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Data and Tech: The Future of Commerce is data-driven

Kees Jacobs
Jul 20, 2023

Part 2: Handle data with care

Welcome back to the second part of my blog series on the future of commerce and the role of data and technology. In the first part, we discussed the importance of channel-less commerce, the impact of disruption in the industry, the need for connected capabilities, and the significance of data-driven competencies and value. In this continuation, we will explore additional key elements and strategies for companies to succeed in the data-driven era of commerce.

Data sources: Whoever masters the data owns the future

In a connected commerce landscape, the data landscape expands beyond traditional, modern, and omnichannel models. While it’s critical for companies to master their owned data, including transactional data, first-party customer data, marketing and customer experience data, and supply chain data, there is a vast array of external data sources that further enhance consumer and business intelligence. This includes third-party data from ecosystem partners, social media data, search data, ratings and reviews, location-specific data, and even cross-sector data collaborations. The more companies can blend internal and external data, the more powerful their insights and decision-making capabilities become.

Data collaboration: If you want to go far, go together

There is an African proverb that I really like: ‘If you want to go fast, go alone – but if you want to go far, go together’. The future of commerce is defined by ecosystem collaborations. Companies need to be able to share and blend their internal commercial data with data from existing and new connected commerce ecosystem partners.

By combining data resources and knowledge, companies can create powerful ecosystems that generate valuable insights and provide enhanced services to consumers. Collaboration can range from partnerships with last-mile intermediaries, social commerce platforms, and third-party marketplaces, to direct-to-consumer/business initiatives. Nestlé’s example of Purina’s digital ecosystem showcases the power of data collaboration in providing holistic and personalized experiences to pet owners throughout their pet’s lifetime.

Predictive and generative analytics: Even more intelligent intelligence

Recent technological advancements have significantly enhanced our ability to analyze data and identify patterns. For example, we are all amazed by the power of Generative AI to create original and realistic content, such as images, music, or text, by learning from vast amounts of data. We are seeing how predictive analytics can determine likely future outcomes, and how prescriptive analytics provides recommendations on what actions should be taken.

These advanced analytics capabilities can be leveraged to optimize merchandising, pricing, marketing and sales execution, individual consumer experiences, and operational efficiencies. Data products that deliver this level of intelligence can take various forms, from strategic storytelling and business solutions to tactical self-service dashboards and operational execution through real-time algorithms and machine-learning models. I see that successful companies are able to automate and industrialise more and more the delivery of these data products within the business and that they are able to effectively take advantage of the new innovations that arrive almost daily.

Data foundations: Quality in, quality out

While the value of data and analytics lies in their ability to drive business decisions and actions, it is crucial to have a robust data foundation. What I often see is that companies embark on specific data-driven business initiatives, but only at the end of such programs realise that they need to organize for proper data foundations, at scale. Quality data input leads to quality output.

Organizations must proactively manage their data and technology platforms, ensuring data availability, trust, governance, and master data management. Data capture, processing, cleansing, modeling, analytics, sharing, and consumption are all vital components of a well-managed data foundation. The architecture should support flexible data capturing, data storage, and data preprocessing. Competencies in data engineering and data science are essential for generating and activating data products.

Composable tech: Plug-and-play

To stay competitive, retailers and consumer goods brands are modernizing their technology architectures. Composable architectures, such as Crafted Commerce, provide a modular approach that enables development and consumption across channels, touchpoints, and modalities. These architectures combine cloud-native, headless, API-first, and microservices principles, offering better interoperability, scalability, and innovation flexibility.

Composable tech empowers companies to differentiate themselves and adapt to evolving consumer-centric business models. Agility, lean delivery processes, and continuous innovation are key to leveraging composable architectures effectively.

Does your organisation manage its data estate properly, both internally and externally, and does your organisation operate from a future-fit data and technology architecture?

Say no more, it’s clear that the future of commerce is data-driven, and it’s essential for companies to organize their data capabilities. Stay tuned for part three of my blog series, where I will focus on data cultures and business-transformational data journeys at scale…

About the author

Kees Jacobs

Consumer Products & Retail Global Insights & Data Lead, Capgemini
Kees is Capgemini’s overall Global Consumer Products and Retail sector thought leader. He has more than 25 years’ experience in this industry, with a track record in a range of strategic digital and data-related B2C and B2B initiatives at leading retailers and manufacturers. Kees is also responsible for Capgemini’s strategic relationship with The Consumer Goods Forum and a co-author of many thought leadership reports, including Reducing Consumer Food Waste in the Digital Era.

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