Q&A with Dr Jillian Ney, Dr of Social Media Intelligence & Behavioural Science

We caught up with Dr Jillian Ney, Dr of Social Media Intelligence & Behavioural Science , ahead of her participation at this year’s Audience Analytics & Insight Forum in the opening keynote on understanding the cost of “creepiness”.

AAI: Why is it that more data can lead to fewer insights?
JN: There is a distinct irony that having too much data can actually lead to fewer insight. The more data that is collected, the less they overlap which creates holes in the data. The data also collected by marketing teams might not be causal. While we can correlate search advertising data with purchase, it does not always follow that ads caused the sales.

There has been too much focus on gathering and collecting data, as an industry we are awash with data. Many data points are useless and hold no value in optimising advertising effectiveness, they serve as a barrier to success with marketers getting too caught up in analytics. To overcome these issues, marketing teams need to first consider what they want to do with the data, and then explore which data they need to do it. While we can all be confored that we are collecting as much data as possible, it actually makes it more difficult to do our job!

AAI: As an industry are we losing the art of interpreting human motivation?
JN: This is a difficult question to answer but I’d say no. We have more ways than ever to interpret and understand human motivation, this is a very exciting time in history where we have the data to answer almost any question, the challenge is getting the right data. As an industry we are becoming more reliant on technology to help us understand and target our customers, mass automation plays a large part in this process but this is not necessarily the right way. We have become reliant on technology providers to determine the key metrics to be measured, and their interpretation of what this means and what should happen next. We need to re-evaluate the effectiveness of this. Many metrics have been taken from old thinking and re-purposed in today’s world, like we measure car speed in terms of horsepower not engine power.

AAI: GDPR should mean cleaner better data, has it?
JN: This is not necessarily true. Unstructured data is always going to be unstructured so it is always going to be messy. I work with social data which has its own challenges even with GDPR. I still don’t believe that the data will be cleaner or better in other areas too. Companies are still collecting data without knowing what they want to do with it or the best way to process or analyse it.

AAI: If AI and machine learning could deliver one improved solution to help your understanding and targeting of your audience, what would it be?
JN: Propensity to purchase and attention.

AAI: Block chain, could it be the answer to all advertisers problems, 100% accurate behavioural data or just the next new shiny thing?
JN: I think we are a way off this solving advertising issues. Yes, we have behavioural data but the biggest challenge is that advertising is sent at the wrong time. We need to get better at understanding behavioural triggers and the optimum time to send adverts. It is.


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