The eCommerce industry is competitive and fast-moving. To thrive in such an environment and maximize the potential of online sales, consumer packaged goods companies (CPGs) need an agile data stack that provides actionable business insights based on aggregated multi-marketplace data in a timely, comprehensive, and consistent manner. This article delves into 3 critical challenges that CPGs face without an agile data stack implemented.
eCommerce is wrought with complex data collection challenges since online customer interactions generate more data than in-store sales. Successful consumer goods companies aiming to drive strategic decisions, and manage daily tactical operations (gauging customer demand, improving product visibility and search traffic, and targeting effective advertising spend) are often at a loss as to how to wrangle all their eCommerce data. In order to make sense of this data, companies need agile data analytics capabilities that can provide actionable insights based on aggregated multi-marketplace data in a consistent and comprehensive manner. Without this robust analytics base, CPGs may find themselves losing market share to their competitors.
How CPGs Can Overcome the The Intrinsic Challenges of eCommerce Data Collection
eCommerce involves selling multiple products across a variety of platforms, whether they be global marketplaces (like Amazon and Walmart), D2C platforms (like Shopify), advertising platforms (like Google and Facebook), or CRM platforms (like Nielsen). As such, one of the first challenges faced by CPGs is how to aggregate and analyze data that is heterogeneous as a result of being housed across multiple marketplaces and platforms.
Simply loading and transforming operational data into a data warehouse is a necessary first step. But when trying to clean, prepare, and make sense of this data, organizations find that data aggregation is only the tip of the iceberg. The following three processes are crucial to ensure business teams are supported by insights that truly benefit their decision making.
A Complex Challenge
The three processes highlighted in this article – harmonizing data, enriching it, and using AI models to derive actionable insights – are critical factors to consider when building out an eCommerce analytics technology stack. But they also represent technical challenges that cannot be underestimated.
Building out a proprietary data stack using in-house resources is inevitably going to generate obstacles. Finding and retaining staff with the right skill sets, particularly in the field of AI, is a significant hurdle. Equally, given that the landscape continuously evolves – as eCommerce marketplaces and platforms update their data models, APIs and other relevant services – in-house data stacks will require significant resources to maintain, which needs to be taken into account when deliberating whether to buy versus build.
Irrespective of how organizations go about building out their eCommerce data and analytics stack, they will need to stay focused on a clear goal: ensuring clean, fresh data is easily accessible by all relevant stakeholders, and that those stakeholders are armed with AI-driven models to scale data analysis and derive actionable insights that drive business profitability.
The Noogata Advantage
Noogata ensures that the challenges highlighted in this article can be easily solved. Our platform aggregates, harmonizes and enriches data with accessible, and transparent AI-driven models. Get in contact to see a demo of those capabilities and see for yourself how Noogata can help your business!
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