What was your journey/career path to your current position?
I started my career in public relations, rising quickly through the ranks and some leading agencies until I was named head of the innovations group at Cohn & Wolfe, and became a member of the WPP.com board. The board was an effort headed by Ester Dyson to help all the members of the holding company reinvent themselves for what was rapidly evolving digitized word. My guiding light then (and now) was that the antiquated 'command and control' models for communications were rapidly being tuned out and deemed irrelevant, especially in a world where individuals could self-publish and share opinions (early on being GeoCities and Myspace). Then the trend was clear, we needed to create new systems of communication that better aligned to real 'human' communication - listen first, understand what is being said, define what you are going to say, express it effectively and understand, again, the reaction.Repeat. The tools and technologies were only emerging then to empower this solution, but we had a pretty clear vision of where this was all headed. This was informed, in part, by early efforts by others to also revolutionize the industry (expressed very poignantly and powerfully by Doc Searles and David Weinberger in the 'Cluetrain Manifesto'). 'Markets are conversations' was the mantra and, in fact, inspired the naming of 'Converseon'.
I came to realize that real innovation was not going to happen in a large holding company, so I set off to create what Shel Israel, industry observer and author of 'Naked Conversations' called the first pure play 'social agency' in 2001 (the term social media didn't really emerge until 2003). We won many awards over the next several years, including Digiday 'Best Social Agency'. In 2008 we felt this was great room for improvement for ‚'listening' and built out a software group to better empower the capabilities we needed. We were fortunate to work with some of the world's best NLP experts to build our technologies and only two years later (2010) we were named the Forrester Leader for Enterprise Social Listening. Fast forward several years and, recognizing there was a commoditization happening in the space, we decide to slightly pivot away from being a traditional social listening platform to focus more exclusively on the heart of our technology - our machine learning focused NLP and analytics technologies (the 'intel' inside) so that we could help power other platforms and entire 'listening and engagement' ecosystems. We were named best NLP company in 2019 (AI Breakthrough Awards) and continue today to help pioneer the transformation of conversation data into true predictive ‚'decision intelligence' by combining that technology with advanced analytics and modeling both through partner integrations and directly though dashboard solutions like our new Social Brand Reputation System solution, among others. Our long journey in the space tells me clearly that this data is still underutilized and that it is possible through machine learning and human in the loop AI to transform it from reactive and descriptive, to predictive and, most importantly, prescriptive. Clearly, in my view, the most exciting opportunities and impact for the industry are yet to come.
What's your proudest achievement of your career to date?
My proudest achievement probably is that we started life as a pioneer in the space, and 20 years later remain at the forefront of much innovation and evolution, while remaining fully independent. Many of our early competitors have come and gone, but we have been able to continue to remain highly agile to evolve and innovate to stay ahead of the curve and stay true to our vision. While we raised some investment to help scale several years ago, we have largely declined other external investment and not long ago decided to buy back our outstanding shares so that we could be fully in control of our destiny and stay agile and flexible. The fact that we have been able to largely bootstrap our success over many years, remain profitable while pushing innovation, and invest heavily into our people and the 'right' core technologies is really the most important element of success and our culture. As a direct result of being able to invest in the right people and solutions for the long term, I'm very proud that we have been able to stay deeply partnered with some incredible, well recognized clients over many years. We have forged true partnerships with our clients who invest with us for well over a decade as we work together to continue to drive even more value to their organizations. Trust is earned and I'm proud we have earned theirs and continue to work hard every day to make sure keep it. Of course, these partnerships don't also happen without a strong internal team of dedicated leaders. We have benefitted from having great people with long tenure here together with a stellar cast of up-and-coming colleagues who were attracted to us for our history of innovation and impact and want to make a difference (and they are).
What does social intelligence mean to you?
The term is one I wrestle with. We no longer usually say 'electric lightbulbs' - they're just lightbulbs. And 'social intelligence' is just really 'intelligence' - a powerful collective consumer intelligence powered though those ‚'synapses' provided through social networking technologies. The fact we can demonstrate that this data is indeed serious 'data' with strong predictive and explanatory power for a world where even real time isn't always fast enough really is essential to level up the respect this data provides at organizations. Unfortunately, 'social' has become somewhat of a negative modifier that we hope we'll grow out of.
What's been the biggest challenge you've faced while trying to get brands to integrate social intelligence within their growth strategy?
Organizations are drowning in data. The challenge they have is understanding which data to actually listen to and act on. And that decision is increasingly being clearly driven by that data which helps them foresee the future most clearly and correlates to business performance, whether its predicting and explaining areas like shareholder value or sales. It's easy to criticize social for issues around perceived representation etc., but if we can show that this data does indeed predict business outcomes to allow organizations to get the future first, we win, the client wins, and some of the issues that have hindered growth of 'social intelligence' simply melt away. Transitioning this data into true decision intelligence where organizations can get to predictions and utilize the data in business simulations to predictive potential ROI of various business decisions and investments is, in our view, a powerful way forward and where we are investing our time, technologies, and energies.
What do you think is the biggest missed opportunity for social intelligence?
I think the industry gets too caught up in technology vs 'humans' the qualitative vs the quantitative. It misses the point. It requires a thoughtful combination of human in the loop AI technologies and human expertise that can be scaled at the speed of software. I think it's important the industry gets beyond this and embraces that 'social intelligence' is all the above and what matters is successful outcomes. There is generally too much obfuscation of technology and too light of investment in advanced analytics. If social listening is to be taken seriously, it needs to break out of the social listening silo and integrate deep into organization's insight and analytics functions. Companies are data driven and we need to be integral part of that data ecosystem. We need to speak the language of data science and analytics with fluency; to dive deeper into the 'AI' tech beyond superficial description. It needs to get beyond broad generalizations of capabilities and not over promise and under deliver. The traditional social listening approach, focused heavily on platforms, often misses the requirement that many companies have of integrating this data and insight deeply into their organizations own internal stack and combine it with first party data. We need to provide not just social listening ‚'tools', but help enable true 'listening organizations' via robust API capabilities, customized data classification, frictionless data integrations, and advanced analytics.
What's on the cards for you and your team/organisation in 2022?
We have much on our plate. During Covid we invested heavily in our machine learning powered NLP and advanced, predictive analytics. As a result. we are now rolling out a series of 'decision intelligence' offerings more broadly especially in the areas of brand reputation and brand relevance. We have been providing these to clients in 'beta' but now ready for scale. These solutions combined custom and pretrained NLP, Bayesian statistics, core measurement frameworks, predictive analytics, and business simulators all into one package. We will also be expanding our NLP integrations through our Conversus 'auto NLP' platform with a broader range of partners in the social listening, VoC and customer experience industries so that they access highly effective and precise custom and pretrained models for their clients' more advanced analytics demands. This is also expanding to predictive models so that Conversus can provide the core NLP and the predictive models all in one platform and integrate via API deeply and broadly across complimentary solutions.
How do you see social intelligence and its use evolving?
We believe the social description will become of secondary importance to what we see our clients demanding - real time, essential intelligence to help them make rapid and informed decisions to navigate through for complex world brimming with a polarized society and rapidly changing consumer needs - or what we call decision intelligence. We must combine opinion and behavior data to not just understand what they say, but what they do. This requires data they can count on to be reliable, accurate, predictive, prescriptive, and most importantly useful - all which social can indeed be properly classified and modeled. But this also requires tying social data to other first party sales and other data. As such, we see 'social intelligence' fragmenting from a one size fits all 'listening platform' approach that still largely dominates the market to more use case specific solutions that also brings in more sophisticated, predictive analytics capabilities in areas like reputation, social responsibility, customer experience, innovation, and brand tracking/relevance. These solutions 'powered by' social but have many other additional capabilities and features so that 'social' will not often be a primary descriptor.
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