Here at Quant, we pride ourselves on the talent and expertise of our team. Dedicated to finding actionable consumer data intelligence, our analysts, strategists, marketers and technologists work hard to develop solutions and get results that take our clients’ businesses forward.
With so much data knowledge and innovation at our disposal, we thought it only right to ask some of our key team members for their unique perspective on what we do. Here, our Director of Insight and Analytics, Paul Parker, describes the method behind extracting meaning from customer behaviour, how he surmounts challenges, and what he believes is next in the world of consumer behaviour analytics.
Paul and his team work with multiple sector clients with an array of differing needs, from marketing strategy, data infrastructure and CRM system implementation through to data science and advanced analytics.
PP: “We begin by asking our customers what their core business challenges are and therefore what they want to achieve with their data – we try to avoid just ‘diving in’.
A client usually has a set of business hypotheses they want to prove or disprove, sometimes based on experience, and sometimes fact. Often we tend to draw the development of insights from this position, to generate trends and correlations, and look for key patterns in the data.
We also share the insights we find with the client and work with them to expand on such findings, rather than just producing a deck of slides which don’t get to the heart of the customer’s goals. We often have a good idea of the advanced analytic methods we’ll use thereafter, however it is key we have explained enough to the client in a clear and logical way before we become too scientific! “
PP: “Absolutely. Clients tend to see or seek a binary outcome or answer to a problem or business challenge.
We use machine learning to ‘train’ artificially-intelligent algorithms to spot patterns and trends within large datasets, patterns which can often go unnoticed by human eyes. The beauty of using AI and machine learning modelling approaches is that they often unearth hidden correlations which tell a compelling story and surprise clients about their customers’ behaviour.”
PP: “It’s key to understand the data well, to know the nuances in how it has been constructed and the underlying business rules applied to it. Only the top candidate models ever make it and therefore they are most accurate in predicting what customers will do in the future. Any model will have a shelf-life, so it is fundamental to be realistic here and have the right checks and balances in place so you know when a model should be optimised or discontinued.”
PP: “In a nice way, I don’t have a typical day! My work tends to be a broad mix of strategic, operational and leadership tasks, of course with a strong thread of analytics. We have so many great things happening at Quant and I am heavily involved many, many activities.”
PP: “We’re developing and refining a product recommendation engine for one of our leading clients. We have great ambitions this will generate a great customer experience and of course further revenue for them.”
PP: “My team tackle data and analytics challenges of all shapes and sizes. Sometimes there’s a lack of data to work with, or the data we have is not of sufficient quality, which is when we have to be a bit more innovative in our approach. Client surveys and loyalty schemes are both effective means of gathering data.
In other cases, we need to fully understand the context of the data to drive out the best insights. Overcoming any data analysis challenge requires a high degree of skill, patience, collaboration and a steely determination to extract meaning from the data and reach a set of compelling stories. Luckily, the Quant team has all of these qualities in abundance!”
PP: “It might not always feel that way, but GDPR actually presents us with opportunities, in terms of a meaningful value exchange between businesses and consumers. It is important we can trust the customer understands what they are opting in to and what for, and that businesses offer a better customer experience in return for using personal data.”
PP: “Plain and simple, that data science and analytics is used at the forefront of business decision-making. For any business to innovate or stay ahead of the pack, analytics is a key toolset – the future of this really excites me.”
PP: “Each year I see more businesses adopt data intelligence capabilities across their organisations, and a recognition that low-latency, high-quality data reporting is now a standard.
Similarly, we’re also seeing the use of open source programming language like R and Python moving from a ‘nice to have’, to a ‘must have’. These languages are the backbone of data science, and the fact that skills in this area are becoming an essential investment should make machine learning and AI-powered data insight accessible to even more businesses.”
Interested in hearing more from Paul and the rest of the team? Contact us today.