Building an Agile Enterprise Analytics Strategy from the Ground-Up

Objectives of Digital Analytics

Digital marketers have traditionally been the primary beneficiaries of digital analytics. However, today’s leading organizations recognize analytics’ abilities to empower managers across all business functions, far beyond marketing.

Experienced analysts leverage data from their web analytics platform of choice and merge them with other sources such as Customer Relationship Management (CRM) and sales systems. The “digital” aspect is secondary—a euphemism even—given analysts should leverage the appropriate data at their disposal, be it from digital channels, social media outlets, and other quantitative sources, or from qualitative sources such as surveys and call center records.

The newly gained information will help stakeholders involved in digital marketing, website, and app development, social media or Internet of Things devices to understand, measure and optimize marketing campaigns, customers experience, sales, conversion flows, quality of support and services, and any other processes by leveraging data from online and nonline customer touch points.

A multitude of other disciplines are dependant on the data gathered through behavioral analytics solutions: Search Engine Optimization (SEO), Conversion Rate Optimisation (CRO), Social Media Optimization (SMO) and so on. 

In fact, data has become so important to organizations that some of them are creating specific senior positions for a Chief Data Officer (CDO); a corporate officer reporting directly to the Chief Executive Officer (CEO) who is responsible for enterprise wide governance and utilization of information as an asset, via data processing, analysis and data science.

Major Analytics Platforms

The uncontested leaders in the web analytics space are Google and Adobe. A study of the Internet Retailer 1000 by digital agency Cardinal Path proves these two account for well over 80 percent of the market share. 

But there are other players offering point solutions such as Parse.ly in the media industry, creative solutions from Chartbeat and Snowplow or strong foothold in geographic regions such as Webtrekk in Germany and AT Internet in France. The Marketing Technology Landscape Supergraphic (2018) shown below encompasses an impressive 6,829 technologies in various categories.

We’ve extracted just the “data” part of it... 

Google Analytics comes in two flavors - free and as a suite named Google Analytics 360 Suite. The free version offers the essential capabilities that introduced thousands of marketers and analysts to the discipline of digital analytics. Google Analytics 360 Suite offers a strong rival to the Adobe Marketing Cloud, with advanced capabilities such as tag management, multivariate testing, surveys, attribution, visualization, and a data management platform.

Google Analytics is the ubiquitous and de facto solution not only to get started in the field but also to accompany you as you climb the digital analytics maturity ladder. Ninety percent of the top retailers in the U.S. are using Google Analytics (vs about 40 percent for Adobe Analytics; and note that many retail sites use multiple platforms).

The 6 Components of Google Analytics

Google Analytics can be organized into the following components:

• Collection: a few lines of JavaScript code on every web page (or within a mobile app) collect behavioral data such as sessions, pageviews, events, social interactions and ecommerce transactions. A measurement protocol can also be used to send data directly to the platform from any internet-connected device.
• Storage: the vast amount of data is stored securely and efficiently in the Google Cloud environment, where it is optimized for later use.
• Reporting: Google Analytics comes with an intuitive user interface where you can view dozens of reports broken down by Audience, Acquisition, Behavior and Conversion dimensions and metrics.
• Extending: beyond “out of the box” data collection and reporting, you can extend the platform by importing additional data or expanding existing dimensions; extracting data through exports or programmatic APIs, create custom reports and dashboards and many more capabilities.
• Suite: Features including Tag Manager, Optimize and Data Studio are available at no additional cost, which allows you to manage native and third-party tags/instrumentation, testing,and visualization, respectively.
• Ecosystem: one of the biggest benefits of Google Analytics is its very extensive ecosystem of tools, learning resources and support from a vivid network of agency partners and third-party vendors.

The Many Ways to Jumpstart Your Digital Analytics Plan

The classic approach to launching an analytics strategy, similar to waterfall project management, would be to go through the following steps:

• Conduct a rigorous requirement phase with stakeholder interviews and detailed documentation.
• Create a solution design precisely describing how the technology will fit the above requirements.
• Deploy the technology and instrument the required tracking codes.
• Conduct extensive tests.
• Launch the tracking and hope for the best.

However, given the quickly evolving nature of digital channels, changing and evolving requirements, as well as the ever-expanding technical prowess required to do sophisticated and hypothetically perfect implementations, this has become an unrealistic endeavor.

The Radical Analytics approach prescribes a more agile methodology of quick, iterative improvements and refinements in order to collect data as early as possible. Going through this exercise shouldn’t take long and will allow you to uncover the unknown-unknowns. Even if imperfect, starting early and improving over time-based on the data you will uncover is by far preferable in today’s business environment. 

Before you get started with Google Analytics instrumentation (aka “tagging”), follow these simple, yet too often skipped steps. Ask yourself (or your stakeholders) the following: 

1. What is the greater good objective? Learn about the organization. What is the unique value proposition? In short, how would you define success (from a business standpoint)?
2. What is the customer life cycle? If you’ve never thought about it, start with the classic Awareness-Familiarity-Consideration-Purchase-Loyalty funnel, the AIDA model (Awareness-Interest-Desire-Action) or the more dynamic circular journey of Consideration-Evaluation-Closure and Postpurchase proposed by McKinsey. The important thing here is this: stop and think about it, use a terminology that makes sense for you and your organization, and get everyone on board.
3. Who are the target audiences? If personas were used in designing the website, it sounds natural to start there and adapt your segments as you uncover more attributes of your audiences through data. As pointed out in this McKinsey article, “customers’ behavior usually coalesces around a few major variables.” What are those variables?
4. Map persona journeys to website content and features. Think of the micro-moments which can serve as triggers and reveal the micro-conversions and how they should be measured. The goal is to be able to “move the needle” for each target audience, at every stage of their lifecycle. Some of those metrics will become your few, precious Key Performance Indicators.

Pick Your Approach

Google talks about the Zero Moment of Truth  (ZMOT). Industry guru Avinash Kaushik promotes his See-Think-Do-Care framework. The most important point is to find a suitable approach that is easy to understand, easy to communicate, and easy to embrace by all stakeholders. They all describe evolutionary steps to be undertaken by specific segments of your target audience in order to achieve mutually beneficial outcomes.

As the analyst, it is your duty to uncover those, educate people about them, and reinforce the importance of working toward the measurable goals of answering specific audience's needs and desires that are aligned with the business strategy. If they do not readily exist, take the lead and propose an objective, describe the customer lifecycle the way you see it, uncover segments and look at the existing digital assets to identify relevant content and functional components which your customers are interacting with.

This way of proceeding is adapted from what is called Product Thinking, a recently developed approach that readily applies to the field of digital analytics.

Source 

In short, instead of trying to define a SMART Objective, fill this simple statement:

Selecting an analytics platform and a strategy is a great start, but it’s only half the battle. Next is the hard part: taking that data and putting it to use in a way that your business can gain actionable insights. Read Using a Lean Approach to Gaining Actionable Insights from Your Analytics for details on what to do with your data.