The Phonies are Delaying your Dinner: How Customer Buying Behavior Drives Costs

By Christine Carragee
July 24, 2014

In the past 30 years, Americans have shifted to eating nearly 40% of their meals away from home.  This increase has been driven by some fundamental changes in our way of life, including an increase in the number of women working outside the home, the decline in the cost of food relative to household income, the myriad of new business models restaurant owners have developed and a hundred other things.

Dining Out

So many of the trends prompting Americans to eat out have been well researched, as have the impacts of our doing so, namely obesity.  Research on the impact of consuming so many meals outside the home has filtered into popular media so fully that we can now debate the merits of counting ketchup as a vegetable in school lunches, the effect of fast-food wrappers on marine life, and the absurdity of a $50k kitchen in the modern home.

A small recent operations research study at a New York restaurant managed to find a new angle which as far as I know, had been unexplored up to this point.  Following a spat of negative customer feedback through social media channels regarding the time it was taking patrons to be served, the business commissioned a time study comparing 2014 to 2004.  Although the study suffered from limited data availability and results were drawn from just a 1 day comparison from each year, the differences between customer behavior in each period is so pronounced it bears repeating.  The use of smart phones during the dining experience increased the time from seating to exit from an average of 1:05 to 1:55, 77%.

How did smart phones cause such massive delays?  Were waiters checking their Twitter feeds and therefore ignoring the patrons?  Was the restaurant management enforcing the “clean plate” rules from our childhood, ensuring that patrons stayed at the table until all the sautéed kale was consumed? Certainly not.

The extended stays at the dinner table resulted from changes in customer behavior.  Some examples the study identified include: 5/45 customers requesting help from the waitstaff in connecting to the Wi-Fi, 8/45 customers requesting that the servers take (and then retake) digital pictures, the diners arranging and photographing their meals, only to send them back to be rewarmed.  The distracted walking of 8/45 caused delays and impacted the servers too.  The multi-function phones were so captivating that menus were ignored for the first 10 minutes after a patron was seated.  Every step of the dining out process was insidiously impacted by customer smart phone use.

Can you imagine if customer buying behavior in other industries had changed so dramatically in the last 10 years that it was causing manufacturing or sales cycles to be 77% less efficient?  Unlike the restaurant whose impotent response to this information was a plea on Craigslist to customers to be more considerate, the more effective response to this type of time study in other industries would be to monitor variations in customer behaviors and segment customers in such a way that desired behaviors could be incentivized and costly ones could trigger surcharges or other off-setting payment structures.

As always, we would capture individual customer cost-to-serve adjustments on the right side of the waterfall and then work to reduce or offset them.  If, in the case of the restaurant, mitigating steps weren’t taken, customer behaviors would persist or even worsen, profitability would suffer and even non-offending customers would be impacted by rising costs and slower service caused by other customers.

I love the use of the time-study to get to heart of the Smartphone-delay-phenomenon, but the response to the new knowledge is not that of a good pricing organization.


  • cost increase , customer buying behavior , pricing , smartphones

    Christine Carragee

    Christine has a diverse background in pricing analysis and implementation across industries. As a pricing practitioner, she has worked in both B2B and B2C environments and collaborated across functional areas to improve margin performance. Applying her passion for data analysis, Christine has helped Vendavo customers to anticipate their data and reporting needs during requirements gathering in anticipation of the on-going the value realization process. Another component of her work has focused on corporate education and training; ensuring strong project ROI through user adoption and increased pricing understanding.