Since the dawn of modern marketing, consumer-facing brands have striven for better, more accurate information about their potential customers. Which audiences should they be targeting? What are their needs and desires? Where and when are they most likely to form and make their purchase decision? These questions are all asked with the intention of making smarter, faster marketing decisions to gain a competitive edge.
The types of questions marketers are asking haven’t changed, but the tools we have at our disposal to answer them have, and will continue to rapidly evolve over the next few years.
The speed of how information is now disseminated means brands and innovators need to think differently about how they discover and deploy consumer insight. No longer can brands wait 24 months from inception to execution for their latest new flavour innovation or product reformulation. The traditional barriers to entry for big brands have been eroded by today’s digital economy. Smaller, nimbler startup brands are reacting to consumers’ changing preferences faster, gaining crucial first-mover advantage. Speed isn’t always everything, however; it’s also crucial to be able to evaluate whether a trend has long-term potential to impact a category, and therefore warrants investment.
So, what should big brands do? The answer lies in using 21st-century tools for 21st-century business. The internet represents the world’s largest public record of consumer attitude and behaviour, and brands that can utilise this robust and rich data source to deliver real-time consumer insight will be best placed for future success. This abundance of data and availability of artificial intelligence (AI) opens up the possibility of true prediction, moving research away from traditional rear-view techniques.
This is how ‘social prediction‘ was born. To clarify, social prediction is not social monitoring or listening. It has similarities with these methods – in that it uses technology to analyse social data – but social prediction is fundamentally different. It utilises consumer-defined category data sets to analyse a subject of interest, in relation to all the other topics in that data set.
For example, if I wanted to find out about the latest personal care ingredient trends, social listening tools will retrieve and analyse the content containing the keywords I queried. But it won’t surface unknown ingredient trends that I couldn’t possibly query because I wasn’t aware of what they were in the first place.
It won’t tell me how important a particular trend is in comparison to other trends in the category. Nor will it tell me anything about the maturity and future trajectory of this trend – is it a short-term fad, or something that’s here to stay? If social listening is akin to shining a torch in the dark, social prediction is finding the light switch that illuminates the entire room.
PepsiCo has deployed it as part of an industry leading always-on trend detection programme, fast-tracking the launch of new and emerging ingredients and flavours for key brands such as Sensations and Off the Eaten Path. Leaders at PepsiCo were among the first to recognise big data’s role in their strategic imperative to not only detect trends faster, but also to be smarter in determining which to respond to and how.
Tim Warner, vice-president of insights and analytics at PepsiCo, calls it a “robust, always-on trend detection engine. We can distinguish between the emerging trends that matter and the many that don’t, and scientifically predict which trends will sustain growth, versus those that will fade away.”
Social prediction allows brands not only to understand what consumers are talking about now, but also to accurately predict how much they will be talking about it in the future. But we’re only at the tip of the iceberg of its potential. As the field of big data and predictive analytics is increasingly applied to how marketers make decisions, I see many more exciting use-cases that will shape the future of the industry; from surfacing and predicting unknown and emerging trends, to executing dynamic content in response to real-time changes in audience behaviour, to identifying undervalued targets for potential merger and acquisition investment.
I believe social prediction is here to stay, and AI and algorithms will become – if they’re not already – a marketer’s new best friend.
Hugo Amos is chief strategy officer and co-founder at Black Swan Data, a data science and analytics company which specialises in utilising social prediction to better understand consumer behaviour and trends.