email and support desk . Importantly , behavioural data captures not just the decisions that people make , but how they make them and the context they are made in , making it much more
BEHAVIOURAL DATA CAN DESCRIBE NOT JUST THE SEARCH TERMS THAT WERE ENTERED , BUT THE CONTEXT THAT THE SEARCH WAS CONDUCTED .
explanatory and predictive than demographic or transactional data .
For businesses , this helps drive a much deeper level of customer understanding and a better basis to personalise the customer service . For example , B2B SaaS companies can use it to build a picture of how different people in prospect organisations learn about their technology , trial it and decide to evolve their usage . Media companies can understand how users engage with content and what they like , and retailers can understand how customers make purchasing decisions : what information they require and how they consume it .
How behavioural data improves customer service
Behavioural data can improve customer service in many ways . It can help create a single customer view , shorten the time taken to resolve a problem , or by providing a personalised customer service experience . What ’ s more , it supports a range of use cases . Here we look at three that help deliver an enhanced customer experience :
Use case 1 : Delivering customer insight to the helpdesk
Support agents have a difficult task . They have to engage closely with customers to understand their problem and the context , to help them resolve it . And often by the time a customer gets in touch with an agent , they are already frustrated and in no mood to carefully communicate all the information needed to provide the most effective support .
Behavioural data can help enormously here . In the first instance , it can provide support staff with a detailed view of the customer ' s journey before submitting the ticket , as well as a view of what the customer is doing in-product .
This saves the customer having to explain too much while providing the agent with a good understanding of the problem .
Because behavioural data is so rich , it can also be used with AI to help predict the customer issue and potential routes to resolution . For example , it can be used to dynamically route requests to the agent with the best track record of resolving those types of issues quickly . Or it can be used to proactively spot the issue before the customer has to contact support so that an automated intervention can be initiated .
Use case 2 : Capturing and modelling data to drive search optimisation
Behavioural data is also an important enabler of better search optimisation . Companies typically optimise search performance based on data collected by the search engine itself , on which results were selected against each combination of search terms . This enables them to optimise the ranking of results based on click-throughs .
Behavioural data can describe not just the search terms that were entered , but the context that the search was conducted in . By showing customers results they ' re more likely to purchase , rather than ones that look promising but on inspection prove to be disappointing , organisations can deliver a much better customer service .
Use case 3 : Understanding customer data through customer journey analytics
Organisations can also use behavioural data to deliver customer journey analytics . Successful brands can use this capability to understand customer behaviour across disparate systems and channels . This could be things like the best time to engage a particular customer or what channels are best to engage with them .
Getting the most from behavioural data
The above examples highlight how behavioural data can be used to enhance customer experience . But to get the most from their behavioural data , organisations must optimise its use .
The problem is many organisations lack highquality behavioural data . This is in part due to factors such as customer opt-outs , technical challenges around implementing tracking , human error and privacy-based measures like Intelligent Tracking Prevention ( ITP ) which make collecting high-quality data difficult . However , it ’ s also down to the fact that previously many
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