Did you see the movie Moneyball yet? Billy Beane changes the game of baseball by using data. And the same trends have been happening in the digital marketing arena for just as long.
According to leading web analytics guru Avinash Kaushik, many large organizations still practice online marketing as a faith-based initiative – that is, they place their trust in intuition, bad data, and inefficient indicators when making strategic decisions. Unfortunately, the use of weak metrics like clicks and page views often makes web analytics results look more impressive or meaningful than they actually are. This allows managers to feel comfortable and content with their marketing decisions, without digging deeper to determine if their efforts are actually yielding useful results. We think those types of metrics are generally ineffective. We call them “caveman analytics”.
We agree with Avinash, who recommends that marketers look beyond these types of vague metrics, and instead use the following indicators to determine whether your digital marketing strategy is successful:
Loyalty – How many times are the same users visiting your site within a set period of time?
Recency – How much time has passed since your users’ most recent visits?
Economic value – What valuable activities are users completing when they visit your site? This includes not just making purchases, but also downloading white papers or apps, watching videos, and signing up for catalogs or email newsletters.
Task completion rate – How frequently are users able to achieve their desired outcomes when visiting your site? This indicator is best measured by a visitor survey that asks a single, simple question, such as “Were you able to complete your task today?”
Filter, Segment, Funnel and Test – It’s a crime to look at data in the aggregate. Filter your data by identifying the segments of folks you really want to reach. Many times this means bringing in external data from CRM systems, Point of Sale systems, Call Centers and other databases that present more information about your users at the Personally Identifiable Information (PII) level. Create funnels for each segment, and then create content that speaks to those segments more directly.
Avinash’s advice should lead all those interested in digital marketing to one principal conclusion: instead of competing on the basis of traditional marketing metrics (clicks, page views, conversion rate) and other vague, faith-based elements, organizations should use quantitative statistical analysis and predictive modeling to uncover deeper truths about their customers, and then adjust their digital marketing strategies accordingly. Stakeholders and decision makers must move away from making decisions based on a combination of weak data and intuition, and instead make use of the sophisticated data analysis tools that are increasingly accessible to businesses of all sizes.
Never in the history of marketing has there been so much data available as there is today. Marketers must use that information to know and understand client behavior, then create compelling services and experiences that meet their customers’ needs. It’s not a goal that can easily be achieved over night. Increasing brand value through the thoughtful, insightful use of analytics tools is a journey, not a destination. And those that abandon vague, outdated metrics to instead seek a deeper understanding of customer behavior through the use of web analytics will find their batting average increase in a big way.
