Challenges in Leveraging Big Data in Retail

1. Consolidate and cleanse data from various sources:
Retailers want to bring data together from multiple internal, third party subscriptions, public, and social sources to create a complete and accurate view of their customers. They want to uncover relationships, not just between consumers and products, but locations and family members as well to solve the householding issues. They want a single source of truth of customer data across functional areas and a reliable data foundation for accurate customer segmentation and identification of the influencers.
2. Gain relevant insights from omnichannel data:
There were several discussions around retailers wanting to blend interaction data from various channels with consumer profile information, giving marketing, e-commerce, and customer support teams visibility into customer preferences, product interests, and channel choice. Retailers want to deliver insights like churn propensity, lifetime-value, and abandonment rates to relevant teams in the context of their role and objectives. Furthermore, many leading retailers are leveraging machine learning and predictive analytics to suggest next-best-actions to send relevant and consistent information, across all channels, to the customer and find opportunities for up-sell and cross-sell. However, there is still a concern about the reliability and completeness of the data utilized to run such analytics.
3. Create a global product master:
Several retailers want to create a complete product or SKU profile to understand the supply chain relations, contract adherence, consumption per location, overall global business value and even social sentiments about their brands. They want a worldwide real-time view of the product, especially during a launch, to gain critical insights into accurate targeting and managing key influencers in the marketplace, designing relevant promotions and devising social media strategy.
4. Break data silos across departments:

Retailers are looking for ways to encourage collaboration across teams, in real time. With global multi-functional teams, multi-product portfolio, and big data scale consumer information, it is critical to allow as well as secure access to a convergence of information, with the proper level of role-based access and visibility. Data management has to be a shared responsibility across all functional groups and tools for social curation of internal data in the form of annotating, workflows, tagging, and voting allow every member to contribute and continuously improve data quality and the enterprise knowledge.
5. Exchange data with external parties:

There were some interesting discussions about the possibility to share the data externally with the suppliers to establish a single holistic view of the supply chain. Historically, most retailers do not have the infrastructure to process and make transaction-level data accessible easily. Fortunately, this technology is now available as Data as a Service (DaaS). Retailers can efficiently carve out a data view in the cloud and share it with partners or even monetize their data to create new revenue streams. The advantages of retail data sharing include improving on-shelf availability, better demand forecast accuracy, and improving the customer experience, among many others.

6. Be compliant:

With so many teams working with consumer data, retailers need comprehensive auditing and tracking features to guarantee compliance. They want a historical trail for any data merged or updated and want to get alerted to abnormal data viewing patterns by application users for possible information breach or theft. Compliance and transparency need to be inbuilt into the data management rather than treated as reports developed as an afterthought.

According to a McKinsey study, the continued adoption and development of big data levers have the potential to increase US retail productivity by more than 0.5 percent a year through 2020. Such a boost in profitability is especially significant in a sector where margins are notoriously tight.

Are you ready to address the above pain points and turn your big data into a valuable asset? Answer these seven questions to learn how prepared you are to manage your retail data effectively