How to Analyze Customer Satisfaction and Spending

The essence of customer satisfaction and spending analysis is to improve performance without over investment or under investment. It should be noted that it is difficult to improve the performance of a business without incurring some extra costs. However, there is a point whereby a business owner should stop investing because any extra investments do not result in additional return. The reason attributed to this is that the customers cease from buying any more of the products offered or stop being loyal anymore. In this consideration, customers do not always respond in a linear fashion to an improved fashion of a product or service. This is because most customer behavior is impacted by non-linear diminishing returns. Therefore, increasing customer expectations via investments may sound as a good idea towards increasing sales and revenue but the fact is that this does not make sense to the business. As such, there is a need for Michael Tanaglia to recognize that there is a point when he should stop investing becausean further investments to improve customer satisfaction misses the opportunity for better returns elsewhere. 

From the provided data, it is evident that the customer satisfaction does not depend on the food and services that the business offer, but it also depends on the distance that the customer must travel to get to the restaurant as well as the cost of the food and services that the business offers. Moreover, it can be seen that some customers dine in the premises while others purchase food substances but take them from a different location. In brief, the factors that are significant in predicting customer satisfaction that Michael Tanaglia should put into consideration is the quality of services and products that the restaurant provides, as well as the distance that the customers cover to reach the business. Another important factor that determines customer satisfaction that Michael Tanaglia must put into consideration is the price of his products and services. 

Concerning the distance traveled by the customers, Michael Tanaglia must understand that if the distance is longer, customers will tend to be unsatisfied. The reason attributed to this assertion is that if consumers travel for longer distances, they view it as expensive because they incur transport expenses. As such, they will prefer getting the same services and products that Michael Tanaglia’s business offers from a nearer location; regardless of the quality of that particular service or product. The first recommendation is that since customers who travel from longer distances express dissatisfaction due to the costs involved, Michael Tanaglia should consider establishing several branches in Arizona. 

Concerning the prices of the products and services that Michael Tanaglia’s business offers, he should realize that customers tend to express dissatisfaction when the prices are too high. Therefore, he should consider lowering the prices of the products a bit to maintain the customers and attract newcomers. However, before lowering the prices, he should consider the costs involved in the production of those goods. Considering that Michael Tanaglia’s business is a for-profit business, he should look at the production budget so that lowering the prices do not result into loss. Next, he should understand that not all customers like cheap products. As such, while lowering the prices, he should not lower them too much because customers may start doubting the cheap products and services. Also, Michael Tanaglia should consider improving the quality of his products and services because consumers tend to be satisfied with the quality of services or products. As such when satisfied, the customers will spend more; meaning that the business will improve its sales and subsequently its revenues. 

Forecast of customers based upon demand 

Forecasting is defined as the process analyzing trends using the past and present data to make certain predictions. During the process of forecasting, it is important for the individual analyzing the data to determine the degree of uncertainty. In other terms, uncertainty is key in forecasting. For this reason, individuals are encouraged to analyze the most recent data to increase accuracy in predicting and minimize their errors ( Box, Jenkins, Reinsel, & Ljung, 2015) . There are various methods of forecasting, but the most common ones include qualitative vs. quantitative, average approach, naïve approach, drift method, seasonal naïve approach, time series methods, econometric forecasting methods, judgmental methods, and artificial intelligence methods. The time series forecasting methods include moving averages, Kalman filtering, exponential smoothing, autoregressive moving average, autoregressive integrated moving average, extrapolation, linear prediction, trend estimation, and growth curve. This paper uses moving averages and exponential forecasting techniques to analyze Michael Tanaglia’s business data obtained for the last eleven months to forecast of customers based upon demand 

In moving average forecasting method, which is also known as rolling mean/average or moving/running mean/average, the data is analyzed by creating a series of averages. In this case study, two types of moving averages are used; namely, 4-month moving average and weighted moving average. In the 4-month moving average, from the provided data, the numbers of customers visiting the restaurant from January to November are 650, 725, 850, 825, 865, 915, 900, 930, 950, 899, and 935. Therefore, the fourth moving average in December is calculated as (930 + 950 + 899 + 935) ÷ 4 = 928.5. Weighted moving average is related to 4-month moving average in that the two methods embrace the use of the most recent rates. However, the two techniques differ in that a weighted average returns a number that depends on the variables of both value and weight. The numbers of customers used in this case are for the two months, namely October and November, which are 895 and 935. The weights of 8995 and 935 are 0.15 and 0.3 respectively. To calculate the number of customers for the month of December, the sum of the two numbers is divided by the sum of their weights. The answer is 923. 

Exponential smoothing methods are used to carry out forecasting on data that is changing slowly with time. This is because this forecasting method does not give equal weight to the observations made. In light of this, exponential smoothing gives more weight to the recent observations and less weight to earlier observations in a given set of data (Brown, 2004). The formula for forecasting the number of customers using exponential smoothing will be S t+1 = αy t + (1−α)S t . The formula can also be written as S t+1 = S t +αϵ t , where ϵ t is the forecast error (actual - forecast). Using the first formula, the number of customers predicted using exponential smoothing is 745. 

Among the forecasting methods used in this case study, it can be concluded that exponential smoothing is the most optimal forecasting model. As mentioned earlier, exponential smoothing assigns exponentially decreasing weights to observations as the weight of the involved observations get older. In other terms, the older observations are given little weight while the recent observations are given more weight. On the other hand, all the observations in moving averages are given equal weight regardless of their age. In this consideration, the reason exponential smoothing is a better forecasting model than moving averages for this data is that the error obtained in the former is smaller compared to that realized in the latter. 

Staff scheduling 

The total number of employees is 24. Considering that the total number of workers is constrained to 15, the recommended number of staff for each shift to accommodate the minimum requirements for customer service is shown in the table below: 

References 

Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015).   Time series analysis: forecasting and control . John Wiley & Sons. 

Brown, R. G. (2004).   Smoothing, forecasting and prediction of discrete time series . Courier Corporation.