Improve Customer Experience with Machine Learning
Renee Kalia - CX Strategy
Aim for simplicity in Data Science. Real creativity won’t make things more complex. Instead, it will simplify them.
Machine learning’s (ML) many applications make it a powerful tool in creating amazing customer experiences. The ML approach enables true digital innovation for customer care that has the potential to deliver world class customer experience if understood and applied correctly.
If you want to improve customer experience, then measuring the omni-channel customer journey at an individual level is essential.
What are the key takeaways for me so far? The first is maybe the term ‘machine learning’ creates misconceptions when compared to what it means. The obvious one being that the task of machine learning is to think and make decisions like humans – this is incorrect. It’s “applied statistics and algorithms to identify probabilistic relationships in data”
Machine learning is about learning patterns in data. It is good at predicting the future when the future looks like the past. Once they are deployed and over time, ML models become less accurate until they are retrained. Accuracy is not a good measure of model performance and ML learns whatever biases you and your data have.
Having spent more than 15 years being obsessed with perfect service and digital empathy, I can tell you that the prospects of machine learning to improve customer experience is a game changer – and should be taken seriously if companies want to truly get ahead of the competition and avoid being left behind.
Here are some well known examples where machine learning has been deployed successfully:-
Netflix: 75% of what people watch come from recommendations based on each viewer’s preference, demographics and watch history
TechCrunch: This chatbox enables users to keep up with tech news. The bot then tracks the articles by user and delivers new articles they would like. Certainly does narrow down the overwhelming amount of information out there
Amazon: 35% of sales comes from recommendations. The site tracks customer viewing and purchase history and finds the right products to recommend. Great conversion rate!
Uber: Algorithms use data from previous rides to match riders and drivers and determine how long it will take for drivers to arrive. The system gets smarter the more it is used
There’s many more, so check out Pinterest, Wells Fargo, Starbucks, Instagram, Nike, BMW, North Face, American Express, and Disney.
There are 3 x common uses of machine learning.
Applying Machine Learning to enhance CX?
The process outlined below in practice should be circular. A cycle of continuous improvement where enhancements to CX can be realised through business process optimisation. This is also a key aspect of the newly launched version of ITIL4.
The diagram below has been used at basic levels before machine learning, however is now also used with Machine learning to accelerate progress and outcomes for improved CX.
For example, reports are generated to measure performance, scope and realisation of customer activity and trends which can now translate to predictive analytics. Learnings from these enable optimisation of processes and enables the building blocks to be established for service management – with less human interaction and higher conversion rates – of course, all of this has to be managed with execution through cross functional collaboration and stakeholder engagements which leads to delivery and monitoring of functionality.
Measure and Learn
Comprehensive dashboards. Granularity is the key to opening up the most powerful Machine Learning analytics capability
Understand how your customer base is engaging with the business and/or digital applications. Enable tracking whether individual or anonymous
Customer profiling. Create and monitor target audience based on common characteristics and behaviour
Optimise
Understand how content is performing at molecular level by customer or site
Build and Manage
Range of ML approaches include:
Supervised and unsupervised machine learning
Reinforcement learning
Artificial neural networks
Deep Learning
Artificial Intelligence
Descriptive, Diagnostic, Predictive and Prescriptive analytics
Conclusion
The future of Machine Learning is unstoppable. It is not a passing trend, but a mega trend that will transform business and society. It is the fundamental building block of artificial intelligence.
As a starting point, there would be no harm is working with a partner to establish a set of machine learning algorithms whether to solve business problems, or improve business process or enable a differentiation in customer experience. There is a clear case for a symbiotic relationship between business leaders and data scientists to support the advancement of AI and ML.
Business leaders cannot know what ML is good for without knowing what it is and there is a clear knowledge gap. Without a good understanding, there are cases where ill-conceived ML projects were rolled out with impact to costs and time with negative impact on the bottom line. The usual fallout in addition to losing customers is staff turnover.
With thanks to all the CX futurists that I have discussed and shared ideas with, and to the data science and machine learning course I have attended that has enabled me to translate the capability into CX possibilities. I am still learning!
