Customer 360

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<ul><li><p>Customer 360Understanding Your Customers In The Digital Era</p></li><li><p>2Contents</p><p> Background Why Customer Data Matters Data Warehousing and Big Data Data Blending Solutions Data Management Platforms The Road Ahead</p></li><li><p>3Background</p><p> When most people think of marketing and advertising, they think of the archetype of the Mad Men era ad agency </p><p> But with surprising speed, the rise of digital media (and the accompanying explosion of customer data) has revolutionized marketing </p><p>Moving from Mad Men to Math Men</p></li><li><p>Why Customer Data Matters</p><p> We are in the Age of the Customer Customers have more power, choice </p><p>and influence than ever before What we think and feel about our </p><p>interaction with an organizations products and services is increasingly important due to the rise of social media </p><p> Consumer behavior has shifted dramatically in recent years: how we research, evaluate, purchase, and engage with brands has changed</p><p>Power is in the hands of the customer</p><p>4</p></li><li><p>5Rapid Shifts In Customer BehaviorIn recent years, most people have changed</p><p>How they watch TV</p><p>How they research </p><p>What they expect</p><p>How they communicate</p><p>How they shop</p><p>on-demand via NetFlix, Amazon, </p><p>HBO GO, etc</p><p>anywhere and anytime using smartphones and </p><p>tablets</p><p>based on experiences with Apple, Amazon, </p><p>Trader Joes, etc</p><p>using social media; Facebook; Pinterest</p><p>Showrooming and buying it cheaper </p><p>online</p></li><li><p>THIS IS A DEALBIG6</p></li><li><p>7An explosion of new devices</p></li><li><p>An explosion of touchpoints</p><p>88</p></li><li><p>An explosion of content</p><p>9</p></li><li><p>Quantity of global digital data, exabytes</p><p>130 2005</p><p>1,227 2010</p><p>2,720 2012</p><p>7,910 2015</p><p>Source: EMC/IDC Digital Universe Study, 2011 </p><p>An explosion of data</p><p>10</p></li><li><p>Farewell FunnelDuring the Mad Men era, the purchase journey was more predictable and linear</p><p>Customers </p><p>Prospects</p><p>Leads</p><p>11</p></li><li><p>Hello Decision Journey</p><p>Purchase </p><p>Prospects</p><p>LeadsOnline Review</p><p>Ask FB Friends</p><p>Online Chat</p><p>Online Search</p><p>Store Visit</p><p>Banner Ad</p><p>View Video</p><p>Purchase</p><p>Today, the consumer decision journey is non-linear, multichannel, and consumer-driven</p><p>12</p></li><li><p>56% of customer interactions happen during a multi-channel, multi-event journey</p><p>13Source: McKinsey &amp; Co.</p></li><li><p>Business OutcomesUnderstanding customers and customers decision journeys helps companies drive significant business outcomes</p><p>Marketing &amp; Advertising</p><p>Customer Service</p><p>Retention &amp; Loyalty</p><p>Customer Experience</p><p>CSAT</p><p> Sco</p><p>res</p><p>ROM</p><p>I</p><p>Waste</p><p>Call ReductionCr</p><p>oss S</p><p>ell</p><p>Churn</p><p>14</p></li><li><p>15</p><p>Customer DataCompanies have access to lots of data that can help them understand their customers and customer decision journeys</p><p>Online Review</p><p>Ask FB Friends</p><p>Online Chat</p><p>Online Search</p><p>Store Visit</p><p>Banner Ad</p><p>View Video</p><p>Purchase</p><p>Social Media</p><p>Retail</p><p>Mobile</p><p>Purchase</p><p>Call Center</p><p>Survey</p><p>Chat</p><p>Web Branch</p></li><li><p>16</p><p>The ChallengeMore often than not, customer data is fragmented and locked away in physical and organizational silos</p><p>Social Media</p><p>Retail</p><p>MobilePurchase</p><p>Call Center Survey</p><p>Chat</p><p>Web</p><p>Branch</p><p>MARKETING CUSTOMER SERVICESALES</p><p>16</p></li><li><p>17</p><p>Customer AnalyticsNew approaches have emerged to help companies unlock and analyze their customer data </p><p>Data Warehousing Solutions</p><p>Data Blending Solutions </p><p>Data Management Platforms (DMP)</p><p>traditional, batch-oriented ETL data </p><p>integration for reporting and analysis</p><p>real-time blending or mashing of data from different sources for </p><p>analysis</p><p>platform to collect, organize and activate </p><p>audience data from any source; integrated with </p><p>execution systems</p></li><li><p>18</p><p>Data WarehouseTraditional approach to integrating data for consistency and quality</p><p> For many years, traditional business intelligence and data warehousing technologies and approaches have been used to capture and analyze customer data. </p><p> Beginning in the 1990s, companies pulled data from their transactional systems into separate, centralized data warehouses to support reporting and analysis. </p><p> 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. </p><p> 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.</p></li><li><p>19</p><p>Data WarehouseTraditional approach to integrating data for consistency and quality</p><p>ETL</p><p>Data CleansingData Sources Data Warehouse Example Use Cases</p><p>CRM</p><p>ERP</p><p>Operational System</p><p>Flat File Data Mining</p><p>Analytics</p><p>Reporting</p></li><li><p>20</p><p>Data Warehouse ChallengesThe explosion of data has strained the traditional approach &amp; technologies</p><p> Explosion of data, particularly unstructured data, generated in recent years has strained the traditional data warehousing approach and underlying technologies </p><p> 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. </p><p> 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.</p></li><li><p>21</p><p>Evolved Data WarehousingNext generation approaches and technologies for big data analytics</p><p>Cloud Computing Big Data TechnologiesData </p><p> Visualization</p><p>Cloud computing decreases cost of computing resources and creates agility. Resources </p><p>spun up and shut down quickly and easily.</p><p>Big data technologies support greater variety, volume, and velocity of data. They also </p><p>speed the time it takes to mash up different data sets. </p><p>Data visualization provides user-friendly visual analysis and </p><p>helps decision makers move from insight to action.</p></li><li><p>Data BlendingApproach to blending data from different sources for analysis</p><p>22</p><p> Historically, analysts used tools like Microsoft Excel or Access in situations where they needed to analyze data not available in the data warehouse. </p><p> But, in recent years a new type of solution, data blending (also sometimes referred to as data discovery), has emerged. </p><p> Using data blending tools, analysts themselves can access, cleanse, and blend data from multiple sources without having to write a line of code. </p><p> 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. </p><p> 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.</p></li><li><p>Data BlendingApproach to blending data from different sources for analysis</p><p>Internal Data Sources</p><p>CRM</p><p>ERP</p><p>Operational System</p><p>Flat File</p><p>Data BlendingExternal Data Sources</p><p>Market &amp; Customer Data</p><p>Example Use Cases</p><p>Analytics</p><p>Reporting</p><p>Data Mining</p><p>23</p></li><li><p>24</p><p>Data Management PlatformsApproach to collecting, organizing and activating customer data</p><p> 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. </p><p> 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. </p><p> 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. </p><p> DMPs are optimized to allow marketers to define target audiences and then activate campaigns to reach those prospects and customers.</p></li><li><p>25</p><p>Data Management PlatformsApproach to collecting, organizing and activating customer data</p><p>Internal Data Sources</p><p>Display (Ad Server)</p><p>Web Analytics</p><p>CRM</p><p>Data Management PlatformExternal Data Sources</p><p>Market &amp; Customer Data</p><p>Example Use Cases</p><p>Email/ Inbound Campaigns</p><p>Targeted Display Advertising</p><p>Email Database</p><p>Ad Execution</p><p>Mktg Automation</p><p>Advanced Customer Analytics</p></li><li><p>26</p><p>DMPs Support Demand-Side PlatformsDMPs support programmatic approaches to targeting specific audiences</p><p>Marketers</p><p>Demand-Side Platforms &amp; Data Management Platforms</p><p>Exchanges (Supply-Side Platforms)</p><p>Publishers (Websites)</p><p>Audiences</p><p>DSP/ DMP</p><p>DSP/ DMP</p><p>DSP / DMP</p><p>DSP/ DMP</p><p>DSP/ DMP</p><p>DSP/ DMP</p><p>DSP/ DMP</p></li><li><p>27</p><p>How Do They Compare?Each approach has benefits and limitations</p><p>Data Warehousing</p><p>Data Blending</p><p>Data Management </p><p>Platforms</p><p> Integrated data to provide a single version of the truth for reporting and analytics </p><p> Minimizes any performance impact to operational systems</p><p>Benefits Limitations</p><p> Long cycle times to integrate new data sources </p><p> Business user-driven approach </p><p> Speeds time to integrate and analyze new data sources</p><p> Increases risk of data quality issues due to user errors </p><p> May impact performance of operational systems </p><p> Enables real-time activation through integration with execution systems </p><p> Speeds time to integrate and analyze new data sources </p><p> Bringing offline data online results in data loss</p></li><li><p>The Road Ahead</p><p> Customer analytics is not about the data or technology, but about the business decisions that the insights enable. </p><p> Customer insights have maximum value when the focus is on real-time insights connected with front-line execution. </p><p> Many customer insights can be found by mashing up different data pools. But, it is important to begin with whatever data is available today. </p><p> 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. </p><p> 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? </p><p> Keep people, your prospects and customers, constantly in mind in terms of improving their experience and meeting their needs and expectations. </p><p> 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.</p><p>Your 90 Day Plan: Recommendations to Consider</p><p>28</p></li><li><p>The Road Ahead (cont.)</p><p> 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. </p><p> 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. </p><p> Make learning and talent development a key part of the agenda. </p><p> Take an agile, iterative approach to managing, analyzing and activating data. </p><p> 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. </p><p> 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 whats working in your workplace</p><p>Your 90 Day Plan: Recommendations to Consider</p><p>29</p></li><li><p>30</p></li><li><p>31</p><p>Contact Me !</p><p>@DaveBirckhead</p></li></ul>