Month: May 2019

IT IS WELL DOCUMENTED THAT AI CAN NOW recognize images. Such technology is behind Facebook’s ability to automatically tag people in your photos or how your iPhone cleverly sorts the various people in your life into image folders.

In other areas of AI research, like understanding text and language, similar models have proved more elusive. But recent research from Open AI has signalled potential breakthroughs through the creation of new language models.

Here at Jaggu we have gone once step beyond this- creating technology that understands the text that actually appears in images. Being able to identify the language in images, within the contexts they appear, has a myriad of different business uses.

Our technology has the ability to remove text from millions of images, in real time, and input this information into a text recognition model. Once that happens our technology users’ classifiers to comprehend the context of the text and the image together and make sense of what it is seeing.

This technology can pick up text of all different sizes and shapes and is as adept at detecting both printed text and more handwritten styles. Once these words are understood they can then be placed in a readable format for searching and organisation. Suddenly labels like captions, product names, text logos, street names, licence plates and product names become mappable and identifiable at scale, eliminating the need for anyone to manually scan each image for the relevant text.

For brands this technology could be revolutionary. Being able to search images based on the text they contain means that they can find and measure who, when and why is interacting with their products. Suddenly even if their product is placed in the back of an image it can still be turned into meaningful and actionable data. Furthermore, by being able to detect your brand’s logos on people’s accessible social media, brands can reach people that already have an interest in their products. In this way, understanding text in images will contribute to a new, more personalised form of customer relationship.

Another use of this technology is to ensure compliance in adcopy. The technology can look at the visuals and text within copy, scanning them in order to ensure that all logos and descriptions say what they should, where they should. We recently worked with a global computer company that wanted to automate the process of checking if their compliance name mentions were being correctly placed alongside the compliance logo on their products. The amount of time and money that this would normally take employees to manually go through this content is frankly unthinkable. The automation of this task improved the productivity of their teams- freeing up their time to focus on more creative and strategic tasks and reducing the chance of any human error.

Jaggu’s computer vision helps our costumers you find, see, understand, and unlock the text insights that lay dormant in your image data. These in turn can be the key in helping you achieve your goals in today’s ever-changing world.

What would your Sunday morning dash to Waitrose or your Monday morning coffee in Pret look like as data? To have these personal, physical journeys mapped and understood would be goldust to retailers. Of course, all our consumer journeys online are already tracked, computed and turned into actionable data already so the next logical step is to apply this technology to the real-world.

It is well documented that Bricks n’ Mortar is not doing so well. According to PwC a record net 2,481 stores disappeared from the UK’s top 500 high streets in 2018 – 40% more than in 2017 – a worrying statistic for the future of the high street. Now, more than ever, retailers need to understand their customers physical journey through the store and get some much-needed insight into customer patterns to understand what is working and what is not.

AI technology can help by applying a sort of Google Analytics to the real world. Working with brands, this technology provides companies with a much-needed picture of customer journey instore- making trends like customer loyalty, foot traffic depending on locations/markets, demographics of customers and even customer emotions measurable. Of course, once this data is tracked it can then turn into actionable information that can make the difference between a purchase or not. It’s about who, what, where, why and how people are shopping physically and crucially will help brands understand how their performances measure up to their competitors.

Some of the ways this AI data collection can manifest as real solutions are:
• Camera systems can detect the “fresh” status of perishable products before on-site employees and the machine learning technology can reorder supplies without need for any manual operation.
• Camera technology can also work alongside an AI platform to monitor how long a customer has been in a certain aisle as a way to measure how marketing and placement is working best and with what demographics.
• Retailers can monitor wait times in checkout lines with camera and AI technology to understand store traffic and merchandising effectiveness at the individual store level—and then tailor assortments.

A mix of sensory technology and AI can be used to follow consumers as they journey through stores, gaining invaluable insight on what attractive products arrest attention or why bottlenecks in stores are occurring. By gathering this data, automatically and in real-time, on how customers are interacted with various areas of a store, brands can map and realign the customer experience.

According to Forbes, UK retailers have the potential to see a 0.5–1.0% increase in annual productivity growth if they start properly utilising their in-store data. But cost, a lack of analytical talent and siloed data within companies remain barriers to this sort of progression.

AI and machine learning can help scale the repetitive analytics tasks required to drive better leverage of the available in-store data. When deployed on a companywide, real-time analytics platform, they can become the single source of truth that all enterprise functions rely on to make better decisions.

So how will AI and machine learning change retail analytics, as they are currently understood? We expect that AI and machine learning won’t kill analytics as we know it, but give analytics a much needed and impactful makeover what will in drive the future of physical retail. In the future we could see:

• Analytics will increasingly focus on analysing manufacturing machine behaviour, not just business and consumer behaviour.
• Analytics will happen in real time and act as the glue between all areas of the business.
• Retailers will include machine learning algorithms as an additional factor in analysing and monitoring business outcomes in relation to machine learning algorithms.
• They will use AI and machine learning to sharpen analytic algorithms, detect more early warning signals, anticipate trends and have accurate answers before competitors do.

AI is a huge opportunity for retail and one that it shouldn’t waste any time in grabbing wholeheartedly. It is the key to understand who, why, how and what people are purchasing and with that knowledge comes profit, growth and accuracy. It is time for Google Analytics to get real.