Managing Customer Data Efficiently

If your CX Program simply consists of surveys, it's like trying to understand the whole movie by watching a single frame. You have to integrate data, insights, and actions if you want to understand how the movie ends, and ultimately be able to write the sequel. But integrating multiple customer signals isn't easy. In fact, it can be overwhelming. I know because I successfully did this in the past, and counsel clients on it today. So, here's a 5-step plan on how to ensure that the integration of diverse customer signals remains insightful and not overwhelming: 1. Set Clear Objectives: Define specific goals for what you want to achieve. Having clear objectives helps in filtering relevant data from the noise. While your goals may be as simple as understanding behavior, think about these objectives in an outcome-based way. For example, 'Reduce Call Volume' or some other business metric is important to consider here. 2. Segment Data Thoughtfully: Break down data into manageable categories based on customer demographics, behavior, or interaction type. This helps in analyzing specific aspects of the customer journey without getting lost in the vastness of data. 3. Prioritize Data Based on Relevance: Not all data is equally important. Based on Step 1, prioritize based on what’s most relevant to your business goals. For example, this might involve focusing more on behavioral data vs demographic data, depending on objectives. 4. Use Smart Data Aggregation Tools: Invest in advanced data aggregation platforms that can collect, sort, and analyze data from various sources. These tools use AI and machine learning to identify patterns and key insights, reducing the noise and complexity. 5. Regular Reviews and Adjustments: Continuously monitor and review the data integration process. Be ready to adjust strategies, tools, or objectives as needed to keep the data manageable and insightful. This isn't a "set-it-and-forget-it" strategy! How are you thinking about integrating data and insights in order to drive meaningful change in your business? Hit me up if you want to chat about it. #customerexperience #data #insights #surveys #ceo #coo #ai

"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

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.

If you feel like your business operations are feeling sluggish, listen up... Odds are quite high poor data management could be part of the problem. In my experience, companies that struggle with inefficiencies often overlook how their data is being handled. If you want to streamline operations, you must take control of your data. Here are 3 actionable steps to get started: 1️⃣ 𝗖𝗼𝗻𝘀𝗼𝗹𝗶𝗱𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 Many businesses store data in silos across different departments. The result? It’s hard to get a clear picture of what’s really going on. The solution is to integrate your data into a central platform. This will eliminate redundancy and create a single source of truth that everyone can access. 𝗔𝗰𝘁𝗶𝗼𝗻 𝘀𝘁𝗲𝗽: 𝘈𝘶𝘥𝘪𝘵 𝘺𝘰𝘶𝘳 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘥𝘢𝘵𝘢 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘵𝘰 𝘪𝘥𝘦𝘯𝘵𝘪𝘧𝘺 𝘸𝘩𝘦𝘳𝘦 𝘺𝘰𝘶 𝘩𝘢𝘷𝘦 𝘥𝘶𝘱𝘭𝘪𝘤𝘢𝘵𝘦 𝘰𝘳 𝘪𝘴𝘰𝘭𝘢𝘵𝘦𝘥 𝘥𝘢𝘵𝘢 𝘴𝘰𝘶𝘳𝘤𝘦𝘴. 2️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗱𝗮𝘁𝗮 𝗲𝗻𝘁𝗿𝘆 𝗮𝗻𝗱 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 Manual data entry is not only slow but also prone to error. Automation tools can help you capture, process, and organize data in real-time. This frees up your team to focus on higher-value tasks. 𝗔𝗰𝘁𝗶𝗼𝗻 𝗦𝘁𝗲𝗽: 𝘐𝘥𝘦𝘯𝘵𝘪𝘧𝘺 𝘳𝘦𝘱𝘦𝘵𝘪𝘵𝘪𝘷𝘦 𝘥𝘢𝘵𝘢 𝘦𝘯𝘵𝘳𝘺 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘦𝘴 𝘪𝘯 𝘺𝘰𝘶𝘳 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘢𝘯𝘥 𝘦𝘹𝘱𝘭𝘰𝘳𝘦 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯 𝘴𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘭𝘪𝘬𝘦 𝘡𝘢𝘱𝘪𝘦𝘳 𝘰𝘳 𝘗𝘰𝘸𝘦𝘳 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘦 𝘵𝘰 𝘴𝘵𝘳𝘦𝘢𝘮𝘭𝘪𝘯𝘦 𝘵𝘩𝘦𝘮. 3️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝘂𝗽-𝘁𝗼-𝗱𝗮𝘁𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 It's essential to work with the most current data to make well-informed decisions. Ensure your analytics are refreshed regularly to give you accurate, up-to-date insights. This allows you to respond to changes and make decisions based on the latest available data, improving your business agility. 𝗔𝗰𝘁𝗶𝗼𝗻 𝘀𝘁𝗲𝗽: 𝘚𝘦𝘵 𝘶𝘱 𝘢 𝘴𝘪𝘮𝘱𝘭𝘦 𝘥𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥 𝘪𝘯 𝘵𝘰𝘰𝘭𝘴 𝘭𝘪𝘬𝘦 𝘗𝘰𝘸𝘦𝘳 𝘉𝘐 𝘰𝘳 Looker 𝘵𝘰 𝘵𝘳𝘢𝘤𝘬 𝘺𝘰𝘶𝘳 𝘮𝘰𝘴𝘵 𝘤𝘳𝘪𝘵𝘪𝘤𝘢𝘭 𝘮𝘦𝘵𝘳𝘪𝘤𝘴 𝘪𝘯 𝘳𝘦𝘢𝘭 𝘵𝘪𝘮𝘦. TLDR: Streamlining your operations starts with managing your data effectively. The more accessible and accurate your data is, the faster you can make informed decisions. P.S. What’s the biggest data challenge your business is currently facing? Let me know in the comments!

Did you know dealerships use, on average, 9 systems that require customer data? And most don’t talk to each other. Every time your data is exported to a vendor, here’s what happens. ❌ You lose control. ❌ Your data becomes stale. ❌ You increase your risk of breaches. Meanwhile, you’re left managing fragmented systems that don’t give you a clear picture of your customers or inventory. Sound frustrating? It is. QoreAI fixes this. ✅ Seamless integration: Connect all your tools—DMS, CRM, inventory, marketing platforms. ✅ External data enhancement: Add Equifax data and predictive AI models for precision targeting. ✅ No more silos: Everything works together seamlessly. The result? Proactive strategies that give you answers like this and more. • "Who’s in-market for a car this month?" • "Who declined brakes and tires last week?" Dealerships spend thousands of dollars moving data between systems. Imagine if you could put that money to work analyzing and enhancing your data instead. How many systems does your dealership rely on every day? Imagine if they worked together. Let’s discuss how to make that happen. 👇 #QoreAI #DataIntegration #AutomotiveTechnology #CustomerInsights #DealershipSolutions #AIinAutomotive #DataManagement #BusinessEfficiency #PredictiveAnalytics #SiloBusting #EnhancedTargeting

Are you using Customer Insights to advance your business? Every company has data, but let’s be honest— data alone won’t transform your business. I often see channel partners sitting on valuable insights, yet struggling to turn that information into impactful decisions. Why? Here’s what I’ve learned works best: First, assign someone on your team to own data management. When it’s everyone’s job, it’s no one’s job. A dedicated data lead ensures your information is consistently updated and enriched beyond basic demographics, translating insights into actionable strategies. Next, prioritize data quality over quantity. It sounds boring, I know, but it’s critical. I had a client ready to launch a new offer, only to discover their database was a mess—full of info@ email addresses. Imagine losing momentum because of something so basic. Clean data isn’t glamorous, but it’s the foundation for any effective strategy. Finally, don’t overlook the resources your IT vendor provides. If you’re a Microsoft partner, you have access to Cloud Ascent for propensity data — use it! I’ve seen too many partners miss out on golden opportunities simply because they weren’t tapping into the tools available to them. And if you’re stuck or don’t know where to begin, I can help connect you with our trainers and analysts to get you started.

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.