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The Weekly Blog is curated content from the Evolytics staff, bringing you the most interesting news in data and analysis from around the web. The Evolytics staff has proven experience and expertise in analytics strategy, tagging implementation, data engineering, and data visualization.
While our implementation and marketing analytics teams specialize in both Google Analytics and Adobe Analytics, Google catches the spotlight this week, recently publishing thought-provoking articles on data-driven leadership, lifetime value as a key metric, and artificial intelligence for uncovering analytic insights.
In this stats compilation based on Econsultancy research, Google makes the case that leading marketers prioritize “data-driven insights” over “gut reactions” at their companies. For example, the study claims that, “nearly 2/3 of leading marketers say their execs value data-driven insights over gut instinct.”
The post also points out that leading marketers make data a key part of company culture, integrate marketing data from different sources, break down organizational silos, and value analytics training across the organization.
Over several years of guiding organizations to higher levels of analytic maturity, we’ve discovered that two key components of building a data-driven culture are aligning on success metrics and providing relevant, timely access to data to team members. We achieve alignment through a discovery process of stakeholder interviews, business objective definition, and measurement planning. We enable access to data through a combination of data engineering and data visualization, making sure each stakeholder has access to the right data in the right formats for making timely data-informed decisions. Alignment on key metrics and access to data lead members of an organization to optimize toward common goals using shared, flexible analytic tools and dashboards.
In this Think With Google guest post, Experian’s Senior Director of Digital Analytics Jan Yu explains how her team is ditching conversion rate and embracing customer lifetime value as the key measure of success. It’s difficult enough setting key metrics at the beginning of a website or marketing effort, let alone changing them up at some point. Sometimes it’s easier to just keep measuring what you’ve always measured for the sake of continuity, but that historical success metric may not reflect the changing needs and evolving business objectives of your company.
We’re sure Experian took several deep breaths before finally deciding to change the key success metric from conversion to lifetime value. Experian’s decision reflects a perspective we share at Evolytics however, that it’s important to measure and connect data throughout the customer journey, as far down the funnel as possible. We also expect Experian conducted customer data modeling exercises to determine the best way to calculate customer lifetime value based on digital metrics – a service we also provide for our analytic clients.
Perhaps the key learning from this article is that measurement planning is not a one-time, set in stone process. You can’t just set it and forget it. Companies change. Business objectives change. Therefore, it’s also necessary for success metrics to evolve from time to time. We recommend our clients re-evaluate digital success metrics at least once a year, usually coinciding with the company’s annual planning process. If a company’s objectives do not change substantially from one year to the next, then success metrics probably won’t change either, but it’s always best to check just in case.
Digital analytics history lesson time! Once upon a time, Google Analytics automatically surfaced data trend anomalies, calling the service Intelligence Alerts. Basically Google Analytics would let you know if something weird (and statistically significant) happened in your website data. Then Google Analytics took this feature away from desktop and put it in the Google Analytics mobile app. Now Google has reintroduced a newly revamped Intelligence service back on desktop with a killer new feature – ask your data questions.
Natural language queries are becoming increasingly prevalent in analytic tools. Similar to Google Analytics’ new Intelligence service, Microsoft’s Power BI allows you to ask questions of your data in the same way you might search Google to find out how old your favorite movie star is. Tableau is working on similar functionality also that should be “able to handle ambiguity, colloquial expressions, and conversations with your data.”
Our reporting and analysis team is excited about the possibilities of analytic tools automatically surfacing key trends and statistically significant changes in data. Such functionality will allow our analysts to spend less time digging in the data for such trends and anomalies, focusing more time determining root causes and deciding appropriate next steps for testing and optimization. We’re also excited about new ways of interacting with and analyzing data with natural language queries. As the analytical toolbox continues to grow, our team makes sure to understand the most appropriate applications of each new tool to meet our clients’ measurement needs.
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