This travel season is shaping up to be the busiest since 2008, according to results from several surveys that gauge consumer intent. The question for marketers in the travel and hospitality industry is: How do they capture more than their fair share of this potential market?
They need to go beyond their customer relationship management (CRM) data and focus on indirect data.
The Internet is awash in indirect data, which is often user-initiated and comes from the ongoing dialog between a brand or marketer and its customers. It includes non-user-initiated data sources such as the Facebook “public feed” and “keyword insights” data sets, the Twitter “search” and “tweets about places” data sets and the Google Trends data set.
Mining the information available within web searches (Google Trends), blog posts (Trendland, Feather and Flip, A Luxury Travel Blog, etc.), reviews (TripAdvisor, Fodor, Oyster, etc.), and social posts (Facebook, Twitter, Pinterest, Instagram, etc) can help to paint an up-to-date mosaic of the industry. Monitoring these sources, both manually and via technical automation and curating the information can yield incredible results when combined with analysis techniques performed on CRM data.
Sure, you may know that certain individuals in your CRM database are of high net worth, but this can help take it a step further and tell you what they’re specifically interested in. Imagine further how valuable it would be to know that current general sentiment is leaning towards Monte Carlo instead of Monaco or Ibiza.
Let’s look at an example from Google Trends. The following lists show the current top and fastest growing searches related to travel destinations.
Top Searches in the travel destination category
- Zoo: 100
- Disneyland: 55
- Brazil: 50
What information can we glean from this data? First, we see that individuals predominantly start with single-term searches. For these search terms, “zoo” is searched for most and has a baseline of 100. “Disney” is searched for 80 percent as much. “Disneyland” and “Brazil” are searched at lower percentages, 55 and 50 respectively.
We can also see that a “local” term is searched for most often. “Zoo” is a search for a destination that is local to any particular location. The others are global and reference the same specific definition. Local searches like “zoo”, “beach” and “waterpark” generate more widely varying result sets than global searches — especially when geolocation is used.
Fastest rising searches in the travel destination category
- Recife: +180 percent
- Wroclaw: +120 percent
- Brazil: +110 percent
- Legoland: +90 percent
What we can gather from the fastest-rising searches is best used in a supporting context as the actual number of searches for a particular term is not presented. The most interesting tidbit here is that “Brazil” is on both lists. This makes the strong case for Brazil as an important and growing destination for the coming season.
How to capture the full potential of indirect data to move the sales needle
- Produce and consume blog, review and social content, and participate with the persona of an opinionated marketer. Content spawns opinion, and opinion generates data. Shape the discourse and learn from it. Although you are focusing on data sources outside of your control, it does not mean that you cannot contribute to the process and learn from the experience.
- Perform web searches, monitor the results,and drill into the travel section of services like Google Trends. Play the persona of your target market. Perform individual searches with the relevant drill downs to gather data that can be used to classify the results. This isn’t about search engine optimization (SEO) or search engine marketing (SEM) but about the sentiment of the unknown individuals performing travel-related searches.
An organized approach to accessing indirect data can accelerate marketing and engagement efforts in the travel sector. Combining the one-to-one information that exists in a CRM database with the global sentiment that can be gleaned from indirect data sources creates an understanding of the market that is greater than relying on either of the data sets alone. Read more…