Social media has become ubiquitous and critical to modern marketing strategies. Perhaps at one time considered juvenile and distracting, social media platforms do not discriminate when it comes to bringing significant value and measurable ROI to companies across all industries. For modern marketing professionals, participating in online conversation is an essential part of nearly every strategy. Like everything else, marketing is evolving. Social media management platforms enable mass communications by arming marketing teams with baseline insights into their respective audiences. But as more information becomes available and companies look to gain further insight into their audience, the trick is figuring out how to efficiently interpret meaning from the data deluge. However you parse it, data gives marketers tangible strategic advantages.
Stitching unstructured behavioral data to structured, customer information can produce personas full of quantitative data that can then be leveraged as astute market research. These rich profiles allow marketers to personalize marketing content, then strategically target it to relevant audiences—where and when the customer wants it. Unstructured data can be defined as scattered information that is unorganized and informal, i.e., tweets, posts, check-ins, comments and recommendations. Structured data is information that lives in fixed or pre- defined fields, i.e., email lists, customer loyalty lists, CRMs and marketing automation platforms. Even data found in databases can exhibit significant amounts of unstructured content. For example, a patient record contains structured data such as the treatment the patient received, their vitals and specific medications that were administered as well as unstructured comments from the doctor regarding the patient’s consultation. To cite another example, location information on a Twitter profile is entered into a structured field that is easily identified as such, but the content itself is freeform, allowing users to provide values like “Austin, Texas,” “ATX,” “H-Town” or even “Chris Pine’s Heart.”
Context is critical to understanding your audience. Modern data science has made it possible to turn characteristics and nuances into quantifiable items that marketers can act upon. From the information derived using data analysis techniques, marketers are able to gain insights into the personas in their audience without missing some of the finer points of individual expression. Key to these innovations are advanced methods like predictive analytics, machine learning and natural language processing. Predictive analytics is a broad term for statistical inference techniques that can be used to analyze current and historical data to understand trends and make predictions about the future. Predictive analytics can be used to model everything from the way people will behave, from the outcome of a basketball game to the future value of an investment portfolio. Machine learning encompasses a range of methods that allow patterns to be learned from data creation models that associate given inputs with clusters and classes. For example, clustering methods can take a collection of texts or social profiles and group them with respect to which are most similar to one another. Classification in machine learning involves obtaining labels from humans for a set of examples,
learning a model that associates new inputs to those labels and then applying that model to new data. Natural language processing is a field at the intersection of computer science and linguistics that makes sense of unstructured linguistic data, including text, speech and even sign language. It is closely connected to information retrieval, and it relies heavily on machine learning methods. Researchers and developers operating within the field of natural language processing work on ways to automate algorithms that perform intriguing computations on the things people say, including well-known applications such as machine translation, spam filtering, and sentiment analysis.
Companies using traditional marketing techniques tend to have match rates between structured and unstructured data of 15 – 20%. In other words, for every 100 emails found in a database, there are 15 to 20 social media profiles that match up. This model leaves marketers with little to no detailed information about 80 – 85% of their audience, and this equates to huge opportunity loss. By analyzing data acquired directly from structured and unstructured sources, marketing teams can learn valuable information about their audiences, such as:
• Personal and brand affinities
• Pain points
• Engagement with brands
• Influence level
• Device use
• Comparison to audiences of competing or complementary brands
By tactically gathering and parsing into meaningful audience segments and personas, marketers can not only validate existing audiences, but find new ones that may never have been considered. And when an audience is accurately understood, marketers gain the ability to communicate directly to each persona in a voice that is persuasive yet authentic. Today’s data science can help brands understand more about their audiences than ever before. When used strategically, audience data has the potential to bring exceptional value to marketers and marketing campaigns.
People Pattern is a Software as a Service platform that supplies meaningful Audience Insights to the world’s biggest brands. Via semi-supervised machine-learned algorithms and natural language processing, People Pattern turns vast, messy public expression into actionable persona sets, helping brands gain an edge in the race to win, retain and serve customers.