|Title||A Study on Loyalty Cards and Shopping Behavior for Fast Moving Consumer Goods in Southeast Turkey|
|Publication Type||Conference Proceedings|
|Year of Publication||2013|
|Authors||Fadda, S, Gursoy, O, Durur, E, Ozguven, K|
|Conference Name||EuroTecS - 2013 European Conference of Technology and Society|
|Keywords||CRM, Data Mining, FMCG, Loyalty Programs|
Loyalty programmes in fast moving consumer goods industry (FMCG) have a history of over 20 years. The main drive behind loyalty cards is to gather data from unique customers on their shopping behavior and product preferences so as to further analyze them and have knowledge to act on in their operations. In this study we analyzed loyalty card and checkout receipts data provided by the primary FMCG chain of the Turkish market. We have as a result discovered interesting consumer behavior traits, and suggested future possible research interests.
|Full Text|| |
A Study on Loyalty Cards and Shopping Behavior for Fast Moving Consumer Goods in Southeast Turkey
Keywords: CRM, Loyalty Programs, FMCG, Data Mining
Loyalty program in fast moving consumer goods industry (FMCG) have a history of over 20 years. The main drive behind loyalty cards is to gather data from unique customers on their shopping behavior and product preferences so as to further analyze them and have knowledge to act on in their operations, such as placement of products in shelves, pricing, opening times, promotion tactics or procurement.Additionally it helps win the customer loyalty, and try to insure that he/she does not go to other stores.
The loyalty program should insure higher purchase frequencies and lower number of visited stores for the loyalty program members than for non-members. The price promotion also generates excess loyalty but less than the loyalty program.
Small and medium-sized retailers consider competition from large chain stores as a noteworthy obstacle; while looking at regulations, macroeconomic uncertainty, costs of financing financial and transportation facilities as being only partially important. Another interesting study shows consumers’ almost exclusive preference for traditional retail shops when buying high involvement goods (e.g. household appliances), while the most purchased goods in hypermarkets are essentially convenience products and, consequently, of low involvement.
In an industry which is notorious for its low margins, many producers and retailers are collaborating to ensure that the final price paid by consumers remains competitive. Being provided with the data of the chain of the Turkish market, the following part will include some analysis of the data, followed by some suggestions for further research.
The Figure 3.1 shows monthly sales of each store for two years of 2010 and 2011. Evident positive change in revenues is shown in the second half of 2011, which applies in parallel or every of the 12 stores. Interestingly at no point in this 24 month can it be seen that any store is gaining at the cost of another. It could be another market factor that bring rise or fall in store revenues.
It can be seen in this graph that there is a common change on the monthly basis in the second half of both years. Looking from the month of June till December, in 2010 and 2011 there is a constant rise in July and August, followed by a fall in September, then growth in October, followed by a fall in November, and ending with growth in December. If such characteristic does follow for the years of 2012 and 2013, it can be easily used for Collaborative planning, forecasting and replenishment (CPFR).
As the Figure 3.2 shows all the shops have a reasonable increase in their revenues on the weekends, and the maximum sales occurring on the day of Sunday. On the other hand, majority of the shops have the minimal revenues taking place on Wednesday, with the exception of the sales unit #103. In total, on average the lowest revenues occur on Thursday.
The Figure 3.3 shows the sales of different goods throughout a day. As can be seen in the graph the movement of the sales by the product does not change with significant difference with the time, rather the movement is almost parallel. It implies that there is no difference in the change of demand difference between these products on the timely bases, as there is no point where the demand lines intercept.
Rather the demand for all the products shown in the Figure shows an increase followed by decrease in sales in the timely manner all through a day. Starting from the morning hours up until 5 pm, there is a continuous increase in revenues, whereby the continuous decrease starts from 5 pm and follows on until the midnight.
Shopping by Gender
As shown in the Figure 3.4, continuing analysis of the distribution of customers in the market, it can be seen that majority of the customers of the market are male. Despite the day of the week, male customers make at least two thirds (twice the number of female customers).
On the other hand Figure 3.5 shows the average spending by the female customers (total spent by the female customers through the day divided by their number during that day) divided by the average spending by male customers. As it can be seen in the figure, every day of a week a female customer on average spend more than does a male customer.
The difference ranges between 3.6% and 11.87%. Keeping in mind that the data contains 104 weeks. It can also be noticed that the minimal difference occurs on weekend days, assuming that likely the pairs go shopping together. On the other hand, looking at shopping during the working days, on average female customer spends between 7.95% and 11.87% more than average male customer does.
When comparing the outcomes of the customers by the Store (the 18 of them numbered from 101 to 118) on the basis of the customer gender, looking at Figure 3.6, it can be seen that male customers in total spend more than female customers.
Having the 18 stores of the market, it can be seen that some stores have much higher revenues than others. On the other hand, also the difference is very significant between stores in the ratio of total amount spent by the gender. While in Store 111 male customers spend more than 3.5 times more than females, in store 101 they do just 1.7 times. Further details would be required to identify the reason for that significance.
As the Figure 3.7 shows the gender based average spending by store, there is no shared characteristic, as some stores have female customers spending on average more than males, while others show the opposite. The difference could be based on some other factors, as average household income, household employment or culture in the area of each store.
As was seen in the product sales throughout the day, similarly the customer showing-up through the day is being distributed as seen in Figure 3.8. The number of customers during the day increases until five o’clock, when it reaches its peak. This data represents average daily shopping for all the stores. The number of male customers at each point through the day is greater than the number of female customers.
Whereas looking at the average spending throughout the day based on the gender, the Figure 3.9 shows that most through a day female customers “on average” spend more than male customers do. The only exception is the early morning before 7 am, and late evening after 10pm. The two possibilities are that female customers either buy more expensive products, or they buy a bigger amount of products.
The Figure 3.10 shows the ratio of each product frequency of being bought by a male customer as opposed to its being bought by a female customer. Looking back at total number of units bought, male customers buy 2.16 times more than female customers do. It can be seen on this graph that there are a few exceptional products being outside the 1.5 – 2.5 range.
Among the top products that male customers buy more frequently, and standing above 3.0, are newspapers/magazines, hardware-car accessories, and shaving products. The less outstanding products, ranging between 2.5 and 3, are baby-food, seafood, and cigarettes. On the other hand, those appearing below the range of 1.5 include skin-care products, diabetic sweeteners, diet biscuits, liquid creams, and color cosmetics.
Having realized that the lower sales occur in the first half of each year, it should be given more attention how to attract customers in that period. On the other hand, similar attention ought to be given to the months of September and November, which in both of these years showed negative performance in all the stores.
While looking at the shopping timing, the highest rate of customers appears on weekend and they do daily in the afternoon. Further analysis should be done, whether those times are favored for better revenues, or they do bring stress and need to be distributed otherwise through additional activities or offers.
Having found that female customers do on average spend more than male customers do, while on the other hand female customers are less frequent than male, further action could be considered to encourage female customers to shop more often. On the other hand, having identified that it does not hold for all the stores, as in some stores male customers spend on average more than female ones, despite females having in total higher average spending decision should be made separately for each store based on its outcomes.
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