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Customer 360


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As the strategic importance of data has increased, new approaches to customer analytics have emerged as well. As customer interactions with companies grow and diversify, the need to integrate data faster and deliver real-time insights is critical. This presentation explores the underlying trends driving companies to become more data-driven and invest in customer analytics. And, it outlines three types of approaches to capturing, managing, analyzing, and activating customer knowledge and insights.

Published in: Marketing
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Customer 360

  1. 1. Customer 360 Understanding Your Customers In The Digital Era
  2. 2. 2 Contents • Background • Why Customer Data Matters • Data Warehousing and Big Data • Data Blending Solutions • Data Management Platforms • The Road Ahead
  3. 3. 3 Background Moving from “Mad Men” to “Math Men” ‣ When most people think of marketing and advertising, they think of the archetype of the Mad Men era ad agency ‣ But with surprising speed, the rise of digital media (and the accompanying explosion of customer data) has revolutionized marketing
  4. 4. Why Customer Data Matters Power is in the hands of the customer • We are in the Age of the Customer • Customers have more power, choice and influence than ever before • What we think and feel about our interaction with an organization’s products and services is increasingly important due to the rise of social media • Consumer behavior has shifted dramatically in recent years: how we research, evaluate, purchase, and engage with brands has changed 4
  5. 5. 5 Rapid Shifts In Customer Behavior In recent years, most people have changed… How they watch TV How they research What they expect How they communicate How they shop on-demand via NetFlix, Amazon, HBO GO, etc anywhere and anytime using smartphones and tablets based on experiences with Apple, Amazon, Trader Joes, etc using social media; Facebook; Pinterest “Showrooming” and buying it cheaper online
  6. 6. THIS IS A DEAL BIG 6
  7. 7. 7 An explosion of new devices
  8. 8. An explosion of touchpoints 8
  9. 9. An explosion of content 9
  10. 10. Quantity of global digital data, exabytes 130 1,227 2005 2010 2,720 2012 7,910 2015 Source: EMC/IDC Digital Universe Study, 2011 An explosion of data 10
  11. 11. Farewell Funnel During the Mad Men era, the purchase journey was more predictable and linear Leads Prospects Customers 11
  12. 12. Hello Decision Journey Today, the consumer decision journey is non-linear, multichannel, and consumer-driven Leads Online Prospects Purchase Review Online Chat Ask FB Friends Online Search Banner Ad Store Visit View Video Purchase 12
  13. 13. 56% of customer interactions happen during a multi-channel, multi-event journey Source: McKinsey & Co. 13
  14. 14. Business Outcomes Understanding customers and customers decision journeys helps companies drive significant business outcomes Marketing & Advertising Customer Service Retention & Loyalty Customer Experience CSAT Scores ROMI Waste Call Reduction Cross Sell Churn 14
  15. 15. 15 Customer Data Companies have access to lots of data that can help them understand their customers and customer decision journeys Online Review Online Chat Ask FB Friends Online Search Banner Ad Store Visit View Video Purchase Social Media Mobile Retail Survey Call Center Purchase Chat Web Branch
  16. 16. 16 The Challenge More often than not, customer data is fragmented and locked away in physical and organizational silos Social Media Retail Mobile Purchase Call Center Survey Chat Web Branch MARKETING CUSTOMER SERVICE SALES
  17. 17. 17 Customer Analytics New approaches have emerged to help companies unlock and analyze their customer data Data Warehousing Solutions Data Blending Solutions Data Management Platforms (DMP) traditional, batch-oriented ETL data integration for reporting and analysis real-time blending or mashing of data from different sources for analysis platform to collect, organize and activate audience data from any source; integrated with execution systems
  18. 18. 18 Data Warehouse Traditional approach to integrating data for consistency and quality • For many years, traditional business intelligence and data warehousing technologies and approaches have been used to capture and analyze customer data. • Beginning in the 1990s, companies pulled data from their transactional systems into separate, centralized data warehouses to support reporting and analysis. • The typical extract-transform-load (ETL)-based approach to data warehousing captures data housed in disparate source data systems, transforms the data, and then moves it into the data warehouse, where the data is arranged in a way to help facilitate access. • By centralizing data in the warehouse, companies could create a "single version of the truth" and avoid the errors and discrepancies that often plagued them when reports were created from various transactional and source data systems.
  19. 19. 19 Data Warehouse Traditional approach to integrating data for consistency and quality Data Sources Data Cleansing Data Warehouse Example Use Cases ETL CRM ERP Operational System Reporting Analytics Flat File Data Mining
  20. 20. 20 Data Warehouse Challenges The explosion of data has strained the traditional approach & technologies • Explosion of data, particularly unstructured data, generated in recent years has strained the traditional data warehousing approach and underlying technologies • The foundational infrastructure of data warehousing has been the relational database, which stores data into tables (or "relations") of rows and columns and is used for processing structured data. • As the volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources) of data has increased, relational databases often aren't able to provide the performance and latency needed.
  21. 21. 21 Evolved Data Warehousing Next generation approaches and technologies for big data analytics Cloud Computing Big Data Technologies Data Visualization Cloud computing decreases cost of computing resources and creates agility. Resources spun up and shut down quickly and easily. Big data technologies support greater variety, volume, and velocity of data. They also speed the time it takes to mash up different data sets. Data visualization provides user-friendly visual analysis and helps decision makers move from insight to action.
  22. 22. Data Blending Approach to blending data from different sources for analysis 22 • Historically, analysts used tools like Microsoft Excel or Access in situations where they needed to analyze data not available in the data warehouse. • But, in recent years a new type of solution, data blending (also sometimes referred to as data discovery), has emerged. • Using data blending tools, analysts themselves can access, cleanse, and blend data from multiple sources without having to write a line of code. • These tools allow customer data to be blended together from multiple internal sources as well as external sources immediately to support a more agile approach to customer analytics. • This is increasingly important because if companies know what their customers are doing better than their competitors, or can get to those insights faster, then they have a very distinct advantage.
  23. 23. Data Blending Approach to blending data from different sources for analysis Internal Data Sources CRM ERP Operational System Flat File External Data Sources Data Blending Market & Customer Data Example Use Cases Reporting Analytics Data Mining 23
  24. 24. 24 Data Management Platforms Approach to collecting, organizing and activating customer data • A DMP allows companies to centralize data, both their own online and offline data as well as third party data, and use it to create target audiences and optimize their online advertising. • Using a DMP, companies can measure how campaigns perform for different customer segments and optimize their media buys and creative elements over time to improve effectiveness. • DMPs differ from data warehouses since they more provide more rapid data integration and are tied to execution systems, such as digital ad execution, content management and marketing automation systems. • DMPs are optimized to allow marketers to define target audiences and then activate campaigns to reach those prospects and customers.
  25. 25. 25 Data Management Platforms Approach to collecting, organizing and activating customer data Internal Data Sources Display (Ad Server) Web Analytics CRM External Data Sources Data Management Platform Market & Customer Data Example Use Cases Targeted Display Advertising Email/ Inbound Campaigns Email Database Ad Execution Mktg Automation Advanced Customer Analytics
  26. 26. 26 DMPs Support Demand-Side Platforms DMPs support programmatic approaches to targeting specific audiences Marketers Demand-Side Platforms & Data Management Platforms Exchanges (Supply-Side Platforms) Publishers (Websites) Audiences DSP/ DMP DSP/ DMP DSP / DMP DSP/ DMP DSP/ DMP DSP/ DMP DSP/ DMP
  27. 27. 27 How Do They Compare? Each approach has benefits and limitations Data Warehousing Data Blending Data Management Platforms Benefits Limitations • Integrated data to provide a “single version of the truth” for reporting and analytics • Minimizes any performance impact to operational systems • Long cycle times to integrate new data sources • Business user-driven approach • Speeds time to integrate and analyze new data sources • Increases risk of data quality issues due to user errors • May impact performance of operational systems • Enables real-time activation through integration with execution systems • Speeds time to integrate and analyze new data sources • Bringing offline data online results in data loss
  28. 28. The Road Ahead Your 90 Day Plan: Recommendations to Consider • Customer analytics is not about the data or technology, but about the business decisions that the insights enable. • Customer insights have maximum value when the focus is on real-time insights connected with front-line execution. • Many customer insights can be found by mashing up different data pools. But, it is important to begin with whatever data is available today. • The best approach is business question or hypothesis-driven. Often the biggest challenge is to follow the 80-20 rule and identify the 20% of the data that provides the right insights. • Where possible, begin with simple and then evolve to more sophisticated approaches. For example, is it possible to approach early attempts at multi-channel, multi-touch marketing attribution with heuristic approaches? Can you begin predictive modeling using simple, linear regression models that are easy to understand and implement? • Keep people, your prospects and customers, constantly in mind in terms of improving their experience and meeting their needs and expectations. • Don't just focus on customer acquisition and retention data. There is additional value in insights derived from the full life cycle of prospect and customer touchpoints. 28
  29. 29. The Road Ahead (cont.) Your 90 Day Plan: Recommendations to Consider • Gain an outside perspective. Consultancies can help provide an assessment of where you are today and recommend roadmaps and best practices based on their experience with other clients. • Rather than approach customer analytics in terms of a single business use case, consider a full range of uses when determining appropriate levels of investment and communicating the full strategic value. • Make learning and talent development a key part of the agenda. • Take an agile, iterative approach to managing, analyzing and activating data. • Approach customer analytics as a journey rather than a one-time project. Most companies require cultural, organizational and process change to become more data-driven--not just a new data store or technology--and this evolution takes time. • Success with transforming to data-driven marketing also requires executive support and involvement. Persuade senior executives to champion and support these efforts.Let me know what’s working in your workplace 29
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  31. 31. 31 Contact Me ! @DaveBirckhead