giosg Blog

Are you interrupting your online visitors - or are you leaving them hanging? 

Most industries working with large amounts of data have already recognized the value of AI and machine learning technology. By using machine learning algorithms to build models that uncover connections and identify important insights in data, companies can make better decisions without human intervention. This means that companies can work more efficiently and gain an advantage over competitors.

Ecommerce websites can use real-time analytics and machine learning to recommend visitors items they might like based on their buying history – and to promote other items they could be interested in. And, this ability to utilize the data collected to provide even more personalized shopping experiences (or implement targeted marketing campaigns), is the future of retail. Visit any major eCommerce sites and you may notice that this future is already becoming a reality. 

Business goals, football goals, life goals, the list goes on. Goals belong everywhere. In an online business environment goals tend to be total online visits, bounce rates, CTRs and total engagements among others. However, have you considered what goals are actually relevant to you?

Following these generic goals does benefit your business, but they are just the tip of the iceberg. Following numbers for the sake of it seldom pays off, so let’s explain why a dynamic business goal approach is the way to gain competitive edge - in football terms.

Chatbots are your first line of customer service and a crucial part of your virtual assistance solution. As exciting it might be to have a face to face conversation with a robot, the technology still doesn’t live up to online users’ expectations.

[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?

Customer experience in an online environment is about service quality; how well you can meet your customer's expectations. Offering live chat service on a website has proven to be an effective tactic to attract leads and convert them into customers.  Still, as an eCommerce professional, you are also painfully aware of the cost of offering real-time personal support which is why you need to continually look for ways to target your sales and support efforts more efficiently.

In the past decade or so, the way people search for homes has changed drastically as more and more house-hunters now start their search by looking on one of the major property portals. Recent research in the UK showed that 47% of the population had used a property search website during the last 12 months.

Technology will undoubtedly continue to take away barriers to information, so that portal visitors have even easier and more efficient ways to search for properties that match their requirements. Innovations are also changing the role of an estate agent so that the real issue real estate companies need to address is how to be more responsive to potential customers.

Estate agents are apparently always looking to get more listings but how to find those seller leads on property portals? The answer is quite simple: try asking portal visitors if they already have a home that they need to sell before they can buy a new one.

An estimate of 4 trillion USD worth merchandise was abandoned in 2016. Every time consumers are asked for a reason for this behaviour one of the most common answers is higher than expected shipping costs. What this usually means is that either lower or free shipping costs haven’t been offered in the first place, or the customer hasn’t qualified for them. Either way, this is a great example of a situation, which could have been turned into a victory quite easily. In this particular case, for example, by reminding the customer of a perk (e.g. free shipping for orders over 100 EUR).

Then, how to identify these situations so that you can give the customer a gentle push towards completing the purchase? Like discussed in our previous blog, there are four (4) conditions that e-retailers should pay attention to when trying to turn online visitors into customers. Visitor's shopping cart information is the first one of these. That is why in this blog we will focus on explaining how having real-time visibility of visitor's shopping cart size and shopping history can create a lot of opportunities for enhancing online customer experience and increasing sales.

Like mentioned in a recent Finextra article, there’s one fundamental thing that separates banks from many other businesses; customers don’t want to do business with them. Customers don’t want a mortgage; they want a home. They don’t want a loan; they want a car, or a holiday trip, or something else. Banks for a customer are a means to an end. And those banks that get this also recognise that they need to be offering services beyond the banking product.

Then, how can banks offer services online? A typical situation now is that a website has been designed to bombard visitors with everything possible. Still, for example, new and returning visitors often have different kind of needs which means that it would be wise to personalise the offering to meet the customer's actual situation better.

The most important thing, when trying to engage customers online, is to take a look at what customers are experiencing and then think about how to get their attention and help them. One way to do this is to use different kinds of situational reminders, like personalised and informative banners or proactive live chat, on a company’s website.

Let us show you how!

Have you ever thought that instead of spending more and more money on trying to get website visitors back to your site once they have left, you could offer them better service in the first place?

Beyond the buzz, personalisation offers online retailers real potential. 86% of consumers consider personalisation to have some impact on their purchasing decision and businesses that personalise their web experiences see sales increase by 19% on average.

The definition of personalisation is changing continuously. For some time, personalisation signified exclusivity which has traditionally been expensive. However, rapid developments in technology have made personalised communications, products and services more affordable and accessible.

Offering different things to different people based on their known or predicted preferences may still sound like a lot of effort but this is where machine learning comes into play. Giosg Target, our machine learning tool, analyses hundreds of thousands of possible behavioural patterns and then decides how likely each individual visitor is to convert. Based on the information we collect, machine learning algorithms determine the optimal personalised intervention to maximise the chances of conversion.  

In this blog we will talk the two strategies for converting online visitors; proactive and reactive chatting. We will also give our recommendations based on what we have come to find from our experience and results.  

Let us start by explaining shortly what the difference between these two approaches is. A reactive chat means that the online visitors have to, by themselves, click on a button that would prompt the chat to start. A proactive message is an automatically configured message that appears to the visitor.

Now, let's imagine you are a sales rep in an electronics warehouse, a customer walks in and you see him looking at one product for a long time, wouldn’t you go up to him and ask if he needs any help? The likelihood is that he is looking for some information that will satisfy his requirements in order to make the purchase. Well, the same idea applies to a website.

A Forrester research study has shown that a reactive chat will give a return on investment (ROI) of 15% while engaging online visitors with proactive chat will give a return on investment (ROI) of 105%. We have also seen these same results in the field,  although they vary by industries and domains. So in the end of the day, when done right, meaning carefully placing the proactive messages in order for the visitors to be prompted and converted, it is just good service.

Most industries working with large amounts of data have already recognised the value of machine learning technology. By using machine learning algorithms to build models that uncover connections and identify important insights in data, companies can make better decisions without human intervention. This means that companies can work more efficiently and gain an advantage over competitors.

Ecommerce websites can use real-time analytics and machine learning to recommend visitors items they might like based on their buying history – and to promote other items they could be interested in. And, this ability to utilise the data collected to provide even more personalised shopping experiences (or implement targeted marketing campaigns), is the future of retail.

The brutal fact is that roughly 70% of shopping carts are abandoned and the number is most likely going to increase as more consumers shift to online and mobile shopping. By 2018 the expected value of mobile commerce will exceed six hundred billion dollars but the difficulty is that there are numerous potential causes leading up to mobile conversion rates being even lower than with desktop shopping. 

This may sound like a challenging situation, but can actually provide a great opportunity for differentiation and competitive advantage if approached the right way. Because, no matter the channel, at the end of the day the problem usually roots in the same cause: Your online store fails to meet the needs of the visitor. As highlighted in this previous blog, most purchase funnels are still built on someone’s best guesses of an optimal path. And, this doesn’t really match with the expectations of today's customers as 70% of them say they want more personalized shopping experiences.

To succeed in the future, businesses need to rethink their customer experience. And what better time than now because getting into customer’s head and finding ways to analyze their behavior enables enhancements. And, a rich and personalized live customer interaction and showcasing products in more engaging ways also contributes to a better customer experience and to higher conversion rates.

What does personalization mean and why does it even matter?

Let me start with a real-life example. Couple of years ago a friend of mine decided to enter a triathlon race. That was not something I had ever dreamed of before, but since I have a relatively competitive nature, it didn’t take too long for me to jump aboard. If he could do it, so could I.

At this point, I was not concerned about the fact that to actually finish the race; I needed to improve my non-existing swimming skills significantly. Instead, I was determined to make sure that I have the best possible equipment for completing the task. The one thing I needed the most was a new bicycle.

I spent the next weeks searching for information and making comparisons online. All this only to end up going to a brick and mortar store to make the purchase. So, what exactly happened and what did the online store lack?

While most companies track web store conversion, the diversity of possible factors affecting conversion, and it's evil twin, abandonment, makes it a difficult process to manage. A variety of online customer service tools exist today, but knowing which customers to approach is still difficult. Also, providing live customer service is valuable in many cases, but not in all. As customer service is also costly, the effort should be directed in a way providing best value.

This may sound like something out of a science fiction movie but the process is actually quite similar to human comprehension. Machine learning tools use data to detect patterns from extremely large data sets and then adjust actions accordingly. By doing this they also constantly learn more and improve their own understanding.