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Analysing the ‘why’ in customer loyalty

Building brilliant customer loyalty is a mixture of science and art – of analytical thinking and emotional intelligence. The raw material for it is a combination of terabytes of behavioural data and empathetic connection through regular conversations across multiple channels.

The data is great for measuring the outcomes of customer experience and identifying behavioural patterns. The conversations are vastly superior in providing insights into why those things occur.

However, for most organisations, aligning the two to draw absolute connections between conversations and actions is almost impossible, even for those running sophisticated CRM platforms.

This makes understanding why people behave the way they do much more challenging across the entire customer base, or even segments within it. Pursuing the answer to why is what keeps many research firms in business.

Knowing what drives customer loyalty and, most importantly why, is the key to company growth, to maximising retention while attracting new customers.

Knowing why certain customer experiences drive loyalty enables teams to better prioritise enhancements to the things that really matter over the customer journey. It enables vastly more efficient processes and effective, well-targeted communications.

AI’s coming of age in data science is now offering a pathway to drawing together the art and science of customer loyalty. Its most obvious advantage is its analytic capacity and speed, but its ability to tease out the why things are happening is less appreciated.

AI digs deep to explain your data

If we return to the original point about science and art, these are underpinned by data in two basic forms – structured (mostly numeric material captured in business systems) and unstructured (call centre conversations, web chats, emails).

Many organisations, particularly those with CRM platforms, are pretty solid on the structured customer data – value and frequency of transactions, even birthdays and some personal information obtained with permission.

But what about those ad hoc communications that may be attached but remain unaggregated and unanalysed? These unstructured records hold the key to why things are happening across the customer.

Using AI, we can unearth the why using ‘explainer’ algorithms, which mine and align both sets of data – structured and unstructured. Not only does this provide us with improved insights for individual customers, but also an overarching view, which identifies patterns in conversations that strongly and consistently influence customer propensity to either make additional purchases, or to defect to other providers.

These powerful behavioural insights empower customer relationship teams to address and reduce the issues having most impact on designing and guiding next-best decisions or actions, or to influence improved customer retention.

Empowering better customer experience

Harnessing the capacity of AI to dynamically deliver updated insights at dashboard level every day of the year can substantially improve the process of even with the best-designed customer research, including Human Centred Design (HCD) processes.

HCD has proven capacity to design business systems, processes and interactions to improve customer experience. Imagine the additional benefit that could be brought to this by backing the design process with AI data science that can measure the customer reaction and impact of HCD deployments in the field, allowing testing and fine tuning on the fly.

One of the core beliefs behind HCD is that every organisation and its attraction and retention of customers is unique. Every HCD output is unique to that organisation and its customers.

AI is no different. There is no one-size-fits-all answer to data science analytics and modelling. To successfully support business growth, AI processes and insights must be precisely moulded around each company’s customers, products, capacity and strategic goals.

Is our business ready for AI?

Return on AI investment for each enterprise will depend on various combinations of the scale and complexity of its customer data. Some will have relatively small customers bases with complex data sets (think financial planning businesses), while others in FMCG will have large databases but, most likely, less intimate data on each customer.

To date, data science has been the province of large, well-resource companies, but that is changing. As of now, AI is offered on a similar ‘Software as a Service (SaaS) basis to the ubiquitous Microsoft365. Data Science as a Service (DSaaS) is democratising access to the power and benefits of AI at a price affordable to a much broader array of businesses and not-for-profit organisations.

With this in play, the first question customer service executives should ask is: why wouldn’t I want to invest in better understanding why?

Asanka Fonseka
Author: Asanka Fonseka

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