“Data is king!” and “Data is the new gold” are common phrases, but do we truly grasp its power or how to leverage it for real business success? In today’s digital economy, being data-driven is the goal, but is a spreadsheet before a board meeting really enough? While eCommerce leads in data usage, many brands still fail to unlock its full potential. This article dives into the A-Z of eCommerce data - why it matters and how to use it effectively. Data is the cornerstone of every successful brand strategy, yet transforming raw data into actionable insights remains a challenge. We will explore data management, system integration, and the crucial role of clean data in driving forecasting, machine learning, and AI.

The Current State of Data in eCommerce

eCommerce and retail brands collect vast data from platforms like ERP, CRM, IMS, and PIM, yet many underutilise or misinterpret it, leading to poor decisions. This often results from disparate systems, inconsistent entries, and weak internal analytical skills. Brands also miss asking the right questions or adapting to insights. Proper data flow and analysis are crucial; without them, brands risk missed opportunities and losing competitiveness.

Data Collection Systems in Focus

To become truly data-driven, eCommerce brands must understand the data landscape and the systems that capture it. Each system collects unique data points that, when integrated and cleaned, provide a comprehensive business view. Here's how to maximise data collection across key systems:

eCommerce Platforms: These platforms centralise transactional data, customer behaviours, and marketing insights.  

• Improvement Tips: Use custom analytics dashboards to monitor KPIs like conversion rates and average order value. Leverage SEO tools for search term data to inform product strategy. Focus on live data for market agility.

• Utilisation Tips: Utilise heatmaps and session recordings to understand customer behaviours. Cross-reference with sales data to identify top-performing products.

Enterprise Resource Planning Systems: The backbone of eCommerce, ERPs centralise finance, supply chain, and customer service data, providing a "single source of truth."

• Improvement Tips: Link inventory management with sales for real-time visibility. Automate data entry to reduce errors and streamline order processing.

• Utilisation Tips: Analyse consolidated ERP data to find cost-saving opportunities and inform strategies like pricing and market expansion.

Customer Relationship Management Systems: CRMs collect data on customer interactions, preferences, and sales funnels.

• Improvement Tips: Standardise data fields and automate workflows. Use dropdowns instead of text fields for easier analysis.

• Utilisation Tips: Segment customers for personalised marketing campaigns, boosting retention and lifetime value.

Inventory Management System: IMS track inventory, supply chain movements, and product turnover.

• Improvement Tips: Set up automated stock alerts and regularly audit inventory data.

• Utilisation Tips: Analyse turnover rates to clear slow-moving stock and optimise future purchasing.

Warehouse Management System: WMS optimise warehouse operations, from tracking to shipping.

• Improvement Tips: Use barcode scanning for real-time tracking and optimise warehouse layouts to reduce picking times.

• Utilisation Tips: Identify bottlenecks and adjust workflows to improve efficiency and reduce order processing times.

Product Information Management Systems: PIM systems manage and distribute product information across channels.

• Improvement Tips: Standardise product attributes and use data validation rules to ensure accuracy.

• Utilisation Tips: Use enriched product data to improve customer experience and conversion rates.

Optimising data collection across these systems enables brands to capture actionable insights and lays the groundwork for advanced analytics. These systems are the backbone of data capture, so ensure they are optimised, scalable, and aligned with your business needs. Avoid the common pitfall of choosing software for a single function; select systems that integrate seamlessly to support a truly data-driven approach.

Streamlining Data Across Systems

Integration streamlines data flow across systems like ecommerce platforms, CRMs, IMS, WMS, ERP, and PIM. However, it's effective only if data is clean and standardised. Brands must ensure consistency in data fields, such as product names, SKUs, and customer IDs. Without standardisation, integration can result in duplicated or conflicting data, complicating decision-making.

The Importance of Clean Data in Integrations

Clean data is essential for successful system integration, enabling accurate insights and effective decision-making. Free of duplicates and errors, it ensures reliable reporting and strategies. Conversely, dirty data leads to incorrect reporting and flawed actions - like poor customer segmentation and marketing. Unstandardised SKUs and inconsistent data compound issues, causing duplicate records and skewed analytics.Therefore, thorough data cleansing must always precede integration efforts to avoid these pitfalls.

Reporting and Analysing Data

Clean, integrated data allows brands to unlock its full potential through comprehensive reporting and analysis. By consolidating data into a centralised dashboard, brands gain real-time insights into key metrics like sales trends, inventory turnover, and customer lifetime value. Advanced analytics tools can then interpret this data, revealing patterns such as seasonality in sales and identifying high-value customers for targeted marketing. However, the accuracy of these insights depends on the quality and completeness of the data analysed.

Machine Learning and AI: The Next Frontier

Clean, well-structured data is essential not just for operational efficiency but also as the foundation for machine learning and AI applications. When data is consistent and standardised, machine learning algorithms can effectively identify patterns and predict trends, such as inventory needs, customer behaviours, and market shifts. However, the primary reason AI initiatives often fail in ecommerce is poor data quality; AI models are only as reliable as the data they are trained on. Flawed input leads to flawed predictions, resulting in brands investing heavily in AI without achieving the desired outcomes.

To harness the full potential of AI, brands must prioritise data cleanliness and integration. By doing so, they can leverage machine learning and AI to enhance customer experiences, streamline operations, and stay competitive. Although AI is a powerful tool, even the fast-moving ecommerce industry struggles with data quality challenges. Addressing these challenges is key to leading the industry forward with AI-driven innovation.

Maintaining Data Integrity

Maintaining data integrity is an ongoing process that requires regular audits to identify and correct inconsistencies, errors, and duplications. Automated tools can help flag anomalies and potential data quality issues, but the human element is equally crucial. Teams responsible for data entry and management must be trained on the importance of data cleanliness and proper procedures for updating information. This training ensures data remains accurate and actionable, underscoring that while data is a vital resource, its reliability depends on those who manage it.

Misinterpretation of Data and How to Avoid It

Data misinterpretation in ecommerce often arises from misaligned definitions, incomplete datasets, and lack of context. For example, increased traffic doesn't guarantee higher sales, and high acquisition rates are meaningless without retention. Brands need a comprehensive data strategy with clear definitions, context-setting, and cross-verification. Interpreting data within the broader business context is crucial for accurate insights.

Why Data Challenges Persist and How to Overcome Them

Many eCommerce brands struggle to leverage their data effectively due to poor data quality, weak integration strategies, and viewing data management as a one-time task rather than an ongoing process. Overcoming these challenges requires investing in a robust data management framework that prioritises data quality, integration, and maintenance. This means adopting best practices for data entry, using automated data cleaning tools, and integrating systems to create a single source of truth. By taking a proactive approach, brands can turn data from a mere operational byproduct into a strategic asset.

Final Thought

Data is more than numbers; it's the driving force behind successful eCommerce brands. In a fast-changing industry, those that fully leverage data gain an edge, while others fall behind. The power of data lies in guiding strategic decisions, streamlining operations, and enhancing customer experiences - but this is only possible with clean, integrated data. With today's advancements in predictive analytics and AI, clean data is more vital than ever. Brands that prioritise data excellence will not just keep up with the market - they'll lead it. Are you using your data to its fullest potential? In ecommerce, data isn’t just king; it’s the future.

For more information on data please see the Loncom Consulting website - Loncom Consulting