What are the best practices for collecting, storing, and analyzing multi-channel retail data?

Creating an integrated customer experience is a crucial strategy for multi-channel success. It means that customers should be able to switch between channels without interruption. This requires consistent branding, product information, pricing, and promotions across all channels. To achieve this, retailers must implement centralized inventory management systems, integrated customer databases, and synchronized pricing strategies.

Conquering Multi-Channel Retail Data: Best Practices 1. Set your goal: What do you want to learn (e.g., who your customers are, what campaigns work best)? 2. Gather all the info: Collect data from everywhere your customers interact with you – online, offline, you name it! Make sure it's all in the same format. 3. Store it safely: Think of a secure cloud storage, like a digital vault, to keep your data safe and organized. 4. Make sense of it: Use special tools to analyze your data, like a detective looking for clues. Group your customers based on their habits and see what's working well. 5. Take action! Use what you learned to make better decisions, like targeting the right customers with the right offers.

Clarify also WHY and for WHAT purpose you are gathering the data to make sure you are gathering the right kind of data on right format on right amounts.

Setting clear and specific goals is essential, with measurable metrics like sales growth percentages or customer satisfaction scores to track progress. Goals should be achievable, relevant to your business objectives, and have clear deadlines. Key Performance Indicators (KPIs) such as sales revenue or net promoter scores are crucial for monitoring success. Regularly reviewing and adjusting these goals and metrics is key to staying aligned with your business's evolving needs.

To optimize the collection, storage and analysis of multichannel retail data, it is critical to integrate data collection systems across all customer touchpoints. Data must be stored in a centralized and secure location, facilitating access and analysis. Using advanced analytical tools to extract relevant and actionable insights is essential. Additionally, it's important to regularly monitor market trends and customer behavior, adjusting strategies as needed to remain agile and competitive in the ever-changing retail environment.

Channel Voltaire --- don't let perfect be the enemy of good. Technology is the enabler, not the objective in and of itself. Don't get distracted by the pursuit of a perfect solution - it doesn't exist. Better to get going, learn, and refine, than to be stuck in a 3 year tech infrastructure initiative that doesn't deliver meaningful business impact...impact that your CFO can see and believe.

Some of the best Platform's I've used Collibra: Best for reference data management. It provides a wide range of capabilities, including data cataloging, data quality management, data lineage tracking, and data privacy and compliance. Oracle: A comprehensive data management platform. Tableau: Known for unified data management and analytics. Google Cloud Platform: Ideal for existing GCP users. Profisee: Best for managing large volumes of data. Hevo Data: Ideal for digital transformation. Ataccama ONE: Best for master data management. Panoply: Suitable for small to medium-sized businesses. Azure Data Factory: Best for data transformation.

- Selecting the appropriate tools and platforms for multi-channel retail data management is crucial. It involves assessing factors like scalability, security, compatibility, functionality, and cost to ensure they align with your needs and budget. Compatibility and integration capabilities are also vital to ensure seamless operation with existing systems. By carefully choosing the right tools and platforms, you can effectively collect, store, and analyze data across multiple channels, enabling informed decision-making and optimizing business performance.

Governance & quality are always difficult to develop, manage & measure. Given the number of systems, teams & users, I have found that simplicity is of the upmost importance. It is best to start with the most commonly known & easily defined pieces of information to document (and provide examples) as you begin this journey. It is also a great opportunity to drive clarity in the business around what pieces of information actually are. As the process evolves, the documentation becomes a reference for the business to increase it's knowledge.

- Implementing data governance and quality standards is essential for effective management of multi-channel retail data. Establishing clear policies, procedures, and guidelines ensures consistency, accuracy, and reliability of data across all channels. It involves defining roles and responsibilities, data ownership, access controls, and data lifecycle management processes. Regular audits and monitoring mechanisms help maintain data integrity and compliance with regulatory requirements. By adhering to data governance and quality standards, retailers can trust their data for making informed decisions, enhancing customer experiences, and driving business growth.

Segmentation: RFM (Recency, Frequency, Monetary) segmentation is a popular method in retail. It segments customers based on their purchase history, how recently they made a purchase, how often they make purchases, and how much they spend. Segment customers based on demographic data like age, gender, location, etc. Segment customers based on their behaviour, such as browsing history, purchase history, product preferences, etc. Personalise customer experiences across all channels. This involves delivering consistent and relevant experiences based on the customer’s unique needs and preferences. Use customer data to recommend products that are most likely to interest each customer.

Tell a relatable and compelling story. Don't simply report on the 'What' as in 'what happened'...do the critical thinking and creative solutioning work to be able to explain the 'So What (does it all mean)' and 'Now What (do I do about it).' What/So What/Now What.

Data has to be communicated clearly and precise to all who have a relevant need for it. It has to be easily accessed but also well protected from unrelevant participants who might misuse this data unethically.

Putting the data to work and testing the hypotheses that derive from it allows marketers to know their target audiences better and to forge a more personalized relationship with them.

Adopt a culture of experimentation where you continuously test hypotheses and learn from the outcomes. Use your data to try new marketing tactics, product recommendations, or customer engagement strategies, and measure their effectiveness against your defined KPIs.

Loyalty programs are fantastic vehicles to collect and use zero-party customer data. If your program has a point mechanic, consider rewarding members for sharing data that will help you make your marketing more personalized and relevant. Take SEPHORA's recent Passport to Beauty and Ready, Set, Sephora activations. They reward Beauty Insiders with points for in-store Color IQ shade matching, or completing an online beauty routine quiz. Other great examples include Hungryroot's customer onboarding. Customers fill out a robust questionnaire on their nutrition goals and preferences. It's clear how these activations will help the brand improve the member's experience, and so members are happy to share it.

Handling multi-channel retail data presents several challenges that organizations need to address: Data Integration Complexity: Diverse Data Sources: Retailers collect data from various channels such as online stores, physical stores, mobile apps, social media, and more. Integrating data from these disparate sources can be complex. Data Formats and Schemas: Each channel may use different data formats, schemas, and APIs. Harmonizing this data for analysis is challenging. Data Quality and Consistency: Inconsistent Data: Data inconsistencies across channels (e.g., product names, customer IDs) can lead to inaccurate insights. Duplicate Records: Duplicate customer records or product entries can skew analytics results.