Information overload is a pressing challenge for any industry these days, but this is especially true for the hospitality industry. The data that hospitality companies need to operate comes from a diverse mix of online and offline platforms, including social media, booking websites, metasearch sites, online reviews, and internal documents. And there’s a steady stream of data emanating from all of these data sources, which seems to grow exponentially and in real-time.
How can hospitality leaders sift through growing volumes of complex data to make the right decisions?
The challenges are three-fold: first, hospitality companies need to harness the data from multiple diverse and distributed sources in real-time. Second, these companies need efficient, secure, and privacy-preserving analytics mechanisms to process large volumes of sensitive consumer data. Finally, hospitality leaders need to select the most insightful metrics in an exceptionally crowded field.
The solution largely comes down to identifying the data metrics that accurately capture a comprehensive view of hospitality operations, consumer expectations, and market trends. However, building effective data analytics is not just about selecting metrics and interpreting results. It’s also about leveraging those results to improve operations.
The following are three key areas in which hospitality organizations can develop metrics to increase their overall effectiveness.
Generally, optimizing pricing models is all about selling the right service to the right people at the right time. For that, you need a wealth of data on past and present performance.
Hospitality pros can leverage data analytics to select pricing models based on a combination of current and historical trends. The dynamic pricing model – also known as demand pricing – is a pricing strategy in which businesses adjust the prices of their products or services based on shifting demand in real-time. Conversely, a yield management pricing scheme looks more at historical data and trends – like predicting a spike in hotel reservations around holiday times – to anticipate future demand and maximize profits from a limited resource.
These two pricing models can be used in tandem, however, because they’re based on simple supply and demand. What differentiates them is how the level of demand is calculated. Yet in the hospitality industry, both current and predicted demand should affect price. While yield management allows hospitality organizations to forecast demand, dynamic pricing allows businesses to respond to demand in real-time. A hybrid pricing model will allow hospitality providers to maximize profits at any given moment from a fixed supply of tourism, lodging, and recreational facilities.
To optimize revenue streams, hospitality professionals can use advanced machine learning algorithms to adapt the pricing models based on a combination of previous market performance, changing user behavior, facility capacity, and current market demand.
This requires organizations to capture real-time data streams to feed the algorithms. The data should specifically pertain to online searches and activity, as well as traditional key performance indicators (KPIs) such as average occupancy rates, available rooms, and peak season pricing.
The hospitality industry serves customers from all walks of life, but a single hospitality provider can only optimally tailor its service to a limited number of customer segments. Traditionally, customer segments or groups have been created along demographic lines. But the challenge with traditional demographics-based customer segmentation is that it fails to segment consumers by behavior, and it instead assembles groups together that don’t share the same service expectations or purchasing power.
Instead of using demographic groups, the hospitality industry should segment customers based on behavioral metrics. Such metrics can come from customer online activity, such as session frequency, spending habits, and average order value. Tracking these metrics rather than mere demographics can help hospitality professionals better predict customer behavior and create more cohesive segments for targeting.
When analyzed in real-time, these metrics help identify the behavioral patterns that lead to a purchase of specific products and services. Hospitality organizations can use this knowledge to customize their marketing efforts to different customer segments and attract the most relevant audience.
One of the best KPIs that hospitality organizations can capture comes straight from the customers’ mouths: performance reviews or, more technically, customer sentiment scores. Customer sentiment measures the overall feelings, attitudes, and opinions that customers have toward your brand. These metrics help hospitality organizations understand how customers are receiving their services, identify issues that can potentially compromise the customer experience, and ultimately deliver services that encourage customers to visit again.
Standardized surveys on booking websites traditionally offer a simplistic approach to collecting users’ responses on their experience. But to obtain more useful quantitative feedback, AI tools can capture a broader view of customer sentiment by accounting for metrics ranging from repeat purchases and website revisits to online reviews and social media engagements.
In practice, customer satisfaction is affected by a range of factors that change in real-time: service cost and availability, quality of services, and alternatives on the market. In order to develop an accurate view of customer satisfaction, hospitality organizations need to analyze these factors and use predictive analytics to proactively adopt measures that can improve customer experience based on the changing operational circumstances.
ABOUT THE AUTHOR
Dan Marks is VP of Business at Mactores, leading the business automation consulting. Dan leads collaboration with CXOs and their teams around solving complex business problems with 15+ years of experience aligning the right solutions and products to build a growth mindset driven by data.