Artificial intelligence, machine learning and the science of customer engagement

James Forbes

James is the head of marketing and digital for InfoReady. The pure-play information management and business analytics consultancy specialises in helping organisations transform data into actionable intelligence.

There is no let up for today’s CMO who needs to be the master of an ever-increasing variety of trades. Digital changed the game years ago, and now the CMO must be a skilled publisher, technologist and data analyst.

Keeping across such a diverse array of specialisations requires a diverse team of experts. And in the area of data analytics leading CMOs are increasingly turning to the skills of a data scientist to help then make sense of the deluge of data they confront daily.

So what is a data scientist? According to a recent report by NYU, data science involves using automated methods to analyze massive amounts of data and to extract knowledge from them.

There is some debate as to the exact definition, but statistics, mathematics and computer science are the common elements with techniques that extend to signal processing, machine learning, artificial intelligence, pattern recognition and predictive analytics.

While the title may be new the skill set is not. Data scientists or at least employees with these skills have been employed in specialist roles in typically larger organisations for some time. What’s changed is the name and the accessibility and relevance of data science in mainstream business functions such as Marketing.

There are a number of factors at play here. The massive growth in the volume and variety of data is driving demand, while increasing computer-processing power combined with ready access to advanced analytical toolsets is supporting supply.

As a result for CMOs the role for data scientist is becoming increasingly relevant. Today it provides a genuine option to mine the mountainous datasets and unearth new insights into customer attitudes and behaviour to support the delivery of superior customer experiences.

Data science techniques can be applied to help answer a wide range of different problems, these could include: what product should you offer to a customer to purchase next?, which customers are most likely to churn?, which customers are the best match for a given product? or which channel is the most effiecient to reach a consumer for a given offer?

Sounds complex? While the techniques are highly specialised and require an experienced practitioner, getting started with Data Science may not be as hard as you think. In fact adopting the approach in some cases can actually be a faster path to gaining insights from your data than many traditional business intelligence and analytic approaches.

In some cases a data science exercise will benefit from accessing data in its raw form. The rationale being that some information is lost as part of the process of structuring, normalising and cleansing the data that can occur as it is processed into a traditional warehouse.

Further, with the number of skilled practitioners on the rise, increased computer processing power and unprecedented access to advanced analytical tools, the barriers to get started with data science are lowering by the day. Additionally, the ability to tap in to expert external partners means you can reduce the risk associated with starting up your internal data science practice. This allows you to start small with some bite size projects and iterate the discovery of insights in a more agile manner without prohibitive start up costs.

It goes without saying that the underlying integrity of the data you are working with needs to be sound, otherwise it will be a case of garbage in, garbage out. However, the net result is that establishing an agile data science practice within your Marketing team is closer than you might think. And the prospect of generating genuinely new insights to better the customer experience, what are you waiting for?

Tags: data analytics, artificial intelligence

Show Comments

Featured Whitepapers

More whitepapers

Blog Posts

Cannes 2017: The Machines Are Here

It’s day 4 in Cannes and among the ever-growing divergent panels, presentations and workshops spanning from one end of the Croisette to the other, there has been a very real emergence of how artificial intelligence (AI) and machine learning needs to fit into the marketing ecosystem of today and tomorrow.

Aden Hepburn

Executive creative director and managing director, VML Australia

Is your content marketing missing the mark?

Does it ever seem like the content you create falls flat on its face or that the leads you’re generating aren’t worth following up?

Dan Ratner

managing director, uberbrand

​ Creating a purpose-driven brand

So you want to be a brand with purpose. But what does it actually mean to build a brand with real meaning?

Paul Chappell

Partner and managing director, Brand + Story

Typo"claiming no other email service protects its users form spam, hacking and phishing as successfully as Gmail"I'm sure Google can help...

OlliesBlog

Google to stop scanning personal Gmail accounts for ad targeting

Read more

It is interesting. Rebrand is very good. Perhaps they should change the logo to something more modern. For example as it is - https://www...

David Hill

Grace Group undergoes first rebrand in 30 years to unify and contemporise

Read more

Hey Guys, just one small typo that changes a part of the story :“That was a really big step forward for our company because we didn’t hav...

Damian Young

Chobani tastes success with data analytics platform

Read more

This is amazing! Congratulations to Cochlear for continuing to lead innovation in every way!

Chris

How this marketing ops leader is lifting the automation ante at Cochlear

Read more

Interesting case! It seems like universities can also benefit from marketing automation. I've started using getresponse some time ago and...

Aaren

How marketing automation is helping drive CX change at Adelaide University

Read more

Latest Podcast

More podcasts

Sign in