Enhancing the customer experience with behavioural data

Deeply understanding customers to deliver a personalized service is critical

However, many businesses are still falling short of the personalization game and missing a mark when it comes to enhancing customer experiences, as they continue to undervalue structured high-quality behavioral data. Whilst investing in artificial intelligence (AI) to remove the data bottleneck that’s preventing delivery of advanced analytics use cases, brands should simultaneously start utilizing behavioural data to understand customer better.

Rich, custom and unified behavioural data doesn’t just capture the decisions people make, but also how they make them and the context in which they are made, providing a better basis to personalize customer service. For instance, retailers can understand the information customers need to make purchasing decisions and how they consume it.

In the context of customer service

The use of behavioural data improves customer service in various ways, such as allowing businesses to gain a single customer view. This reduces the time taken to resolve a problem and helps deliver a personalized experience. It also enhances the customer experience in several other ways:

1: Customer insight

Support agents have a difficult task as they engage closely with customers to understand their problem and help resolve it. Often by the time a customer gets in touch, they are feeling frustrated and are reluctant to carefully communicate all the information required to provide the most effective support.

Behavioural data can help by providing support staff with a detailed view of the customer’s journey, including the details of the customer’s last order in question and any previous touch-points the customer engaged with to try and resolve the issue themselves. This saves a lengthy explanation from the customer, while providing the agent with necessary details.

This rich behavioural data combined with predictive and flexible AI-powered insights helps agents ascertain the customer’s problem and potential routes to resolve. For instance, it can be used to identify which agent has the best track record of resolving a particular type of issue and send those requests to them. Or it can be used to spot a problem before the customer has to contact support, so that an automated intervention can be initiated.

2: Using data to drive search optimization

Behavioural data enables better search optimization. Businesses typically optimize search performance based on search terms collected by the engine itself, allowing them to optimize the ranking of results based on click throughs. Behavioural data doesn’t only describe search terms, but the context in which that search was conducted. For example, what happened before the search was run? More importantly, it can capture what happens after the user clicks on each result – did they see the result and dismiss it or did they go on to buy it?

3: Customer journey analytics

Brands can use behavioural customer journey analytics to understand customer behavior across disparate systems and channels. This could be for things like the best time or channels to use to engage with a particular customer. Reliable, accurate and explainable AI-ready data is a key enabler to this.

Maximizing behavioural data

Customer opt-outs, technical challenges around implementing tracking and privacy-based measures such as Intelligent Tracking Prevention (ITP) can all be the root cause of the lack of high-quality behavioural data across businesses. It’s certainly a challenge that needs addressing if brands are to optimize the use of AI-ready data sets.

Many organizations rely on existing data sources and third-party tools to prepare their data pipeline and allow a more deliberate use of data. Often, this is via commercially packaged analytics tools, like Adobe Analytics and Google Analytics. While these are good for measuring and optimizing marketing campaigns, they are not as good at driving the deep customer insight required to power personalization. Leaders in AI are already developing end-to-end data platforms and data schemas in-house, enabling them to break through traditional data constraints and execute upon advanced AI opportunities.

Starting on behavioural data journey

A behavioural data platform (BDP) allows organizations to create and capitalize on AI-ready and behavioural data securely to power innovative use cases. AI-ready data enables the evolution of an organization’s data schema over time, maintaining a human-readable data grammar.

A BDP facilitates the ability for businesses to change what data they capture as their customer service functions evolve. This equips support teams with actionable customer insights, allowing organizations to proactively and quickly predict and solve customer problems. With a BDP and AI-ready data in tow, the possibilities are endless, such as the creation of a composable customer data platform (CDP), enabling full ownership and future scalability.

Businesses that focus on understanding their customers to deliver optimum service will have a competitive edge over their rivals. They can do this by investing in a BDP to collect, build and manage behavioural data to improve the overall customer experience.


About the Author

Yali Sassoon – CPO & Co-founder At Snowplow, Yali gets to combine his love of building things with his fascination of the ways in which people use data to reason; something he cultivated when studying his BA in Natural Sciences and MPhil in the History and Philosophy of Science, and further developed as a Strategy & Operations Consultant (at PwC, Bestshore and Keplar LLP) and a Data Analyst at OpenX.

Featured image: ©James-Thew

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