Hotels.com adopts more flexible expiration policy for its loyal guests

Online travel site hotels.com’s loyalty programme welcomerewards has implemented a more flexible expiration policy.

The site has decided that as long as members engage in qualifying activity on the account every 12 months, they will be eligible to continue earning free nights on that account. The loyalty credits will not expire and each qualifying purchase extends the credits for another 12 months.

Also, the loyalty programme that allows members to earn a free hotel night (guests are required to pay taxes and fees) after booking and staying 10 nights is now open to customers in all 60 countries where hotels.com has global websites. The site now allows welcomerewards members to earn more free weekends, or two consecutive nights, instead of just single free nights. Also, they have more than 65,000 properties to choose from, with no black-out dates, stated the company. Members can stay at participating hotels including multiple major chains or bed and breakfasts.

Trends

Whether it’s a free upgrade, on-site perks or exclusive pricing, online travel agencies have been luring their loyal customers with a flexible offering and are designing their loyalty programmes in a way so that travel ends up being as rewarding and hassle-free as possible. Other offerings include priority customer service during the travel booking process, dedicated concierge service for tickets, tours and transportation, hotel price guarantees and exclusive offers.

For instance, earlier this year Expedia announced that all members of its Expedia Elite Plus programme will earn free room upgrades and VIP perks at VIP Access hotels in select markets nationwide. Expedia customers in the US automatically earn membership in Expedia Elite Plus when they book more than 15 room nights, or spend more than $10,000 on hotels and airfare, in a calendar year.

Predictive modeling

Travel companies are using customer loyalty programmes and predictive modeling techniques to identify and retain the most profitable customers.

A company like Expedia says predictive analytics helps in prioritising projects and investment in resources with expected revenue and profitability impacts.

Talking to EyeforTravel’s Ritesh Gupta about the utility of predictive analytics as part of the group’s advanced data management strategies earlier this year, Edward Nevraumont, senior director - customer loyalty, Expedia, said one of the ways in which the group is involved is for the analysis of the profitability of different types of customers.

Expedia’s team maintains dashboards, writes custom SQL queries and is involved in structured ad hoc analyses. Also, the group has a team that builds predictive models that complement these types of analyses. These models come in two basic types – one for long term planning and strategic analyses (such as customer segmentation analyses) and another for more tactical marketing programmes (such as website personalisation).

As far as applications of predictive modeling for optimising customer relationships is concerned, the travel industry is looking at measuring and managing the asset value of their customer relationships. Other applications include personalising the way customer relationships are being managed and also detecting any significant change in customer behaviour that may indicate a service or retention issue.

Expedia acknowledges the usage of predictive analytics to mine and analyse the data gathered from web traffic and bookings on a website.

“Anything that helps us understand the customer at a deep level so that we can provide them the best offer that meets their needs is something that we’re going to invest in. In particular, my team has invested in models that try to predict long term behaviour based on what customers are doing on our website today. From this we can generate an alert that tells us if a customer’s behaviour has changed for better or for worse. If there’s a problem, we can try to intervene – either with a special offer via email or potentially with direct customer contact for our Elite customers who are very important to us,” Nevraumont said.

One of the most useful applications of business intelligence is developing effective customer retention solutions and reducing costs by identifying the greatest number of customers likely to churn within a small percentage of your customer base. Predictive model tells you which new customers are likely to return and which are probably one-timers.

Another area of focus is behaviour analytics that helps online travel companies in understanding and predicting their customers’ desires and to more effectively serve relevant content and products in real time, ultimately increasing satisfaction and conversion.

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