giosg Blog

Conversions. Conversions everywhere. The things we are all longing for - right? But as we blindly aim towards increased clicks and purchases, we simultaneously forget our all-important brand. We push people towards filling-up their shopping carts and forget to put focus on their overall customer experience which should represent who we are as a company. The eCommerce trend of recent years continues in 2019: customer experience is about convenience and personalization. These two in turn transform singular customer transactions into true customer engagement. How?

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. 

Your eCommerce website acts like your online, virtual shop window, and unless it engages prospects immediately, they will leave and visit a competitor. December is the time of retail decline as the Christmas purchases are more and more often made already in November - so what will help you to minimize this trend?

After the excitement and chaos of Black Friday, Cyber Monday and Cyber Week dies down, it’s been proven that UK retail sales drop in December.

UK’s Office for National Statistics (ONS) released data showing that in 2017, shoppers reduced their levels of spending after Black Friday by 1.5%, even when Christmas is around the corner. What is this all about?

Black Friday first began in the US and has spread like wildfire internationally. Last year 91% of retailers in the UK and 81% in the US offered Black Friday discounts and promotions, according to a SaleCycle’s study.

[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?

Goal-oriented chatting is more than just live chat, it’s building a strategy of goals and measuring results to keep growing your business. So what is it?

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.

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.

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.

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.

Hesitant visitors are thorns in the flesh of many eCommerce leaders. Around 35% of visitors bounce before getting even started. And out of those who get started, almost 73% vanish at the final strecth and abandon their shopping cart.

So, what can you do to help your hesitant visitors to overcome their hesitance - to push them into purchase? In this blog post we'll go over the two-step process of identifying them and choosing the right method to push them into purchase.

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?