with the right analytics for any level of business problem — responding in the moment, prioritizing for the future and every kind of performance improvement in between. You can also attend our Dec. 4 webinar for ways to incorporate these analytics into your 2020 strategy.

Here are the four key types of data analytics so you can be ready for any change.

Understanding the four layers

  1. Descriptive analytics bring together historical data from a range of sources to yield valuable insight into the changes that have occurred across a business. With descriptive analytics, you can accurately describe different aspects of your organization and its operations. However, this first layer alone is not enough, as it fails to tell us why these changes occur. For this reason, it’s best to think of descriptive analytics as a solid foundation on which to conduct further analysis.
  2. For more in-depth insights, you can adopt diagnostic analytics. This represents the next step in identifying patterns and correlations around a particular problem or challenge faced by the business.
  3. Highly data-driven companies take this a step further by mastering the world of predictive analytics. This third layer offers a more complex, proactive approach which builds on the findings of descriptive analytics and diagnostic analytics to answer the question: “What is likely to happen?” Thanks to predictive analytics, businesses can anticipate future trends and changes likely to impact their bottom line.
  4. The latest rock star of data analytics however is prescriptive analytics. The fourth and final layer enables businesses to prepare for the challenges ahead and anticipate problems before they arise. This means you can find out exactly what actions will maintain a competitive edge over your rivals.

What to consider

One layer of analytics isn’t optimal. When implemented effectively, the four layers of analytics should complement one another to add maximum value to how your team derives insights from the data.

So how can you better identify your organization’s specific needs and find the right strategy for your business? Sure, you’ve got a long wishlist, but the key question you need to ask is “what is our number one priority?” This will make the task more manageable as opposed to taking a one-size fits all approach.

Whether you want to improve benchmarking and competitor analysis, monitor internal performance or use consumer insights to anticipate future demand for your products or services, you’ll need to draw on the layers of data analytics to answer these questions.

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The four layers in action…

Creating a new air travel market sector

The first airlines to unveil a Premium Economy product took a huge leap of faith when introducing a new cabin type to the market. Some argued there was no room for a ‘middle of the road’ option for passengers. The economics involved in aircraft cabin design and the space taken up by passengers has almost become a science in itself. Each extra inch has to be justified against high yields and even tighter profit margins.

Diagnostic analytics, identifying patterns in customer cabin preferences, helped bolster the strategic introduction of Premium Economy, alerting carriers to a gap in the market for an ‘economy plus’ or ‘business lite’ cabin option. Now, Premium Economy cabins and expanding seat options are a common feature onboard many airlines around the world, offering greater comfort and flexibility for customers.

Modernizing customer support

Another area where data analytics has had an impact is the way in which travel companies communicate with their customers. In a digital world, we expect higher levels of customer care and round-the-clock availability of service agents. In 2017, Dutch flag carrier, KLM became the first airline to sign up to the WhatsApp Business platform, enabling customers to receive check-in notifications, boarding passes and flight updates via the popular social media app. In this case, customer feedback revealed strong consumer demand for diversity in the channels used by businesses to communicate with them about a particular product or service.

Predicting growth at Netflix

It’s not just companies in the travel space using data to get ahead. ‘Failing fast’ innovator Netflix, supported its scaling business through predictive analytics. As a fledgling start-up, the streaming service had to work out how to make its business model sustainable in light of rapidly increasing cloud storage needs. Through competitor analysis and forecasting industry changes, the streaming service identified the AWS cloud computing platform as the most effective hosting service to provide the capacity needed to grow its scaling media services enterprise.

The same tools helped the streaming giant make the transition from distributor to producer, generating its own original studio productions in addition to streaming content from more established film studios.

Feeling inspired?

It’s important to outline your desired outcomes, from early-stage monitoring to insight-based action. With clear outcomes, you will have a better understanding of what combination of the four layers works best for you and your data-driven strategy.

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Feeling inspired? Get in touch with one of our expert advisors to see how Cirium can help your business harness the power of data analytics to improve the traveler experience or read more articles like this.

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