Aisling McCarthy explores the relationship between AI and marketing, and explains how the pairing will see the marketing industry flourish.
AI is no longer just making appearances in science fiction; it’s here already and it’s not going anywhere. However, marketers have no reason to fear it as the application of AI-powered technology can make their jobs quicker and easier.
The focus of AI in marketing has largely centred on improving the customer experience in order to drive more sales. Using AI-powered technology allows marketers to communicate with each customer in a personal and meaningful way. However, this is not all that AI has to offer marketers.
Reasons why AI is a marketer’s best friend
1. AI lets marketers really understand what consumers want
At the 2017 APEX Awards
, ex-Googler turned banker Brett St Clair explained
that the exponential growth in technology has changed the way in which people interact with the world around them, and that personalisation is key to winning consumers over.
“Listening to your customers is vital – hyper-personalisation is a huge deal for consumers.”
With every activity and habit of users logged in online, marketers have a way to see the interests and preferences of every single consumer. However, processing that data is not possible without the use of AI.
AI-powered technology is able to process consumer data quickly, analysing it and identifying trends. This technology also makes it possible to personalise adverts to consumers based on what they have purchased before, or what products are frequently purchased together.
2. Marketers can be more efficient with AI
A huge part of working as a marketer involves resource-intensive processes to complete before serving the right ad to their audience. AI-powered technology can automate many of the time-consuming steps, such as data analysis.
In an article for Digiday
, Mindshare CEO Norm Johnston says that this leaves marketers with more time to communicate with their clients, improving the customer experience.
“The rapid evolution of AI in media will enable our people to focus on innovation and intelligence rather than repetition and reports,”
Ilyse Liffreing says that there is no reason to fear this development.
“The journey really is no different from what we have seen in past decades. Jobs and activities that need low levels of skill and intellect, and those that are replicable, get automated.”
3. AI can help marketers pre-empt customer service needs
Companies already gather tons of data from customers – logging purchases, customer-service enquiries and various other online and in-person requests. Using AI technology, marketers can utilise this information to pre-empt consumer service needs.
Not only can AI capture the data (saving marketers valuable time and effort), but it can mine the data as well, quickly finding trends and outliers. This will allow customer concerns anticipating issues before customers even log a service request.
“Look for [areas] where you can apply customer service data to solve trouble spots in your toughest customer journey,” says marketer, Jeff Foley in CMS Wire
“Then, identify when and how you can improve your existing systems and processes to tap into customer data. Finally, make sure that pre-emptive service using customer data is a centrepiece of your company’s digital transformation plans over the next three to five years.”
AI technology isn’t going anywhere; it’s here to stay. To stay one step ahead of competitors, marketers need to embrace this technology and use it to its full potential.
AI terms every marketer should know
So, now that you know why you should include AI as part of your marketing strategy, you’ll need to know the lingo.Here are 10 AI terms that you should add to your vocabulary:
A set of steps that runs on a computer program and is designed to solve a problem. These problems are solved by running through a number of processes, which all have their own specific rules.
Algorithms can be trained using machine learning, and are either programmed by humans or created and modified by machines during the machine learning process.
2. Artificial intelligence (AI)
A branch of computer science that focuses on creating intelligent systems that can perform and react like humans. AI systems are developed to perform tasks like visual perception, decision-making, speech recognition and language translation.
3. Chatbot (or “bot”)
A computer program that can simulate conversation with human users, via a chat interface. Numerous brands have invested in chatbots on their social media accounts or websites to answer questions that consumers have about their products or services.
4. Data mining
The process of extracting information from extremely large sets of data, and establishing patterns within the data. It is a useful way to process Big Data, as it can be difficult to manually identify trends and find actionable insights within mounds of data.
5. Natural language processing
A component of machine learning that processes, sorts and categorises various elements of text. While a human can inherently understand the meaning of sentences and paragraphs, a computer cannot really understand language. Using the mechanics of language, computers can, however, identify the context and grammatical use of words in sentences.
6. Image recognition (or ‘computer vision’)
This technology allows computers to identify patterns and recognise objects in images. It is used by some advert tracking companies to detect the logos of thousands of brands and businesses in newspapers and magazines. This technology allows the computer to understand what they ‘see’, which is why it is being used in self-driving cars, augmented reality as well as the development of robots.
7. Machine learning
A process in which machines are fed data and continually develop the algorithms they have been programmed with. The more data the machines are fed, the more accurately it can process similar information in future, and the more sophisticated the algorithms will become.
The training of machines by humans, where the algorithms learn from historical data submitted to it. The data that the machine learns from needs to be consistent and accurate so that learning can take place as efficiently as possible.
8. Neural networks
A method of processing vast amounts of data, which is modelled on the way the human brain interprets information. Neural networks consist of layers of nodes that receive data, extract the most relevant information and send the data along to the next node.
Using neural networks allows computers to process complex information in a different way to using traditional algorithms. While algorithms will always follow the same steps every time data is processed, neural networks gather more connections, and become more complex, with each piece of data.
9. Sentiment analysis
An AI process that studies opinions, attitudes, views and emotions that people express in text. Once these have been analysed, the sentiment analysis can mark the text, sentence by sentence, as being positive, negative or balanced. Analysing the sentiment of text – whether it is in the form of an email, newspaper article or social media post – can reveal the attitude of its writer towards the topic they are addressing.
10. Turing Test
A test created by Alan Turing in 1951 was designed to determine whether or not a computer could be classed as ‘intelligent’. It tests a machine’s ability to behave in a way that is inseparable from a human.
The test is conducted by having human judges chat to several people via a computer. Most of the people the judges will be speaking to are humans, but one will actually be a chatbot. The chatbot’s objective will be to convince the human judges that they are speaking to a real person. If it does this, it has passed the Turing Test.