— February 13, 2019
We live in a world where we are incentivised to share. We digitally share our photographs, thoughts and buying habits more than ever before. There is a wealth of data available on consumers and if brands want to capitalise on this through their social media marketing, they need ways to collect and understand it.
However, the need for this data is far surpassed by the fact that it is unwieldy for brands to manage – the volume and velocity of data alone can make it a monumental task to understand. That is why the industry is changing and adapting to make sure this data is much more accessible.
As we’ve seen in the digital marketing sector as a whole, the use of artificial intelligence (AI) is increasing rapidly. With applications from voice search to chatbots, there’s a wealth of information that AI is already harvesting. But, there will be so much more it can do with social media in the future.
Assisting with social media marketing analytics
Steve Wozniak, when asked what his dream product would do, said that he’d love something that would give him “more time”. When a 400-millisecond Google delay results in 8 million fewer searches, the speed to insight needs to be lightning-fast.
Companies like Brandwatch, which provide a social listening service, are looking to use AI as a way to reduce the number of hours that social analysts spend looking at brand data. Instead of an average of 3.2 hours a week looking at basic analysis, social analysts could get on with the bigger things while AI makes that data easy to understand and easy to access across an organisation.
The way Brandwatch does this is by analysing the peaks and troughs in the charts and pulling together data from a number of different sources. This is then used to work out why charts may have peaked at a particular point – maybe a social post coincided with a news event from the same industry that drove new viewers to that channel. These AI insights make reporting on social media marketing far more straightforward, since they take out the guesswork of social analytics.
Integrating customer experience with social apps
As with chatbots, AI is becoming more of a feature on social channels, integrating customer care and social analytics through customer service.
As Donika Ruseva, the Digital Owner Experience Coordinator from Jaguar Land Rover, says, “There’s no better way to show off your brand than good customer service”. Many brands use automation to implement holding messages for complaints and comments on social media, to varying degrees of success, but there’s more that can be done with social and customer experience.
Messenger apps, such as Facebook Messenger and WhatsApp, aren’t just for personal conversations anymore, but have become social media marketing platforms in themselves, providing access to both commerce and service apps. Personal banking apps, like Cleo and Plum, can help you save or manage your spending, while retailers like Made.com have created a conversational commerce experience for their customers. While these apps already exist, there’s a lot of scope in 2019 to see more businesses from different industries embracing this new use of messenger platforms.
From these AI apps, businesses can gain information on what their customers are primarily using them for, what aspects are important to them, and what trends occur on a regular basis.
AI and customer care
Supported by AI, businesses can achieve the quick, responsive and transparent response times that today’s customers expect. What’s more, AI can analyse what customers say in tweets, posts or comments.
Using AI to analyse sentiment and recognise key terms in messages to identify positive or negative feedback is already available, but there’s much more that AI can do for the customer care aspect of a business.
Many AI systems have machine learning and natural language processing (NLP) capabilities, and these are key to real-time self-service on customer service platforms. AI can respond to automated queries quickly and generate responses with accuracy and speed that humans can’t match; this is especially effective, as we’ve mentioned before, when applied to chatbots on messenger services.
There are a number of other services that AI can provide in the customer service sector, such as providing an easier way to identify customer issues on social by processing and learning from gathered information, defining customer behaviour patterns, such as when or how they might complain or talk about a product, responding with suitable solutions, products or discounts after receiving complaints or messages, and much more. We’re looking forward to seeing more intuitive measures, developed over the next year and beyond, applied to social media.