What was your journey/career path to your current position?
Most of my career has been on the client side in Insights. I started out at BASES, then spent 9 years at PepsiCo in US, UK, European, and Global Insight roles. From there I went to Healthcare where I started Spectrum Health's first Consumer Insight and Data Science team. After Spectrum I decided to join Black Swan Data, and being a start up at Black Swan I've worn a lot of hats. I've led the Product team, the Insights team, the Data Science team, and now I lead the Datasets team as the Chief Data Officer.
Fundamentally I see two foundational forces changing the market research industry - Data Science and Behavioral Economics - both different ways of achieving the same goal, which is to enable companies to make better decisions - more predictive decisions - based on observing behavior instead of asking a question. So, I went about learning as much as I could about those two fields, and I came to Black Swan to help apply Data Science to Market Research.
What's your proudest achievement of your career to date?
I think seeing our SaaS product, Trendscope, come to life. When I started at Black Swan it was just a beta product - 1 client, 2 datasets, and a handful of engineers, insights consultants, and data scientists hand stitching code together to make it work. Now we have users from several of the world's leading consumer brands regularly using the platform and accessing our syndicated datasets. We've also built several internal big data software tools that enable our datasets and insight teams to curate and maintain the most comprehensive, sophisticated, and predictive category trend datasets on the planet across different countries and languages.
What does social intelligence mean to you?
Social intelligence is a bit of a broad church. We talk at Black Swan about Social Prediction, but there are a lot of similarities. At its heart, it's about enabling businesses to answer primary research questions - without having to ask a question. This would not have been possible 15 years ago, as there wasn't enough content online to generate meaningful insight, nor were the Data Science tools available to make sense of the data. Now however there is an abundance of content online - everything from food to cosmetics to home cleaning to politics is being discussed, debated, and introduced online. The pandemic has only poured fuel on what was already a vast and rich data source. So, the challenge has now moved from - how do you find this data and is twitter et al representative - to how do I harness this data, how do I use it in decision making more effectively?
What's been the biggest challenge you've faced while trying to get brands to integrate social intelligence within their growth strategy?
Part of it is education. When I first started at Black Swan 4 years ago, brands were still hung up on how 'representative' social data was. For the past 2 years however, no one really questions the usefulness or representativity of social data - instead they are asking more useful questions like - "how do I integrate this into my innovation process?" or "how can I use this to inform my brand positioning/brand equity?" So, the challenge is still education - but on a different level.
The other challenge, which is industry wide, is how we help over-burdened client-side insights teams extract from value social data to increase their agility and create new ways of working. Our customers are under a lot of pressure to get more from less - and that means that they need to be equipped with more self-service tools rather than relying solely on traditional full-service market research agencies that can be expensive and slow. Our goal is to democratise the use of social data and help users' get to insight quicker, so they can enable faster decision making. No one wants to spend time writing queries or doing endless configuration.
What do you think is the biggest missed opportunity for social intelligence?
I'll speak to two missed opportunities. The first is that we need to educate client-side leaders on how to enable their teams to move beyond short-term digital marketing use cases that are based on what's trending today, to how we can use SI as a unique data source that can understand, prioritise and predict long-term behavioural trends and inform higher order innovation and marketing commercial decisions: i.e. Where To Play? and How To Win? The second is - how do we help people understand context better? No trend happens in isolation - and a topic or trend's meaning can vary dramatically based on the context it appears in - yet many of the tools available today for clients don't help them to understand or navigate this context effectively, which can lead to a false sense of security and poor decision making.
What's on the cards for you and your team/organisation in 2022?
2022 is shaping up to be a big year for Black Swan Data! We'll soon be releasing a new version of our SaaS platform, Trendscope, that will make it easier for customers who license our datasets to do high level Innovation tasks like White Space Identification, Competitor Intelligence and New Product Concept Optimisation. We are also pushing the boundaries of exploring how data sources come together in trend prediction - linking social data with search data and sales data. And of course we are continuing to expand our dataset coverage, building datasets in new categories like OTC and new countries like Germany.
How do you see social intelligence and its use evolving?
Social Intelligence is in the beginning of its journey. It's figuring out what it can and can't do, and where it fits in the research and analytics continuum, and that's an exciting place to be. There are some research use cases that I think it will completely replace like quantitative surveys and traditional qualitative work - because it is a better methodology for answering questions, particularly in the Innovation world. There are other use cases that it will power when it is connected with secondary data sources like search and sales data - where it will then act as an early lead signal, enabling businesses to anticipate and predict behaviour earlier, and further ahead than their competitors. And then there are some use cases, like pricing, where it won't be a great data source and we'll continue to use other sources to serve those needs.
I also see a greater movement in standardisation. At Black Swan Data, we are doing what Nielsen and IRI did to syndicate Sales Data, but we are applying this to social data. We create syndicated datasets that track every topic/trend in a given product category and country combination - every brand, every product type, every claim, every ingredient is tracked. We think clients shouldn't have to worry about finding new trends or writing complicated Boolean queries to get value out of social data. Clients should be focused on what decision they want to make or what action they can take from the data, rather than how they obtain the data in the first place. It's the way clients respond to the information they receive that is their competitive advantage - not their ability to get information in the first place. Equally, we think social data is so important that in the future clients will need to report on social data metrics - like a key trend's prediction value to justify a new product launch - much like they use market share and sales data in order to manage their businesses today.
There will likely be other firms in the Social Intelligence space that move to syndication as well, and so the next phase of evolution will more like how people evaluate and compare IRI vs Nielsen for their sales data today. Things like - what's your trend coverage, how many do you track? What data sources and partnerships power your datasets? What value-add metrics do you provide?
This will help the industry enable those higher level use cases more effectively - by moving the conversation from better ways to create the data, to better ways to implement the data for marketing and insight decision making.
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