Words & Pictures

You might have come across articles that discuss artificial intelligence (AI) as something that will ‘steal your job’ or become your ‘boss’, in a kind of dystopian society that could happen sooner than we think. While it would be naive to assume that AI will not change the workplace, it will certainly change our future jobs and careers, AI can also be seen as a tool within a bigger process to increase efficiency or enhance a person’s productivity. It can also create unique and personal experiences for customers meaning brands can have a bigger impact. Research suggests that companies are increasingly investing in AI over the coming years, and it makes sense to do so. Machine learning within computer science is the application of algorithms being able to learn and interpret data without having seen it before. Artificial intelligence has been around for years and is used in products and services you probably use on a daily basis. Famous chess player Gary Kasparov lost a game of chess against a machine called ‘Deep Blue’ about 20 years ago, he recently spoke out and said we need to collaborate with AI rather than fight against it to get the most out of the technology. In this blog, we will look at machine learning specifically and how it has been applied by brands and how we have started to apply this to some of our very own clients. Machine learning within computer science is the application of algorithms being able to learn and interpret data without having seen it before, it has not been specifically programmed to be able to do this.

Companies that use AI to enhance a customer’s experience

Facebook has introduced new image recognition features to their platform, which uses machine learning. Before Facebook would simply suggest the names of your friends you might want to tag in your photos, now it also finds pictures of you uploaded by friends that you haven’t been tagged so you have more control over what images of you are shared on the platform. Facebook explains how this works as followed:

Our technology analyzes the pixels in photos you’re already tagged in and generates a string of numbers we call a template. When photos and videos are uploaded to our systems, we compare those images to the template.” — Facebook

Similarly, Google announced more machine learning features in its next system update, Android Pie. Google will collect more of your data, on how and when you use specific apps, in return it will analyse behavioural patterns to customise your phone experience further to your individual needs, such as save battery life or adjust settings on your phone such as brightness levels. And Mozilla are releasing an experimental browser extension that will suggest articles of interest to you, based on your browsing history.

 

AI is also becoming a huge part of the automotive industry, not only to get data on customers in new ways in order to serve them better, or to do the groundwork of data processing, but also by integrating AI tech within vehicles to customise your driving experience. The Jaguar E-Pace uses machine learning to store driver profiles that are stored on the driver’s key fob. The aim is for the AI to detect patterns over time based on the drivers behaviour, it will adjust settings in the car based on the driver’s preferences.

 

A more personalised brand experience for customers is easily achieved using AI, StitchFix is a good example of this. This online personal styling company relies heavily on machine learning and data inputs from customers to determine what outfits would be suitable for them. The volume of customers is high, and the volume of personal stylists is far less, machine learning allows to offer more accurate recommendations to a higher volume of customers. Some companies take quite a creative approach to recommendations for customers, you can read about that in our senior strategist’s Dave’s blog.

Using machine learning to improve accuracy within a business to send consistent messaging to customers

So we have looked at a variety of examples on how machine learning has been used by companies to enhance their service and to provide a better user experience for customers. Essentially, you derive an output from a set of inputs, and this becomes more accurate over time the more data you feed it which helps inform what you present to customers.

We created rules for an algorithm taken from existing brand guidelines. The tool allows you to enforce brand guidelines, and present a consistent brand image to your customers.

Another application is to apply machine learning to improve a process within a business, which enhances the experience or productivity of employees and ultimately also improves your end-user’s experience with the brand. Our Tone of Voice Tool is a tool that helps employees write on brand for various document types, both internal and external and contains the first steps to incorporating machine learning.

 

We created rules for an algorithm taken from existing brand guidelines. The tool allows you to enforce brand guidelines, and present a consistent brand image to your customers. Quickly employees can see their score and how on brand they are, plus receive suggestions for improvement. This automates what ordinarily might be an opinion of a qualified human being who manually checks someone’s work, though a person is always needed to oversee the process and development.

Machine learning can help flag up inconsistencies to improve messaging and create consistency across multiple territories within a business, as part of marketing or brand activities.

Some rules can be dismissed, which the tool will log and apply the next time you use it. Over time, this means we can see how often specific rules have been dismissed and for what reason, to adapt and improve the tool and its uses over time and can provide valuable feedback on a company’s tone of voice guidelines. The next step for this for us is applying machine learning, this will allow the tool to establish patterns over time the more data we input. It would allow you to recognise desirable and undesirable patterns within communications, help flag up inconsistencies to improve messaging and create consistency across multiple territories within a business, as part of marketing or brand activities.

At TheTin we are very excited to continue to find solutions for brands and seeing how technology can enhance a user’s experience or achieve a specific outcome, we can’t wait to implement machine learning to our Tone of Voice Tool and see how it can add value to other client projects in future.

Stay tuned for more on Artificial Intelligence in our upcoming blogs, and if you want to learn more about how you could apply machine learning for your brand or see a demo of our Tone of Voice Tool, get in touch.

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We’ll make sure that the way we work is the right fit for your business, and we’ll ask the right questions to make sure you’re set up for success.

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