As a business relying on e-commerce and external marketing platforms to differentiate your brand, and convert leads into paying customers, it is important to continuously identify potential limitations and effective digital strategies to stay ahead of the competition.

But e-commerce alone does not prove effective, shopping has been reinvented by personalized commerce, with a focus on applying innovative big data solutions for improved customer experience. This includes well known tools like AI chatbots with automated purchase processes, visual search shopping experiences, and machine learning algorithms that use data to predict customer behavior for personalized targeting. An example of this is Netflix’s House of Cards strategy where their team created ten trailers pushing the same series using a different focus specifically tailored to audience past viewing behaviour.

How does machine learning work?
Data analysts can use big data and machine learning to build tools that find patterns and predict outcomes. Our own GetSmarter analytics team built a tool to predict the likelihood that someone who opens an opportunity (usually through downloading an information brochure) will convert into a paying student. Here is a great case study that compares two sets of data and demystifies how the machine learning model works.

‘Natural’ AI conversation is possible.
Following on Siri and the likes, e-commerce businesses have jumped on the bandwagon to try and create similar personalized online experiences for their users. The North Face have managed to create a ‘natural’ conversation with users on their website using IBM Watson technology.

In light of the growing customer expectation for personalized experiences, how can we integrate technology in our products and marketing to win a greater following?