We've all heard it by now: we're living in a golden age of data.
Marketers (and entire enterprises) are leveraging advanced analytics and machine learning technologies to get the most out of their customer data, as they should. But as datasets grow more complicated, so too does the potential for error.
Customer data banks can open countless doors for businesses across industries: energy and utility providers can use smart meter data to analyze homeowners’ usage trends, and marketers are pinpointing individual customers’ web surfing patterns to ensure efficient targeting.
However, somewhere along the line — from data analytics system processors to Chief Data Officers to frontline employees and marketers — data can lead organizations astray.
Here are a few strategies marketing leaders can deploy to accurately analyze and leverage data analytics platforms.
Don’t Place All of Your Trust in Dashboard Data
Marketers love dashboards, and for good reason. Data snapshots provide a glimpse at the fruits of their labors, indicating the success of recent campaigns, comparisons with past initiatives and where adjustments need to be made. But too often, marketers use dashboards as the main source for customer information, and fail to dig deeper.
Data is incredibly nuanced. And while in an ideal world we could simply scan a dashboard to determine the next steps in a campaign, it’s important to designate data-savvy employees to take these findings a step further. These individuals can be responsible for analyzing datasets themselves to estimate the reasoning behind dips or spikes in trends. This way, spurious correlations on dashboards won't mislead marketers — a mistake that could result in their placing significant financial and labor investments in the wrong initiative.
Choose Specific KPIs to Analyze
Many marketers use Google Analytics as the top source for measuring the impact of their efforts.
While Google Analytics offers a number of different KPIs to track success, marketers must use these metrics as one piece of their larger growth strategy rather than obsessing over the numbers alone.
To avoid spreading your team’s resources too thin, analyze the KPIs that matter to your business’s unique goals (whether that includes lead volume, conversion rates or number of transactions) and place the others aside. Additionally, before considering these KPIs, partner with industry experts to perform a Google Analytics audit. This way, you can accurately correlate quantitative findings with business goals and avoid common Google Analytics misconceptions.
Be Wary of Customer Journey Tracking Accuracy
Trackable cookies open a window of possibilities for marketers. But today, customers are operating across more devices than ever before.
With users switching from work desktop computers to personal smartphones to tablets within minutes, marketers only have a fragmented view of their decision-making processes.
On top of that, they may be focusing on the wrong customers: Pew Research Center found that single device ownership largely correlates with demographic factors like age and income. Thus, when marketers analyze customer touchpoint sequences on desktop computers alone, for example, they are seeing only part of the picture. This leads them to make an incorrect assumption about their overall customer base, resulting in misdirected marketing efforts.
While they can be helpful in instances where demographics aren’t a factor, take customer journey tracking applications with a grain of salt.
The Key to Analytics Success Is Balance
For marketing decision-makers who’ve become dependent on their data, it’s often an eye-opener to learn that sometimes the data is open to a great deal of interpretation. Understanding the context and looking through the right lens results in the right outcome.
Of course, this doesn’t mean that marketers should see data as the enemy: it is one of the only windows to our customer bases, is growing in value by the day and will drive the future of the entire marketing industry.
However, the key to analytics success is balance. Data can’t be the end-all be-all in marketers’ decision making processes, but instead it should be a piece of the broader puzzle to help inform decisions.