HOW MACHINE LEARNING WILL DELIVER YOU BETTER CUSTOMERS
First we are not talking about Skynet in Terminator or HAL in 2001: A Space Odyssey, Machine Learning is where we want a machine to learn from the huge amounts of data we give it, and then apply that knowledge on new pieces of data that streams into the system.
Machine Learning can be seen as a subset of AI (Artificial Learning) and defines the process by which a machine can learn by its own without being explicitly programmed, and that’s the key it delivers insight of its own accord.
Machine learning will help us finally realise the promises of how to optimise and develop truly engaging and effective customer experiences. However very few companies believe they have the necessary technology or that implementing it is easy. But the technology is only the start and today it doesn’t represent the challenges it used, however the company or brand needs to re-orientate the business to the customer.
By doing this and implementing machine learning that focuses on the customer journey, brands can start to redefine the customer experience through data insight inexpensively.
Taking multiple data sources including transactional machine learning models can begin to specifically tailor and personalise the customers journey using machine learning algorithms. Whether it’s a motor retailer looking to identify the moment when a customer will be seeking to change their car or an insurer looking to identify those customers least likely to commit fraud to speed settlements up. These companies are successfully harnessing machine learning to deliver higher sales, more efficient service and most importantly higher customer loyalty, through more effective on-boarding and retention.
WE ARE SEEING COMPANIES BEGINNING TO SUCCESSFULLY HARNESS THE POWER OF MACHINE LEARNING AND SEE RESULTS IN HIGHER SALES AND GREATER CUSTOMER ENGAGEMENT
Using machine learning we can identify customers and tailor their journeys to deliver the right products at the right time, making engagement and the customer journey more relevant. Through algorithms we can understand prediction problems, propensity modelling, classification and regression problems, and more to effectively target communications at the moment in time when a customer is the most receptable to communications and messaging, to effect choice, selection and purchase. Through technology we have been able to identify predictive ways that customers spend and what they are likely to buy.
Through using data derived from customer interactions with a brand, machine learning can go far beyond the scope of human analysis, driving deep insight into the feelings, needs and desires of the customer. As customer touchpoints continue to grow at speed, creating more and more data we need to develop solutions that can cost effectively generate models to answer this. Customer experiences used to involve only a few hard to track touchpoints such press ads, store visits and direct mail, these have grown exponentially in the last few years to include mobile apps, call centres, social media and instore tracking. This growth will continue as wearable tech and the internet of things becomes a reality creating yet more data sets that can be tracked to a customer’s engagement.
Because todays consumers no longer follow the linear journeys they once did, accessing and using this data will be fundamental to the success of a brand and the business. Without finding ways to cost effectively reach these customers with targeted, personalised and relevant activity they will be lost to the competitor that does.
AS TECHNOLOGY ADVANCES, AS DOES THE NUMBER OF TOUCHPOINTS AND THE AMOUNT OF DATA CLIENTS HOLD
Preparing for the journey
Using machine learning to drive insight from a customer’s engagement should include:
Push existing customer data sets into a data processing refinery, to begin the process of understanding your customers and the data you hold – the start of a single customer view. If you are lacking data, develop a strategy to acquire it, such as data capture exercises through existing digital touchpoints.
Understand the customer journey and their touchpoints – how can this be improved, where are the customer drop offs or the cross-sell opportunity.
Match what the data is telling you with the business goals and objectives, has it highlighted new challenges or opportunities for customer growth?
Apply machine learning to the data to begin developing algorithms and data models that will start to drive change in the way customers are engaged with
Identify the shortcomings of the data and where models can be improved or the customer journey adapted
Develop a test and learn strategy from the customer sets that have been produced through the data processing exercise and initial machine learning models.
Machine learning represents an exciting opportunity for brands to finally and cost effectively begin to drive their customer engagement strategy with real insight. Even a few years ago many of the aspects of ML would have been beyond a normal business without the recruitment of an in house team to pour over and analyse the data it holds, now we have clients gaining insight on millions of records in weeks and under realistic budgets.
Machine learning in customer insight still has room for improvement, but it is something that all businesses should be looking at and making plans to include in their future engagement strategies.