Advanced business intelligence with google analytics and tableau

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<ul><li> 1. Advanced Web Data Analysis with Google Analytics and Tableau</li></ul><p> 2. Advanced Web Business Intelligence using Google Analytics and TableauAdvanced data analysis with Google Analytics and TableauGoogle Analytics is a powerful, versatile and FREE Web Analytics tool that provides nearly limitless possibilities for web channel measurement through compelling features and a vast network of third-party plugins that extend its capabilities in a variety of innovative ways. However, while its usage is pervasive, its true potential remains highly underutilized in the Web Analytics strategy of most small and medium sized businesses. This is in large part due to the fact that an overwhelming majority of Google Analytics customers attempt to use it as a reporting tool and get quickly frustrated with its lack of support for advanced reporting and analysis features that are critical to any meaningful marketing optimization program. Notwithstanding the introduction of a number of enhancements to its reporting interface, Google Analytics native reporting remains good enough at best and falls woefully short of expectations of inquisitive Digital Marketers who need something a lot more specialized and capable to fully realize the benefits of their Web Analytics investmentsIn this whitepaper we discuss an alternative Web Analytics implementation approach whereby the role of Google Analytics is restricted to that of a clickstream data collection repository while the reporting and analysis function is transferred to Tableau Software-a robust, specialized business intelligence tool that is designed from grounds-up to support advanced reporting, analysis and visualizations of large volumes of data. Making this adjustment allows business users to significantly improve the quality of their analysis efforts and provides a highly compelling alternative to paid web analytics tools www.lvmetrics.com 2012-20152 3. Advanced Web Business Intelligence using Google Analytics and TableauSwitching the roles-6 reasons to start using Tableau for analysing Google Analytics data Using Google Analytics as a FREE, massively scalable data repository while transferring the analysis engine role to Tableau has several distinct advantages. Before presenting an architectural overview of how the two technologies work in tandem, here is a quick run-down of some of the key business capabilities enabled by brining Tableau into the mixPreventing data sampling For large Google Analytics accounts, reporting can be severely distorted given that Google Analytics provides sampled results when querying on large amounts of historical data. This can be disastrous in scenarios that require accurate metric count across a number of dimensions. In contrast, Tableau is designed from ground-up for Big Data Analysis and can quickly analyse large data sets through its inbuilt processing engine that is optimized for handling complex queries. Drip-feeding Google Analytics data into an intermediate database which Tableau can then connect to helps companies analyse large amounts of raw, unsampled historical data for hidden patterns and elusive insights.Advanced data visualizations Numbers certainly tell a story but without effective visualization, it remains largely impossible to quickly identify patterns and isolate areas for deeper analysis. This consideration becomes paramount when working with Big Data in that there is a critical need for quickly identifying patterns through visual indicators without having to run multiple reports and manually screen through numbers. Tableau supports a number of advanced visualization types (charts, trends, overlays) that can provide visual clues to quickly narrow down analysis focusAnalysing data spread across multiple profiles Large Google Analytics accounts often use multiple profiles to better analyse specific aspects of their digital channel performance. Reporting on consolidated data from acrossREPORTING VS. BUSINESS INTELLIGENCEWeb Analytics tools have long provided elementary reporting features that provide macro information about count based metrics such as visits, bounce rates, conversions etc. Many products (including Google Analytics) have developed advanced features around segmentation and multidimensional reporting but notwithstanding these feature additions, intrinsic reporting in these tools remains largely primitive in comparison to the capabilities offered by fullfledged Business Intelligence tools such as Tableau. The primary reason for this gap would be the fact that Business Intelligence requires complex data manipulation and modelling in order to properly analyse datasomething that is impossible to achieve while staying within the architectural constraints of a Web Analytics tool Traditional arguments against implementing Business Intelligence for Web Channel data revolved largely around the complexity of implementing old school BI technologies that required complex ETL processing and data modelling-skills that were largely absent from Marketing teams. With the advent of next-generation tool such as Tableau, these considerations have become largely mute thanks to the advanced capabilities of these tools in doing away with complex data modelling and providing business user friendly interfaces for rapid fire exploratory analysis www.lvmetrics.com 2012-20153 4. Advanced Web Business Intelligence using Google Analytics and Tableauthese profiles is not supported within Google Analytics and can only be achieved by pulling data out of Analytics and aggregating/analysing it offline.Calculated metrics and dimensions Effective analysis routinely requires building custom metrics and dimensions on the fly after data has been collected by the Analytics engine. Ever tried analysing an arbitrary metric such as the bespoke mathematical relationship between cost and revenue within Google Analytics? Or the weighted average of revenue realization Tableau-A Relentless from various channels? Or perhaps the running total of all focus on Business website conversions in a given window? While not possible within Google Analytics, running such analysis in Tableau Users and Analysts would usually require nothing more than a bit of common sense and a basic knowledge of mathematical expressions The adoption of older Business Intelligence tools amongst business users was low given the steep learning curve involved in critical activities such as data modelling, cube design and writing complex MDX queries. With its highly user-friendly interface and a business user focused architecture, developing complex reports in Tableau becomes a simple case simple drag and drop-Miscellaneous Advanced Features Apart from the above high level drivers for integrating Google Analytics, there are a number of other advanced reporting capabilities that are not supported within the existing interface but which can be easily enabled by plugging in Google Analytics data into Tableau What if analysis-This technique forms the bedrock of strategic planning for most established digital marketing departments at intermediate to advanced levels of marketing maturity. Examples includeHow does the conversion rate change if traffic to landing page x is increased by y%? What is the impact on revenue if paid advertising budget in channel z is decreased by 5%? How many additional new visitors would be required to drive overall email signup rate by 2%?Tableau can help quickly answer such hypothetical questions using parameterized inputs and provides visually appealing display of output based on automatically calculated (or custom generated) models . Marketers routinely use such analysis for better planning and prioritization of their optimization efforts. Trend analysis and Forecasting-Unlike what-if analysis, trend analysis techniques project future value of metrics based on historical data but without any user input. For example, trend analysis of website conversion rates can be used to estimate website revenue at a future time and then work back to calculate marketing costs for delivering a certain cost per acquisition Outlier detection-Outliers can wreak havoc on any analysis output no matter how well planned. Being able to quickly identify outliers through visual display allows Marketers to present a more accurate analysis of data at hand. This is especially relevant in Web Analytics where average values of metrics are used for practically all analysis output. For example, making judgements about overall conversion rates based on a few exceptionally high converting days in a campaign is ill-advised and almost sure to provide misleading results On-the fly calculations-Ever tried measuring the percentage change in conversion rates since the launch of a campaign? Or the percentage change in revenue contribution of various channels as percentage of overall paid advertising spend? Or perhaps the lifetime value of customers across a www.lvmetrics.com 2012-20154 5. Advanced Web Business Intelligence using Google Analytics and Tableaugiven period? There are numerous such analysis use cases that cannot be implemented within the native Google Analytics interface but all of which are supported out-of-the-box in TableauHandling data quality issues The quality of data in most Google Analytics implementations tends to be suspect at best. Without properly pre-processing this data, it remains largely impossible to create accurate reports let alone conduct any Tableau meaningful analysis. Take the case of campaign tagging for Application Stack example. Even a moderate size campaign running across multiple channels requires consistent tagging of inbound links in order to show proper data. Multiple trafficking teams Tableau Desktopwork on various elements of the campaign and Development environment for creating dashboards and notwithstanding the rigour of internal quality assurance reports. Can connect to procedures, link tagging is rarely implemented consistently multiple sources across multiple teams. Spelling mistakes, varying letter cases, arbitrary semantics and naming conventions all lead Tableau Server-Used for to high quality data chaos resulting in Analysts having to hosting dashboards and bend over their backs to address data quality issues. Within reports. Required for Google Analytics for example, Google is not the same as automated, real-time refresh google and AdWords is not the same as AdWord. Even of data though these words refer to the same entity, they show up differently in Analytics thereby distorting any out of the box reports. Even without a proper data pre-processing solution in place, these scenarios can be easily accommodated within Tableau using simple GUI based features (filters, custom fields etc.)Using Google Analytics with Tableau-Key Architecture considerations Using Tableau as the business intelligence interface for Google Analytics involves two phases The Design phase-This is where analysts build the actual reports and dashboards. Two options should be considered for the design phase. Plugging Google Analytics directly into Tableau Desktop-With the release of version 8, Tableau provides an inbuilt Google Analytics connector that business owners can quickly configure to pull live data into Tableau. While easier to implement, this option is of limited utility in scenarios that need to analyse large amounts of historical An effective method of data from multiple profiles on an on-going basis.analysing Google Analytics data inside Tableau is to use a data warehousing approach whereby Google Analytics data is periodically extracted into an intermediate database which then feeds the Tableau engineThe Data Warehouse approach-In this option, Google Analytics data is first extracted into a data warehouse which can then be connected to Tableau Desktop. The obvious advantage of this option is that it allows access to large amounts of historical data in Google Analytics and also the ability to implement a variety of data transformations beforehand (rather than in Tableau). Data is regularly extracted from Google Analytics and custom transformation rules are applied to it before storing it in an intermediate storage. Tableau desktop can then be connected to this intermediate store to build out all required analysis. www.lvmetrics.com 2012-20155 6. Advanced Web Business Intelligence using Google Analytics and TableauThe Deployment Phase-This is the phase where the reports created using Tableau desktop are physically delivered/rendered to end-users. Once again, 2 options need to be considered Using Tableau Public-Analysts have the option of saving the output from Tableau Desktop in Tableau Public which is a free web-based hosting environment provided by Tableau. Dashboards hosted under Tableau Public are publicly accessible and cannot store visualizations that use data above a certain number of records. Also, dashboards hosted with Tableau Public are static in the sense that they have to be regenerated at source (Tableau Desktop) to reflect changes. For these reasons, using Tableau Public for deploying dashboards is rarely an option for most mid/enterprise commercial environments Using Tableau Server-Dashboards deployed in Tableau Server do not suffer from the constraints of Tableau Public. Tableau server is a paid tool that allows Analysts to quickly deploy their dashboards and then share them securely with consumers. Dashboards are automatically refreshed from source without the need for rebuilding them in Desktop and also there are no constraints on the number of records that go into making visualizations With the wide-spread availability of a number of open source technologies for ETL(Talend, Pentaho) and inexpensive but highly scalable cloud based technologies(Amazon Cloud, Rackspace) for quickly building and hosting data warehouses, the most rewarding option is to use the data warehousing approach for Design Phase while using the Tableau Server based deployment option for physical hosting of dashboards.Despite a widespread belief to the contrary, the end-to-end process for implementing this configuration is surprisingly simple and cost effective. Typical implementation times range from 2-4 weeks even for some of the most complex Google Analytics implementations and costs tend to be a tiny fraction of those of traditional business intelligence solutions. Capital investment in hardware and data warehouse software licenses is almost zero given the pay-as-you-go pricing model adopted by www.lvmetrics.com 2012-20156 7. Advanced Web Business Intelligence using Google Analytics and Tableauproviders such as Amazon. Different consulting providers handle Tableau licenses differently and clients have the option of purchasing their own Tableau licenses (both Desktop and Server) or pay a flat rate to their service provider for accessing Tableau functionality for the duration of contract. Notwithstanding the actual figures, the option of using a combination of Tableau and Google Analy...</p>

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