A way to ensure your new guests turn into loyal regulars is to offer a tightly-curated version of your regular food and drink menu. Generally, happy hour is not some mysterious affair that needs to be shrouded in secrecy — unless you run a speakeasy! On the contrary, this time is seen as a social event that can generate a lot of buzz for your business.
Using social media, table signs, and even word-of-mouth marketing , you can build brand awareness by promoting your happy hour drinks and food specials. But actually pulling off the perfect happy hour is a little more complicated than it might seem.
As the National Restaurant Association found, customers see happy hour as much more than an opportunity to grab half-priced margaritas. By looking at POS reports and analytics, you can figure out what items sell well at different times of day.
You can then create a happy hour special around popular, high-margin options. For instance, your Sales by Menu Item report might reveal that your clientele is largely business people who prefer wine over beer and cocktails. With this information, you can then tailor your promotions to this audience by offering discounts on wine later in the day.
Traditionally, these promotions take place during that sweet lull between the end of the workday and the beginning of the dinner rush.
But while the post-work happy hour is classic, virtually any hour from could be a happy hour with a little bit of planning. For instance, late-night happy hours have become a popular way for many bars and restaurants to bring in a younger crowd.
In other words, half-priced cocktails at 10 p. could bring in some younger customers and their friends. This means you need to go beyond a curated drink menu to ensure you attract a crowd. As you test out different happy hour menus, remember to keep an eye on check sizes.
Paying attention to this important metric will help you understand the popularity of each dish, how much customers are ordering, and the impact on your bottom line. If your promotion only focuses on alcoholic drinks, you could be missing out on a huge part of the market.
When it comes to pulling off a successful happy hour, your staff are a huge part of the equation. Not only should they know the specials inside and out, they should also understand the goal of your happy hour.
Are you trying to bring in new clients on a slow night or boost sales among your regulars? What about getting the after-work crowd to stay for dinner? Or do you want to attract a whole new type of clientele? Your front-of-house team is also the best way to spread the word about your happy hour specials.
Encourage your servers and bartenders to tell customers about upcoming promotions. With so many restaurants offering their own happy hour promotions, it can be hard to make yours stand out. Instead of offering rock-bottom prices, using entertainment like trivia or live music can make your establishment stand out.
While entertainment might seem like an added expense, it can play an important role in filling tables and increasing check sizes. During these chilly winter months, you could host a singles-only happy hour and let customers break the ice with discount drinks.
If you have a modern POS system, you can easily track whether your happy hour tactics are helping you turn a healthy profit. Thanks to business reports such as Sales by Order Types and the Drill Down Report, you can see whether strategies like additional menu modifiers are putting more change in your pocket.
Whether you opt for a post-work social hour or some late-night drink specials, happy hour is a great way to increase traffic and sales during untapped parts of the day.
Katherine is the Content Marketing Manager at TouchBistro, where she writes about trending topics in food and restaurants. TouchBistro is an all-in-one POS and restaurant management system that makes running a restaurant easier.
Customer Experience What Is Happy Hour? Generate Buzz for Your Restaurant Generally, happy hour is not some mysterious affair that needs to be shrouded in secrecy — unless you run a speakeasy! Experiment with Your Happy Hour s Traditionally, these promotions take place during that sweet lull between the end of the workday and the beginning of the dinner rush.
Detailed calculation of serving-standardized discounting for each item and subsequent aggregation to category is provided in Appendix S 1 and Supplementary Fig. S 1 in the Supplementary Information File. A lagged association between time-varying outcome log-transformed sales quantity and exposure discounting is commonly captured by a distributed lag model, which is a regression model that contains multiple time-lagged values of an exposure.
Regression coefficients for these time-lagged variables have functional constraints i. One such constraint is the Koyck lag decay [ 29 ], which captures the monotonic decay of the effect of an exposure over time by two regression coefficients: β as the immediate effect at lag zero and λ as the lag coefficient that quantifies the decaying rate.
The functional form of the Koyck decay is represented by a polynomial of form:. An estimated value of the lag coefficient λ closer to 0 indicates the absence of a lag, while its value closer to 1 indicates a stronger lagged effect. The visual interpretation of the lagged effect represented by this polynomial function is provided in Supplementary Figs.
S 2 a and b Appendix S 2. The Koyck lag variables were added to a linear time-series regression, dynamic linear model [ 30 , 31 ]. The details of the model, including the intercept and the lag coefficients, are provided in Appendix S 3.
We accounted for seasonal trends of sales by adding the sine- and cosine-transformed harmonic wave of a week variable as detailed in Appendix S 3. Covariates were weekly varying variables that are likely to temporally correlate with price discounting and sales.
These included non-discounting promotion: weekly-varying display promotion and flyers, which often co-occur with price discounting although not always and are associated with higher sales [ 3 ]. Display promotion is temporarily placement of items into prominent location of stores such as store front.
We calculated the value of these variables at the level of SSB category at each week by aggregating binary promotion status across items. Specifically, display promotion was coded as 1 if an item was temporarily placed at any one of prominent retail locations from the original shelf space, such as the end of aisle, entrance to store, or by the cashier.
Flyer promotion was coded as 1 if an item was listed in flyer, and 0 otherwise. Additionally, an indicator variable for whether the week contained national and provincial statutory holidays was added.
Other covariates were regular baseline price of each beverage categories, mean daytime temperature in each week, and the lagged value of sales itself autoregressive of order 1. We fitted a separate model for each of the five food categories independently under the Bayesian framework.
We therefore specified prior distributions for regression parameters Appendix S 3. We used the Stan software, which uses Hamiltonian Monte Carlo methods and accessed through the Rstan package in R software [ 32 ].
Model selection, specifically selecting a subset of variables from the covariates described above was guided by the value of the Watanabe-Akaike Information Criterion WAIC indicator of model fit [ 33 ].
As sales of many food categories are expected to have seasonal trends a priori, we did not perform any selection of the seasonal terms and thus they were retained in all models. A lower WAIC value indicates a better-fitting model. Codes are publicly available in an online repository [ 34 ].
The latter specification implies that, rather than assuming monotonic decay seen in Supplementary Figs. S 3 , a sharp reduction of sales below pre-discounting period immediately after discounting [ 3 ]. Theoretical explanations for the post-promotion dip are provided elsewhere [ 3 , 35 , 36 ].
The study was approved by the Institutional Review Board, Faculty of Medicine, McGill University IRB approval : AEB , which did not require a written or verbal consent from human subjects, as the study used aggregated store-level secondary data.
All methods followed the institutional guidelines and regulations. However, these two categories, along with coffee and teas and potentially drinkable yogurt, exhibited a mildly decreasing trend during the study period Supplementary Fig. S 4 relative to that of sports and energy drink, consistent with the trends between and in Canada [ 37 ].
The sales of sports and energy drinks exhibited strong seasonal cyclic patterns in this store but did not show the prominent increase in Canada and worldwide in the same time period and reported elsewhere [ 37 , 38 ].
Discounting of soda, fruit drinks, energy and sports drinks, and sweetened coffees and teas appears to have modestly increased trends over time Supplementary Fig.
S 5 relative to that of drinkable yogurt. Average percent discounting over the study period was highest for soda and lowest for yogurt Table 2. Mean and median regular non-discounting price per serving were the highest for sweetened drinkable yogurt followed by sports and energy drinks, and the remaining 3 categories had far lower baseline prices Table 3.
The store neighborhood, as defined by Forward Sortation Area first 3 digits of Canadian postal codes in which the store was located had comparable census characteristics to the larger Canadian Census Metropolitan Area of Montreal consisting of Forward Sortation Areas Table 4 , as measured by the Canadian National Household Survey [ 39 ].
However, the store neighbourhood had a notably larger proportion of recent immigrants. The summary of the time-varying intercept for each of the SSB categories shows its temporal path capturing the local fluctuations and overall declining trends of the SSB sales, as seen in Supplementary Fig.
S 6 a-e. Inspection of autocorrelation plots of residuals suggests little serial correlation Supplementary Fig. The final set of covariates included the holiday indicator for all SSB categories, in addition to display promotion and regular non-discount price for some SSB categories Supplementary Table S 1.
As noted above, all models included sine- and cosine-transformed harmonic wave of a week as covariates with no selection performed on these time-related seasonal variables. The estimated coefficient β indicating the change of sales during the time of discounting is shown in Fig.
This immediate effect was highest for drinkable sweetened yogurt and lowest for soda Fig. Soda beverages show the weakest immediate effects. The estimated immediate effect β for price discounting on the sales of five sugar-sweetened beverage categories.
The extent of lagged effect the coefficient λ for each food category is provided in Fig. The estimated value of λ is close to zero for all SSB categories but somewhat larger for sports and energy drinks. The visual interpretation of the lagged effects in the form of the above mentioned Koyck polynomial function for each SSB category Fig.
These shapes suggest a diminishing effect immediately after the period of discounting i. Posterior summary of the estimated lag coefficient, λ, for price discounting on the sales of five sugar-sweetened beverage categories. The variable λ represents a unitless quantity, whose value ranges from 0 to 1, with 0 representing the absence of lag.
This is the posterior distribution of the difference between exponentiated fitted sales generated by the model containing the lag parameter λ and the exponentiated fitted sales generated by the model with the immediate discounting effect alone i.
We also performed additional analyses applied to sports and energy drink categories separately and to diet soda soda containing artificial sweetener rather than sugar products Supplementary Figs.
As in its sugar-sweetened counterpart, diet soda did not show evidence of a lagged effect. We investigated time-lagged effect of price discounting for five SSB categories for a supermarket located in Metropolitan Montreal, Canada.
The results indicate that the association between discounting and sales of sports and energy drinks persisted even after discounting ended. To the best of our knowledge, the extant public health research estimating the association of price discounting and sales has evaluated only the immediate effect, thus potentially not capturing the total immediate and lag effect price discounting on the sales of some food categories.
There is an increasing number of studies investigating within-store food promotions as a modifiable obesogenic environmental drivers of un healthy food selection and nutrition disparities [ 5 ], and price discounting is likely to have the most influential impact on food purchasing [ 4 , 23 ].
Similar to the exposure to environmental stressors e. The lagged effect on the SSB category of sports and energy drinks may have occurred due to repeated trials induced by discounting among peoples who are previously unexposed to the consumption of these rapidly expanding SSB beverages, thus inducing purchase reinforcement.
Sales and consumption of these beverages, in particular energy drinks, exhibited a steady and global growth during the study period [ 17 ], mainly propelled by aggressive and ubiquitous marketing activities within and outside retail settings, including sponsoring of sports and youth events [ 19 , 41 , 42 ].
While the percent increase of sales due to the lagged effect appears modest relative to the immediate effect, the absolute quantity of sports and energy drinks attributable to the lagged effect is concerning.
Aside from their sugar contents, a single serving of energy drinks often reaches the recommended daily dose of caffeine intake among youth [ 43 ] and associated with caffeine-related acute health outcomes including neurological, psychological and often fatal cardiovascular events [ 42 , 44 ].
Possible reasons for the absence of discounting carryover effect in the other SSB categories include rationale planning of shopping activities i.
This forward-looking planning may be relevant for categories that are discounted heavily, namely soda, fruit drinks and coffees and teas as seen in the descriptive analysis.
As well, the lower baseline prices of these three categories may have further diminished the lagged effects discounting. It is also possible that the lagged effect is masked by the aggregated measure of sales and discounting by SSB categories in this study.
Thus, the overall category sales might not have increased at post-discounting period. This explanation also applies to the results of the sensitivity analysis: the lack of post-promotion sales dip frequently observed in the disaggregated brand-level analysis [ 35 , 36 ].
While it is reassuring that the lagged effect is absent for the SSB categories such as soda in the store investigated, the presence of such effect for the sales of sports and energy drinks implies potentially unaccounted sales due to lagged effects in previous studies targeting these beverages, including our previous study [ 17 ].
Therefore, performing lag analysis in studies investigating the influence of food marketing exposure is warranted. We remark that, while the analytical approach provided in this study is a flexible form of distributed lag model no need to specify the lag length a priori , there are alternative and readily implementable regression models to capture lagged effects built upon the past two decades of lag analysis on exposure-outcome associations in environmental epidemiology [ 27 , 45 , 46 , 47 ].
Although our study focused on capturing linear exposure-outcome lagged associations between discounting and sales, existing lag models, including our transfer function models, can readily incorporate non-linear exposure-outcome associations as well [ 29 , 47 , 48 ].
These methods are accessible as existing software libraries typically implemented within a frequentist framework obviating the need for complex statistical programming [ 27 , 49 , 50 ]. Our study also highlights the need for consumer behavior individual shopper-level research investigating behavioral explanations for time-lagged purchasing in response to price discounting and potentially other forms of promotions, which are important food environmental exposures and may also modify the effectiveness of policy interventions, such as beverage taxation.
Our findings should be interpreted with several limitations in mind. First, while one of the key contributions of this study is to introduce an exposure lag modeling approach applicable to other populations, the data in this study are not recent — Given that the sales of energy drink are forecasted to grow further [ 51 ], the study motivates further investigation to confirm lagged effects on more recent sales and promotion data.
As well, our findings are based on shopping patterns in a single supermarket. Population-level influence of discounting across varying socio-economic status at the shopper- or store neighborhood-level needs to be estimated based on a regionally representative sample of stores or people.
This would require panel data, which in turn would bring significant increases in the computational complexity, requiring hierarchical analyses of lagged models with spatial correlation across geographical locations of stores, which remains our future research.
As in any observational study, we note the potential for unmeasured confounders of price discounting, such as media advertising or a community or school-based health promotion program that took place near the target store. We also note that potentially important individual product-level information, such as the size of products e.
Future research should investigate the lagged effect of other forms of sales promotions, including couponing, volume discount, display and flyer promotions, which independently and jointly influence selections of energy-dense and nutritionally poor food items [ 3 , 5 ].
Overall, our results provide insights into the lagged effect of price discounting on unhealthy beverage purchasing that should be further investigated by other observational studies, as such effect may represent a previously overlooked source of bias in the association of sales and within-store food marketing activities, which is recognized as a potentially important but largely unregulated component of obesogenic food environment.
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