In the decade of 2010, many companies faced the great challenge of big data. Since the financial potential was likely to be more visible and the increasing number of digital channels easily provided a lot of new data, The use of customer data for marketing purposes quickly became essential. This logic is hard to deny: you need to know your customers in order to better serve their needs and thereby create value. Forward-looking marketing, ultra-personalization of the customer experience and loyalty programs, as well as monetization of anonymized customer data are levers that make it possible to convert a prospect into a customer, to maximize the value of a customer and, more generally, to generate growth and new sources of income.
If the results are long in coming for some, despite the investments made, the question is no longer whether it is relevant to invest in customer data and their marketing activities, but rather Rather, you know which use cases are most profitable and create valueand how to industrialize them (organization, skills, operating methods, industrialization process, etc.).
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However, The data strategy of companies today has to go far beyond simple marketing applications in order to capture the entire value chain. Big data requires “thinking big”, especially when it comes to investingits potential cannot be limited to customer data or the exclusive playground of a marketing or digital director to generate an ROI. Data must make it possible to modernize, optimize and improve the performance of all building blocks in the company’s value chain.
In order to fully realize the potential of data, the challenge for the company is to transform its operating model to become data-centric.
As, crossing provides a good example of a company that has grown into a data-centric company under Alexandre Bompard’s leadership. Through its partnerships (leading Google), its investments (2.8 billion euros by 2022) and the acquisition of data skills (creation of a data laboratory), Carrefour is starting this Spreading a data culture and providing use cases for many activities – For example: optimization of the offer on retailer sites thanks to prediction algorithms, monetization of customer data via the Carrefour Media unit (turnover of EUR 50 million in 2019), personalization of promotions thanks to purchase history, etc.
In the industry Air Liquide is a pioneer in digital and data-centric transformation. Supported by a sensitive top management with knowledge of digital and data levers, data is used at all levels of the group and integrated into all important strategic projects. The use of “Smart Innovation Centers” on a global scale now enables remote management of production units, predictive maintenance and the adaptation of production to demand. This scale change from Thanks to digitalization, Air Liquide can proudly report a sharply increasing operating margin for the 2019 financial year (+90 basis points compared to 2018).
The implementation of this data-centric business model makes it possible to realize the value creation opportunities that result from the data on the various links in the value chain. Here are a few examples.
Use real-time data to increase corporate strategy
Let’s start with a topic that is aligned with current affairs, strategic and operational management thanks to real-time data. In a time of the COVID-19 crisis, the duration of which remains unlimited, it is more important than ever not to be exposed to the uncertainty of events and the environment and to take an agile approach to adapting your short-term strategy (opening of stores), resource management etc.) and in the medium term (new offers and products etc.). To achieve this, a real-time view of all the parameters of your environment (competitive, regulatory, political, etc.) is essential in order to make the right decisions in terms of investment, strategic plan and operational management …
Our ability to aggregate and merge data in real time must enable companies today to react and make decisions with many more elements and parameters in times of uncertainty. In this regard, the most progressive companies in various sectors have relied on it Real-time data platforms to respond to the COVID-19 crisis ::
- In the automobile a Automaker uses real-time modeling of its environment and demand too Reassign production and warehouse policies (between different models and countries – depending on the expected COVID effects)
- By doing Insurance sectorhave some actors integrated real-time and external data to adjust your risk estimate and ultimately their prices and their recovery process in an unprecedented situation where algorithms based only on history proved out of date
- In the end, Bowl has been based on a methodology from Scenarios for estimating changes in its political, economic and market environment. Recently, these scenarios have been monitored and refined using real-time data
Improving operational performance
Optimizing processes through process mining
Internal processes are both a strength and a handicap of a large company. Difficult to analyze, and attempts at optimization often face resistance to change and unforeseen obstacles, especially with accurately quantifying the time spent on one process among a dozen others? In view of this complexity associated with subjectivity, data offers its solution: process mining. What are we talking about Process mining consists of identifying and assigning all the steps in a process – logs of transactional IT applications, in all its variantsthat is, with a level of accuracy and completeness that it would be impossible for a human to transcribe on his own. This completely transparent view of the process therefore makes it possible to diagnose it: Identify inefficiencies and redundancies as well as points of process optimization and automation. In addition to diagnosis, process mining enables direct intervention in existing processes and tools by anticipating the “next steps” of the process and suggesting them to the operating personnel.
The potential is therefore enormous, as it can be applied to all processes in the company. Process mining made it possible for Deutsche Telekom For example, to optimize procure-to-pay: This was not only made possible by the optimization of the purchasing processes and their management in real time Savings of € 10 million for the purchasing department in 2019 – especially by reducing duplicate payments and penalties for late payments – but the automation of processes and the time saved for teams executing them have also created the ability to identify themselves € 12 million additional savings.
In connection with RPA, Process Mining enables a new level of automation in the service of real-time corrections in important business processes.
Reduce operating costs with predictive maintenance
The development of IoT technologies (Internet of Things) and the gradual equipping of production facilities and tools pave the way for this numerous management and cost optimizations : Robotization, production authorization, predictive maintenance, etc. By providing the digital tool Predity Vision, a clever mix of data, IA and IoT, Engie understood that Potential savings through predictive maintenance : With more than 4 million maintenance jobs per year, it was important to optimize the journeys of the traveling technicians as much as possible. Today the management tool anticipates the maintenance needs of its around 15,000 locations and can even intervene remotely for certain processes. In addition to reducing maintenance costs for Engie, this tool was also of particular importance during the COVID-19 crisis, as it allowed 800 remote audits to be carried out on sensitive customers (hospitals, nursing homes, etc.). ).
Kone is also a good example of data transformation: predictive maintenance has been at the heart of the brand’s quality of service strategy for almost five years. With convincing results: 50% fewer physical interventions and 80% customer loyalty. By converting these 250,000 elevators into connected objects, the company now wants to monetize services such as personalizing messages on screens on behalf of third parties (trustees or advertisers) and identifying users where the elevator is to be positioned, or even within Good Floor COVID times the call and smartphone opening of the elevator. In some hospitals, elevators interact with auxiliary robots for the distribution of meals to facilitate their movement.
Define your pricing strategy and use data to manage your margins
By grouping, Structuring and analysis of all data that influence pricing, especially data on the competitive environmenta retailer can gain new visibility of its multi-channel price positioning and the Opportunities to optimize margins. With these means and goals, we were able to support an international retailer and inform its pricing policy, thanks to a more detailed knowledge of its price positioning at national and local level.
The synthesis of this data made it possible to define the brand-related reference price indices in comparison with the competitors. By defining the price positioning and reducing the dispersion of the price indices, the company discovers the potential for an annual increase in sales of € 50 million. [1%CA].
This is just an overview of the various levers for data evaluation. Many other possibilities should be explored.
These are internal optimizations that cannot be identified, but which enable companies to significantly increase their competitiveness compared to their competitors. These are also levers where far-reaching organizational changes must be fully exploited: The entire company must undergo its digital transformation and become data-oriented.. The desilotation of data involves technological investments integrated into a robust IT ecosystem, the acquisition of new skills, the acculturation of all employees and the governance involved.
Data are therefore a Share of senior management (for those who still doubted it): It enables added value both by generating additional income, by creating customer satisfaction and by optimizing costs and processes. Just like the customer portfolio or know-how, it becomes an important asset for the competitiveness of companies. To reach your full potential, it is required technical, cultural and organizational transformation of the company to bring data to all parts of the company – beyond the marketing and digital departments.