People want marketing teams to use the data available to them intelligently. However, data analytics is often used to make decisions behind the customer interaction, while the process of interaction itself is designed by the user interface team. This means the data rarely supports a real-time, intelligent interaction with a customer.
Instead, rules are set up in the interface, and based upon the data that feeds into them, certain outcomes might occur, such as a pop-up window, a script that the call-centre operative has to read, or a simple ‘computer says no’. Using artificial intelligence (AI) can allow your systems to understand personal space a bit more, thereby making your customers more comfortable during your discussions.
1. Give people space, with support
Often, providing people with an appropriate amount of distance is key to comfort. Following a customer around a shop can often be off-putting, making them feel crowded, but customers want assistance when they need it. Judging the right level of contact and space is an art that marketing professionals and sales professionals learn through experience.
When we use technology to enhance our own capabilities, we need to think how it will support us in maintaining the right level of contact, and how it provides feedback to support our awareness of the customer, and their comfort.
In the digital space, when we think about customer experience (CX), that sense of helpful, informative and self-aware service is important for customers. There is a risk that, with access to ever greater amounts of data on customers, targeting them too aggressively becomes the equivalent of that overly attentive store clerk.
Balance can be achieved through the smart use of new martech which surpasses the older, siloed systems by offering a single view of the customer, with dynamic interaction that intelligently meets the needs of the client by following their patterns of behaviour and not by looking over their shoulder.
2. Bring your intelligence to bear through bots
AI is already being employed to make sense of enormous data sets, and to track and spot patterns in order to support sophisticated businesses as they seek to gain and retain clients. AI systems do not need to be retrained, as they can adapt to new circumstances based on the patterns they have learned, using historical data.
Examples are found with SunLife and Bank of America, which have created automated assistants to help customers manage their insurance plans and financial affairs respectively; BoA’s Erin bot was picked up by 1 million customers during its first three months of operation.
Functionally, AI tools offer a significantly superior service to legacy tools. The legacy model for financial firms offering credit was to map out life events for a given demographic and then push products timed to match the average age customers would buy a house or become a parent. At a simple level, AI can help financial firms to spot behaviours that indicate a need for greater credit, without making assumptions based on simple models or requiring alerts that the customer has gotten married or become a parent. As a result, the firm can recommend products or services in a timely manner. It can also bring in elements such as credit ratings, existing product portfolio to offer a joined-up approach to the customer relationship.
This is impossible for legacy platforms, which contain valuable data but inevitably are a patchwork of different systems with limited or no integration. When the inputs and outputs are not connected, the messages they use cannot be passed between systems without considerable middleware to support the transfer of information.
3. Get it together, behind the scenes
Digital firms are inherently data-focused, building up clear pictures of their customers and their behaviours. For businesses that are either working to become digital or lack the regular, consistent customer contact needed to build a meaningful picture, having the right marketing technology stack is imperative in order to compete effectively with newer or more digital rivals. In 2018, Vasbourne Research reported that some 60% of businesses work with a suboptimal martech stack.
Using this legacy tech costs more and works less. Fragmented, siloed data sets cannot be used to form a clear image of the client and they cannot provide the feedback necessary to moderate marketing behaviour in such a way that it delivers a better relationship.
Cloud technology is a major contributor to data efficiency; cloud can be a repository for data that is accessible by multiple systems. Tools can also be delivered via cloud services; that potentially means that older tech does not have to be replaced, like-for-like, but newer tools can be rolled out incrementally allowing legacy systems to be phased out, rather than forcibly removed.
Once cloud is adopted, the scale of data being used by systems is less of an issue and the massive data sets that can be assessed by AI systems, or alternative data that needs to be crunched and then quantified by big data systems, can be employed within marketing campaigns and ongoing client relationships.
At this point, the concept of personal space in marketing becomes real. Using AI and machine learning tools to monitor activity and responses to contact helps the marketing team to understand how activity is working with clients in an interactive way, rather than through a trial and error model.
For example, if a client is exhibiting behaviour in a pattern that typically is a precursor to leaving one service provider for another, the marketing team can respond by either reducing or increasing contact as needed, and specifically targeting the requirements of that customer.
Such a tailored service might seem impossible to businesses that have not stepped into the digital world of martech. I would urge them to start looking at the adoption of cloud, big data and AI tools, in order to revolutionise their customer relationships. Give your customers the right amount of space to drive your business forward.
