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Data wars and the challenges an eCommerce brand may face when implementing analytics and how can they overcome them

This article originally appeared on Times Now

By harnessing the power of data, businesses can gain valuable insights into every aspect of their business, whether it is customer behaviour or product performance or marketing campaigns, thus gaining the ability to optimize and enhance overall performance.

“You can't improve what you don't measure”. In today's data-driven business landscape, analytics plays a pivotal role in driving strategic decision-making for ecommerce brands. By harnessing the power of data, businesses can gain valuable insights into every aspect of their business, whether it is customer behaviour or product performance or marketing campaigns, thus gaining the ability to optimize and enhance overall performance. However, implementing analytics in the ecommerce realm is not without its challenges.

With eCommerce generating massive amounts of data, businesses need to consider the 5 “Vs” of big data.

  • Volume - the number of data points in a data set.

  • Variety - the number of parameters at each data point.

  • Velocity - how quickly data is generated and how quickly it moves.

  • Veracity - the accuracy and quality of a data set.

  • Value - the potential economic value that the data might create.

The exponential growth of e-commerce activities, especially since the pandemic, has led to data lethargy. With such an overwhelming amount of data available advertising data, content data from multiple storefronts on multiple marketplaces, inventory and order data, warehouses, last mile and fulfilment data - businesses struggle to extract meaningful insights and strategize next actions effectively.

The sheer volume of data can lead to analysis paralysis, where decision-makers find it challenging to identify actionable trends. This is a major burden on any internal/external team that is held responsible to consolidate and analyse this data and leaves room for human error.

Consequently, consolidating data from various sources is a big hurdle that eCommerce brands encounter. Data may flow in from sources such as websites, marketplaces, ad platforms and social media platforms, as well as Content Management Systems (CMS), Order Management Systems (OMP), Supply Chain Management Systems (SCM), Warehouse Management Systems (WMS) and more, making it difficult to consolidate and integrate effectively.

This fragmentation increases the risk of errors and inconsistencies in the data, which can lead to inaccurate analyses and flawed decision-making. Only with data veracity can algorithms make reliable trends and predictions. It’s important to implement robust data governance practices and utilize data integration tools that can help streamline the data consolidation process and ensure the accuracy and reliability of insights.

Another challenge lies in the diversity of algorithms and parameters across different platforms. Almost every platform operates with its own unique algorithms and data structures. They measure and analyse data using parameters that are specific to the platform. As a result, comparing data across different platforms becomes a complex and time-consuming task. The lack of standardized metrics and methodologies makes it challenging to draw accurate conclusions and compare performance consistently.

One critical challenge in implementing analytics for e-commerce brands is the time and effort required for data preparation. Before analysis can take place, substantial effort goes into cleaning and organizing the data, aligning it with comparable parameters, and ensuring its quality and consistency.

This preparatory work can be time-consuming and resource-intensive. Moreover, as time elapses during the preparation stage, the data may become outdated, reducing its relevance and usefulness. Not only will this result in inaccurate/irrelevant decisions but also is a waste of time and energy that could be expended elsewhere. Another challenge that businesses typically face revolves around stakeholder buy-in from different business units. For example, in a customer’s decision-making process, a purchase can be attributed to any of the different sources that the buyer may have interacted with the product - a Google Ad, an Instagram sponsored Ad, or marketplaces like Amazon and Flipkart.

With each of these platforms claiming attribution, it is not easy to accurately ascertain who contributed to the success. This data overlap can often result in lack of clarity, leading to ineffective decisions on which channel to allocate ad budgets to.

As the eCommerce industry matures, we are beginning to see the ne algorithms in business operations that were traditionally performed manually. Algorithmic eCommerce tries to solve the data problems that businesses face through a multi-step process. Firstly, connecting data from disparate systems into a unified data pool, thereby reducing operational complexity. Secondly, using algorithms to analyse the data to derive trends, patterns and insights. Lastly, turning these insights into actionable recommendations that can be applied across the entire business, be it content, ads or inventory. When implemented effectively, algorithmic eCommerce has the potential to directly impact the bottom line by increasing margins and driving revenue growth.

Authored By Prem Bhatia, Co-Founder and CEO, of Graas.

Prem Bhatia

11 Jul 2023

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