Main Menu

My Account
Online Free Samples
   Free sample   Data analytics case study of coles supermarket

Data Analytics Case Study Of Coles Supermarket

Question

Task:
In this data analytics case study, you will be required to identify an opportunity for the development of a data analytics-based application within an industry that is meaningful to your current or future occupation or career.

Answer

1.0 INTRODUCTION
Organizations in this current age of technological advent make extensive use of data to make various decisions. Data-driven has emerged as a buzz-word, where companies are analyzing big-data for informing decisions and processing large amounts of information. Technological companies devising software and packages for industry participants are increasingly making use of data-driven decisions. One such company operating as in the retail Australia segment is Coles, Australia. The company to compete in the current market and provide customer-centric products as well as services is undertaking more data-driven insights. They extend the digitalization of core processes for meeting customer-centric economy requirements and to enable their clients to attain greater business connections as well as competency in an advisory. The current scope of discussion pertains to the evaluation of mode of operation of the business, ways data is used to enhance efficiency, and methods are undertaken to communicate to stakeholders.

2.0 THE CURRENT MODE OF OPERATION
Coles supermarkets, a retail chain of stores across Australia offers its products by way of physical store format as well as online stores. Coles has physical stores of various formats presents across entire Australia and they are catering to their customers by way of various retail products present which are cost-competitive and meets customer demands. By use of data analytics tools, Coles is successfully offering various products as well as services through online mechanisms as well. Currently, Coles makes use of data analytics for harnessing more than 2500 data points to understand the performance of their products (Cameron, 2020). The Company has been successful in extending its data analytics tools for its suppliers as well, such that data-driven decision-making can be performed by them also. The data-analytics tool used by Coles is the Coles Synergy tool which the Company has developed in partnership with global market analytics provider IRI.

3.0 POSSIBLE INEFFICIENCIES
Coles Supermarkets makes extensive usage of data analytics-driven decision making for supporting its business operations across different platforms. Through its new platform, it analyses big data received by the Company from sources internal as well as external for increasing efficiency throughout the Company. With this technology, Coles is harnessing more than 2500 data points to provide suppliers with unprecedented insight into their product performances. Coles Synergy makes use of IRI’s Liquid Data platform is already being used across supermarkets globally across other retail chains. Coles will make use of the Synergy tool for gaining detailed insight into stores, products, sales channels, and geographies. It provides suppliers a better understanding of customer needs as well as performances, in turn effectively collaborating with Coles. One possible inefficiency arising from the use of this data-driven insight is ignoring customer insight into the business model that is used by Coles Supermarkets.

The data-driven decision tool currently being used by Coles Supermarkets is supplier centric. The data-driven decision making is mainly used for informing supplier decision-making on the topic. It enables them to inform operational decisions primarily by stretching across products. Suppliers with the use of this data can undertake demand forecasting as well as sales promotions. On the other hand, the other aspect of the business which is customer data will need analysis such as feedback regarding supplier products, services, and so on (Cárdenas et al, 2013, p 75). Coles possibly does not take into consideration data insights from customers for their suppliers. So, an inefficient and ineffective supplier may continue operating with Coles despite dissatisfied customers. It is crucial to analyze supplier network information by Coles so that it continues to lead in the market and maintain competencies across Australia. However, data-driven decision-making is crucial for attaining competency in the Coles business.

4.0 AVAILABLE DATA SOURCES
Coles makes use of available data from internal as well as external sources for analyzing data sources for arriving at data-driven decision-making. Coles Synergy makes use of relevant insights into market-based data for easier planning and fostering deep relationships such that joint business can easily be conducted (Xu et al, 2016, p 1564). When suppliers and the Company will have a common understanding of the market, then they will have a deeper understanding hence will be able to create innovative products which render great values to customers. IRI with its partnership with Coles has been able to lift data-driven decision-making with digital capabilities for extending innovative products as well as services to its customers as well as suppliers. This data-driven decision sources technology strategic partnership with Microsoft in 2019 as well for transforming operations as well as deriving greater business by deriving insight through data along with long-term collaboration with Accenture for smart selling as well as revitalizing customer strategies. They are also implementing SAP S/4Hana, Infor GT Nexus, and SAP Ariba for enhancing financial processes, procurement, enhancing availability, and reducing freight import costs. These platforms such as Microsoft, Accenture, SAP S/4Hana, Infor GT Nexus, and SAP Ariba make use of the open-source platform to collect data from a wide external marketplace. Then by using a data filtration tool, relevant data is arrived at so that it can be processed further for arriving at crucial data with which decisions can be made effectively (Elgendy, and Elragal, 2014, p 225). These platforms have extensive data sources available with which they can undertake decisions. Then from the vast data repository, they can retrieve such data and then analyze them as well.

5.0 DATA TO PROVIDE EFFICIENCIES
5.1 THE TYPE OF ANALYTIC TECHNIQUES
Data collected by the Company provides efficiency to the Company also to its suppliers. The Company makes use of extensive data analytics techniques for arriving at outcomes with which decisions can be undertaken. There are multiple types of analytic techniques that are used by Coles for analyzing the data collected by them. Some of them which are used includes;

  • Descriptive analytics: Making use of descriptive analytics Coles arrives at a summary of the performance of the bulk business actions. These transactions include inventory changes, transactional history, promotional success, and so on (Chiang, and Yang, 2018, p 180). These types of analytics have been used for a long time now for analyzing outcomes of direct mail campaigns to determine response rates, conversion rates and cost per lead, and so on. With big data coming into being, Coles using making use of website tracking data for ascertaining visitors of their website, the webpages they saw and products they went through, time spent on each page, links that were clicked by them, links which led them to acquisition and so on.
  • Diagnostic analytics: Similar to descriptive analytics, diagnostic analytics analyses past performances. However, the capability of diagnostic analytics is adding context to data for the discovery of trends or casual relationships amongst variables as well as outcomes (Ohlhorst, 2012). By the use of diagnostic analytics, it is possible to arrive at probabilistic insights into the results of the outcome.
  • Predictive analytics: With the use of predictive analytics Coles can anticipate trends as well as shopper behavior based on historical relationship amongst variables as assessed by way of diagnostic analytics. Coles Synergy currently makes use of machine learning as well as data mining tools for forecasting trend lines and bodies of data (Slavakis et al, 2014, p 26). However, there always remains uncertainty with predictions with predictive analytics managers need to verify their analysis as well as the source of data. Such verification needs to include whether the data represents customers, outliers present in the data, key assumptions present in the data, conditions which would arise in a case such assumptions were to emerge invalid, and so on.
  • Prescriptive analytics: By using this type of analytics method, Coles can undertake increment adjustments for the anticipated changes in consumer demand, sentiment, supply shocks, and so on (Griva et al, 2018, p 12). For example, Coles Supermarkets makes changes to their prices for accommodating changes in demand. For accommodating the same, recommendations come in a real-time manner by the day or by the hour.

5.2 PRELIMINARY ANALYSIS OF A SAMPLE DATASET
In case Coles wants to undertake descriptive analytics of its promotional performance. The following data is collected and analyzed (only an example, hence used smaller figures for analysis).

Table 1: Sample Dataset
Source: Author

Data Source (Promotion Location)

Total Number of Reach (clicks) every week

Customer Conversion

Percentage of Reach Out within the total

Effectiveness of Customer Conversion

1.      Website Promotion

1000

 

200

44.44%

30.77%

2.      Facebook

500

120

22.22%

18.46%

3.      Instagram

300

100

13.33%

15.38%

4.      Twitter

200

50

8.89%

7.69%

5.      Physical Poster

250

180

11.11%

27.69%

Total

2250

650

100.00%

100.00%


6.0 OUTCOMES & METHODS OF COMMUNICATION TO STAKEHOLDERS
6.1 USE OF EXAMPLE VISUALISATIONS
The data analyzed needs to be communicated to different stakeholder groups such that data-driven managerial decision-making can be undertaken. The outcomes of the data need to be communicated to important stakeholders of the communication in the best possible method such that it is easier to understand and then decisions can be made using the same. While making use of data analytics procedures, data analyzed from big data sources might be vast and nearly impossible to derive for stakeholders without knowledge of data analytics (Talia, 2013, p 99). Such data might appear to be extensive and impossible to comprehend. Hence such data analyzed by the Company needs to be communicated using data visualization techniques of charts and graphs such that anyone without the knowledge of data analytics can easily understand. Sample data visualization of the above data prepared using the line graph is given below;

Data Analytics Case Study of Coles Supermarket 1

Figure 1: Data Visualization Example
Source: Author

Data visualization is considered to be the first step towards making sense of data. For translating and presenting data as well as correlating between varied data presented simply, data analysts make use of varied techniques such as charts, maps, diagrams for data representation. Reasons for making use of such data visualization techniques include audience (stakeholders), content, dynamics, context, and purpose.

  • Audience: Firstly, to meet the target specific audience, which here includes diversified groups of stakeholders, such as employees, managers, suppliers, staff, and so on. For the manager to ascertain if target sales of the Company are being attended, he/she needs to go through uncomplicated visualizations. However, for analytics personnel within the organization more complex charts can be used.
  • Content: Secondly, the content of the data being dealt with will in turn enable determination of tactics to be used. This is especially true for the marketing department, where a time-series plot can enable determining the relationship between two elements such as sales and product promotions. For comparative analysis bar graphs or scatter, plots can also be used.
  • Context: Depending upon the context for which data is being analyzed different types of data visualization techniques can be used. For example, for showing profit growth shaded charts (box plots) can be used, and then the highest values can be highlighted. Then again for differentiating elements contrasting colors can be used.
  • Dynamics: When representing different types of data, complex analysis with visualization might be compiled into controllable as well as dynamic dashboards that work as visual data analytics techniques as well as tools.

Hence depending on the requirement of the above factors, different data visualization techniques are used. Thereby, Coles Synergy with its capabilities creates dashboards for representing different types of data as well as with their visualization for stakeholders to use them.

7.0 CONCLUSION
In conclusion, data-driven decision-making is bringing businesses success. The effectiveness of the use of big data has been increasing considerably enhancing business operations as well as their competitiveness. The current scope of discussion includes the Coles Synergy application with which the Company is deriving success and accommodating efficiency in its supply-chain processes. There are multiple data visualization tools used by the Company which are being circulated amongst its stakeholders such that data-driven decisions can easily be arrived at.

REFERENCES
Cameron, N. 2020. Coles launches new data analytics tool for partners. IDG Communications. Retrieved from [https://www.cmo.com.au/article/679940/coles-launches-new-data-analytics-tool-partners/]

Cárdenas, A.A., Manadhata, P.K. and Rajan, S.P., 2013. Big data analytics for security. IEEE Security & Privacy, 11(6), pp.74-76

Chiang, L.L.L. and Yang, C.S., 2018. Does country-of-origin brand personality generate retail customer lifetime value? A Big Data analytics approach. Technological Forecasting and Social Change, 130, pp.177-187.

Elgendy, N. and Elragal, A., 2014, July. Big data analytics: a literature review paper. In Industrial conference on data mining (pp. 214-227). Springer, Cham.

Griva, A., Bardaki, C., Pramatari, K. and Papakiriakopoulos, D., 2018. Retail business analytics: Customer visit segmentation using market basket data. Expert Systems with Applications, 100, pp.1-16.

Ohlhorst, F.J., 2012. Big data analytics: turning big data into big money (Vol. 65). John Wiley & Sons.

Slavakis, K., Giannakis, G.B. and Mateos, G., 2014. Modeling and optimization for big data analytics:(statistical) learning tools for our era of data deluge. IEEE Signal Processing Magazine, 31(5), pp.18-31.

Talia, D., 2013. Clouds for scalable big data analytics. Computer, (5), pp.98-101.

Xu, Z., Frankwick, G.L. and Ramirez, E., 2016. Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), pp.1562-1566.

NEXT SAMPLE
Plagiarism free Assignment

FREE PARAPHRASING TOOL

PARAPHRASING TOOL
FREE PLAGIARISM CHECKER

FREE PLAGIARISM CHECKER

PLAGIARISM CHECKER
FREE PLAGIARISM CHECKER

FREE ESSAY TYPER TOOL

ESSAY TYPER
FREE WORD COUNT AND PAGE CALCULATOR

FREE WORD COUNT AND PAGE CALCULATOR

WORD PAGE COUNTER



AU ADDRESS
9/1 Pacific Highway, North Sydney, NSW, 2060
US ADDRESS
1 Vista Montana, San Jose, CA, 95134
ESCALATION EMAIL
support@totalassignment
help.com