Many marketers would agree that analytics can be considered a source of enrichment or empowerment – the essential catalyst to smarter decision-making, and in turn, better results.
However, insomuch as data harbors the power to arouse change, the practice of data analytics can also ensconce companies by validating existing belief systems. Given a lack of alternatives, many companies would favor inaction over probable loss. In this sense, the data are driving, but the business itself is stuck in neutral.
Most of the literature concerning data analysis favors tactics and tools over principles and acculturation. This knowledge gap means that many companies lack the proper framework to learn from their results and gradually improve. However, according to Google, top operators don’t just view their metrics as numbers – they view analytics as an opportunity to ask more questions.
This is the lens through which data analytics holds true promise.
In this article, we examine how a focus on the cognitive aspects of data analysis can provide for an enriched perspective, sound strategy, and better long-term results.
The Role of Data
In the literal sense, data are just a collection of static numbers. Rarely do these figures enjoy meaning in isolation; however, when it comes to analytics, context begets purpose. eCommerce analytics, for example, are often collected to describe outcomes related to consumer behavior, and as such, invoke an underlying narrative comprised of organic yet interrelated events.
We are tasked with teasing out this substratum, and doing so requires that we adopt a new perspective – that is, analytics is a mindset.
In small and medium-sized enterprise, data are viewed as the byproduct of activity: a sale is announced, a product is launched, or something new is otherwise introduced. Tactic-oriented marketers operate under the casual assumption that these data are baked into their tools, thereby limiting their ex post facto reporting capabilities to shallow metrics. These metrics and reports can describe simple events and answer outcome-oriented questions, but they do little to provide causal indicators – the insights that inform strategy.
In academia, most quantitative research requires considerable forethought with respect to analytical methodology. Researchers outline and validate all of the metrics, measures, and means they plan to incorporate into their studies before collecting their first observations. This accommodates for hypothesis testing using statistical methods against the data to extract insights– in other words, this is the process that enables data science.
It stands to reason that the same principles apply to eCommerce analytics: if you can anticipate the need (e.g., the set of circumstances under which you would take action against a result), you can design analytical methodology into your tactics and build a deeper understanding of how customer interactions impact business outcomes.
Such data can inform audience targeting, campaign budgeting, and other marketing strategies designed to drive bottom-line results. In fact, a joint Bain and Google study concluded that the top 100 most sophisticated marketers (in terms of measurement) are four times as likely to exceed business goals, including both market share and revenue, than the 100 least sophisticated marketers.
That said, brands with more developed data measurement practices usually have the means to justify upmarket growth vectors such as real time bidding ecosystems, Taguchi-based optimization, and other tactics that can take advantage of artificial intelligence and/or machine learning.
Innovatory growth tactics notwithstanding, eCommerce analytics can also serve as the foundation upon which brands can prioritize the customer experience. Several ancillary eCommerce functions, including segmentation, personalization, and eCommerce merchandising all rely on data analytics to connect brands with customers.
In an increasingly complex retail landscape, merchants who prioritize measurement are at a competitive advantage. eCommerce analytics enables a suite of fashionable strategies and growth vectors; however, it’s important to understand that practicing sound measurement means incorporating analytical methodology into your tactics rather than relying on reports with limited material or long-term benefit.