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The next in our articles on the movers and shakers in Big Data is by Daniel Cantorna, Head of Data Products at ICLP.
We recently spoke with Daniel Cantorna who gave us his insights with regards to the future of big data in 2017 and his specialty which is data driven marketing. Daniel has worked on marketing automation programmes and has architected solutions to collect, store and understand customer data, as well as having created several data products that allow marketers to drive better decisions. Here are some of his thoughts:
The Difference Between Data-Driven Marketing or Data-Informed Marketing. There is a big difference between data-driven and data-informed marketing, but the two approaches can strengthen each other.
Essentially, data-driven marketing is the direct use of insights or outcomes from data analysis to inform a marketing decision which then impacts on a customer experience in some way. For example, offering special discounts to customers who click on your ad, visit your web page, or sign up for your mail list.
On the other hand, data-informed marketing describes a process whereby the outcome of some data analysis may inform or direct a future decision e.g. we have noticed that there is a dip in sales when products are advertised using these (x, y, z) words, so let’s try using a different product description.
Both are essential in a modern marketing, customer experience focused business, which is why my role lies at the intersection of data and marketing execution. This is where analysis becomes action.
Growth of Data-Driven Marketing
Customer expectations have risen meteorically in the last few years, whilst expectations of brands to know their customer have increased significantly. As such, data-driven marketing is no longer the preserve of industry leaders but is rather a basic consumer expectation. The result? It has become a must for all companies who want to provide a rewarding customer experience.
Several interrelated factors have driven this growth. The rise of marketing clouds such as Salesforce, Oracle, Adobe, and numerous other players (IBM, Microsoft, etc.) has led to extensive acquisitions and the creation of product suites. These suites curate the best tools to fully address the end to end needs of digital marketing.
Cloud, big data, marketing automation and AI (such as chatbots, predictive content engines, adtech, and 1:1 personalisation) have converged. The intersection of several technology trends, particularly in the big data and machine learning space, has enabled huge and rapid evolution. Reflecting this, data science has been promoted to a core competency for any credible marketing organisation. And whereas these skills historically fell within the remit of IT, they are now firmly embedded on the business side of the organisation.
Growth of this magnitude always brings its challenges. We have had to ensure that our skill set is up to date to meet ever-evolving client needs, even as both of these diverge, becoming more fragmented and complex.
The dizzying pace of change can outstrip the ability of some organisations to adapt. Given how long it can take to demonstrate the benefits of introducing new technologies to key stakeholders, innovation will continue to be a fraught process within many organisations.
2017 and beyond
I expect to see the huge growth of predictive marketing, Machine learning, and AI to continue. As ever, we will tackle the challenge of integrating these growth areas into the customer experience for maximum impact. We will also focus on technologies that streamline delivery of the right message, at the right time, on the right digital channel.
We can make all of this happen in 2017 if we know our priorities. That means keeping up to date. It means curating the right ecosystem of technologies and skills for sustained innovation. And it means building data products to support compelling personalised marketing communications.
By exciting people about the possibilities and opportunities within these challenges – by driving data awareness, showcasing the benefits, and strengthening multi-disciplinary working – we can succeed because of these challenges, not in spite of them.
At the same time, we must ensure we can justify the cost and attribution of the returns from using data to drive the business forward, especially at a time of increasing regulation and demands for data security.
Article Credits: Daniel Cantorna (ICLP), Sam Burt, Amir Rasool (Sumner & Scott)