According to a new white paper from Gigya, “Harnessing Big Data to Grow Revenue,” there is a significant gap between collecting data from social media and using that data to create targeted brand marketing campaigns. Gigya explains that by using social logins on brand websites, companies can close this gap and generate new revenue.
In simplest terms, social logins enable website visitors to log into that site (or mobile application) using their preferred social media profile usernames and passwords. Rather than remembering a separate password for the brand’s site or application, the user logs in with his Facebook account or Twitter account.
In its white paper, Gigya explains that, “users who log in to a website using an existing social identity are essentially granting that website permission to access their first-party social data such as full name, interests, education, social connections, email address, location, and more.” It’s a huge opportunity that most companies are missing.
However, collecting user data through social logins is just the first step to actually generate revenue with that data. Gigya offers five steps to successfully collect and use that data:
- Use a “dynamic-schema, secure, cloud-based solution” to store and manage all of the social and behavioral data that is collected.
- Implement social logins whenever possible.
- Use automated data capture to ensure data collected is accurate since social profiles change frequently.
- Increase user engagement to collect more data through social plugins on brand websites, commenting, sharing, and so on.
- Integrate the collected data into third-party marketing platforms, such as email marketing, advertising, web analytics, and recommendation tools, to personalize marketing campaigns.
I would add that another important step is to hire talent that understands how to extract meaningful and actionable information from the data that is collected. This is an area where most companies are faltering. Collecting, storing, and integrating data into various systems and tools is the easy part. The hard part is figuring out what the data actually means.
Data can be extracted and manipulated to tell just about any store an analyst wants, but hiring a person or team of people who understand data and marketing can be challenging. In other words, great marketing data scientists are few and far between in 2013, but the importance of the role to a company shouldn’t be undervalued. After all, effective data evaluation can make the difference between revenue generation and revenue losses.
What do you think about the big data gap? Leave a comment and share your thoughts.
Image: Dave532
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