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Sebastian Karrer
STRATEGY
How to turn data analytics into a competitive advantage for your company
Data analytics bears enormous potential for companies to win in the market. Analytics champions show how this works. And with the right approach, your company can also run a professional analytics program.

Domino's Pizza is one of the largest pizza delivery services. If you order a pizza there, it will be delivered to your home within 30 minutes or the pizza is free. In addition to this sympathetic idea, Domino's also has a performance on the stock market that was only surpassed by Netflix:

Domino's stock is outperforming the largest tech companies

When Patrick Doyle became CEO of the company in 2010, this success story was not foreseeable. Domino's was still heavily indebted in 2008, and the share price was only three USD. Under Doyle's leadership, Domino's developed a less than obvious core competency. The company invented technology to make it much easier for customers to get a pizza than any of its competitors.

To cite a few examples, while competitors were diversifying their offerings, Domino's introduced “ One Click” ordering via mobile phone after realizing that 80 percent of customers always ordered the same pizza. Domino's developed the " Domino's Tracker" to appease hungry and impatient customers with regular updates on the status of their orders. Today, Domino's is experimenting with deliveries by drone or self-driving cars.

This example does not necessarily apply to companies in other sectors. Nevertheless, similar case studies of such analytics champions can be found in almost every industry and their innovations often lead to the development of completely new business models. It is therefore important to take a closer look at data analytics in a business context and the advantages for your company. What exactly makes the difference between the companies that gain a real competitive advantage through analytics and those companies whose analytics programs produce little or no added value?

Analytics is for the business and for the people

Domino's success is because the company simply better understood the needs of its customers and served them with offers tailored to them. It wasn't a marketing tactic that led to this success, but a change in the company's mindset: Patrick Doyle described his company as a "tech company that happens to make pizza". All digitization measures were aimed at making ordering as easy and quick as possible for customers.

Domino's has consistently used and expanded its customer data and relied on data analytics as the key tool to find the right ideas for better customer service in this data. And, of course, the company also managed to implement the new ideas very well. When it comes to setting up and running an analytics program, there are a few basic patterns, which can be found not only in Domino’s, but in almost all companies that use digitization and analytics particularly successfully.

Analytics champions always start with the question of how they can use data-driven methods to strengthen their core business. Analytics can be applied in various ways, for example to increase customer benefits, accelerate decision-making processes or produce in a more resource-efficient manner. The key question that analytics champions ask themselves is, where and how will it have the greatest impact.

Developing a data and analytics strategy that focuses on the core business starts with the definition of the most important goals or the description of the target state of the company. Based on this, entire business areas or processes are selected as the object of investigation and ideas for use cases are collected. These use cases are then grouped and ordered to ensure they complement and build on each other and the results are applicable to day-to-day business. This is important in order to systematically develop competencies, structures and databases in addition to operational improvement measures, so that the program becomes scalable and does not fail at the boundaries of the department in which it was initially launched.

Analytics champions also take into account that their initiatives mean uncertainty and change for many employees. In order to gain the support of the workforce, they therefore communicate openly about goals, benefits and what is changing in everyday work. In addition to the findings from the program, they also systematically incorporate employee feedback into the analytics strategy. This not only increases the quality of the program, but also ensures the necessary participation in the company.

Step-by-step implementation of data analytics programs: Divide & Conquer

With the right strategic framework, the foundation has been laid. And there is also a proven approach to implementing the analytics strategy. Analytics champions often use a Divide & Conquer approach to structure their progress.

Many use cases are described very widely and abstractly at the time the idea is found. An example would be “Reduce customer churn by x%”. If an attempt is now made to process such a big comprehensive use case in one project, the analytics team will need a long time to achieve a proof of concept. Very often, for the rest of the company, the analytics team "submerges" and the rest of the company loses visibility of whether the use case is feasible, what the concrete implications will be and what the team is doing at all.

The far better Divide & Conquer approach is to break down large use cases into several projects that can be implemented quickly and easily and are tangible for everyone involved. The individual projects are complementary or build on each other and are done step by step . In this way, the analytics program is always accountable, delivers results and gives the company the opportunity to contribute with new ideas.

Divide and Conquer use-case structure

This method generates knowledge with each project and enables adapting the roadmap towards the full use case in an agile manner. Incidentally, it is just as important to recognize early on, if a use case cannot or should not be implemented.

An important aspect, especially at the beginning of the analytics program, is to create processes and structures so that the analytics team can implement projects efficiently. This is supported by reducing the complexity of the individual projects as much as possible. For example, data structures often have to be created first, which can only be done in a targeted manner if the process is carried out step by step and the analytics are developed in parallel. As with training in sports, the motto here is: From the simple to the complex.

If the first use cases are successful, the foundation has been laid and the program can be scaled. An early proof of concept is very important as it ensures acceptance of the program by those employees and departments, who are supposed to contribute to the scale-up.

Domino's , for example, gradually aggregated more and more data from all the channels through which the company communicates with its customers. According to Domino's data warehouse manager Cliff Miller, while this happened, various departments began to use this data foundation individually in order to work faster and more cost-effectively. Scalability has, of course, been critical for Domino's to roll out technology to more than 15,000 restaurants in 83 countries.

Implement analytics projects quickly and learn from each one

Operational efficiency in the analytics team is necessary so that the team can quickly establish competence in the area of analytics, but also the routines and communication channels with the specialist departments. Here too, it is crucial to avoid unnecessary complexity, which arises from the use of independent specialized tools or communication barriers. Digital champions therefore build a integrated ecosystem of tools, processes, data sources and guidelines that minimizes the effort to implement a project, but is at the same time fully scalable in all directions.

Furthermore, a consistent plan should be pursued not only to implement projects, but also to systematically build up knowledge. Especially at the beginning, not all projects lead to successful results or require several iterations, for example to develop the data basis. The plan must therefore be to draw insights from each project or iteration and make all gained knowledge accessible. An analytics program becomes a sustainable competitive advantage for a company, if it continuously produces process improvements as well as new knowledge and ideas .

Conclusion

Data analytics offers companies enormous leverage to gain sustainable competitive advantages. Domino's Pizza is a prominent example of how what companies stand to gain, if they better understand their customers and put that that knowledge to use.

For an analytics program to achieve this potential, it must be managed properly. The data strategy needs to align with and become part of the core value creation of the business. Agile processes should be used in the implementation and unnecessary complexity must be avoided. With this approach, analytics champions are able not only to achieve process improvements through their program, generate knowledge and ever new ideas and turn the analytics program into an asset that gives an edge over competitors.

To illustrate how such teams work, let us quote Patrick Doyle one last time: “Find things that are broken and fix them. Do this over and over as an organization and you will thrive.”

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