DTP: Ahold’s Implemention of Big Data Analysis (team 31)


Introduction

Ahold is a large retailer that operates in both the European Union as in the United States. The core business of the company has been “selling great food” and serving the needs of their customers through their different channels (Ahold, 2014). According to their business model, they offer a value proposition that promises a better place to shop, to work and being a better neighbor for customers. They fulfill the needs of customers by using the ‘wheel of retail’ (constant investments to ensure growth) to lower prices. Next to that, Ahold is targeting the mass market through its various companies. Their portfolio consists of grocery stores and stores in other segments (Bol.com, Etos, Gall&Gall and Peapod) through traditional and online channels. At last, they want to increase customer loyalty by practicing a customer intimacy driven strategy and offering good quality and up to date products through a well-organized distribution, purchasing and selling platform. Ahold currently uses its IT to gather consumer behavior data of each branch, which makes sure that there are still opportunities not being utilized. They simply do not use external data in order to improve data analysis. A proper vehicle that might enable Ahold to utilize these opportunities is Big Data analysis.

The Digital Technology Transformation and its impact

The way of using IT in business, especially in the supermarket industry has its shortcomings. Moreover, current IT strategies in this industry are not effective as it could be. This is due to the fact that opportunities are not being accomplished. Big Data analysis helps Ahold to accomplish their opportunities. Particularly, other sources of data can be valuable as well (sensor data, social media posts, weather at that location of the customer (Brown, Chui, & Manyika, 2011)). Big data also can increase the efficiency of Ahold’s business operations. In order to clarify, Big Data analysis influences the core objectives to ensure growth. The use of Big Data will help expanding geographical growth, since it has access to geographical diverse data. It also will help broadening their offerings as they will know what kind of products consumers will prefer to have. This will enable Ahold to increase consumer loyalty and improve their way of being more responsible for customer’s lives by providing them convenience. Big Data analysis will eventually increase the efficiency of Ahold’s operations, so that they will be more able to emphasize their value propositions and to be competitive.

Of course this disruptive technology has an impact on the supermarket industry as well. The net impact of Big Data analysis on the Porter’s Five forces will be positive. Also the implementation of a Big Data strategy will influence the SWOT-analysis as it allows Ahold to expand their current strengths and to utilize opportunities. However, the threat of customer privacy will increase after the implementation of this strategy.

Conclusion

Big Data can predict what customers are really looking for. Knowing your customer is key in predicting trends and creating value. Big Data helps Ahold to provide itself with valuable information, by collecting, integrating and analyzing large amounts of various sourced, timely data (Brown, Chui, & Manyika, 2011). To derive value from Big Data, Ahold needs to know how to analyze it. It has to follow a multi-step process in order to differentiate better: ‘Acquisition, information extraction and cleaning, data integration, modeling and analysis, and interpretation and deployment. Many discussions of Big Data focus on only one or two steps, ignoring the rest.’ (Jagadish, et al., 2014).

References:

Ahold. (2014). Ahold . Retrieved October 9, 2014, from Ahold – https://www.ahold.com/#!/Media.htm

Brown, B., Chui, M., & Manyika, J. (2011, 10). Are you ready for the era of ‘big data’? . McKinsey Quarterly , 1-12.

Jagadish, H., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J., Ramakrishnan, R., et al. (2014). Big Data and Its Technical Challenges. COMMUNICATIONS OF THE ACM , 57 (7), 86-94.

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