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.
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.
In 2015, Connells Group, one of the UK’s largest real estate groups – operating across a number of well-known UK estate agency brands, was looking for new ways to drive revenue from its websites. Their biggest revenue stream was the market appraisal bookings for properties but, at the time, website visitors could only book one through online forms or by phoning the estate agency branches directly. As both of these options required a high level of commitment from the website visitor, finding an easier option for the customer to get in touch seemed like the most natural way to increase the number of market appraisal bookings.
After some careful consideration, Connells Group decided to implement giosg solutions on a number of its brand's websites, enabling them to have a discussion with the website visitor via live chat. Eventually, this ability led to a significant increase in market appraisals. But adapting to a new way of connecting with customers wasn’t without its challenges.
In 2014, Finnair faced the same problem many customer oriented companies face: customer service costs a lot. Customer service is important and engagement with the customer is directly linked to upselling, but the costs associated with traditional customer service channels are sometimes unbearable. Each call takes several minutes out of a service agent’s workday and each minute costs.