The growth of Social Media over the past decade has been swift and widespread. Companies of every kind now engage with customers on the various platforms in order to develop a competitive edge and create a community of like-minded fans and followers. Meanwhile, the exponential growth of data that brands have about their audience and their behaviors has opened up new opportunities for forward thinking marketers.
Businesses understand that in order to effectively target consumers, they have to know who their consumers are, what their consumers are consuming, how they are consuming it and how their product fits in the mix.
Recent developments in Machine Learning and Computer Vision have made it possible for algorithms to understand and describe the elements within an image or video. Computer Vision mimics human perception of visual data and acts as an intelligent eye that can quickly identify, classify and categorize said data. It can provide accurate, timely and actionable insights into large image data sets, and has a wide range of applications across various industries.
Take a Food Processing Company for example. Computer Vision can identify which flavors and colors are selling best and what recipes are driving demand. Retailers can use it to optimize store space and inventory placement. And in fields such as Logistics, Computer Vision can transform the way companies transport, store and deliver merchandise. Doctors now use Computer Vision to identify, diagnose, and treat diseases by being able to read, analyze and understand thousands of x-rays in fractions of a second.
Here are a few more use cases:
Computer Vision and Social Media
In 2017, as the first round of companies began launching products using Computer Vision, the world of Social Media had become huge and unwieldy. Consumers were sharing millions of images every second, live-streaming took off, and the world was producing more content than ever. Computer Vision AI arrived at an opportune time for Marketers, and today promises to transform how companies manage Social Media going forward. HelloScribe is deploying one such tool which is capable of mimicking human perception of the visual content consumers share on Facebook, Instagram, Twitter, and Pinterest.
As an Agency, we are particularly motivated by business impact, and the early results of the use of Computer Vision for Social Media have been astounding. And while we can’t go into specifics, it is clear from our analysis that the technology holds tremendous promise for Marketers in almost any industry.
Here is the context…
Images, graphics and photography have always been a crucial component of Marketing Communications and this has only increased with the rise of social media and the consequent abundance of imagery available. In fact, between 65 and 85 percent of people now describe themselves as visual learners , meaning they extract more information from what they see rather than what they read.
If you are a Marketer spending hours per day combing through social media to find things worth posting, at some point you ask yourself, how exactly are images and video clips ultimately selected for posting? What makes certain visual posts engaging for your audience? And how can you really be sure if your post is going to take off? Knowing what works is tough, and relies on educated guessing, which is most often, not reliable.
This is where Computer Vision AI can be most helpful.
Marketers can use Computer Vision to extract user insight from unlabeled images and videos. Our tool takes in different data sources, including image and video data, user reactions (likes, shares) and other meta-data, such as hashtags, and combine these into a quality score — the higher the score, the greater the likelihood of success. By cross referencing publicly shared social media interactions against a pre-trained dataset of 35,000 brands,each with 3 years worth of Social Media data, our AI can find patterns that provide answers to the question; What post will get the most engagement?’
This approach offers a number of advantages. Key among them the fact that Marketers can now easily retrieve a set of relevant data, provide relevant recommendations, and create an experience customized to how their customers prefer to engage with their brand. In addition, by using AI to make more informed decisions, insights can be shared across the whole Marketing team. And team members can tap into a centralized repository of AI-derived knowledge, instead of relying on their own experience or memory of what content works for which channels.’
How Successful Marketers use Computer Vision
One of the key drivers for marketers to try AI technologies is discovering new ways of engaging with customers. Undoubtedly there are ways to do this that are not AI driven. But being able to use Data Analytics and AI to connect with customers at scale, and in more innovative ways is significantly more advantageous.
Most current engagements with Marketing AI generally, and Computer Vision AI specifically, are either isolated experiments, or pilot projects designed to help companies understand the parameters, triggers and behaviors needed to achieve business outcomes. Though 61 percent say artificial intelligence is poised to become the most important aspect of their data strategy, Marketers remain skeptical. Yet despite this, there is strong recognition that businesses cannot afford to ignore the insights AI can provide. By the same token, there are companies which are seeing the real-time benefits of Computer Vision for Social Media, among them brands across Tourism & Hospitality, and Retail.
A recent case study outlined how ‘Coca-Cola uses Computer Vision to analyse social media and understand where, when and how its customers like to consume its products, as well as which products are popular in particular localities. With over 90% of its consumers making purchase decisions based on social media content, understanding how its billions of customers are discussing and interacting with the brand on platforms like Facebook, Twitter and Instagram is essential to its marketing strategy.
Coca-Cola analysed engagement with over 120,000 pieces of social content to understand the demographics and behavior of its customers and those discussing the products.’ Computer Vision analysis and natural language processing of social media posts, as well as deep learning-driven analysis of social engagement metrics today allows Coca-Cola to produce marketing communications that resonate with customers and drive sales of its products.’
To achieve Coca-Cola – like success, marketers must begin placing focus on data that moves beyond highly structured, transactional data to unstructured data. And that is what Computer Vision AI enables. Your ability to tap into all forms of customer data, regardless of how it is structured, is the advantage.
Of course, the real goal of social media is conversion. Conversion comes down to knowing what your audience is interested in and giving it to them. Where should you spend your attention? What messages does your audience really want to hear? What new information do they need to know in order to act?
With the right answers to those questions and the right social media strategy, you can help your audience understand your products, services, and values better, and build a deeper, more profitable relationship over the long term.
To learn more about our tools and how we can help, request a demo.