“It is a well-known fact that nearly 98% of a website’s daily visitors are anonymous and disappear without a trace”
Website traffic is often a result of well thought-out inbound marketing strategies, and the frustratingly low visitor conversion rates can be attributed to the use of wide net, catchall kind of software solutions with little or no personalisation.
Traditionally, most of the businesses have been relying on click-stream data analytics tools to show rule based tailored content to new visitors, without much impact on conversion rates.
It’s time to shift to a multi-pronged approach for Hyper-personalizing new visitor experiences with Artificial Intelligence.
How can AI help in Hyper- personalization?
Data suggests that businesses which personalize web experiences see an average of 19% increase in sales. Being able to hyper personalize and modify content according to the visitor's context using AI based dynamic persona creation is therefore highly relevant to marketers today.
The age old click stream based tools have now become irrelevant. In order to reach a desired recommendation accuracy level of at least 80%, it is essential to provide consolidated and personalized content/offers/deals to visitors. This is impossible to achieve manually without leveraging AI.
Customer behaviour analysis comprises of complex mix of attributes like:
- Click stream : Intention of visit can be understood to an extent based on the links or products people click on.
- Location and weather : Customers from different locations have different preferences. The weather around the customer’s location can also be an influencing factor when it comes to customer behaviour.
- Device and IP address : Customers behaviours change depending on the device they use and act as an important attribute while segmenting the customer. The IP address of visitors can help track their digital footprint as well. This can then be used to understand the visitor better.
- Time spent on every page : The time spent by a visitor on the website can be a valuable parameter to judge their purchase intention.
- Time and day of the week : Customer behaviours change depending on the time of the day they are browsing a website. In some cases customer requirements also vary according to the day of the week.
Personalizing content/offers based on a single or few of these attributes is akin to spray & pray with low accuracy
Further, the approach lacks “in the moment“ relevance, because the context of the visit isn’t considered. This will hardly move the needle on visitor conversion rates.
The solution lies in using Humanised AI analysis & a mix of Machine learning to create a Dynamic persona, Peer mapping to identifying nearest neighbours and using current context like Humans would do, to make the engagement hyper-personalised and relevant, for manifold increase in conversion.
For example:
A compelling proposition for a returning visitor to the Brand’s website would be a personalised offer for a BOGO brunch of particular cuisine(lebanese) based on location (within 5 -7 kms) + time of day(evening) + day of week (Saturday) + web interaction(search for brunch offers) + context (offer for 2).
Let’s take a deep dive -
Creating dynamic customer persona
Every customer situation is different, the huge amount of customer interaction data is siloed & unstructured , compounded by the fact that it’s humanly impossible to identify, analyse and predict events accurately.
Here, machine learning can help identify the hidden patterns to create Dynamic Persona to actively engage the customers based on reinforcement learning and new data of other customers.
Amigo CX recommendation engine consumes structured & unstructured data about new visitors & continuously updates the AI model to keep the persona concurrent with the behaviour patterns.
Finding the Nearest neighbor
Peer persona mapping - through advanced clustering and classification algorithms, the AmigoCX recommendation engine identifies customers who have personas closest to that of the new visitor. This helps us in finding the gap and predicting Next best action (NBA) or Next best offer (NBO) for the customers.
Pairing persona with current context
Armed with the NBO/NBA information about the persona, we marry the current context to bring relevance & deliver truly personalized recommendations to first time visitors.
Outcome- Hyper personalized recommendations and at least 30% increase in visitor conversion rates.
Amigo CX AI combines a visitor’s contextual data with a dynamic customer persona to deliver hyper personalized recommendations.
This consolidated approach offers 60-70% higher accuracy when compared to rule based recommendation.
Move from actionable insights to insight based actions with Affinsys cognitive CX AI recommendation engine. Leverage dynamic customer persona with contextual data to offer hyper personalized user experience and improve first time visitor conversion. Head over to our website to know more.