Data + Content = Power… Examples From the Hospitality Industry

The Secret Weapon of Review Aggregators in the Hospitality Industry

Reviews are all around us. They have a well proven impact on customer sentiment and purchase decision, and, if you’re anything like me, seeing a 3.5 or lower on any product or service will make you hesitant before completing a decision.  Reviews are also nothing new, casting back my mind, the first reviews I can think of were of gladiatorial battles where the Emperor of the time would signify to the combatants with a turn of the thumb, reviewing their performance, and fate, in the process.

Unlike ancient Rome, in today’s world, reviews are largely democratized. Although certain platforms such as Yelp will provide a cache to power-users whom review more frequently, while amazon offers the ability for subsequent users to rank whether provided reviews are valuable or not, the impact of the review within the customer purchasing decision is now widely acknowledged and accepted almost universally without question.

The Hidden Value of Data Aggregation

Earlier this year, Yelp created a nifty fact sheet that showed the number of reviews on its platform now exceeds 100,000,000 reviews. This is not a small number, but is dwarfed by giant Tripadvsior, boasting 570,000,000 reviews spanning 73 million unique listings across their site. That’s an average of 8 reviews per listing, but I expect the deviation to vary greatly based on the type of listing and popularity.

From a browsing perspective, observing that a Hotel has a large number of reviews and an overall high score is a useful indicator. For the hotel itself however, there is a goldmine of data to be unlocked that can be used to identify and address issues, find highlights to promote regarding capabilities and service, and much more. In fact, the sum total of the reviews received can and should be seen as your truest brand value proposition. If everyone is saying that the hotel is beautiful but the service stinks, guess what, that’s your current value proposition, and the biggest opportunity to harness and make change happen within your brand.

Data aggregation is nothing new. “Data Lakes” and “Cloud Datastore” are now commonplace, and the previous roar of marketers and others in businesses crying out that they have a lack of data has faded out, now replaced with a whisper of ‘What the heck do I do with all this data?’

How to leverage Big Data Insights to Drive Customer Experience

If I were a brand… say Hilton or Starwood. I would be looking towards these aggregation sources and asking some very pointed questions regarding their data and how it can be used to improve my customer experience. Delivered in the right format and timely manner, I would probably be willing to pay handsomely for it too.  This is because for a business, seeing that their rating is at 4.2 /5 isn’t enough. It isn’t enough data to pinpoint who their top performers are, what elements of their service offering are failing to exemplify the brand standards they’re striving for, and what needs to be addressed both on a customer by customer basis, and at an institutional level to elevate their brand. The gatekeepers to this data used to be the service providers themselves, but it’s outdated and a poor model.

Playing out the Scenario

To play this out, lets take the example of a restaurant review:

ISSUE: Let’s say that I encountered extremely poor service in a middle-tier franchise restaurant and my fountain drink was never refilled after I had drunk it early in the meal.

RESULT:  Being English, if asked by the staff how my meal was when leaving although parched, it would be my natural reaction to respond that “everything was fine”. If not, I would possibly write a comment card flagging that I wasn’t offered any refills of my drink, and post a 3 star review on yelp saying the food was fine but service was poor as I didn’t get any drink refills offered.

OUTCOME: The furthest this is likely to go is 1-2 layers of management within that specific branch of the franchise, and would likely not get consolidated into overall feedback and sentiment around the organization. Possibly, if they’re a larger organization or particularly socially active, an eager SMM would contact me on Yelp apologizing and offering me improved service next time I visit.

So, what is wrong with this picture?

It may be a training issue and a one-off, but the issue that occurred may also be endemic to the organization. Every individual interaction elicits an individual response, but aggregating these into a coordinated view of customer experience is something that we simply have not had the power and sophistication to do digitally until very recently.

Key questions that marketing and Operations leaders should be asking in this:

How can we raise the bar? How can we elevate the game? How can data make us smarter and proactive to issues, both as customers and business managers?


I’ve come up with a not particularly scientific, but easy to grasp formula to help make businesses better at harnessing the power of reviews:

Deep Insights = Neural Networks + NLP + AI

Neural Networks = Teaching computers to think and understand the world in the way that people do, while retaining their speed, accuracy & lack of bias.

Natural Language Processing (NLP) = Interpretation of phrases and application of sentiment against these phrases. Also applies to strings of combinations of words and inference given the structure of a phrase.

Artificial Intelligence (AI) = Interpreting insights, groupings, and categorizations based on sets of criteria in a manner that is continuously improving and refining.

So, Now, lets take the same example as before, but with the application of ‘Deep Insights” applied (and from the perspective of an aggregator like Yelp).

ISSUE: Issue is the same, my drink wasn’t refilled or asked to be refilled.

RESULT: As before, I undertook the same action in the restaurant, but lets focus on my digital touch point, which is the most easy to aggregate and utilize. I went to Yelp and wrote:

“I wish I could give this restaurant 5 stars! The food was excellent, the décor was beautiful, and they had a giant fishtank on the wall that was stunning. However, the service wasn’t up to the same level, and my drink was never refilled, nor was I approached to have it refilled during the entire meal.”

OUTCOME: Through a combination of Neural Network, NLP & AI, this review can be interpreted to identify the following positives, negatives, and section them out.

  1. Food was mentioned in a highly positive sentiment
  2. Atmosphere (décor =synonym) was mentioned in a highly positive sentiment
  3. Service was mentioned in a negative sentiment, with focus on attentiveness (never refilled = indicative of sentiment classification)

Well, any person could interpret the details above from reading the review and parsing the data in their head.

However, the power of Deep Insights is to now take this same view, but taking the combined set of sentiments and perceptions from not only the 400 reviews for the same restaurant over the same period, but also the 1,000,000+ reviews for restaurants across a platform. Combining this with internal data association from my POS or Loyalty to the customer that’s on Yelp, I can determine as a business:

  • Was this issue an isolated incident or endemic to the branch or wider business?
  • What corrective actions need to be taken?
  • Where do my customers place value and what positive and negative sentiment are they most impacted by?
  • What is the level their expectation that customers hold for for my business and how do I adjust to be the best possible?

The power that this aggregated data analysis holds for businesses when hundreds or thousands of reviews are analysed, scored and interpreted through Deep Insights is palpable.

For leading brands such as Hilton, to compare quality across their business using aggregated and machine learning based insights is an exciting new area, while for aggregators like TripAdvisorGoogleYelp and others, this arena represents a potentially highly lucrative new approach to their business-side service offering.

The next wave of business intelligence is right on the Horizon, and I’m equal parts excited at what it will bring in terms of protecting and raising brand equity, and scared at the power stakes it puts in the hands of those who have the widest visibility across the landscape and sit in the aggregation space.

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