Ai Editorial: Managing assortment of ancillary products – how to make a precise offer?

Passengers see different products, different prices, different offers or bundles. Airlines are trying to overcome such inconsistency.

We all avoid irrelevant offers. It could be that the offer isn’t fit for that moment or just isn’t for me at all.

If I receive an offer that is actually invalid, not just uninteresting, it can be quite annoying.

So it could be a product that I could buy but just don’t want. As for the “invalid” part, it could mean there really is a strong reason, based on the data the airline has, that I will never buy that product. For example, I won't buy it because I have already bought it. Another example is that I’m already entitled to that upgrade or lounge access for free because of my tier.

“It is imperative for any airline to having intelligent filters and business rules in place - to do things like remove invalid offers are an essential first step before you can implement recommendations based on data analysis,” says Mark Lenahan, VP of Product Strategy at OpenJaw.

Inconsistency 

E-commerce entities, including retailers, today assert that customers do not see channels. Whatever is offered, wherever it’s seen - needs to be useful or inspirational for a future buy. 

But airlines have some specific challenges.

If we were to talk whether airlines are in control of what they would like to offer or not, Lenahan says there currently isn’t enough control because of both technical and commercial aspects.

“Airlines, in general, feel that their current technology does not provide sufficient control over what they sell. Most airline environments consist of a mix of technology vendors, but some of that technology is very old and inflexible,” he says.  

There are also commercial issues with the way airline product is distributed, for example, there is no such thing as a gentle transition from being “full content” (meaning all fares on the GDS) to having channel-specific pricing. The airlines either live with the status quo or start what gets described as a “revolt”, as we’ve seen with some carriers in recent months.

The combined impact of complexity in both technology and distribution leads to inconsistency for the passenger – and their overall experience.

“The passenger sees different products, different prices, different offers or bundles, all depending on which site they are visiting, or what device they are using,” says Lenahan.  

“If an airline goes to the effort of sourcing or creating a unique product, whether it’s an on-board experience, destination event or hotel package, the decision on where and how to sell that product should be based on what the passenger wants and commercial needs of the airline, not on what channel (or silo) the passenger happens to be interacting with at that precise moment,” says Lenahan.

Getting better at retailing

So technology and infrastructure is definitely one core aspect of ensuring a flyer is offered something that is likely to click with him or her. But this wouldn’t be possible in case airlines continue to stick to non-integrated pricing and merchandising solution providers (these entities can have competing priorities, too). Airlines need to avoid going for multiple merchandising processes/ systems. Managing it across direct and indirect channels is neither scalable, nor technically, economically, or commercially feasible.

The second aspect is data analytics for keeping a tab on the intent of the customer. “I think it’s still early days,” says Lenahan, referring to the maturity of the same.

Delving deeper, he says “inspiration” (meaning capturing the customer’s attention, getting them to shop, or inspiring them to look at a destination) is a source of data (e.g., click through, impressions) but marketing need data to begin with. Search and other datasets from external sources, i.e., Google, meta-search etc., can help airline marketing teams to better understand what to promote, where to target their content curation and allocate digital marketing budgets.

Referring to the concept of retailing experience, Lenahan says, “(At OpenJaw), we try to help inspirational shopping by aggregating live product data such as compelling content, accurate availability and live pricing from multiple sources, for all product types, into one platform.”

This supports inspiration and “browsing”, selling the outcome, focus on the destination, and better customer acquisition.

Permutations and combinations based on data

Continuing further, Lenahan mentioned that after a customer has narrowed down to a specific destination, data helps and informs about priorities for product biasing, bundling, etc.

In theory if you have 10 ancillary products, you have 45 possible 2-product bundles, or 120 possible 3-product bundles. However, many of these bundles don’t make logical sense and in any case, you still have to let the customer choose any of the 10 products individually. While that’s only partly a data problem, it’s also a user experience and design issue. “There’s no doubt that data analytics and behavioural economics can help.  The challenge of recommending the 1st, 2nd, 3rd and so on from 200 hotels is quite different,” explained Lenahan. 

For their part, OTAs believe it’s only after a considerable number of years they have reached a stage where they can rely on repository of data to make ensure whatever is offered sustains the booking flow. For instance, as a B2B specialist, RentalCars Connect, relies on the intelligence that its B2C business has its disposal, and ensures the products offered for a certain destination are likely to be bought.

Equally important is being adept at statistical demand forecasting models to predict demand for ancillary products. Also, price optimization is another component that needs to be factored in.

Also, as it turns out, some automated processes aren’t equipped to come up with precise recommendations when it comes to personalisation. Machine learning algorithms are playing their part, by evaluating which offers were most valuable. If an offer didn’t click with a passenger, the algorithm can capitalize on that intelligence and integrate it to work out for relatively more accurate offers.

Overall, travel retailing, with a broad set of products, requires a broad set of tools. Also human curated product management will not go away, even if machine learning is used to automate some aspects of bundling or pricing, asserts Lenahan.

By Ritesh Gupta

 

 

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