giosg Blog

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 to increase conversion rate efficiently with machine learning

Posted by Otto Nyberg on September 23, 2016

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.  

Next, we will look more in-depth into our approach and compare it to one of the most well-known cases of personalisation online: Facebook’s ad targeting.

How real-time analytics can help you recognize hesitant buyers

Posted by Otto Nyberg on February 29, 2016
As an E-commerce professional you are painfully aware of the fact that only a fraction of all your web store visitors will actually buy. Still, getting visitors to engage and follow a path to conversion is in the core of driving value to your web store.

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.

So, how can you target your sales efforts better? By utilizing machine learning and real-time analytics!

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.