Con is CEO of Strategic Precision which is a company that specialises in Data, Research and Strategy, pulling all three together to offer clients a total overall data and research solution. He also develops bespoke research methods and predictive models for loyalty behaviour, segmentation and frequent flyer member choices, as well as developing text and data mining solutions for big data. Academically, Con is a quantitative researcher and doctoral supervisor.
He lectures in market research, managerial and marketing statistics, price modelling, decision modelling and segmentation, six sigma methodology and statistics, at both undergraduate and post-graduate level. Con’s PhD is in measurement theory and applied market research methodology, with particular emphasis on the measurement of brand equity, using choice modelling and structural equation modelling to further develop an information economics model of brand equity, for the purposes of predicting market share. His academic refereed journal publications and conference papers span branding, human machine learning, choice modelling, structural equation modelling, segmentation methods, research methodology, text mining, data mining, health care and psychopathy.
The world is advancing technology at an alarming pace and the need for speed, depth, reduced costs and self-learning abounds. MR is taking longer and longer when compared to the client’s internal analytics teams. It is not uncommon for larger clients to have over 100 data scientists crunching away on databases and modelling client interactions in Australia alone. Coupled with human analysts are an increasing incidence of AI algorithms which really speed analytics up. There is a bit of a way to go in terms of closing the loop of what MR offers and what transaction analytics offer, but that gap is closing fast!
An effective analytics team today can tell us more about a digital campaign and which segment bought at a particular price, the demographics, the quantity, payment method, next-best-action and repeat purchase propensity instantly i.e., on-the-fly. No groups, no research, just facts hard and fast! This alone should be a worrying issue for MR. We’ve seen the advent of technology hit the MR shores by way of online data collection and advanced analytics in the form of machine learning. AI, which is basically machines doing things that one would consider smart, now takes centre-stage and as an industry we need to be up to date! This paper uncovers four very important and critical areas of big data and AI that as an industry we need to urgently address.
Many online data suppliers though do not fully understand the issue of data quality. Factors that affect data quality such as memory decay, respondent inability to articulate, sensitivity bias and fraud go largely undetected. In addition, economic constraints do not often allow for the deletion of sample based on problematic observations evidenced in response sequencing as one example.
This paper presents a solution to the ever-growing problem of data quality and discusses the following:
1. We firstly discuss why data quality is of paramount importance, and a key responsibility of the market researcher;
2. We secondly highlight key factors and common misconceptions that may affect data quality;
3. Thirdly, using case studies, we illustrate some quick checks to detect anomalous response patterns which denote a poor respondent response pattern; and
4. We provide some approaches for addressing quality concerns, and the statistical tools and software packages necessary.
Co-Presenter: Christine Maddern
As researchers we pride ourselves on being able to find the hidden-treasure. Yet, the most powerful, insightful and advanced tool that we have in our possession is largely ignored and regrettably scoffed at by some of our own best practitioners as being unstable, inapplicable or all too hard. We are talking about structural equation modelling (SEM) or latent variable modelling, which when used correctly, offers insights and client opportunities that no other approach even comes close to. Furthermore, this paper unequivocally demonstrates that SEMs should be the go-to platform for analyzing the way that people see the real world.
We provide clear and easy to understand examples of how the outputs of SEMs can be used in everyday solutions and why it is so important to use this method of analysis. We show how to understand the difference between reflecting and formative latent factors i.e., factors that are mature in the respondent’s mind versus immature factors necessitating caution when using marketing offers. Further, we demonstrate the impact of SEM outputs on segmentation, one of the most revered and important applications of SEMs.