One of the greatest challenges in marketing is to create personalised communication. Time and time again, it has been shown that relevance drives engagement, which in turn drives an increase in turnover and creates long-term brand value. Of course, the customers know that you have lots of data about them – so this needs to be reflected in the communication. But how do you manage to personalise thousands of engaging customer journeys for customers, when not all of them need to receive an email by Tuesday at 11 AM, and where personalisation by using their names or an individual product category is not enough? This is what the AI-driven customer journey can offer.
Deep dive into data
Data, data, data. This is the current mantra in marketing because data holds the key to relevant and engaging customer communication. The challenge is not only the amount of data, but also the ability to create insight by using this data. That is why companies are looking for technologies that can help them to utilise this potential. One of the more recent members in the marketing technology portfolio is artificial intelligence (AI), that can find the connection within large amounts of data and derive insights that you would struggle to achieve without AI.
When you use AI to personalise your communication, the customer journeys shift from being rule-based to being driven by insights from those data streams that the customers’ interactions with a brand continuously generate. These insights can be felt at more than an aggregated level. AI also helps you to gain an insight into your customers on a more granulated 1:1 level, and it can recommend timing, channel, and content based on subtle patterns.
Strategy is still required
AI is not a quick fix that can vitalise your customer journeys from one day to the next. A clear strategy is required, and you have to know what you wish to achieve with your communication. Which“moments of truth” in the customer journey do you want to impact? My recommendation would be to choose 4-5 scenarios, where you could useAI to optimise them. This could, for example, be the repurchasing of products that are about to run out (replenishment). It could also be scenarios such as customer retention (anti-churn) or cross sales (share of wallet optimisation).
The first step is to investigate what characterises the customers that match each moment of truth – i.e. the customers that renew their subscription, frequently re-purchase certain products, or who have a high share of wallet. This will give you a clear idea of how to create more of these customers. What are their behaviour and characteristics: Do they look and click on certain links, categories, products? Do they use different services? Do they phone in? Do they redeem points? Have they purchased other specific products, and what kind of people are they in general if one considers classical qualities such as gender, age, demographics?
Besides being interesting knowledge for the company, the patterns also constitute a potential background for an automated scoring of the other customers in the database. Who resembles the customers with the desired behaviour, and to what degree? How can we help them to make the “right” decision during their customer journey, so that they make it to the target?
From a customer perspective, AI provides a more meaningful relationship to a brand during the customer journey, as well as a higher level of relevancy. Increasingly, communication becomes a service that the customer is interested in, and less of a disturbance that the customers will protect themselves against. Through experience, we know that using AI is rewarded with engagement and loyalty.
AI is a breaking point
There is a reinforcing effect when using AI. When you use AI in your communication, you achieve significantly better results. This is, first and foremost, due to the precision and level of personalisation that you can achieve within the communication. Moreover, the effect is strengthened by the fact that the algorithms are self-learning. The AI analyses the results of each communication: what worked well, in which situation did we achieve the sales target with the communication in question, and in which situations did we not achieve the sales target? Through this, the database continuously becomes enriched and “smarter”, meaning that the next communication will be even more precise in terms of personalisation and efficacy. There is a strong synergy between predictive analytics, AI models, machine learning, and marketing automation. This synergy is paramount in terms of winning and retaining the critical consumer’s engagement and loyalty.
Learn more about the benefits of AI
You are welcome to contact Rasmus Houlind at email@example.com or +4553 88 65 55 if you want more information about how we can help you get started with AI-driven customer journeys, and the busieness value you can expect.