giosg Blog

4 companies and 4 detailed examples of improved customer experience

Posted by Kimi Tiinus on September 26, 2018

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.

giosg & Netflix behaviour prediction model

Posted by giosg support team on June 4, 2018

[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?

If Insurance’s first six months with AI-powered chatbot Emma [Infographic]

Posted by giosg.com on September 26, 2017

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.

UK Real Estate Online Engagement Study 

Posted by giosg.com on August 22, 2017

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.

How machine learning fuels conversion optimisation [White Paper]

Posted by Otto Nyberg on July 25, 2017

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.

How UK property giant Connells Group created a new revenue stream from its websites [Case Study]

Posted by giosg.com on June 6, 2017

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.

How a car dealer creates a lock-in on car portals to boosts lead generation [Case Study]

Posted by giosg.com on January 23, 2017

Finnish car dealer Käyttöauto has experienced the radical change in consumer behavior that is almost too familiar to the automotive industry. It wasn't more than a decade ago when potential car buyers showed up on car dealers' doorstep to browse trough the selection. It was relatively easy task to find out how committed these buyers are, what kind of car they are looking for and recommend one from the dealer's selection.

Nowadays, majority of the browsing occurs on car portals. Each car from a single dealer competes against numerous car adverts from other dealers. This competition happens mostly without car dealers having any power on the outcome - the car portal's search engine determines the order in which the cars are shown. Car dealers' role is traditionally left to sit and wait for the consumer to contact them.

How Finnair managed to drop some of the traditional customer service channels [Case Study]

Posted by Petri Vilpponen on October 11, 2016

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. 

How live chat surpassed other service channels with the most positive feedback  [Case study]

Posted by Päivi Harju on July 22, 2016

Live chat has great potential for it meets the customer at the point of the buying cycle where they're going to have questions. Meaning that agents can connect with customers at the point of sale and gently nudge browsing customers into the checkout line. 

Besides increasing sales (if that wasn't enough), live chat can help you gain also other type of competitive edge; excellent customer experience. For let's not forget that customers actually like it. A survey conducted by Forrester research says that 44% of online consumers indicate that having live chat available while making an online purchase is one of the most important features a website can offer. And other surveys also state that more than half of customers are more likely to return to a website that offers live chat.