Chapter 2
Promoting a truly data-led decision-making culture
DACH retailers have made significant progress in recent years when it comes to building an organisation that is data-driven.
Encouragingly, 85% of respondents report a slight increase in their ability to make data-led decisions over the past two years, with 11% citing a significant increase. This suggests a positive trend towards data utilisation, although room for improvement remains.
When prioritising improvements to data analytics capabilities, retailers are focusing on foundational elements. Improving data quality and accuracy (46%) has been highlighted as the biggest priority, followed closely by operational efficiency (44%) and optimised marketing campaigns (39%). Inventory optimisation (32%), real-time data collection (30%), and predictive analytics and forecasting (30%) are also priorities, highlighting the desire to leverage data for both strategic and tactical advantages.
However, barriers remain to retailers fully leveraging data – particularly around data integration and accessibility. Respondents highlighted merging first- and third-party data, integrating online and offline data, and breaking down data silos between teams as key challenges. Data quality and consistency also remain major concerns, with issues such as manual data cleaning, inconsistent data collection, and poor data tagging hindering insights.
That said, retailers must find a way to leverage data to drive profitability. From optimising loyalty incentives using predictive analytics and improving customer retention strategies to leveraging analytics to address quality issues and prevent returns, our respondents highlighted several key ways they are using data to drive profitability.
How has your organisation's ability to make data-led decisions changed in the past two years?
It has increased significantly
It has increased slightly
It has stayed about the same
"Ecommerce once prioritised accuracy in fraud prevention, leading Riskified to focus its identity engines on achieving high precision. Today, many merchants have reached strong approval rates but now face challenges with false declines. They need AI-driven conversion optimisation to convert good customers, not just detect risky transactions. This requires policy-driven AI decisions and advanced identity resolution, which demand more data and a new approach. While customer data is abundant, the ability to access and process it effectively is the key to balancing trust and security in today's competitive market.”
Eyal Elazar, Head of Market Intelligence, Riskified
What are your main priorities when levelling up your data analytics capabilities?
(Respondents were asked to select three options)
Improving data quality and accuracy
Operational efficiency
Optimised marketing campaigns
Inventory optimisation
Real-time data collection
Predictive analytics and forecasting
Customer insights
Customer engagement
Data security and privacy
Improving data literacy across the organisation
Improved targeting
Other
“First, merchants should evaluate their data access, its quality, and their in-house analytic abilities. We've seen that AI-based fraud detection models have proven transformative for those leveraging partners with broad network data and deep insight capabilities. Unlike traditional rule-based methods, AI evolves in real-time, analysing the merchant's data to establish "normal" consumer behaviour patterns. Once the algorithm understands typical transaction patterns, it can learn to detect anomalies and suspicious behaviour. When risk patterns start to emerge, AI provides real-time alerts, empowering merchants to act immediately.”
Eyal Elazar, Head of Market Intelligence, Riskified
"The biggest barrier to leveraging data isn’t just technology—it’s data fragmentation. Responses highlight inconsistent collection, siloed information, and lack of integration as key challenges. Without a single source of truth for product and customer data, businesses struggle to extract actionable insights. To succeed, brands must unify their data, ensure consistency across channels, and eliminate silos. A strong product information foundation enables businesses to trust their data, act on insights, and deliver seamless omnichannel experiences—without guesswork."
Marcus Albrecht, Director of Customer Success CEE & APAC, Akeneo
“At Riskified, we leverage our network data and modelling to drive higher conversion rates while reducing fraud risks. Machine learning analyses diverse data points across all of our merchants' transactions, revealing the true identity behind shopper accounts. With a seamless view of behaviour, merchants can more effectively trace fraudsters' tracks while distinguishing loyal customers from opportunists. This capability not only deters fraud but also fosters smarter decisions that build trust and enhance satisfaction for valued customers, boosting loyalty and securing revenue.”
Eyal Elazar, Head of Market Intelligence, Riskified
