How to Handle Customer Data in Experience Software

Here's the truth: You don't need a $250K CDP to get a unified customer view. Your customer data is trapped in silo'd platforms...CRM, email, loyalty, reviews, ecom/oms. But none of them talk to each other. You understand the value of unifying and OWNING your customer data, but implementation feels impossible with your limited budget and tiny tech/data teams. Start here: 1.) Document what you already have. List every platform containing customer data. You'll feel better making this first little step. 2.) Pick which system will be your foundation. Often your CRM, but could be your email platform if it has better engagement data. Don't try connecting everything at once. 3.) Use ETL tools, not a full CDP. Tools like Fivetran or Funnel let you authenticate with just your admin credentials. They'll pull your data into a warehouse YOU own for $1-2K monthly. 4.) Own your data. That means storing data in your warehouse. Google Cloud or Snowflake are user-friendly options for under $1k/mo for most mid-market brands. No more data trapped in vendor platforms. 5.) Start basic, get wins. Daily batch syncs of core customer data will solve 80% of your problems. Setup simple pipelines or (eek) scheduled queries to bring data together. Most companies overcomplicate this. They try integrating 20+ data sources simultaneously or request hourly syncs they don't need. Start with 3-5 critical sources syncing daily. 6.) Get help if you need it. There are plenty of partners and data consultants that do this for a living. You'll pay consulting rates, but this can be up and running in weeks vs. lengthy 6/12/18mo CDP timelines. The ROI on the data... - Truly understand customer LTV and retention - Build segmented audiences that sync across channels - Make decisions without having to "trust your gut" I've seen mid-market brands who've abandoned $500K CDP implementations for this approach and gotten better results with 80% less frustration. CFO will be happier. Your marketing team will be happier. What customer data sits trapped in your platforms today? Ps... Yes, I'm aware CDPs do a lot more than 1P data merging. Yes, there tons of other data vendors besides those mentioned. Yes, I know the costs above are wildy vague (iT dEpEnDs). #cdp #customeranalytics #moderndatastack

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.

SAP Customer Data security when using 3rd party LLM's SAP ensures the security of customer data when using third-party large language models (LLMs) through a combination of robust technical measures, strict data privacy policies, and adherence to ethical guidelines. Here are the key strategies SAP employs: 1️⃣ Data Anonymization ↳ SAP uses data anonymization techniques to protect sensitive information. ↳ The CAP LLM Plugin, for example, leverages SAP HANA Cloud's anonymization capabilities to remove or alter personally identifiable information (PII) from datasets before they are processed by LLMs. ↳ This ensures that individual privacy is maintained while preserving the business context of the data. 2️⃣ No Sharing of Data with Third-Party LLM Providers ↳ SAP's AI ethics policy explicitly states that they do not share customer data with third-party LLM providers for the purpose of training their models. ↳ This ensures that customer data remains secure and confidential within SAP's ecosystem. 3️⃣ Technical and Organizational Measures (TOMs) ↳ SAP constantly improves upon its Technical and Organizational Measures (TOMs) to protect customer data against unauthorized access, changes, or deletions. ↳ These measures include encryption, access controls, and regular security audits to ensure compliance with global data protection laws. 4️⃣ Compliance with Global Data Protection Laws ↳ SAP adheres to various global data protection regulations, such as GDPR, CCPA, and others. ↳ They have implemented a Data Protection Management System (DPMS) to ensure compliance with these laws and to protect the fundamental rights of individuals whose data is processed by SAP. 5️⃣ Ethical AI Development ↳ SAP's AI ethics policy emphasizes the importance of data protection and privacy. They follow the 10 guiding principles of the UNESCO ↳ Recommendation on the Ethics of Artificial Intelligence, which include privacy, human oversight, and transparency. ↳ This ethical framework governs the development and deployment of AI solutions, ensuring that customer data is handled responsibly. 6️⃣ Security Governance and Risk Management ↳ SAP employs a risk-based methodology to support planning, mitigation, and countermeasures against potential threats. ↳ They integrate security into every aspect of their operations, from development to deployment, following industry standards like NIST and ISO. SAP ensures the security of customer data when using third-party LLMs through data anonymization, strict data sharing policies, robust technical measures, compliance with global data protection laws, ethical AI development, and comprehensive security governance. #sap #saptraining #zarantech #AI #LLM #DataSecurity #india #usa #technology Disclaimer: Image generated using AI tool.