3 Key Applications of Big Data in Marketing & BI in 2018

Data-driven business and marketing solutions are increasingly improving the quality of products and services and can help reshape consumer experiences.

Photo by Teemu Paananen

Continuing to Personalize the Consumer Experience

Big Data allows companies to sieve through vast amounts of client feedback, quantified-self data and behavioral patterns: from ratings and personal preferences for entertainment (see how Netflix uses Artwork personalization and other custom-tailored strategies to keep you binge-watching) all the way to personalized medicine.

69% of travelers are more loyal to a travel company that personalizes their experiences online and offline — Think With Google
More than 85% of mobile marketers report success with personalization — higher engagement, revenue, conversions. — Forbes
Fragment of the Cognitive Bias Index designed by John Manoogian III

In addition to our own ingrained cognitive bias, technology today secures continuous access to products and services whose algorithms are developed to keep us in a constant loop of what we prefer to consume. Add to this the Age of Individualism, and it becomes apparent why the most engaging marketing strategies are those which focus on a highly personalized approach, targeted at the specific needs of the individual consumer.

Harnessing the potential of Dark Data

Don’t worry: it has nothing to do with the illicit corners of the Dark Web. Dark Data is an umbrella term for all unstructured, untagged data which is not processed, analyzed and put to practical use.

Research shows 80% of data is “dark and untouched,” meaning it’s never actually used to make improvements or changes deemed necessary by the customer — Forbes

In the past business and marketing data was either purchased from paid databases, collected via CRM or accumulated via a limited set of activities (focus groups, surveys, etc.). Today the problem is having too much data: a combination of growing number of internal marketing and BI software solutions and external feedback by customers who are always ready to share their thoughts on social media. However, it is an issue of quality over quantity: all the data in the world would be useless if it is not adequatly processed, analyzed and implemented in relevant processes (strategy, decision-making, etc.).

Source: Deloitte University Press

In this comprehensive report Deloitte recommends several vital steps in dealing with Dark Data. The key takeaways are:

  1. Ask the right questions: without focus and well-defined goals you are merely citing facts and figures. Data should help you solve specific problems
  2. Recognize analytics as a core element of your business strategy, not just an IT function
  3. Explore advanced tools for Dataviz (Data Visualization)
  4. Invest in data talent: experts in modelling and statistical techniques can be of tremendous value for harnessing the potential of dark data
Photo by Ian Dooley

Big Data & Analyzing Visual Content

One of the biggest obstacles to the Media Intelligence projects I’ve been working on in the past 6 years is finding the relevant, quality data without missing on important content. Granted, it is never possible to obtain all the relevant online information: the scope of tools you use to harvest social media data and the way you construct your search query profoundly impacts the volumes and quality of the content you analyze.

Until recently text, (hash)tags and metadata were the key ways to detect brand content, but this has changed dramatically in the past few years, especially after Google’s Andrew Ng led the way in training neural networks to recognize animate objects in unlabelled images through unsupervised learning in 2012.

Photo: Amazon Rekognition

Amazon Rekognition is competing with Google Vision to provide a broad scope of solutions, and many other companies are integrating the service with an emphasis on social media marketing and brand analytics. Brandwatch recently launched Brandwatch Image Insights: image analysis technology able to detect brand logos in visual content.

Crimson Hexagon uses computer vision to go a step further and identify demographic information and sentiment about user posts based on face analysis (including age, gender, emotional expressions). Many other tools and platforms like Ditto Labs, Clarifi, GumGum are trying to tap into this market but are yet to establish themselves on it.

Access to this information will radically change the way brands track, measure and evaluate their online presence. It could also shed light on Sponsorship ROI, provide a fresh look at consumer habits and give visual ideas to designers, advertisers and marketers how to make brands stand out in front of relevant audiences.

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