Images are the new currency. Don’t believe me? Take a look at these statistics
The proverb ‘a picture paints a thousand words’ has become even more relevant in today’s sharing age. But why? We could say that it is easier to express a feeling with an image than text. Or that the human brain processes visual content faster.
We are now living in the world of hashtags, emojis, limited text characters. Images complete the stories these are trying to tell. Images are not only enhancing text but are often standing in place of it entirely.
And wouldn’t it be a shame if organizations today did not leverage the information from this huge ocean of visual data?
It is estimated that by 2021, the global image recognition market is expected to touch $38.92 Billion. The video analytics market is expected to touch USD 8.55 Billion by 2023. So where does image analytics feature here?
Much like how sentiment analysis raised the bar for social monitoring, image analytics is raising the bar for social listening. There is a goldmine of data stored in these images as these help organizations understand visual sentiment especially when text is absent.
Take this tweet as an example.
The entire sentiment of the tweet is summed up in the hashtag #PerfectDay. Nowhere is the airline brand mentioned but only the word ‘plane’.
Now imagine the kind of opportunity Emirates could have created by leveraging logo recognition resulting from image analysis! A tweet like “Thank you for the click! You’re such an amazing mom. We hope you and your son had an amazing time plane spotting”. It is this human-to-human kind of conversations that customers are looking for.
Image analytics isn’t some futuristic technology. You are probably using it without even knowing it. Your smartphone is already categorizing your photos. Your iPhone will tell you easily which photos are from your office party or from your latest adventure trip.
What image analytics essentially does is categorizes images from different sources and sorts them according to contexts such as facial expression, age, action, topics, sentiment, gender, and brand logo.
How does it do so? Quite simply by leveraging automatic algorithmic extraction and consequent logical analysis of information found in the image employing digital image processing techniques.
Fritz Venter and Andrew Stein say that the objective of image analytics is to “bring an unstructured rendition of reality in the form of images and videos into a machine analyzable representation of a set of variables.”
Here are a few reasons why image analytics is something to look out for
There is data everywhere. But how authentic is that data? Organizations across the US spend almost $10 billion each year on third-party authentic data. This sourced data has its accuracy limitations and yet forms the basis of many personalized marketing campaigns. The result? Limited accuracy.
With image analytics, the data organizations source will not be mere numbers from a survey, but actual customer data derived from first-hand sources, think facial expressions. The data is also captured real-time when the customer is experiences something, often before she makes a purchase. Such data becomes more relevant when personalizing offers as the insights derived are deeper, more accurate and also real-time.
The age of digital transformation puts the customer in the center of all focus. And for that, it is imperative to improve customer journeys and customer experience. Leveraging facial recognition businesses can create a positive impact on the same in several different ways.
Image analytics has a huge potential in the retail industry. The Consumer-Packaged Goods industry, for example, could leverage some technology advantage to optimize shelf monitoring, store checks and audits. With this technology, retail outlets can
The analytics derived from accurate in-store insights help to optimize store coverage, identifying performance issues and thereby improving retail execution and recovering lost sales.
Sentiment analysis is gradually becoming an essential contributor to improving customer journeys. Using image analytics, businesses can gain a context that goes beyond words.
Image analytics makes sentiment analysis more complete by giving you the capability to understand what a picture is ‘saying’. So, while any text or hashtag adds sentiment context, image analytics can come into play when there are no textual clues. What happens only when the image does the talking? You need to be able to hear the sentiment, right? And that is what image analytics helps you achieve.
Image analytics also complements social analytics. Social analytics is a complex puzzle where every piece is important (including video thumbnails). By adding image analytics to your arsenal, you can understand the visual side of the social analytics coin and stay ahead of the curve leveraging the insights.
This technology is rapidly becoming a rapidly growing in the health IT sphere. According to reports, the global medical image analytics market is poised to grow to $4.26 billion by 2025 as the industry shift towards value-based care increases.
Accurately classifying complicated texts, images, and other clinical data to improved disease identification capabilities (such as detecting small tumors in gigapixel pathology slides, etc.), to improving patient journeys in the hospital environment, assist clinical decision-making using actionable insights generated from images, image analytics has huge potential in this sector.
As we go deeper into an economy where competitiveness is fueled by technology, image analytics definitely helps businesses unlock insights that would otherwise go unnoticed. Would it then be too audacious to say that while image analytics still is relatively new, it makes sense to add it to your analytics arsenal – because at the end of the day, seeing is believing?