It’s an incredible statistic that 98% of visitors to your website leave without buying or moving forward in their customer journey. This is in sharp contrast with the experience in physical stores, where shoppers are much more likely to make a purchase and engage with brands.
This is in part due to shops providing a more personalised approach, with skilled staff who can see customers face-to-face and are able to better understand their desires and needs.

In the online world, customers can be more anonymous. It’s relatively straightforward to identify, understand and engage with our most loyal and logged-in customers but the issue is making sense of the vast majority of casual website visitors who we understand much less.

Addressing the three current challenges of AI
Artificial intelligence can help cut through the noise and deliver the personalised experience that brands want to deliver and consumers are asking for.

However, achieving this successfully means brands have to address three key issues around the transparency of the technology they implement. The first is choice. There are already a huge number of marketing technology platforms available, and the number is growing all the time. It’s therefore difficult for organisations to pick the right one for their specific needs, particularly as most claim many of the same features and benefits.

The second issue is hype. It can feel that every vendor is now offering an AI-powered solution when in reality ‘AI’ can mean different things to different people. Developing AI powered technology takes time and resources – it cannot be added to products and solutions overnight.

Finally, there are ethics. Brands obviously want to achieve their marketing objectives but need to understand how their systems are actually working. Are they achieving some objectives at the expense of others or are they actually damaging their reputation through unethical or discriminatory algorithms?

Explaining AI personalisation in practical terms
So how do we get AI personalisation ‘right’? Essentially it means applying machine learning to available data sources, resulting in a continually improving “artificial intelligence”. A machine learning system works by analysing data and learning rules based on the patterns it finds, then applying these rules as algorithms on new data, improving its predictions as it goes. Platforms usually analyse data around customer behaviour to predict what consumers will want, enabling brands to provide the personalised experience and actions to deliver on their requirements.

The types of data AI relies on can be either ‘cold’ or ‘hot’. Cold data is based on existing known customer information such as preferences and previous orders from systems such as CRM if the visitor is registered and logged-in. Hot data is much more about their real-time behaviour on your website – what are they actually clicking on, what device are they using, where are they located? Importantly this information is all collected anonymously – there is no link to an identifiable person, protecting privacy and ensuring regulatory compliance.

Both types of data can be analysed in real-time. Neural networks automatically find correlations between visitors in order to define a target group of people with often very complex characteristics. This data could then be used (for example) to analyse which visitors require an incentive to convert, and which will convert anyway without receiving a special offer. It’s important to note that a “true” machine learning approach will always take a separate decision for each visitor, based on the current characteristics of a given individual. In this sense it’s a big step forward compared to more simple strategies such as bandit systems, which rely only on global statistical results and apply their insights to everyone without considering the specific characteristics of each individual's underlying data. That means that every visitor experiences an action (such as seeing a specific offer) tailored to their characteristics, rather than simply seeing the most popular.

How personalisation delivers real results
Transforming visitors into buyers, subscribers or prospects is a key challenge for digital marketers but by personalising each visitor’s experience, it is possible to deliver on their expectations and their needs at a given moment, guiding them naturally towards conversion.

Similarly, by providing targeted content, products and browsing experiences that precisely meet their expectations this naturally leads to more engagement. By identifying the visitors who are preparing to leave a website, personalised actions can be triggered at the right time to retain their interest and hopefully reduce churn and boost customer retention.

Taking action in real-time can also mean identifying the best or most qualified potential leads from website visitors and sending them the right message that will convince them to move forward, such as providing their contact details, making an appointment or engaging with the brand in other ways.

Consumers increasingly want personalised experiences and the brands that provide them will benefit from greater engagement and revenues. AI delivers this but it is vital to overcome the challenges around transparency. This means asking the right questions of vendors to ensure that their AI will deliver to the unique requirements of your business, meeting ROI goals in a clear, understandable way that protects your customers, your reputation and your revenues.

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