Web Analytics Liberation

Transcription

1 AN ENSIGHTEN STRATEGY BRIEF Web Analytics Liberation How Enterprises are Re-Engineering Their Digital Analytics MARCH 2016

2 Executive Summary Just like everything else in digital marketing, the changes in consumer behavior resulting from the explosive growth of mobile and social channels is disrupting the web analytics space. Just a few years ago, web analytics solutions gave brands their best view into the performance of digital business and user behavior. Fast-forward to today, and this is not always the case. With the growth in volume and importance of new devices, digital channels and touch points, web analytics solutions are now just another digital data silo that brands need to deal with and integrate into to get a complete picture across all channels and devices. While these vendors may now offer ways for a solution to run in different channels and on multiple devices, their omni-channel capabilities are often still a work in progress. This Ensighten Strategy Brief explains how modern enterprises are re-thinking their approach to traditional web measurement and re-engineering their digital analytics infrastructure. James Niehaus VP of Analytics and Digital Strategy, Ensighten 2

3 Just a few years ago, web analytics solutions gave brands their best view into the performance of digital business and user behavior. Fast forward to today, and this is not always the case. 3

4 Just an Expensive Data Feed? Many brands find that their current web analytics solution is just another data source downloaded daily into an enterprise multi-channel analytics data store, only to run through visualization tools like Tableau, Qlikview or Domo to provide internal, cross-channel business reporting. Put another way, many businesses are simply paying their web analytics vendor to be an expensive data feed. It is this new reality that is driving some savvy enterprises to cut the proverbial web analytics cord. Coca-Cola Cuts the Web Analytics Cord Coca-Cola is one brand that recently cut the cord on traditional analytics to ensure easier integration of digital data through its customer data layer for analytics and action, rather than having it sit in a proprietary vendor data silo. The company shared its story at a recent industry event in New York City, which was later detailed in an article in AdExchanger entitled, Coke Uncorks Data-Driven Happiness. This trend is gaining more and more interest among enterprise brands that are interested in taking their data strategy to the next level. Some have already made the switch away from traditional, vendor-delivered web analytics, while others are wondering what it would it take to make it happen. In each case, you typically have one of three scenarios from which to choose: 1. Rip and replace the existing web analytics vendor with an internal data collection solution to feed an internal analytics and visualization platform 2. Complement an existing web analytics solution with an internal solution 3. Eliminate the paid vendor and keep a free solution like Google Analytics, while the internal team builds out an in-house approach The key point is that there are many options available to enterprises looking to bring digital analytics in-house. Free Offerings 4

5 Why the Shift? The commitment to making the switch to in-house web analytics to support higher-value digital marketing capabilities is relatively new. Yet the desire to re-engineer analytics has been growing within enterprise organizations for a while. The general complaints about the category have persisted for sometime as the costs for the web analytics solutions continue to rise, and the ROI, along with overall utilization, continues to decline. Companies in the past may have had the desire to switch, but lacked the ability or pressure to take this type of project in-house. Yet recent industry trends have accelerated enterprise interest and, more importantly, your ability to re-engineer digital analytics. Leveraging Big Data The first is a big trend from the last few years: big data. A few years ago many enterprises were just dipping their toes in the world of Hadoop, the open-source software framework providing massive data storage, improved processing power and the ability to run many tasks concurrently. Today it s hard to find an enterprise client that doesn t have a solid Hadoop data store up and running. In addition, most have also hired the engineers and data scientists needed to take advantage of the data, assuring these applications are implemented with the right people and the right tools. Teams are now looking for new data sources to throw at the big data applications to start demonstrating more ROI from expensive investments. Marketing in the Moment A second trend is the industry-wide focus on omni-channel personalization, i.e., delivering highly relevant customer experiences across channels and devices. For enterprises to deliver personalization at scale, they must start collecting data for every user interaction, and analyze and act against this data, ideally in the moment. This has led most brands down a path where they now realize they need to collect, store and analyze all the data from every touch point to make this possible. So many are already committed to data collection to deliver on larger digital transformation initiatives. Web Analytics Just the Beginning? These trends taken together have put enterprises in a position combining resources and desire to take on re-engineering of analytics. And web analytics may just be the first stop where marketers cut the cord within digital analytics. Brands are also looking to bring other digital analytics activities in-house, including more advanced analytics functions like attribution and media performance measurement. After all, once digital data is collected, it becomes easier to layer new solutions on top of this rich collection of data. 5

6 Ready to Take the Plunge? Four Questions to Consider Re-engineering digital analytics is not for everyone, at least not right away. For many brands the right strategy may be to continue with a current web analytics vendor for now. However, if you are interested in exploring this path, it s important to consider a number of factors before taking the plunge. Teams are now looking for new data sources to throw at the big data applications to start demonstrating more ROI from expensive investments. The following lists some initial considerations while looking at options, keeping in mind that the decision needs to be made for the right strategic reasons for your organization. multiple channels? This is the first factor pointing to how necessary or disruptive switching solutions might be. 2. Are you ready to stand up for your own in-house solution? What is your current capability to collect, ingest, process and visualize the data yourself? There are multiple steps in this end-to-end delivery, and it s no small undertaking. Your enterprise needs a data strategy that is committed to building out these core competencies within your organization around the people, process and tools needed to be successful. You don t need to be perfect day one. After all, most of your initial web analytics implementations took multiple years to get right. The critical piece is that leadership sees these collections of capabilities as a strategic investment for the long-term competitiveness of your digital business. 1. How healthy is your current web analytics implementation? Is your current deployment properly instrumented? Is it widely adopted? Are you still driving meaningful insights on an ongoing basis across 6

7 3. Do you understand the full payoff? For this to be worthwhile, your organization needs to understand and appreciate the full potential and long-term upside from the transition. The value you extract from this data should not be limited to simply replacing your existing web analytics capabilities, though for some the cost savings might be worth it on its own. While cost reduction is a nice start, the end goal needs to be the broader application of this data and insights to drive higher-value capabilities like full, multi-channel analytics, deeper segmentation and personalization, custom attribution, and more advanced campaign management and orchestration. The investment to cut the cord really starts making sense when you see this as the first step in a multi-phase effort to augment your digital analytics capabilities. 4. Do you understand the tradeoffs? There is no free lunch. Beyond the resources and time involved to bring digital analytics in-house, you also need to expect to go without some features on day one. You are not going to replicate all the features and capabilities of your current vendor overnight, and you shouldn t expect to anytime soon. Rather, you want to be clear on the tradeoffs. While you may lose certain features, you should expect to gain other more valuable capabilities. Being able to integrate this data with your other digital and offline data, and provide richer multi-channel analytics reporting and insights, should be first on the list of items you gain in the tradeoff. The whole goal of this effort should go beyond cost savings, focusing instead on the new and significant questions and insights you can answer either for the first time ever or faster and more efficiently than before. 7

8 Gartner recently predicted a similar trend where they expect up to 75 percent of enterprises by 2020 to take a new build approach where they will forgo out-of-the box vendor applications and instead opt for building more customized solutions. 8

9 Getting Started Once the decision is made to bring digital analytics in-house, there are four key steps in the planning and implementing of a high-value solution: Step 1: Define your ideal digital analytics endstate and then figure out the data and reporting capabilities required to enable it. Like any well thought out plan, it is important to detail your vision, its strategic value to the organization and specific details on what you will be delivering to help ensure everyone is clear from day one on the end goal. The next step is to define at a high level the data and reporting capabilities required to actually deliver on the vision. This is where you will need to put on a product manager s hat to work on a detailed plan for how the organization will build and deliver on the vision. Fortunately, you won t need to start from scratch as you can look at your current web analytics implementation as a good starting point to both define feature parity, product gaps and, of course, enhancements. After completing step one you should feel excited about the amazing potential of bringing digital analytics in-house, but the excitement should be balanced by the reality that it will require a significant investment from you and the larger organization to deliver on this powerful vision. Step 2: Get executive and key stakeholder buy-in for the vision and plan. To succeed you must have the support of key groups within your organization. This helps ensure the project has priority; funding and resources to support the plan; commitment to leverage the new platform to drive value; and last but not least, ongoing commitment to support additional investments to maintain and mature the platform. This is where having a clear and powerful vision for delivering digital analytics in-house and backing that up with a well-defined plan is critical. You are pitching to your organization a new investment, and like all investment opportunities, you need to demonstrate upside (vision) and reduce the risk (detailed plan of action). Step 3: Define the final solution and select the right tools and technologies to get the job done. Designing the end-to-end solution is often the most critical step because this phase introduces the most potential risk. You need to identify solutions that have the best chance of being delivered on time and on budget, and will be adopted and embraced by the larger organization. To plan capabilities, you will need to collect the data from the various channels in scope and define how it will be processed and stored. You will also 9

10 need to define session-ization, what tools and techniques will be supported for analysis and visualization, SLAs required and much more. Here again we recommend you avoid re-inventing the wheel. Most enterprises have already made prior investments in multi-channel marketing data warehouses or big data platforms. Ideally you will want to join forces with these teams and work toward a common organizational end goal: rich omni-channel marketing database that the entire enterprise can leverage for cross-channel reporting and insights. It is also important to remember that you don t have to go down this path alone. This is where strategic consultants and vendors play a valuable role. Step 4: Determine your implementation plan and success criteria. Now that you have stakeholder support, defined your requirements, and selected the right tools and technologies, the final step is to figure out the execution plan and how you roll out your internal analytics solution across your organization. In this final step the devil is truly in the details. While there are many ways to implement a new digital analytics platform, there are general best practices to keep in mind: > > An agile, phased approach to implementation is often the smart way to deploy. If you have the luxury of deploying the solution over phases, taking the agile approach can help you go live faster, minimize risk, run trials and learn along the way, all while potentially showing wins faster. > > You need to set clear criteria for the completion and success of each phase. The biggest risk in delivery is not getting this right. The entire organization needs to understand what the first phase of implementation will include and how will success will be measured against a set of KPIs. > > Finally, it s important to set the right expectations with stakeholders, reminding them what to expect, both the good and the bad. In the early phases it can be easy to lose sight of the significant strategic value of building a digital analytics system, if certain features don t exist or certain SLAs are not achieved day one. 10

11 Takeaways Digital analytics is changing rapidly, forcing enterprises to rethink how to best use data to their advantage going forward, both to transform the enterprise and to deliver more relevant user experiences. To survive and thrive enterprises are looking to take more control over the data and technologies that drive digital marketing capabilities. Building out a digital analytics platform is now seen by some enterprises as the foundational first step in this transformation. 11

12 Select Brands Using Modern Enterprise Tag Management ABOUT ENSIGHTEN Ensighten enables global brands to simplify the management of their data and technology investments so they can orchestrate smarter interactions across touch points. Ensighten s leading customer data platform delivers enterprise tag management; robust profile creation and management; and powerful omni-channel data collection capabilities that bridge customer behavior information from web, mobile, digital advertising, Internet of Things (IoT) and offline sources. Using Ensighten, organizations can leverage rich, first-party customer data and profiles to fuel personalization and enhanced insight using their existing technology investments. Ensighten delivers industry best privacy and security safeguards, unparalleled scale and performance, and the deep integration with the broader digital marketing ecosystem. Select customers include CDW, Hearst Corp., The Home Depot, Microsoft, State Farm, United Airlines and T-Mobile. Ensighten is headquartered in San Jose with offices in London, Sydney and San Diego. Phone: (650) info@ensighten.com Ensighten. All rights reserved. All product and company names are trademarks or registered trademarks of their respective holders. [v ]