Learn to leverage marketing technology at our free Friday Forum on July 10. RSVP now

Social media analytics have evolved rapidly, leaving marketers in a quandary. Digital marketers now have dozens of tools at their fingertips, such as Sprout Social, Google Analytics, Hootsuite and IBM Watson Analytics for Social Media, which provide valuable insights for customer and brand sentiment, brand awareness and share of voice.

For business-to-consumer (B2C) companies, the information (captured primarily from Twitter and Facebook) is extremely valuable in helping to define and measure advertising and messaging strategies. However, business-to-business (B2B) marketers rely on social media for demand generation as much as for brand awareness, with Twitter and LinkedIn acting as our primary channels.

Unfortunately, LinkedIn has been limited in its analytics features for organic updates, engagement, and activity.

Just recently, LinkedIn expanded its "offering for conversion rate tracking" of its sponsored (paid) updates. Although this information is critical to measure your ROI on LinkedIn ad spend, the challenge of measuring the impact and influence of LinkedIn organic activity remains

As marketers, we are responsible for content and for demand generation, so it is critical to know the impact LinkedIn has on reaching our intended target audience, driving website traffic, and determining what content, asset or campaign is resonating.

Solving LinkedIn's Organic Measurement Challenge

However, marketers don't need to manually crunch data or make assumptions regarding their overall social media organic activity. With the advent of new self-service data preparation (prep) tools, marketers can capture data in all different formats, including organic engagement information, directly from a LinkedIn Company Profile page—and combine it with other data for visual analysis.

Whether the information comes from APIs, database files, Web pages, or spreadsheets, a data prep tool allows you to bring all those formats into a single workspace, and normalize and blend the data into something you can work with and analyze.

Sign up for free to read the full article.

Take the first step (it's free).

Already a registered user? Sign in now.


image of Frank Moreno

Frank Moreno is VP of product marketing at Datawatch, a data preparation software provider.

LinkedIn: Frank Moreno

Twitter: @fmoreno44