Learning from our data: Resolver, machine learning and securing better outcomes
The customer service and complaints sector is no different, and Resolver is at the forefront of these new technologies within the sector as part of our mission to redefine how businesses achieve better outcomes, encouraging better solutions and building stronger relationships between businesses and consumers.
AI and ML
The terms ‘AI’ (or artificial intelligence) and ‘ML’ (machine learning) are often conflated, but this is incorrect – while machine learning is an application of AI, they aren’t the same thing.
AI is typically focused on mimicking human decision-making to accomplish tasks in an intelligent manner. These tasks could include holding a conversation through a “chatbot” or piloting a driverless car.
ML, on the other hand, stems from the idea that computers can mimic human learning – that it isn’t necessary to teach computers about everything, since it’s possible to teach them how to learn from themselves. This way, computers can act without being specifically programmed.
Typically, this requires the input of data. By assigning data to specific categories, we can teach machines to draw connections and predictions from the data provided.
Of course, there are lots of challenges. A good example would be teaching machines to recognise handwriting. Take, for example, the letter “a”. When people learn to recognise letters, we identify the “a” shape as being linked to things like phonetic output. This is because when we see a similar shape, our brain unconsciously compares it to an example we’ve already seen. If it’s similar enough, we recognise it as an “a”, regardless of the minute variations that may occur as a result of different handwriting.
Machine learning allows computers to function in a similar manner to the human brain. If we give a computer a large number of handwritten letters, we can build a system that takes the examples given (known as the training data), to automatically infer a series of rules for recognising letters. As the system makes decisions, it adds examples to its training data, informing future decisions and improving its accuracy. The computer recognises that the letter “a” requires curved lines in certain places, and thereby distinguishes it from other vowels.
A more sophisticated example would be creating a system to distinguish between tweets to determine who sent them. If we were to examine tweets made by Presidents Trump and Obama, it would be clear that there are certain key terms that occur at a higher rate of incidence in the tweets of one than in those of the other. For example, President Trump may be more likely to tweet about “fake news”, while President Obama may tweet more about “affordable care”. By giving the system in our last example a sufficient number of their tweets, we could teach it to distinguish for itself between tweets from both Presidents – not only that, but we could extend the system to make predictions concerning their future tweets.
This is why Resolver believes the application of ML is crucial to the future of complaints-handling. With sufficient data, businesses can build systems that afford them greater insight into their own processes and their customers’ behaviour.
Having worked with consumers and businesses to resolve over two million cases, Resolver has generated a vast amount of clean data around complaints.
Where can we draw value from this data?
For Resolver, the implementation and integration of machine learning offers businesses three key advantages.
The first is prediction. Resolver has developed an algorithm that will identify which cases are likely to escalate to an ombudsman or alternative dispute resolution scheme (ADR). This is exceptionally valuable to businesses, as each instance of escalation can cost a business between £300 and £600 in ombudsman fees alone. By identifying cases that are likely to escalate, Resolver will allow businesses to pre-emptively target problematic cases, saving time, money and customer effort.
The second is vulnerability. Resolver is working to detect health and mental health related vulnerability issues within complaints. Here, we use link data sourced from multiple sources to associate behaviours with certain characteristics. Resolver will draw training data from self-disclosed consumer information to help protect and empower vulnerable consumers.
Resolver is developing a system which identifies patterns where people are in need of help or prioritisation – for example, financial difficulties. This is challenging because it can be exceptionally difficult to identify a single, definitive model representing financial vulnerability. This is because the boundary between “getting by” and being in a vulnerable financial position is an extremely thin one, dependent on a wide field of variables, and can shift at a moment’s notice. For this reason, it’s much easier (and often more beneficial) to flag indicators of financial vulnerability. This will allow Resolver to build a better understanding of when consumers need specific guidance – again, by drawing training data from self-disclosed information. These efforts will help businesses ensure that they are positioned to treat their more vulnerable customers fairly, consistent with current regulation.
The third area of value is represented by sentiment analysis and keyword extraction. Resolver uses the Watson API and is developing various proprietary components to swap in that will allow Resolver to identify the sentiment represented in consumer complaints. This will help businesses identify areas for improvement, isolating the point along the customer journey at which problems occur! By isolating specific keywords, Resolver will be able to pinpoint exactly where businesses need to focus their efforts in order to improve customer retention.
As Resolver’s dataset continues to expand, we will be able to improve the accuracy and range of our predictive modelling, improving the customer journey by allowing businesses to hear from their customers. We’re excited to share the benefits of this technology with consumers and businesses as we continue our mission to build better markets.