AI is already enhancing customer relations for multiple business sectors – can it do the same for research?
Organisations of all types are rushing to understand how Artificial Intelligence (AI) can enhance their customer interactions – and the research community is no different.
In a previous post, we looked at two examples of how AI is being used in the research-gathering part of the sector.
Naturally, this is where much of the conversation and industry focus is pointed but what happens when that market intelligence has been compiled and it’s ready to be made public?
Can AI maximise the opportunity to get relevant information in front of the right people and, furthermore, handle various aspects of a business’s relationship with its customers?
What can research businesses learn from the ways in which AI is being used currently by other organisations?
Email subject lines
Virgin Holidays is using AI technology by Phrasee, a UK startup, to automate headline writing for its marketing emails. According to reports, the machine-powered scribe outperforms humans by up to 10% in terms of open rates.
Those incremental improvements can be worth a significant sum to a multi-million-pound business. The technology also allows the holiday company to test more options and save time in the long-term.
Online supermarket Ocado is using machine learning to detect incidences of fraud as people navigate its website and apps. Every time a customer adds trolley items, books a delivery, and goes through the checkout, they leave information behind. This information can be very useful.
Ocado Technology, the team developing software and systems for the online grocer, applies machine learning to find ways to predict when fraud might occur and to differentiate this from normal customer activity.
It does this to prevent customers being adversely affected by fraud and to prevent fraudulent activity spreading, but this isn’t the only way the online grocer uses AI in customer relations.
Machine learning is also used to power the way it recommends products and generates search results. For example, it claims to have designed systems to avoid suggesting meat to vegetarians or products containing gluten to celiacs.
Virtual customer service
Customers of the Royal Bank of Scotland and NatWest have been interacting with a virtual chatbot for almost a year and a half. It picks up queries when messenger conversations start and attempt to deal with the issues that come its way.
Powered by IBM’s Watson technology, the Luvo virtual agent interacts with customers to perform basic tasks. Anything complex is left to a human.
After a successful trial with RBS’s staff, Luvo went live in October 2016. It has the ability to answer ten questions and the capacity to learn to deal with more complex issues. It even recognises whether a customer is unhappy or frustrated, and can change its tone appropriately.
The technology was put into use to free up the time of the bank’s human advisers. While the chatbot deals with simple queries, the bank’s relationship with its customers is enhanced by having staff dedicate more time to helping customers with complex issues that require a level of empathy of which computers aren’t currently capable.
As we have indicated, in some areas, technology might simply prove to be more efficient than humans at performing key tasks. Other implementations could see AI-powered systems add value in ways humans previously didn’t or couldn’t. However, as our banking example shows, the effectiveness of customer relations could also be enhanced by the combined use of technology and humans.
Just in these few simple examples, we can see how the implementation of AI across the economy is likely to be complex and multifaceted – and this variety is likely to be reflected in how research firms use AI in their customer relations.
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