Finland has been ranked number one among the countries that drink most coffee, and our Helsinki office is no exception. Our office practically runs on coffee and we take it very seriously. We brew a lot of coffee, around seven cups per person per day to be precise. In addition, our team is also particular about getting their coffee at its very best regarding optimum brew time and temperature.The coffee supply had never been a bottleneck until we moved office to new premises little over a year ago. For, although our new office is otherwise a lot nicer than the old one, the downside was that most teams, consequently, lost direct visibility to the kitchen area and the two coffee makers. The problem was that in order to get the ‘current coffee availability status’, you had no other alternative than to walk to the kitchen and see for yourself. Besides taking unnecessary time and effort this frequently lead to disappointment and frustration as the coffee jug was often found empty.
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.
Disruptive innovations are reshaping the way financial services are structured and consumed. Setting up a 'virtual branch', that allows financial service providers engage with potential customers on another organisation's domain, is a great way to meet evolving customer expectations using rapidly advancing technology. By integrating real-time credit counselling seamlessly with the purchase journey of a partner site the context where the service is provided is significantly better than in any other form of digital service.
Take booking for a holiday, for instance. Many of us have a ready-made list of dream holiday destinations in our mind, but the reality is that a loan can sometimes be what is needed to bring our plans for a honeymoon, birthday celebration or annual vacation to live. Then, what better moment to be offered financial advice than when we are online browsing for travel services and comparing options?
We know that today’s consumers expect to get a response from property companies around the clock, not just during the hours that most customer service representatives are in the office. To better understand the current state of customer online engagement in the UK property sector we wanted to analyse data from visitors to UK estate agents’ websites.
As we already work with eight UK property companies, including Knight Frank, Connells and Savills, we decided to focus our attention on the visits of 2,710,592 individuals looking for properties across these eight property websites in a period of one month (May 2017). The data clearly suggests, that although property companies are off to a good start, they could still do more 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.