The ability to optimize customer lifetime value (LTV) is one of the biggest challenges in marketing today. While we can identify, in hindsight, what each and every customer is ‘worth,’ we struggle to identify our most valuable opportunities early on in their purchase cycles — a key opportunity to reach prospects with the right messaging at the right time. There are a few reasons why brands struggle:
- Marketing attribution is easier said than done. There are very few tools out there that can map a user’s full journey through the conversion funnel. From a reporting standpoint, it’s easier to measure success by ‘last touch attribution than to track users’ convoluted paths over the long-term.
- User experience signals can be misleading. For instance, high average browsing times could signify confusion and engagement. People might be interested in the resources you’re sharing or confused about where to find the information that they need.
- Customer data is often fragmented, as online audiences engage with brands from multiple devices. Companies, as a result, are struggling to capture data within the right sequences – and information ends up stuck in organizational silos.
For travel brands, the task of forecasting and optimizing their customer lifetime value (LTV )is even more challenging. Even though people love taking vacations, they are typically only limited to one or two per year. As a data scientist would put it, successful ‘outcomes’ are rare events that are difficult to predict systematically. Not to mention, travelers are likely to shop around for deals — even when they’ve joined a loyalty or frequent flyer program.
Marketers can begin the process of predicting customer lifetime value by creating user experience ‘signals’ at different stages of the conversion funnel. We need to identify individuals who show signs of being ready to make a purchase — and then target them with messaging, offers, and resources to win their loyalty and guide them through their decision processes. Here are some important signals that brands can monitor to optimize their targeting.
1. Keywords and search intent
When we conduct travel-related searches on Google or on a website, we’re likely looking for something very specific — vacation ideas, trip options on a budget, fare deals, or logistical information. In addition to identifying what keywords are sending audiences to our websites, we can monitor whether there are any trends evident in how our website visitors are seeking out specific information.
For example, we may notice that a proportion of our user base are seeking out ‘vacation ideas’ at certain points of the year. We can also map the relationship between searches and purchases. For instance, we may notice that there’s a certain number of searches occurring — or a certain amount of time passing — before the consumer makes his or her final decision.
These are ‘signals’ that could help teams build predictive models.
We browse content to educate and entertain ourselves. When we read about a topic in depth, however, we’re doing more than just ‘scratching the surface’ — we’re actively researching something that could influence an upcoming life decision.
Take a look at the reviews, articles, and trip suggestions that people are reading and try to extrapolate a pattern — are they concentrated around a particular destination? Is that destination related to a place that the traveler may have visited in the past?
This type of analysis will help you learn each users’ research patterns — and then target them with messaging and information that guides them further down the funnel.
3. Loyalty Programs
Give your audiences a compelling reason to create and actively use an account on your website. These types of offers need to be authentic — coupon codes, cash back, and point accrual systems are all compelling value propositions.
The reason is that you can learn more about the audiences who actively log into and engage with your site. You can also connect your marketing efforts to their social media and email presences.
Predictive modeling and forecasting are processes that require continuous refinement. When you learn new points of information and are able to detect patterns, you can build them into your LTV calculations.
Historically, travel companies have created loyalty programs to encourage repeat purchases. Now, loyalty programs bring much more additional value to the table.
It all comes down to data.
Travel brands can use their loyalty programs to collect customer data and track experiences across devices. Companies can then harness these insights and optimize customer lifetime value through retargeting campaigns on social media and exclusive offers. By paying attention to user behavior patterns, travel marketers might even be able to predict their users’ next vacations.
Final Thoughts: Develop Stories Through Personas
Travel brands are already well-aware that there are different types of buyer personas: business travelers, luxury aficionados, bargain-hunters, global explorers, and casual family vacationers. To predict and optimize customer lifetime value, you’ll need a clear understanding of these personalities and how they interact with your site. That’s where qualitative research and storytelling fit in — predictive models are, at their hearts, anecdotes. Tell your best stories possible by getting your core audience involved.