data-driven retailing Retail 4.0

7/18/2023

Towards Data-Driven Retailing: A Strategic Matrix and Maturity Model for digital transformation in the retail industry

In order to facilitate strategic decision-making in the areas of digital transformation and data-driven retailing, we have developed a strategic matrix that includes both dimensions, providing a solid foundation for data-driven retailing, and our suggested maturity model is designed to emphasize the importance of using data in the decision-making process of retailers' manpower, in operational perspective. It is essential for managers when evaluating the digital level of a process or unit to consider how data is being utilized. By doing so, they can identify gaps in their data-driven decision-making processes, which can then be addressed through the implementation of strategies to improve digital maturity.

Retail business model convergence and disruption

Competition in numerous sectors of the retail industry has evolved beyond the traditional battlegrounds of location, purchasing power, workforce, pricing, and promotional strategies. Instead, it has been shifted towards the realm of innovation and data-driven management, revolutionizing the core business models employed. In this context, data has become an equally significant asset for retailers, alongside retail formats, goods, customers, physical stores, and online sales channels.
That is being accelerated by incredible changes related to consumer behavior, data accumulation, and the application of digital technologies. There has been a marked transition from store-centric approaches to customer-centric paradigms, achieved through the creation and enhancement of the overall shopping experience. Consequently, key retail processes and organizational structures are undergoing extensive reformation [12]. The transformation process in the retail industry is reflected in the emergence of new executive positions, including the Director of Customer Experience and Chief Data and Technology Officer (CDTO), both of which represent crucial business functions. Additionally, there is a discernible shift in the principles and cultural norms pertaining to management and decision-making, with an ever-growing emphasis on data-driven approaches to management

To capitalize on this changing landscape, retailers should explore value-creating diversification strategies. This may involve expanding marketplaces to connect third-party sellers with customers, generating commissions in the process. Retailers can also venture into new areas, such as providing business-to-business services and developing products that leverage their existing assets, including logistics infrastructure, customer data, and untapped advertising channels on apps and websites. As an example is transitioning from selling nutritious meals to offering curated diets, gym memberships, health monitoring through wearables, and health insurance for those seeking a healthier lifestyle. Other examples include year-round full-service DIY and maintenance for homes, furnishings, and appliances, as well as exclusive lifelong ticketing and access rights for sports and music fans.
Bain Company’s report indicates that by 2030, these "beyond trade" activities will account for approximately half of industry profits in a typical Western market [2]. As the retail industry continues to evolve, companies must adapt and embrace the opportunities presented by digital transformation and innovative business models to thrive in this new era.

Data-driven business-focused and Data-driven tech-focused strategic matrix

The implementation of the Data-driven retailing approach encompasses multiple areas, including key retail business processes, business models, customer experience, decision-making processes, data-driven management, data scalability and accessibility, innovation management, and more. These areas can be categorized into two dimensions: data-driven business focus and data-driven tech focus. In order to facilitate strategic decision-making in the areas of digital transformation and data-driven retailing, we have developed a strategic matrix that includes both dimensions, providing a solid foundation for data-driven retailing.
In the data-driven business focus dimension, companies prioritize the transformation of their business models by leveraging AI & ML technologies and adopting digital business models such as marketplaces and other platforms. This dimension is typically driven by business owners, CMOs, COOs, and other key stakeholders responsible for crucial business processes.
The data-driven tech focus dimension, on the other hand, pertains to the development of IT architecture, infrastructure, scalability, data security, and the implementation of digital products that may not currently be in high demand by the business. This dimension focuses on laying the foundation for the successful digital transformation of the business model and data-driven retailing.

Data-driven retailing maturity model

How should managers and consultants evaluate where the business is at its digitization journey in order to create and implement the most effective, strategic decisions? The number of experts typically use the metrics for that kind of assessment belonging either to the group of customer values or IT operational processes improvement. The first one could include the projects or sub-projects that bring immediate value and that are recognized by internal stakeholders and external customers. The second group is a fundamental investment into IT infrastructure and the long process of deployment of new integrated software. Therefore, the normal transformation plan is a balancing act between those two interests. Even though they are being presented by a well-known consultant (i.e., 300 use cases for generative AI), these strategies share several fundamental flaws, such as a scant depiction of the future and successes in sustained economic benefits.
From an operational point of view, implementing data-driven retailing is based on the data-driven retailing maturity model. Unlocking the real potential of data is a long journey for the retailer, that could be facilitated by the gradual but consistent movement in the eight categories listed in the Data-driven retailing maturity model: Data-Driven Management, Data and Systems, Organization, Culture and Competence, Data penetration into the Business Processes”, Digital business models applying, Customer Experience CVP and CX focus, Investments and Costs and Innovations Management. Following the properly adjusted plan will raise the percentage of data utilization in the decision-making process and, as a result, lower overall operational costs, the cost of mistakes, and the main difficulties that face customers in retail and moving on next level of data-driven retailing maturity