Facts, figures and detailed insights are often lacking, when it comes to customer service. That's why we wrote this jargon-free article with 4 examples of how different companies have succeeded in providing excellent customer experience online. We hope that by sharing how Finnair, Delete, XXL and If Insurance have improved their CX, you will gain actionable ideas and factual information for your own strategy.
[A summary of a presentation titled 'Tavoitteellinen Chat osa 2: Kohdentaminen, mittaaminen ja tekoäly` at a seminar event on the 27 of April 2018 in Helsinki by Otto Nyberg.]
Is AI better at decision-making than people? Maybe…it’s logical, it doesn’t complain, it can sift vast amounts of data and make automated, information-driven decisions and it doesn’t get tired. AI is an automated assistant that does the work of a small army of analysts, and does it better. So how does it work, how does AI help us making those difficult decisions?
The hype around AI applications that is about to swipe through industries and the customer service function, in particular, is enormous, but so far we've seen little real-life experiences how the chatbots function, how's it like to implement one and what the results are. That's why we put together an interview with one of the early adopters of chatbots, If Insurance.
These days, most house hunters browse for properties online, and they expect to get a response from property companies around the clock, not just during office hours. We work with some of the real estate companies in the UK, including Knight Frank, Connells and Savills, so we were keen to get a greater understanding of the current state of online engagement in the British property sector.
To achieve this, we set out to analyse data from eight of those UK real estate clients, focusing our attention on the website visits of 2,710,592 individuals during a one-month period (May 2017).
The infographic below highlights some of the top findings and conclusions of this exercise. Whilst the data suggests that property companies are off to a good start, it also clearly shows that there is a lot more that they can do to be in sync with the needs and demands of their online visitors.
When talking about machine learning in a commercial context, people often think that their conversion rate will increase just because advanced calculations relating to machine learning are being made in a server room. That is not the case. In order for machine learning to improve the conversion rate, there needs to be a complete use case where a machine learning algorithm figures out something interesting and the customer is able to use this information to their benefit.
In a standard example, you would have a treatment that has an effect of, let’s say, +10% on conversion if offered to every visitor. An example of this would be offering free delivery to every customer in an online store. If you instead targeted this treatment with machine learning, that 10% would set an upper limit to how much your conversion could improve. In the best case, we would find exactly the visitors that bought because they got that treatment and not offer this costly treatment to anyone else. This would land at the same +10% improvement in conversion. In such a case we would have optimised the benefit, i.e. minimised the expense while maximising the return.
In order to reach something better, you need a slightly different setup. If you had a treatment that has a positive effect on some of the visitors and a negative effect on others, you could actually reach a better conversion rate with machine learning than you could by offering that treatment to everyone. This would be a case where you reach some conversion rate precisely because you use it together with machine learning; a case where you could not reach the same conversion rate without machine learning.- As it happens, this is precisely what we have done.