Operationalizing Customer Analytics with Azure and Power BI
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Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
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Operationalizing Customer Analytics with Azure and Power BI
- 1. Operationalizing Customer Analytics October 18, 2017
- 3. CONVERGENCE CONSULTING GROUP We help companies improve business performance through the use of Data & Analytics I would not only recommend CCG to any company, but question why you would engage with anyone but CCG. - Director of Customer Success DATA ANALYTICS STRATEGY
- 4. DRIVING CUSTOMER LOYALTY WITH AZURE MACHINE LEARNING – WATCH ON DEMAND Webinar Series OPERATIONALIZING CUSTOMER ANALYTICS WITH AZURE + POWER BI – TODAY, OCT. 18TH 2017 BUILDING A SCALABLE CUSTOMER ANALYTICS HUB – WED, NOV 8 | 11AM go.ccgbi.com/cloudwebinars.html
- 5. John Bastone Dir. of Customer Analytics, CCG Mike Druta Solutions Engineer, CCG SPEAKERS
- 6. Customer Analytics 101 Operationalizing Analytics Technology Solutions Demonstration Q & A
- 7. Introduction to Customer Analytics
- 8. Challenges We See People Process Technology Data Small Staff Specific to analytics supporting a large scale audience “Finance, Planning and Allocation, Merchandising, Wholesale and Marketing all have analysis needs to support” BI Evaluation Numerous choices of reporting and analysis capabilities “Reporting is clunky, requiring us to build queries and views to make use of it” Disintegration Data sources spread across the organization “We have data spanning, ERP, Point of Sale, eCommerce and Digital residing in different places” More Freedom For line of business to create their own reports “We have 4 different teams running against the same data set, but interpreting it in different ways”
- 9. Evaluate how different people will want to consume information Anticipate demands by implementing processes for triaging requests & questions Prioritize use cases, create reference architecture, and maximize technology investments Prioritized business value should shape your data strategy & architecture Business Value should be evident within 3 months, iterative & high impact. What Is Needed – Analytics That Drive Business Value Whether you are looking at technology investments, data management or advanced analytic solutions, every initiative must drive towards business value.
- 10. Where Companies Are Betting On A Payoff 41% of organizations increasing investments in customer analytics this year Analytics Investment
- 11. 9 Key Analytic use cases every organization should be practicing Optimizing strategies around the acquisition, growth and retention of customers. Acquisition Analytics - Where can I best find customers that will drive an inordinate amount of value? Next Best Offer - What is the best offer I can personalize for each customer, as the basis of my next interaction with that customer? Churn Modeling - Who among my customers are at greatest risk of leaving me for a competitor in the next 30 days? Providing deeper insights on how customer perceive every interaction with your business, beyond just using purchase data. Cross-channel analytics - What are the individual channel preferences each customer has for how they prefer to be engaged? Text Analytics - What is the customer sentiment and immerging topics shared across social, chat and web channels specific to our company and competitors? Marketing Optimization - Which aspects of my digital strategy resonate best with the customers that matter most? Developing a more holistic understanding of how a customer engages with every touchpoint of your business over time. Omnichannel Analytics – Where are you interacting with this customer? What is the most valuable channel? Customer Journey Mapping - What are the moments of truth in the sales funnel have the greatest impact on the conversion of that prospect to a customer? Customer Lifetime Value - What can I expect the customer lifetime value and associated actions to be for differing customer segments? Customer Loyalty Customer Experience Customer Journeys
- 12. 1. Analyze 2. Automate 3. Scale 4. Start all over. Now that analysts are pulling from an enriched, robust data set, can produce more valuable insights to reapply in the database. 3. Providing access to all and adding more data sources requires an enterprise grade storage engine, of which there are many options. 1. This short-term phase focuses on data exploration. Often the work of a single analyst or data scientist to find out something about your customer’s loyalty, journey or experience that you did not know before. Stair Step Approach to Customer Analytics 2. Create processes to remove the need for manual work, socialize the outcomes, and make customer insights actionable. This medium-term phase focuses on automation, as well as data and predictive modeling. 4. Rinse & Repeat…
- 13. Operationalizing Customer Analytics A Practical Approach
- 14. Desktop ML –> Cloud ML Load Data Prepare Data Build & Train Models
- 15. Process Non-sustainable - Handling multiple predictions is untenable Repetitive - Manual work - Prone to user errors Data Siloed Data - Not ready for consumption by a larger audience - Missing attributes Quality Issues - Timeliness - Accuracy Current Set of Challenges People Specialized Skills - Data Scientist - Integration Specialist Resource Allocation - Competing priorities
- 16. Governance Data Quality Data Security Single version of the truth Next Step: Operationalizing Analytics Automation Programmatically invoke predictive models Real-Time or Batch Include Predictive Analytics into your ETL processes Dashboards Data Exploration Data Visualization Self-Service Analytics
- 17. Practical Use Case: Review and Solution Architecture Telco Dataset - Customer Demographics (Gender, Marital Status, etc.) - Products sold (Phone line, Internet Service, etc.) - Transaction Aggregates (Monthly/Total Charges, etc.)
- 18. Demo Part 1 - Demographics and Product Mix - Data Exploration in Power BI Part 2 - Deploy and Run the Churn Model Part 3 - Making Predictive Model Insights Actionable using Power BI
- 19. Key Takeaways Using Power BI to get a better understanding of data and customer Azure ML differentiator: enterprise grade deployment and management Don’t just predict an outcome: take action on it before your business environment changes. “You never know who's swimming naked until the tide goes out.” Warren Buffett
- 20. What’s Next Scale for the enterprise Increase data volumes Lowest grain transactions Historical data Add more data sources Multiple systems (ERP, CRM, POS, etc.) Social data Web logs Solve issues related to volume, variety, and velocity Increase analytical capabilities BUILDING A SCALABLE CUSTOMER ANALYTICS HUB WED, NOV 8 | 11AM
- 21. Watch the full version of this presentation on www.ccgbi.com/resources Watch the Full Webinar
- 22. THANK YOU! www.ccgbi.com | 813.265.3239 | [email protected]