Data Overload: Finding the Right Mix of Social Analytics Metrics

The culprit responsible for the glazed looks washing over the faces of many marketing execs can be attributed to many things, but the current pain is likely being driven by social media. No, not participating or contributing to social media, but analyzing the mounds of data it produces.

The rapid growth in popularity of social media, from brand pages on Facebook to flash sales on Twitter to custom YouTube channels, has transformed social media from a personal engagement channel to a core element of most businesses’ marketing strategies. Unfortunately, the amount of interactions and touch points is dizzying: too much data to sort through, too many sources of data to consolidate, too many vendors to manage, too many ways to interpret the data, and too many meaningless metrics.

Finding success in social media is not just a factor of producing great Facebook pages or engaging viral videos, but leveraging the data to drive ongoing insight and optimization. It’s too late, however, to be starting with the data. First, figure out what are the key business drivers for your social program – what is the business need you are trying to solve for? Without this first decision, social media just becomes another marketing investment with no tangible long-term value.

Align the Vision

Start by understanding why social media is important for your business. Social media can be used to support customer service, drive and convert immediate sales, and even identify trends for future products. All of these are important and valid uses for social media, but the metrics for measuring success are vastly different. An increase in time on site and page views may be good for driving awareness for existing customers, but may actually be bad for businesses trying to quickly move someone through the conversion funnel.

Once there is alignment on goals, then establish metrics to effectively measure and provide guidance for action.

What to Measure?

Actual metrics are very dependent on the business model. Broad metrics like engagement, influence, and sentiment get a lot of attention, but are often not a valuable measure in practice. Just defining something like engagement can be difficult. Is engagement a factor of time on site? Content consumption and sharing? A combination of all three? Often, once there is alignment on a definition, many organizations have no detailed action plan to respond to the data. If engagement goes up, but sales go down, is this an effective campaign and metric? Maybe not.

Instead, start by looking internally at your organization. Is your business transaction focused? Is a primary goal of your social media efforts to drive to a sale or tactical conversion? If so, then leads, sales, and registrations are solid places to start.

However, if your business has a longer sales cycle – or a model that results in most sales being driven by partners (such as most CPG companies) – consider metrics more focused on brand building and influence. Increases in brand awareness, sentiment, Net Promoter, sharing, and purchase intent are all metrics that align with this type of business.

It quickly becomes clear that there is not a single silver bullet metric that gauges the performance of social. Once you have both the strategic direction and the core metrics, you have a foundation in place for effective analysis.

Execute and Optimize

Accurate data collection and measurement, based on tangible business metrics, is worthless if the results are not transformed into actionable recommendations. Many organizations spend an inordinate amount of time synthesizing data, creating dashboards, and getting social performance data out to the organization, but they don’t follow through and optimize with the same zeal. Social data can spot trends in product demand, highlight high-performing content, and support local content targeting. But it requires ongoing data review, testing of hypothesis, and generating new social content to continue beyond reporting to optimization.

Optimization does not have to be complicated – but without it, social data becomes one more expensive metric that provides little tangible return.

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