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Leading marketers in the travel sector use machine learning (ML) not only to measurably improve business outcomes but to fundamentally redefine what those outcomes should be. Simply put, ML is helping travel marketers learn more about what outcomes should matter most. In particular, they’re deploying ML to learn much more about their customers much faster. ML is making travel marketing analytics more predictive.
Our recent global executive study of strategic measurement reveals that a majority of travel marketers embrace ML: 70% of marketing executives in travel have incentives or internal functional (marketing-specific) KPIs to encourage the adoption of ML technologies to drive marketing activities, and 79% report that they invest in new skills or training to make marketing more effective in its use of automation and ML. (See Figure 1.) Measured according to our KPI Alignment Index (discussed in more detail in the “Leading With Next-Generation Key Performance Indicators,” report1), a larger percentage of travel respondents are, relative to the overall sample, advanced users of KPIs. In sum, the travel industry is a quantitatively sophisticated sector making investments in training marketers in ML.
To drive greater returns on their ML investments, aspirational travel marketers need to recognize and embrace the following trends.
The Emergence of New KPIs
Using ML primarily to improve or optimize legacy KPIs can be a fool’s errand. Travel industry marketers — who often confront born-digital disruptors, increasing globalization, and tourists who’ve made “selfies” an integral part of the travel experience — recognize that new value creation demands new metrics to assess it. Using 100 times more data to wring greater efficiencies from existing KPIs isn’t good enough; serious marketers look at orders of magnitude to spark novel insights and test new business hypotheses. ML makes that kind of data exploration fast and feasible. Unsurprisingly, born-digital platform-oriented travel companies appear quick to use more data and more innovative analytic techniques to bring new KPIs to their operations.
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1. M. Schrage and D. Kiron, “Leading With Next-Generation Key Performance Indicators,” MIT Sloan Management Review, June 2018.
i. “Machine Learning,” Techopedia.