More brands are looking to enrich and augment their market research to get the full picture of customers and see what they are missing. And there’s a particular demand for using AI (in all its flavors) to help in bringing together new diverse data sources, processing/visualizing them, and interpreting and blending in the right small data – either distilled down from unstructured (big) data sources or gathered from ‘local” (small) data sources like a focus group or user survey. These are all elements of what my techie friends at Synthesio call “data hybridization.”
In short, hybridization helps you get all your big and small data together. It also does this across virtually any source – whether online or offline, in one insights platform. And just as many insights teams are looking for new ways to spot market trends and see unmet consumer needs, marketing teams have discovered that social data alone isn’t sufficient to fully understand consumers or their intent! Other data sources are often better at offering detailed feedback on your product (forums or blogs), tracking customer satisfaction (opinion surveys), or even uncovering purchase intent in the moment (search analytics), as was discussed last year in this blog post.
Say “hello” to Hybridization
The core of hybridization is a process of combining big and small data from different sources into a single, unified view. This starts with ingestion, and includes AI-powered steps for cleansing, mapping, and transformation. Hybridization allows different analytical functions to work with a “complete” data universe. For example, one that includes survey and social data (and other online sources) in one place, enabling streamlined exploration, cross-analysis, and visualization, and bringing the power of techniques like NLP and data viz capabilities to the research domain.
More broadly, hybridization helps to “export” the power of AI-enabled consumer intelligence (AICI) from insights teams to more parts of their organization, by:
Reducing biases and fostering more precision
To make sound, strategic decisions, brands need the full picture. By incorporating both structured and unstructured data, your team can get both spontaneous insights from social conversations or search data, and refined reactions from surveys – that bring brand perceptions or purchase intent into focus. Plus, being able to compare and visualize datasets in one platform makes it easier to spot correlations and generate compelling reports that help to shorten time to value.
Combining the individual strengths of social, survey and behavioral data
When you can incorporate the broadest set of online and offline sources you can apply the strengths of each data type, while also compensating for gaps in understanding. For example, survey integration can expand on social insights, validate the impact for specific target groups, and (using survey demographics or psychographics) correlate social insights to consumer behavioral data.
Driving better sharing and collaboration across teams
Building around a single insights platform with the right data, algorithms, and analytical framework all under one roof offers unique advantages. It helps to break down silos and also bridge traditionally disparate VOC, social listening, and even big data efforts. Offering interactive visualizations to compare different sources (and their meaning) gives a voice to different teams, and helps to foster peer review and broader adoption of insights.
In a way this is the natural evolution of BI and customer analytics, powered by big and small data.
*this post is based on one written for the Synthesio blog. Check out their solutions at www.synthesio.com.