Friday, June 29, 2012

Using customer feedback to give location-specific, actionable insight ...

By Ashish Gambhir, co-founder, newBrandAnalytics

Co-Found of newBrandAnalytics

Online restaurant reviews have been skyrocketing in their popularity and influence and there is no slow-down in sight. An estimated 129 million consumers are talking about restaurants on social networks daily and that number is expected to swell to 220 million by 2015.

With social media providing a ready platform for one?s voice to be heard, diners are jumping online to share their opinions and evaluate their experiences. And their peers are listening. It is estimated that 82 percent of customers will use their smartphone to read online content when finding and selecting a restaurants in 2013. And, they?re flocking to sites such as Yelp, UrbanSpoon, Facebook, and Twitter to tap into the wisdom of the crowd to help them make their dining choices.

Despite the concerns regarding content credibility, most in the industry agree the better experience you provide, the better your rating and online reputation on these sites. Just as important, the better your online reputation, the more revenue you generate.

Gleaning location-specific insight from these online conversations is the key to driving the coveted positive reviews. By reading and understanding credible mentions online and translating it into location-specific data, restaurant executives can listen to and understand the real issues that impact the local customer experience and earn their continuous loyalty.

For instance, during a monthly review of their comment dashboard, a midscale restaurant?s management team member identified a negative trend in comments about the Chicken Caesar Salad at a particular location. Filtering the data by day part, they determined that most of the negative commentary attributed to the salad was during lunch service. Management retrained the lunch shift responsible for Chicken Caesar Salad food prep and effectively addressed the root source of the guest satisfaction issue: too much dressing. Negative commentary about the salad declined post retraining, positive elements of the dish shined, and unit sales increased over the month after the operational change.

Another restaurant group was able to glean similar operational insight. Reviewing the dashboards, management found a critical mass of mentions online warning potential guests that the restaurant had a slippery floor, particularly for women wearing heels. When drilling into the themes in feedback, management found that the issue was focused to one?specific?part of the restaurant: a high traffic area, on a turn from the restrooms, close to the food and beverage service area, and where airborne grease likely travels. The fix was simple enough: The restaurant now uses a longer mat in that area for an incremental charge of $5/week (they were already renting a mat in front of the area, but was not big enough to cover this area per customer feedback), and they also added this portion of the restaurant as part of a regular management inspection. Since making these slight changes, there have been no more mentions online about the floor being slippery ? thereby avoiding potential guest?embarrassment as well as slip and fall legal issues.

As these examples illustrate, listening to customer conversations and gleaning location-specific operational insight from this feedback enables restaurant organizations to ensure they are delivering a quality customer experience at each and every outlet. With consumers increasingly hopping on their smart phones to geo-locate and evaluate restaurants, arming yourself with location-specific intelligence has never been more important.

* Ashish Gambhir is co-founder of newBrandAnalytics, a provider of social business intelligence solutions for the restaurant and hospitality industries.

?As seen in FastCasual.com, please click here to see this article:?http://www.fastcasual.com/article/196233/Using-customer-feedback-to-give-location-specific-actionable-insight?

Source: http://www.newbrandanalytics.com/blog/2012/06/using-customer-feedback-to-give-location-specific-actionable-insight/

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