An older person recently commented, “I don’t trust my cell phone or computer any longer. I just talk about something, and the next moment I see ads for it all over the place. It is as if they are listening to me.” Well, they are. Conspiracy theories aside, not literally, you might searched, typed a message on social media, or clicked on a related product recently. In a nutshell, AI and machine learning leverage advanced analytics and learning techniques to predict consumer behaviour before they know what they want or need.
What are AI and machine learning?
Simply put, AI (Artificial Intelligence) refers to machines being able to learn, reason and act for themselves. There are hundreds of definitions for AI. Forbes describes it as a method of leveraging technology to improve the customer journey. Whatever the definition, it comes down to systems being able to analyse vast amounts of data to find patterns of behaviour that humans can’t. These patterns and trends can help you as a marketer target your strategies and campaigns throughout the marketing funnel.
Let’s compare marketing done with and without AI to illustrate our point.
AI vs CRM
A Forrester survey found that 74% of consumers research products online before they make an offline purchase decision. Many companies rely on sales data to personalise their customer journey. This process has shortcomings, particularly in human cognitive bias and scalability. CRM platforms like Marketo, Hubspot and Pardot are fantastic for marketing automation. They are largely ineffective at data-driven predictions. Take lead scoring, for example. It has largely relied on arbitrary data based on human emotions with little correlation to real-world facts and behaviours. The result is based on human error and bias. Although these platforms can add your name to a website or newsletter, they fail to understand the whole picture. That’s not what CRM was meant to do. AI and machine learning, on the other hand, are built to predict what a person will do next and can recommend actions you can take.
AI in action
Let’s look at an example of AI in action. Netflix uses an AI algorithm that decides which thumbnail image to show to which audiences. If a person is more into action movies, the AI will show an action-packed thumbnail to a potential viewer. The same goes for animals, males and females. Based on previous viewing patterns, the AI will show a thumbnail containing an animal to those people who find them irresistible. The AI filters out all irrelevant information and shows you relevant data. Or the data that you are most likely to react to.
Identifying an audience based on big data
You’ll find relevant data to gain insight into your audience in your CRM system, social media accounts, Google Analytics and, your help desk data. Here are the four V’s that will help you separate the relevant data from the rest.
- Volume – refers to the amount of data, i.e. number of Google Searches or website clicks
- Velocity – refers to the speed at which the data is created, like real-time search results, stock trades or content recommendations
- Variety – refers to the different number of source types. It includes data from social media to your website to help desk requests
- Veracity – refers to the trustworthiness of the data, like self-reported data vs data that is gathered automatically
To predict a person’s behaviour, you need to understand their mindset, behaviour and demographics. The data you will need to gather will differ from brand to brand. You want data that factors in sentiment, cultural differences, social engagement, and how people search for information. These are some of the data points to gather data from:
- Loyalty programmes
- Social media usage data
- Product affinity data
- Conversion paths
If you take these data points and apply AI and machine learning to them, you can predict when an individual wants to hear from you, when they are likely to buy, and when they’re likely to churn. Combining big data with deep learning, allows you to predict their behaviour months in advance.
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