Reading Between the Numbers

May 28, 2018

Marketing Metrics

Consider the fact that 90% of all the data in the world was generated between 2015 and 2017. It’s no wonder B2B execs aren’t sure what to do with all this information.

Today’s B2B marketing executives charged with turning data into actionable insights have far more raw material from which to draw conclusions than their predecessors. From the outside, the explosion of easily accessible information seems to be a marketer’s pipe dream. But the truth is that simply possessing more data doesn’t automatically increase conversions or revenue. In fact, it often results in feeling overwhelmed.      `

Define ‘critical’

Specific critical metrics will change based on a company’s business model and marketing strategy. However, there are some that can be considered universally valuable, such as customer survey responses, transaction records, web analytics, and qualified online leads.

For example, think about tracking bounce rates for a company’s blog page. For organizations that are heavily reliant on using original content to draw in leads, tracking bounce rates might be vital to the overall health of their business. Conversely, companies that emphasize other means of building lead lists, such as cold calling, may not place much value on this metric.

Real-time vs. historical data

Real-time data provides insights that can be used to influence buyer decisions on the fly. For example, when A/B testing LinkedIn ads, if ad “A’s” call-to-action proves to have a 27% higher click-through than ad “B”, that immediate feedback could compel a decision to divert the remaining budget for the campaign to ad “A.”

Comparatively, historical data that is gathered over weeks, months, and years serves as a guide for future efforts. Suppose that over time, it’s found that the leads from ad “B” (the underperforming ad) followed through on a purchase 50% more often than those from ad “A.” Though real-time data indicated ad “A” would prove most successful, continued data aggregation proved the opposite.

B2B companies that want to maximize their ROI would do well to use both real-time and historical data to make both short- and long-term decisions.

Siloed departments

Poor communication between sales and marketing teams is often a chokepoint for successful use of data. As the duties of these once-separate departments continue to meld, creating open lines of communication is absolutely critical to maximizing data use. For example, let’s assume that both the sales and marketing teams within an organization have access to newly acquired leads from a contact form submission. If the two departments aren’t actively collaborating on their strategies for following-up, missed opportunities and lost revenue result. Leads could receive conflicting or duplicate information, causing them to opt-out of further communication.

But with collaboration tools, both marketers and sales pros can have their cake and eat it, too. The marketing department can target the leads their sales reps are pursuing, and take it a step further, identifying and marketing to users similar to existing prospects and customers. Sellers can see how their prospects are engaging with marketing content and look for targeted prospects who interact with their company’s content in the form of a click through, like, comment, or share.

Tying analytics to objectives

The most important element is for each department to establish their objective(s), so that they can tie relevant data back to their goals. Imagine a B2B marketer is looking to generate inbound leads for a newly released software product. To build a lead list, a series of social media ads drives traffic to a landing page that prompts visitors to submit their contact information in exchange for a free 30-day trial.

Already, several touchpoints have been identified for collecting meaningful data:

  • Ad click through/engagement
  • Landing page bounce rate
  • Number of contact forms submitted

Now, three things must happen in order to turn that data into actionable insights:

  1. Establish desired outcomes for individual marketing tactics as they relate to the organization’s overall goal. For example, if a business is looking to increase sales 11% by the end of the year, the desired outcome could be to drive LinkedIn ad click-through 30%, and contact form submission 15%.
  2. Identify meaningful metrics associated with different tactics. In the case of email campaigns, collecting data on open-rate and click-through are great indicators of performance, while the success of landing pages could be measured by things like form submissions or time on page.
  3. Employ empathy. All the data in the world won’t help if marketers don’t keep in mind that B2B buyers are still consumers at the end of the day. A best practice is to approach marketing as an opportunity to solve an audience’s problems.

As the proliferation of data continues to change the global business landscape, companies of all shapes and sizes are equipped to make smarter, data-driven decisions. You don’t have to be a data scientist to capture, analyze and manipulate relevant data. But you do have to understand your objectives and employ the right tools.

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Photo: Havar og Solveig

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