Managing Customer Data Efficiently

"𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐘𝐨𝐮𝐫 𝐒𝐢𝐧𝐠𝐥𝐞 𝐕𝐢𝐞𝐰 𝐨𝐟 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫" In today's data-driven business landscape, developing a single view of customer (SVC) is no longer a luxury - it's a necessity. But where do you start on this complex journey? Let's break it down: 🔹 𝐃𝐞𝐟𝐢𝐧𝐞 𝐘𝐨𝐮𝐫 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬: Begin by clearly articulating what you hope to achieve with your SVC. Is it to enhance personalisation, improve customer service, or drive more effective marketing? Your goals will shape your strategy. 🔹𝐀𝐮𝐝𝐢𝐭 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐒𝐨𝐮𝐫𝐜𝐞𝐬: Take stock of all your customer data touchpoint - CRM systems, marketing platforms, sales data, customer service interactions, etc. Understanding what data you have and where it resides is crucial. 🔹𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: Before you start consolidating data, ensure you have robust governance policies in place. This includes data quality standards, privacy protocols, and compliance measures. 🔹𝐂𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲: Select a platform that can integrate your various data sources and provide a unified view. This could be a Customer Data Platform (CDP) or a custom-built solution, depending on your needs. 🔹𝐒𝐭𝐚𝐫𝐭 𝐒𝐦𝐚𝐥𝐥, 𝐒𝐜𝐚𝐥𝐞 𝐆𝐫𝐚𝐝𝐮𝐚𝐥𝐥𝐲: Begin with a pilot project focusing on a specific segment or use case. This allows you to test your approach and demonstrate value before scaling up. 🔹𝐅𝐨𝐬𝐭𝐞𝐫 𝐂𝐫𝐨𝐬𝐬-𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: SVC isn't just an IT project—it requires buy-in and input from marketing, sales, customer service, and other departments. Create a cross-functional team to drive the initiative. 🔹𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐬𝐞 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲: Implement processes for data cleansing, deduplication, and ongoing data maintenance. Poor data quality can undermine even the best SVC strategy. 🔹𝐏𝐥𝐚𝐧 𝐟𝐨𝐫 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭: Your SVC strategy should evolve with your business. Regularly review and refine your approach based on new data sources, changing customer behaviors, and emerging technologies. Building a single view of customer is a journey, not a destination. It requires ongoing commitment and investment, but the payoff in terms of improved customer experiences and business outcomes can be substantial. Are you on the journey to developing a single view of customer? What challenges have you encountered, and what strategies have you found effective? #CustomerData #DataStrategy #SingleViewOfCustomer #CustomerExperience

In my previous post, I explored the hidden costs of data silos. Today, I want to share practical steps that deliver value without requiring immediate organisational restructuring or technology overhauls. The journey from siloed to integrated data follows a maturity curve, beginning with quick wins and progressing toward more substantial transformation. For immediate progress: 1) Identify your "golden datasets": Focus on the 20% of data driving 80% of decisions. Prioritise customer, product, and financial datasets that cross departmental boundaries. 2) Create a simple business glossary: Document how terms differ across departments. When Finance defines "revenue" differently than Sales, capturing both definitions creates transparency without forcing uniformity. 3) Implement read-only integration patterns: Establish one-way flows where analytics platforms access source data without disrupting existing systems. These connections create cross-silo visibility with minimal risk. 4) Build a culture of trust: Reward cross-departmental collaboration. Create incentives that make data sharing a path to recognition rather than a threat to influence or expertise. 5) Establish cross-functional data forums: Host regular meetings where data users share challenges and use cases, building relationships while identifying practical integration opportunities. As these initiatives gain traction, organisations can advance to more substantial approaches: 6) Match your approach to complexity: Smaller organisations often succeed with centralised data management, while larger enterprises typically require domain-centric strategies. 7) Apply bounded contexts: Map where business domains have distinct needs and terminology, creating clear translation points between areas like Sales, Finance, and Operations. 8) Adopt a data product mindset: Designate product owners for critical datasets who treat data as a product with clear consumers and quality standards rather than simply an asset to be stored. 9) Develop a federated metadata approach: Catalogue not just what exists, but how data relates across domains, making relationships between siloed systems explicit. 10) Maintain disciplined data modelling: Well-structured data within domains makes integration between them far more manageable, regardless of your architectural approach. This stepped approach delivers immediate value while building momentum for more sophisticated strategies. The most successful organisations pair technical solutions with cultural transformation, recognising that effective data integration is ultimately about people collaborating across boundaries. In my next post, I'll explore how governance models evolve with data integration maturity. What approaches have you found most effective in addressing data silos? #DataStrategy #DataCulture #DataGovernance #Innovation #Management

We've all heard the old saying in advertising: "I know half of my advertising is working, I just don't know which half." For too long, that’s been the reality for many in automotive retail. We spend money on mass marketing to databases filled with dirty, outdated customer data. This leads to wasted ad spend, irrelevant messages, and a frustrating experience for our customers. We've learned that you don't need more leads. You need a cleaner process to get more out of the opportunities you already have. That's the power of a Customer Data Platform (CDP). Our journey with a CDP was about getting "unstuck" from old habits. The first critical step? Getting our data clean and establishing a single source of truth. We found that 52% of our customer data was dirty in some way, full of bad addresses, outdated phone numbers, and sold vehicles. By simply cleaning and enriching our data, our advertising started working more effectively almost instantly. Now, with our CDP, we're not just waiting for a lead form to show up. We’re engaging with customers in real time. We know when a shopper starts filling out a service scheduler or a trade-in form and abandons it. With this information, we can send a personalized, automated message to help them finish the process. The results from this single use case were immediate. This system is changing our business. We’ve seen our sold in timeframe rate jump to 30%, which is more than double the national average of 12.4%. By focusing on a better, cleaner process, March and April were two of the best months in our company's history...a feat that has never happened before. The goal is to control the experience, not just react to it. It's about moving from mass marketing to micro audiences, delivering the right message, at the right time, to the right person. What's the one "dirty data" problem that frustrates you the most? #wearerohrman

Could your team answer this today, and how long would it take? "How many of the customers who... - Signed up through our holiday marketing campaign in Q4 - On the annual family plan - paying with a Chase-issued card Had their first renewal attempt declined for ‘insufficient funds’, ? And of those... - How many were successfully recovered within 7 days - Versus how many ended up churning within 30 days?" An organization that can answer these types of questions quickly and accurately can react quickly, which results in happier customers and satisfied executives. At its core, payments optimization is only as good as the join between internal business data and transaction data. PSPs, networks, and banks give you a rich stream of authorization, settlement, and dispute data, but merchants must bring their own customer and product context in order to produce actionable insights that are relevant to their business. To make this data usable, merchants need to normalize a few key dimensions: A. Customer-level data - Unique customer identifier that persists across systems (CRM, billing, payments, support). - Cohort tags: acquisition channel, geography, subscription tier, customer tenure, annual vs monthly - Payment Event stage: Card verification, free-to-paid trial conversion, non-trial conversion, renewal (1 to N) B. Subscription contract data - Plan/tier ID (standardized across billing and payments). - Start and end dates, renewal frequency, billing currency. - Status flags: active, paused, canceled (with standardized cancel reasons). C. Invoice/order-level data - Invoice ID (must be consistently mapped to payment transaction IDs). - Line items (tier, add-ons, discounts, tax). - Net and gross amounts, including refund/credit adjustments. D. Payment transaction data - Transaction ID (gateway/PSP ID). - Decline reason codes (normalized across acquirers/networks). - Payment method type, issuer BIN, network tokenization status. - Success/settlement status, fraud signals, retries, dispute lifecycle. E. Linking logic - A clean key structure: customer_id → subscription_id → invoice_id → transaction_id. - Consistent timestamping (UTC, ISO8601, normalized across systems). - Master data management (e.g., ensuring “Invoice 12345” in billing = “Payment 12345” in PSP). When structured this way, you can slice performance by cohort (e.g., “Trial-to-paid in LatAm declines more often on first attempt”), payment method (e.g., “BIN ranges in Southeast Asia fail more often at renewal”), or customer lifecycle (e.g., “Annual renewals have higher closed account declines than monthly”). Most merchants I meet with can't answer even a fraction of that first question. Not because the data doesn't exist, but because it isn't structured. If this sounds familiar, let's talk.

"𝗜 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗿𝗲𝗮𝗱 𝗮 𝗽𝗼𝘀𝘁 𝘄𝗵𝗲𝗿𝗲 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘀𝗮𝗶𝗱: '𝘑𝘶𝘴𝘵 𝘴𝘦𝘵 𝘶𝘱 𝘢𝘯 𝘈𝘞𝘚 𝘢𝘤𝘤𝘰𝘶𝘯𝘵, 𝘥𝘰𝘸𝘯𝘭𝘰𝘢𝘥 𝘢𝘭𝘭 𝘺𝘰𝘶𝘳 𝘥𝘢𝘵𝘢, 𝘶𝘱𝘭𝘰𝘢𝘥 𝘪𝘵 𝘵𝘰 𝘵𝘩𝘦 𝘈𝘞𝘚 𝘤𝘭𝘰𝘶𝘥, 𝘢𝘯𝘥 𝘮𝘰𝘷𝘦 𝘵𝘰𝘸𝘢𝘳𝘥 𝘢𝘯 𝘈𝘗𝘐 𝘸𝘩𝘦𝘯 𝘺𝘰𝘶 𝘤𝘢𝘯.' It sounds clean and simple. However, this is not how it works in the automotive industry or any other industry. And oversimplification advice like this is 𝘸𝘩𝘺 𝘴𝘰 𝘮𝘢𝘯𝘺 𝘥𝘦𝘢𝘭𝘦𝘳𝘴 𝘢𝘳𝘦 𝘴𝘵𝘶𝘤𝘬. Let’s break it down and shine a light on a bit of detail that was purposely overlooked and left out. 𝟭. "𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮" • 𝘍𝘳𝘰𝘮 𝘸𝘩𝘦𝘳𝘦? Most DMS/CRM systems don’t offer clean export paths, and the moment you get a snapshot, it’s already stale. • 𝘈𝘯𝘥 𝘪𝘯 𝘸𝘩𝘢𝘵 𝘧𝘰𝘳𝘮𝘢𝘵? Different systems use different schemas, naming conventions, and record structures. It’s a mess without mapping. • 𝘖𝘩, 𝘢𝘯𝘥 𝘭𝘪𝘤𝘦𝘯𝘴𝘪𝘯𝘨? Some platforms legally prohibit you from extracting customer data for centralized use without using the paid-for API. 𝟮. "𝗨𝗽𝗹𝗼𝗮𝗱 𝗶𝘁 𝗶𝗻𝘁𝗼 𝗔𝗪𝗦 𝗮𝗻𝗱 𝗺𝗼𝘃𝗲 𝘁𝗼𝘄𝗮𝗿𝗱 𝗔𝗣𝗜𝘀" • Dumping raw CSVs into a bucket isn’t data modernization. That’s just shifting the mess to a new container. • Real transformation comes from normalization, schema control, enrichment pipelines, and structured ingestion. • APIs are great, but what about real-time syncs, webhook management, identity resolution, and event-based logic? Dealers need more than a “toward” plan. 𝟯. "𝗖𝗹𝗲𝗮𝗻, 𝗲𝗻𝗿𝗶𝗰𝗵, 𝗮𝗻𝗱 𝗮𝗽𝗽𝗲𝗻𝗱 𝗶𝘁 𝘄𝗶𝘁𝗵 𝘃𝗲𝗻𝗱𝗼𝗿𝘀 𝗹𝗶𝗸𝗲 (𝗗𝗮𝘁𝗮 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗫)" • Cleaning is not a one-time thing. • Enrichment is worthless if your systems can’t use the fields. • And no vendor knows your customers better than you do. 𝘠𝘰𝘶𝘳 𝘪𝘯𝘵𝘦𝘳𝘯𝘢𝘭 𝘭𝘰𝘨𝘪𝘤 𝘮𝘢𝘵𝘵𝘦𝘳𝘴 𝘫𝘶𝘴𝘵 𝘢𝘴 𝘮𝘶𝘤𝘩 𝘢𝘴 𝘦𝘹𝘵𝘦𝘳𝘯𝘢𝘭 𝘢𝘱𝘱𝘦𝘯𝘥 𝘧𝘪𝘦𝘭𝘥𝘴. 𝗧𝗵𝗲 𝗳𝗮𝗰𝘁 𝗶𝘀 𝘁𝗵𝗮𝘁 𝗱ealers aren’t struggling because data is “too hard.” They’re struggling because the advice they’re receiving is either incomplete or incorrect. Data in automotive needs a system. One that organizes, enriches, 𝘢𝘯𝘥 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘢𝘤𝘵𝘪𝘷𝘢𝘵𝘦𝘴 data across departments. Not a Dropbox folder in the cloud. That’s what we’re building. And it’s working.

Square Enix, creators of iconic games like Final Fantasy and Dragon Quest, faced a colossal challenge: managing a mountain of player data. Think millions of quests completed, spells cast, and potions drunk. 🤯 With players scattered across different platforms and regions, their data was as chaotic as a random encounter. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦: 👉 Data Siloing: Square Enix faced the challenge of data scattered across various systems, platforms, and regions. This made it difficult to get a unified view of their customers. 👉 Data Quality: Ensuring data accuracy, consistency, and completeness was crucial for making informed decisions. 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: 👉 Data Ingestion: Gathering data from various sources like game consoles, mobile devices, websites, and in-game purchases. 👉 Data Cleaning and Standardization: Ensuring data quality by removing duplicates, inconsistencies, and errors. Standardizing data formats for consistency. 👉 Data Integration: Combining data from different sources into a single, unified view of the customer. This involves matching customer identities across different platforms. 👉 Data Enrichment: Adding additional data points to customer profiles, such as demographic information, purchase history, and in-game behavior. 👉 Data Activation: Leveraging the enriched customer data to personalize marketing campaigns, improve game experiences, and optimize customer journeys. They cast a powerful spell called Google Cloud! 🧙♂️ By combining Google Cloud's data analytics tools, Square Enix created a Customer Data Platform (CDP) that united their scattered data into a single, magical kingdom. 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐢𝐧𝐠 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐥𝐨𝐮𝐝: 👉 Data Storage: Using Google Cloud BigQuery for storing massive amounts of data. 👉 Data Processing: Employing Google Cloud Dataflow or Cloud Dataproc for data transformation and processing. 👉 Data Analytics: Utilizing Google Cloud BigQuery for advanced analytics and insights. The results? A party of epic proportions! 🎉 Square Enix improved player experiences, made smarter marketing decisions, and unlocked new revenue streams. It's like they found a hidden treasure chest filled with insights and opportunities. 💰 This isn't just about gaming; it's a masterclass in turning data into gold. Whether you're a fellow gamer or in a completely different industry, there's something to learn here. Let's chat about how we can level up our own data game! 🎮 More details here: https://lnkd.in/dYKdSkBu #dataanalytics #googlecloud #customerdataplatform #CDP #gamingindustry #squareenix #finalfantasy #dragonquest #cloudcomputing #data #bigdata #gaming #dataengineering #datascience #businessintelligence #marketing #gamingnews #tech #technology #innovation

Here’s how a fast-growing AI company turned a messy flood of 30,000 new contacts per month into a clean, reliable contact database that runs itself. Their key insight Manual data processes don’t scale. To manage contact data effectively, they had to move from UI-driven workflows to API-first automation - No Code used. This is the simple framework I use with clients facing the same challenge 1. Deep data audit Understand what you currently have.. Duplicates, missing fields, inconsistencies, formatting issues… Without a clear picture, every process built on this data will fail. 2. Targeted enrichment through API Decide which fields really matter to your business. Automate enrichment of those fields only. Less noise, more value. 3. Full integration with core systems Your CRM and marketing tools should always have clean, trusted data. Automate validation and enrichment inside those systems. No manual cleanup. No extra work. When you manage contact data this way, it becomes an asset, not a problem. If your team is still fighting messy lists, it might be time to rethink the process.

How should you structure your customer 360? Option 1: Create one row per customer with all attributes (e.g. name, age, address) and computed features (e.g. total page views, num_login_last_7_days, last_5_products_clicked, total_revenue_in_last_6_months) as columns. Option 2: Separate dimensions (customers) and facts tables (login_events, product_click_events) and let downstream users compute features ad-hoc. There’s no universal answer, but here are some considerations: 💾 Storage is cheap, compute is costly If you're referencing the same feature (e.g., last_5_product_clicked) multiple times in dashboards or marketing segments via rETL, it’s better to compute it once and store (cheap) than do a JOIN (costly) on every query. ⚡ Optimize with batch processing Computing features in batch instead of one at a time allows data teams to run multiple SQL queries in parallel, share intermediate results, and significantly reduce costs. 🛠️ Self-serve is great - if the team has the right skills Enabling business teams to self-serve features works only when they are tech savvy enough to do so. Feature computation can get tricky, particularly if ID stitching is required. 🧹 Handling dirty data is a universal challenge With messy data (like having multiple login events, e.g., login_ios_v1, login_android_v2), it's better to have data teams compute aggregates like total_login_last_7_days and make them available to business stakeholders. The ideal customer 360 structure balances efficiency, accessibility, and data quality – and empowers your organization with smart, fast decision-making capabilities.

50 Clean Records a Day / 1 Month = 1,500 Quality Records Ready That's the target. But let’s be real—prepping data for AI isn’t glamorous. It’s like cleaning the kitchen before cooking a great meal. To get there, you can: 1️⃣ Automate the grunt work with data validation rules and deduplication tools. 2️⃣ Dive into bulk updates using Salesforce’s Data Loader to keep things accurate and up-to-date. Just start with these daily tasks: ✅ Spot and merge duplicates that creep into your database (they’re sneaky!). ✅ Check the accuracy of critical fields—make sure emails are real, and phone numbers aren’t missing. ✅ Track missing info and create simple follow-ups to get the data filled in. Here’s what that looks like in action: 🔍 Example for Task 1: Use Salesforce’s Duplicate Rules to merge overlapping contacts and eliminate noise. 📧 Example for Task 2: Verify and update customer emails, flagging any bounce-backs for follow-up. 📝 Example for Task 3: Run a report on incomplete Account Owner fields and assign them to a rep for outreach. Stick with these habits, and you’ll build a clean, reliable database that makes your AI work smarter (not harder). As you progress, add in: 📊 Weekly health checks using Salesforce dashboards to monitor data quality (it’s like a fitness tracker for your CRM). ⚙️ Monthly automation tune-ups to catch and refine any gaps in your data processes. 🗂️ Quarterly data enrichment—tap into third-party tools for a deeper view of your customers. I've helped clients follow this exact Data Quality Playbook for 3 months, and it’s unlocked more accurate AI predictions, faster reporting, and happier end-users. This isn’t just a checkbox exercise—your AI is only as good as the data you feed it. Clean data is the key to making sure your AI doesn’t serve up the digital equivalent of burnt toast. 🥴💡 #Trailblazers #Salesforce #AI