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Demystifying Big Data for Travel #4: Prescriptive Analytics

This is the final part of our “Demystifying Big Data” paper series, so we are focusing on an important emerging area – indeed, the ‘final frontier’ for any travel retailer on a Big Data journey.

The field of prescriptive analytics takes things one step further than predictive analytics – using a combination of the customer insights provided by both descriptive analytics and predictive analytics, prescriptive analytics aims to identify the next best action for each customer to convert these insights into concrete, profitable business outcomes.

You might be thinking: ‘but we do this already’. Sure, aspects of next best action could be implemented in a CRM system or even an e-commerce platform. However, the reality is that very few organisations in any industry do next best action at scale and in a coherent, structured way. Most prescriptive analytics initiatives are either tactical in nature and focused on a single part of the customer journey, or only leverage simple business rules. These initiatives ignore the incredible insights that can be gained through more sophisticated methods like propensity modelling.

Next best action is not a new concept, nor is it only applicable to retailing or marketing. In fact, its origins lie in the military, where it is used as a framework to describe thinking quickly with distributed, local decision making versus planned campaigns and objectives (see Observe, Orient, Decide and Act (OODA) Loop). In the world of business, next best action is essentially a customer centric paradigm that identifies the next best action that should be taken for a particular customer at a specific point in time. This could be a marketing action such as an offer or promotion, or a service action to address a complaint.

Ultimately, the goal of next best action in retailing is to balance a retail customer’s product requirements and preferences against the brand’s business objectives to achieve an optimal result for both parties. Next best action is different to next best offer, an approach that prevails today. The origins of next best offer are product centric in nature and quite narrow – focused on simple up-sells or cross-sells.

In contrast, next best action takes a broader customer centric perspective to determine the appropriateness of any selling action. For example:

  • Is there a complaint or servicing issue to address first?
  • Is the customer loyal and open to up-sells, or at risk of churn, so in need of retention incentives?
  • What propensity scores do we have on the customer that identify optimal products to up-sell or cross-sell?
  • What have they searched for recently on the web-site?
  • What does their online social media influencing scores look like?

All of these factors should be considered before any offer or communication with the customer occurs.

In practice, it is very difficult to implement next best action strategies as described above. A sophisticated technology infrastructure is required to implement it. So, let’s look at a methodology and Big Data tool-set to deliver this functionality. Using the Customer Journey map as a framework, we can illustrate how a library of next best actions can be created to implement the vision of prescriptive analytics for travel retailing.

A customer journey map for travel retailing

Every travel retailer will be familiar with the concept of the customer journey. Many will have developed versions of a customer journey map that identifies the steps a customer will typically follow from the very start to the very end of a trip.

At OpenJaw, we have developed a unique customer journey map that takes into account the myriad of different ways the modern traveller thinks and acts through the full customer journey. This map follows each step of a typical customer journey – all the way from ‘Inspiration & Dreaming’, through to ‘Booking’ and finally the return ‘Home’.

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So what is the significance of this customer journey map for prescriptive analytics? The answer to this lies in the fact that there is always a next best action for each customer as they move through their journey. And this next best action has to balance the requirements and preferences of the customer with the retailer’s commercial objectives. The key to identifying this next best action in the world of travel retailing is to match supply (inventory) with demand (customer desire or intent).

Matching supply with demand in travel retailing

Let’s start with the demand side. The previous articles from our Big Data paper series provide a good overview of descriptive and predictive analytics in travel retailing. If we want to measure demand in a precise way i.e. at the level of individual customers that we want to sell travel products to, then the best place to start is with the customer profile information delivered by descriptive analytics, combined with the propensity to purchase information delivered by predictive analytics.

This information measures demand as it captures both the natural long-term propensity a customer has to purchase a particular travel product, as well as their short-term ‘intent’, as captured by their online searches of flight, hotel or package combinations.

The supply side is the other side of this; here, a finite inventory of hotel rooms, airplane seats, rental cars etc. is actively managed with an offer management process that uses price as the primary lever to maximise revenue. Although this established approach is widespread, it is not complete as a retailing strategy. This is because supply (inventory) is not actively or systematically matched to demand (customer desire or intent).

The principle of this approach is that revenue will be easier to maximise if the additional lever of customer demand is included as part of the offer management process. Conversion rates will increase as the offer management process is enhanced to deliver tailored offers to the right people, with the right message, at the right time.

A framework for next best action in travel retailing

So, in practical terms, how should a travel retailer attempt to match supply with demand as described above? What process or framework can be used?

Let’s take a look at a simple framework that uses the customer insights from descriptive and predictive analytics to determine the next best action at each step of the customer journey. This is best explained with a worked example.

Let’s assume we are a travel retailer and one of our customers is ‘John Smith’ who lives in Dublin, Ireland. John Smith has a history of transactions with us over the past 5 years. This history tells us many things, including that John regularly travels with his wife and 2 kids to European cities and holiday destinations. This transactional information enables the data science algorithms used by the OpenJaw Big Data platform to generate descriptive analytics e.g. using clustering and RFM (recency, frequency, monetary) methodologies, John is classified as a loyal customer.

The OpenJaw Big Data platform also supports predictive analytics. Using a series of propensity models, it identifies that John has a propensity to purchase rooms in 5 star family-friendly hotels, as well as travel brands similar to Disney and European holiday destinations.

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With this customer insight, a meaningful set of next best actions can be readily identified for each step of the customer journey, as depicted in the diagram above. For example:

  • Deliver personalised content for a Facebook Dynamic Ad during the inspiration & dreaming phase;
  • Deliver a follow-up email with more detailed information to support research and planning;
  • Execute a personalised offer once shopping commences;
  • Execute an appropriate up-sell once booking commences.

What does a technology solution for this use case look like?

A technology solution to implement this framework can take many forms. However, if we focus on the first four steps of the customer journey map, then we can map out a core of the technical solution (in simplified form):

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Here, we have consumers accessing a travel retailing web-site to purchase a travel product or combination of travel products (on the right-hand side). Behind the scenes an e-commerce platform is working to process inventory information from suppliers, implementing business rules for pricing and offer management.

If no Big Data capability exists for the travel retailer, then offers are generated on the basis of supply of inventory only i.e. in a product-centric way as described above, using price as the primary lever to maximise revenue.

However, if customer insights are available from a Big Data platform as depicted in the diagram, then the offer management process can be enhanced in a customer-centric way to market inventory to customers that are most likely to purchase it, as well as personalising the offer to maximise the probability of conversion.

Naturally, there are many other uses for the customer insight data that is generated by the Big Data platform. In particular, there will be internal users in marketing, revenue management and product management teams that can use this information to enhance their marketing campaigns that sit outside of the e-commerce platform, or to provide insights for product strategy.

Note too that the principles and methods of prescriptive analytics described here can be implemented in an additive way i.e. built on top existing revenue management and pricing algorithms used by travel retailers today, without significant reengineering of these processes or tools.

More information on the technical underpinnings of this diagram and the products available from OpenJaw to implement it (namely t-Data and t-Retail) can be found at www.openjawtech.com or by contacting the author.

Conclusion

Our goal here was to highlight how prescriptive analytics is in many respects the ‘final frontier’ for any travel retailer on a Big Data journey. It is undoubtedly a challenging area that requires a sophisticated technology infrastructure to implement, but given its potential to complete a travel retailing strategy by matching the supply of inventory with ongoing customer demand in a systematic way, it is an important field that must be taken seriously.

A second goal was to tie the pieces together from each of our four Big Data articles to illustrate how there is a natural roadmap for every travel retailer on a Big Data journey. This roadmap can be broken into 4 parts:

  1. It starts with building a scalable Big Data warehouse that can supply high quality integrated data for analytics;
  2. It then evolves to implementing descriptive analytics to make that data accessible and useful across the organisation;
  3. Once this foundational work is complete the challenging work of building a predictive analytics capability can commence;
  4. Finally, the complex domain of prescriptive analytics can be tackled.

Once the ‘final frontier’ of prescriptive analytics is reached and the principles of next best action are implemented in an e-commerce platform, the potential to truly transform and optimise a travel retailing business is reached as supply is systematically and continuously matched to customer demand.

Next time: Now that we are finished our introductory “Demystifying Big Data” paper series, we will start to look at the deeper technical aspects of creating a Big Data solution, with specific, practical insights and tips for any travel retailer on a Big Data journey. Stay tuned!

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