Artificial intelligence, machine learning and the science of customer engagment
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?
Source: Artificial intelligence, machine learning and the science of customer engagment
Via: Google Alert for ML