Yali Sassoon, co-founder of Snowplow, discusses what businesses need to consider when it comes to driving value from behavioural data
Behavioural data describes what people (and machines) do. With the birth of smart phones more than a decade ago, and the subsequent development of connected devices like wearables and smart appliances, we now have the opportunity to collect rich, granular, behavioural data, that describes how people perform a growing range of activities, second-by-second and minute-by-minute. These activities include how we work collaboratively with our co-workers (on productivity applications like Microsoft Teams, Atlassian‘s Jira and Slack), how we meet and fall in love (on dating apps like Tinder and Bumble) and how we manage our health and fitness (on apps like Strava and MyFitnessPal), to give just three examples.
For businesses, the rise of behavioural data presents a huge opportunity. Not only can organisations now collect data that describes how their customers, employees and partners engage with them through different digital platforms, but they can use this to enhance services, such as providing personalisation or informing product development.
For example, retailers can understand how customers make purchasing decisions: what information they require, how they consume it, what options they identify, and how they weigh those options up against one another to make a final decision. Media companies can build detailed pictures of how users engage with different types of content at different points in their day, what they are most interested in, and what they are not. Meanwhile, B2B SaaS companies can build a picture of how different people in prospect organisations learn about their technology, how they trial it, and how they go about deciding to continue to use it and grow their adoption over time. That data is incredibly valuable.
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Combining behavioural data with artificial intelligence
Behavioural data is the best type of data to drive artificial intelligence (AI) applications, because it describes how people make decisions, and it captures the different factors that influence those decisions. AI technology can then be used to identify what factors actually drove those decisions, so that they can be understood and effectively influenced going forwards. Behavioural data can be used with AI to optimise the customer experience, power dynamic pricing, drive effective promotion and spot and prevent churn. It can be used to maximise basket value and lifetime value. It can power lead scoring, optimise ad yield and identify and prevent fraud.
This single data set can power an endless number of use cases that drive value for customers, whose needs are better understood and met, and businesses, who can use it to meet key objectives such as including customer acquisition, retention and lifetime value. However, despite the benefits it can deliver, most organisations do not collect and use behavioural data effectively. So why is that?
Using behavioural data effectively
Currently, most organisations are using behavioural data to power a limited set of use cases via packaged solutions. The problem is these solutions are focused on specific use cases for a specific set of industries. As a result, they make specific assumptions about what the behavioural data describes and how it should be structured and processed – a hard task given the enormous variety of behavioural data that different organisations could collect and use.
Similarly, while these tools are great for easy reporting, they are often not built to enable the integration of data sources, leaving organisations unable to see the full picture and drive meaningful impact to their overall revenue and profit. If businesses want to use behavioural data and AI to drive more effective customer acquisition, customer lifetime value, pricing, promotion and personalisation, they need to start by collecting behavioural data that is fit-for-purpose. And the only way to do this is through data delivery solutions that provide flexibility and control over how data is collected, such as behavioural data platforms, rather than relying on packaged tools to collect it and export it as a by-product.
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The benefits of behavioural data platforms
Behavioural data platforms enable organisations to generate, validate and deliver high quality behavioural data in real-time. More importantly, they provide flexibility, enabling organisations to evolve the underlying behavioural data as they grow in data sophistication, using the data to drive more use cases, and iterate on each of those around evolving customer experiences.
Behavioural data platforms also provide high levels of assurance in data quality, guarantee that data is both accurate and complete. Access to high-quality behavioural data is essential if data is to be used to power AI models and a behavioural data platform enables the generation of much richer, better structured data, with more predictive power, than packaged tools can support.
Businesses typically start using behavioural data to execute on a simple set of use cases, and then evolve these over time to adopt more sophisticated approaches to drive better performance. As they move from simpler to more sophisticated approaches, packaged solutions start to be much less effective, and the benefits of a behavioural data platform become more pronounced.
Take the example of marketing attribution. Organisations often start with simple approaches (last touch, first touch, linear) based on data from packaged solutions like Google Analytics. Over time, organisations want to start to understand how different campaigns across different channels work together to drive up likelihood to buy. This requires moving to build a complete customer journey across different channels including on and offline, understanding ad exposure and interaction across those channels for each prospect, and then algorithmic approaches to understanding how those campaigns work collectively for each prospect across the userbase. A behavioural data platform provides the robust framework necessary to build an accurate picture of the customer across all channels in a consistent structure and format, suitable for being fed into different models to do this sort of analysis, enabling organisations to achieve much higher return on ad spend. Packaged solutions do not.
Another example is customer segmentation and targeting. Organisations often start with rule-based approaches for targeted users with different characteristics and campaigns (e.g. target users who bought products in this category with this email) and then move onto machine learning (ML)-based approaches to segmenting audience based on their more specific interests. Behavioural data platforms provide rich, granular, user-level data that describes in intricate detail not just what content and products users have engaged with, but how deep that engagement is and in what context. This allows companies to feed their ML models with much better input data to make much more accurate predictions about what product, content and promotions those same customers are likely to be interested in at this point in time. From here, organisations can drive better engagement levels, customer satisfaction, retention and lifetime value.
Reaping the rewards
In the old world, organisations used to compete on product, and those organisations that had the best product would win. However, in today’s digital economy, organisations compete on customer understanding, and it will be those that best understand and serve each of their customers who will come out on top.
Behavioural data and AI can provide organisations with unparalleled opportunities to build deep customer insight and progressive companies are already putting solutions in place to enable them to leverage this for competitive advantage.
By investing in a behavioural data platform to build the best behavioural data, companies can use data to drive radical improvements in customer experience, acquisition, retention and lifetime value, at every stage in the customer journey.
