Big Data Capabilities for McDonald's: Approaches and Strategies
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This research paper outlines the possible approaches for McDonald’s with regards to big data. The aim is to recognize areas of the business structure of McDonalds that require the application of big data. After the identification, big data approaches are then suggested for McDonalds.
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Management Proposal Big Data Capabilities [Your Name] [Date] Executive Summary Page1of18
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This research paper outlines the possible approaches for McDonald’s with regards to big data. The aim is to recognize areas of the business structure of McDonalds that require the application of big data. After the identification, big data approaches are then suggested for McDonalds. General Content Analysis and Sentimental Analysis look at describing the views of the customers at McDonald’s outlets on the quality of the products they have purchased from the fast food chain as well as the nature of the service they have received. The Cluster Analysis builds on the outcomes of both the General Content Analysis and Sentimental Analysis to categorise the outlets and regions where McDonald’s has outlets. Consumer experience and consumer perception forms an integral part of the business strategy of McDonald’s since it’s a market leader in the fast food industry. The General Content Analysis, Sentimental Analysis and Cluster Analysis would enable McDonald’s to better understand the views and needs of their customers. Page2of18
Contents About McDonald’s4 Key Business priority4 Big data approach5 Information and sources8 Big data technologies10 Big data visualisation examples13 Big data adoption challenges and governance13 Page3of18
About McDonald’s McDonald’s is a global chain of fast food restaurant started by the McDonald brothers in San Bernardino in the state of California in the USA (McDonald 2018). The fast foods chain was established in the year 1940 and mainly dealt with the sale of hamburgers. In the year 1955, Ray Kroc bought McDonalds from the McDonalds brothers and structures the brand to become the market leader it is presently. The headquarters for McDonald’s was previously located in the city of Oak Brook in the state of Illinois in the USA. However, in the year 2018, the fast food chain moved its headquarters to Chicago (McDonald 2018). McDonald’s currently offer a range of fast food products apart from the hamburgers for which they are known for. These products are: soft drinks, salads, milkshakes, chicken, coffee, French fries, desserts and breakfast wraps (McDonald 2018). The products are among other products offered by the food chain that are region specific. The region specific menus consist of fast food products that are indigenous to the given part of the world. Despite being established in the United States of America, McDonald’s has successfully managed to use globalization to enter the fast food markets in other regions of the world. This has been achieved by both having outlets that are owned by the company as well as through franchising. The franchising has helped the company manoeuvre the strict market entry laws set by countries that are not free markets. The collaboration with local businesses involved in franchising encourages governments to ease the restriction for McDonald’s. The extensive scaling and globalization of McDonald’s has seen the food chain open outlets in over 102 companies around the world. This scaling and globalization has also made the food chain the second largest employer in the private sector. Key Business priority The McDonald’s food chain is a market leader in the fast food industry. This implies that McDonald’s is less likely to be affected by market forces or market factors that might affect other smaller food chains. The company size, diversity of products and markets allows McDonalds to enjoy some level of protection that would otherwise adversely affect their sales and subsequently profits. The most important aspects of business strategies for companies that are market leaders are image and brand (Kuehlwein & Schaefer 2017). The image of the company is built by the consumers’ Page4of18
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perception of the service and products offered by a company. At times the strength and influence of a brand may be large to the extent that the company can solely depend on this strength and influence ((Petty 2016; Twede 2016). The aggregate result of this over reliance on the brand influence may be neglect of other competences such as product quality, service quality and marketing (Sirajuddin & Ibrahim 2017; Sheth 2017). The overall effect would be a significant reduction in the brand influence, sales and subsequently profits. This therefore makes the key business priority for McDonald’s fast food chain the quality of products and services. Ensuring that the customers are happy and satisfied with the food and services offered in the McDonald’s outlets will play a big role in maintaining the brand image of the fast food chain. The present age of internet and social media has meant that brand influence can be drastically affected by a single post or review on the service and products offered by the company in question (French 2017; O'Malley & Lichrou 2016). Hence it is important for McDonald’s to guarantee their customers satisfactory levels of services and product quality. Big data approach Customer satisfaction can be determined and inferences made through the use of analysis of big data. Three main big data approaches can be used to address the concerns of customer satisfaction: General Content Analysis, Sentimental Analysis and Cluster Analysis. 1.General Content Analysis Content Analysis is a statistical technique in qualitative research that conducts a language diagnostic of views, reviews, comments or responses on a subject of interest (Pashakhanlou 2017). Content analysis technique applies the use of another data mining tool referred to as text mining. Text mining is a big data collection method that gathers information that are generally in the form of texts and processes it into vector, matrices or data frames that can then be analysed to generate inferences (Badal & Kundrotas 2015). Application of general content analysis as a big data approach for McDonalds will aim at summarising the views, reviews, comments or responses of customers using single words. This will be achieved by trying to singles out the most mentioned significant words from the entire text. These frequently mentioned word then provide McDonald’s with information on what aspects of their products and services they need to focus on. This analysis can be focused on the fast food chain outlets and therefore provide data on the product and service concerns of the customers that purchase in the specific outlet of interest. Page5of18
Content Analysis can also be applied when McDonald’s is interested in determining the product and service concerns in whole regions. This is especially for cases where the region in question is exposed to similar product and service related factors. These factors can include sources of farm produce or supplies for the products and the recruitment agencies used for hiring the staff. 2.Sentimental Analysis Sentimental Analysis is a form of a qualitative big data analysis technique. Sentimental Analysis approach to big data involves the analysis of the contents of views, reviews, comments or responses of individuals with the purpose of determining the underlying opinions of the individuals (Mozetic & Smailovic 2016). Sentimental Analysis measures the attitude of an individual’s view towards the topic or subject of interest. Similar to the general content analysis, sentimental analysis applied the use of text mining for collecting and arranging the views, reviews, comments or responses of individuals. This analysis also applies the Natural Language Processing (NLP) and computational linguistics for the generation of the inferences on the attitude (Van Le & Montgomery 2018). The Sentimental Analysis approach for McDonald’s will involve the collection of consumer views, reviews, comments or responses through text mining. The data would then undergo sentimental analysis where the individual view, review, comment or response will be termed as either positive or negative depending on the number of positive or negative words used respectively. This specific form of Sentimental Analysis is the Polarity Test. Polarity Test mainly involves the classification of sentiments into two main groups, positive sentiments and negative sentiments (Korkontzelos & Nikfarjam 2016). Once the sentimental analysis has been done, the aggregate attitude score for individual McDonald’s outlets can then be developed. This aggregate attitude score will give information on whether the quality of products and services in the specific outlets meets the expectations of the customers that purchase fast foods from the particular outlet. The aggregate attitude score can also be generated for whole regions, especially for cases where the region in question is exposed to similar product and service related factors. These factors can include sources of farm produce or supplies for the products and the recruitment agencies used for hiring the staff. This will give information on whether the quality of products and services in the specific region meets the expectations of the customers that purchase fast foods from the outlets particular region. Page6of18
3.Cluster Analysis Cluster Analysis is an EDA (Exploratory Data Analysis) technique that groups objects with respect to a standard or condition that is of interest (Kirk 2016; Renganathan 2017). Cluster analysis puts together items that exhibit similarity in the condition of interest, hence allowing the identification of items that don’t exhibit similar characteristics (Malki & Rizk 2016; Ying 2016). Cluster analysis allows businesses to make informed decisions as regards strategies for the different categories that are produced by the clustering. This presents an efficient and cost effective means of business strategy development for business entities (Liu & Denxiao 2015). The cluster analysis big data approach for McDonald will comprise clustering depending on both the general content analysis and the sentimental analysis. These two clustering analysis will also be considered in terms of outlets and whole regions. a.Cluster Analysis of Outlets with respect to General Content Analysis From the general content analysis, the most frequently mentioned words can be determined for the different McDonald’s outlets. The results of these analysis can then be used in the cluster analysis where the most frequently mentioned words for the outlets are observed. The cluster analysis will then group together the outlets that have the same most frequently mentioned words. Closely observing the outlets in the same categories will provide a larger sample for determining why customers are having a certain opinion about the product or services of McDonald’s. b.Cluster Analysis of Whole Regions with respect to General Content Analysis From the general content analysis, the most frequently mentioned words can be determined for the different regions (in terms of countries, regional arears or cities) where McDonald’s has outlets. The results of these analysis can then be used in the cluster analysis where the most frequently mentioned words for the regions are observed. The cluster analysis will then group together the regions that have the same most frequently mentioned words. Closely observing the regions in the same categories will provide a larger sample for determining why customers are having a certain opinion about the product or services of McDonald’s. Page7of18
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c.Cluster Analysis of Outlets with respect to Sentimental Analysis From the sentimental analysis, the number of positive or negative sentiments can be determined for the different McDonald’s outlets. The results of these analysis can then be used in the cluster analysis where the number of positive and negative sentiments for the outlets are observed. The cluster analysis will group together the outlets that have the same range in number of positive or negative sentiments. Closely observing the outlets in the same categories will provide a larger sample for determining why customers are having a certain sentiment about the product or services of McDonald’s. The categories of significantly large positive responses provide McDonalds with a strategy template for their products and services. d.Cluster Analysis of Whole Regions with respect to Sentimental Analysis From the sentimental analysis, the number of positive or negative sentiments can be determined for the different regions (in terms of countries, regional arears or cities) where McDonald’s has outlets. The results of these analysis can then be used in the cluster analysis where the number of positive and negative sentiments for the regions are observed. The cluster analysis will group together the regions that have the same range in number of positive or negative sentiments. Closely observing the regions in the same categories will provide a larger sample for determining why customers are having a certain sentiment about the product or services of McDonald’s. The categories of significantly large positive responses provide McDonalds with a strategy template for their products and services. Information and sources The required data for the big data approach for McDonald’s would need to be collected from the client in the form of views, reviews, comments or responses. The information sources for this data will be: a.Online Surveys:through the use of online questionnaires, McDonald’s can get views that their customers have about their products and services in different outlets and regions Page8of18
globally. The filled questionnaires will have to be sorted into outlets and regions for easier analysis. b.Social Media Comments:the comments that are posted by customers on the posts published by the numerous McDonald’s social media sites present an opportunity for data mining. In instances where regions have their own McDonald’s social media site, for instance McDonald’s Australia or McDonald’s Sydney, the collected data becomes more specific and reliable in drawing inference on the quality of the products and services in McDonald’s outlet in the region. The collection of data can be done from any social media platform that McDonald’s has an account in. This could include Facebook, Instagram and Twitter. c.Product and Service Reviews: by setting up a site where customers can post reviews of the quality of products and services at specific outlets, McDonald’s creates a source of information that can provide useful inference once analysed. The site will provide customers with the opportunity of giving an instantaneous review of products and service in the fast food chains. From this reviews, inferences can be made about the quality of both products and services in specific McDonald’s outlets. In the table below we have the big data approaches for McDonalds and description of the nature of the variables that will be considered for each approach. Big Data ApproachSource of DataVariable type 1.General Content Analysis views, reviews, comments or responses Categorical data variable measured on nominal scale. 2.Sentimental Analysisviews, reviews, comments or responses Categorical data variable measured on ordinal scale. 3.Cluster AnalysisOutput of General ContentCategorical data variables that Page9of18
Analysis and Sentimental Analysis. are measured on either nominal or ordinal scales depending on the source of the data. Big data technologies In the big data approach for McDonald’s the following big data technologies will be applied: a.Technology for streaming data processing b.Technology for storage of data c.Technology for analysis of data 1.Technology for streaming data processing Data from the views, reviews, comments and responses represent real time data that requires to be continuously processed once received. The filled questionnaires from the online surveys will be streaming once filled by the participants in the surveys. The comments on the McDonald’s posts on social media will also represent streaming data since the posts will be continuous and the comments are also expected to be made continuously. The site for the reviews will also be a source of streaming data. The Apache sparks technology is a reliable big data technology for the processing of streaming data, which makes this technology appropriate in the big data approach for McDonald’s. 2.Technology for storage of data The output data from Apache Sparks processing of the streaming data needs to be stored as an intermediate stage between the processing of data and the analysis stage for the data. In order to prevent loss of data quality and guarantee the reliability of the inference drawn from the analysis of data, the storage of the data should be efficient and structured (Karolin & Schrape 2018). The NoSQL Database is a big data technology that provides for structured storage of data. This implies that the data can be easily accessed and organized in a way that makes the analysis process efficient. Hence NoSQL is a reliable big data approach to data storage for McDonald’s. Page10of18
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3.Technology for analysis of data R Studio is an R language environment that provides big data analysis capabilities (Galit, et al. 2018). R Studio is capable of running all the big data approaches (Text mining, General Content Analysis, Sentimental Analysis and Cluster Analysis) for McDonald’s. Using a single data analysis technology gives the inferences a degree of consistency. This thus means that there are lower chances of producing conflicting inferences (results with high variability). R Studio will mine the data from the processed streaming data in Apache Sparks first, then send the data for storage in the NoSQL Database. From the database, General Content Analysis, Sentimental Analysis and Cluster Analysis will be conducted on the structured data using R to produce the final inference. The diagram below represents the description of the big data technologies application for McDonald’s. Page11of18
Page12of18 Comments from Social Media Site (Facebook, Twitter, Instagram) INFORMATION SOURCES Reviews from OutletsResponses from online surveys Streaming Data ProcessingAPACHE SPARKS R STUDIOText Mining Data StorageNoSQL DATABASE Data Analysis General Content Analysis Sentimental Analysis Cluster Analysis R STUDIO Results
Big data visualisation examples Figure 1 Figure 1 above represents the cluster analysis results for the big data. The plot shows the grouping of items into clusters of different colours. The items falling within the circles with similar pattern and colours are considered to be in the same category with respect to the condition of interest. Figure 2 Figure 2 above shows a sample of the results from the general content analysis of the reviews given by customers at a McDonald’s outlet. From the analysis we can observe that the most frequently mentioned word is chicken. This implies that chicken is the most sold product for the specific McDonald’s outlet. Big data adoption challenges and governance The table below provides a summary of the challenges that are faced in the adoption of big data approaches and the recommendations for ways of overcoming such challenges. Page13of18
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IssueRecommendations Challenges1.Data Privacy. Restriction of the access and usage of data to individuals with proper clearance is key for any organization (Vicenc 2017). 2.Data Security. Data needs to be secured so as to prevent the leakage and subsequent usage of the data by unauthorised individuals or personnel ((Igor & Lindsey 2016). Apache Rangers Governance1.Data Source. The source for any data has to have a level of integrity attached to the process of the collection of data to ensure high quality inference from the analysis (De Jong-Chen 2015). 2.Data Reliability. The data, even at face 1.IBM 2.SAS Page14of18
value, has to be indicative of the actual data on the ground without exaggerations or deductions (Baack 2015). 3.Data Usability. The usability of data mainly concerns the existence of permission for analysis and inferencing of data from the owners of the data (Lenca & Ferretti 2018). 4.Data Appropriateness. The data used for an analysis has to have the parameters and variables of interest for it to provide useful inferences (Galit, et al. 2018). Page15of18
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