Monica Gessner brings over 20 years’ experience in marketing and market research. Monica has been working with Brian at AOR since 2011. Monica has multi-market experience, across Australia and internationally in the US and Europe. Monica has worked on both client side and agency side, in both syndicated and bespoke research across both qualitative and quantitative research. Previous companies that Monica has worked for include The Clorox Company, A.C. Nielsen and Roy Morgan Research and project work across multiple industries including Government, Media, FMCG, Retail, Alcohol, Banking and Non-Profit. Monica has and MBA from the Kellogg School of Management in Management Strategy, Organisational Behaviour and Marketing and a BSc from Brown University in Applied Mathematics-Economics.
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 center 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: