By Enock Wandera
Artificial intelligence (AI) has grown in popularity in recent years. Voice and facial recognition software has developed in all technical gadgets, and we are beginning to see how it is revolutionizing market research, resulting in faster, cheaper, and better results.
Today’s business environment demands for greater speed and agility, and this has put more pressure than ever on marketers to not only keep up with consumer behavior but also stay ahead of it. Add in the need to make sense of online and offline data and the continuing march to democratize data science, and we have the perfect storm in the world of consumer insights.
Unlike traditional social listening tools, AI-enabled Consumer Intelligence (AICI) platforms harness data from multiple sources (social, search, survey, and more) and apply advanced AI and data-mining algorithms to uncover predictive and prescriptive insights. Most critically, these smart solutions operationalize AI, and extend its value beyond just market research and customer insights groups.
Product and innovation teams are increasingly dependent on understanding (in near real-time) consumers’ preferences and motivations, how they are talking about products and brands, and even broader trends that are important to product development, go-to-market, and brand equity strategies.
As brands need to get closer to the customer and do so more efficiently by reconciling redundancies in tools like social listening, voice of the customer, and text analytics, many are rethinking their approaches to gathering and activating consumer insights – and the role of social data as a whole. Online conversations from social media sites, blogs, and forums have become critical sources of insight that provide authentic, unprompted consumer feedback. By consolidating around one insights platform that blends “behavioral data with solicited and unsolicited feedback,” as Joe Rice from Twitter framed it last year, brands can use both online and offline conversations to drive better business decisions at scale.
A push from business leaders who now see AI as the most industry-impactful technology - according to a recent Gartner survey – has also led to the latest spark for driving AICI into many more organizations.
Brands also need to augment their traditional market research techniques with a global solution that covers all the right sources. Only with this in place can we get a complete view of the market and its consumers. Yet, there is always too much data, creating data overload. And the teams managing data are often not the teams that need it the most – a challenge dating back to early business intelligence and centralized reporting functions. This is where democratizing data, supported by smart machines, aka automation and AI, come in. Machine learning (ML), for example, helps sift through large volumes, reduce “noise” in datasets, and detect signals as they emerge. Advanced sentiment analysis capabilities use natural language processing (NLP) techniques to understand the feeling, tone, and emotion of online conversations. And data-science powered capabilities like image analysis, scene recognition, and logo detection provide critical context about visual content. Yet not all vendors in the space deliver the same flavor of AICI.
Today’s applications of consumer intelligence stretch far beyond traditional use cases; consumer insights impact every stage of the customer lifecycle. While brand health and monitoring teams will always rely on consumer insights data, these insights now play a more strategic role in broader initiatives like understanding shifting behaviors and emerging models in a post-COVID era.
|At Ipsos in Kenya, we employ Synthesio, along with our Social Intelligence Analytics service line, to integrate SaaS technology with custom insight services to support more languages, data types, and use cases – and put the right cultural and contextual lenses on consumer-generated data.|
At Ipsos in Kenya, we employ Synthesio, along with our Social Intelligence Analytics service line, to integrate SaaS technology with custom insight services to support more languages, data types, and use cases – and put the right cultural and contextual lenses on consumer-generated data.
Insights-driven businesses with advanced capabilities are 8.5 times more likely than their beginner counterparts to report annual revenue growth of 20% or more (24% vs 3%). AICI helps brands on their journey to profitable growth by providing users with actionable insights that lead to better business decisions – and enables teams to move from reactive to predictive decision-making.
AI enabled consumer intelligence also complements traditional market research techniques like surveys and customer interviews by providing access to real-time data, online signals, and predictive capabilities. It helps bust through blockers by reducing the time it takes to get insights, improving data quality, identifying market changes sooner. The aim is always to spot emerging trends before their competitors and provide insights to their product innovation and digital marketing teams to develop on-trend products and messaging. To date, the system has spotted 700 – 800 trends that merited study by internal teams.
AI is also key for brand health and MarComms. The traditional social listening users need a finger on the pulse of consumer sentiment and trackable metrics so they can make quick improvements to brand awareness initiatives, Public Relations efforts, campaigns, partnerships, and more. AICI enables teams to embrace more data-driven approaches to marketing.
Product development and innovation teams are pushed to become more customer-obsessed, and to make smarter decisions about possible expansion opportunities by understanding emerging consumer needs, market share potential, and what competitor signals may represent. Social data is a gold mine of innovation opportunities – primarily because “lead users” share their product hacks and ideas online.
Performance metrics that that constantly prove agency value to their clients is another place to explore- and creative agencies especially, can also exploit AI to gather insights that can improve their clients’ brand awareness and boost ROI with better audience targeting, more relevant campaigns, and smarter sponsor/ influencer partnerships.
While many of these users recognize the need for better consumer intelligence that will allow them to explore their markets, spotting unmet needs, and mitigating brand health risk, many still need to make the case for a new solution.
How individual brands adopt and roll out AI for consumer intelligence, majorly depends on their focus, functional needs, and ultimately, their current digital and data maturity. Orienting your plan to where you are in the journey towards better consumer intelligence is critical. Before making a software investment, consider which path suits your organization’s resources and insights aspirations.
Yet, the business case for AICI looks different than that of traditional social listening and brand health tracking. While social media and corporate marketing teams have always recognized the value of social data, AICI has pushed it up the value chain to become a key input in market-moving and revenue generating decisions – and a critical component of enterprise insights initiatives.
For many market researchers, the practice of incorporating consumer-generated data from social, search, and other-real time sources is relatively new. For product and innovation teams, it is a novel concept. Yet for all groups, AI enabled consumer intelligence is a worthy investment.
Brands need to choose a trusted partner that will also bring to the table data science and social intelligence expertise. This is where hybrid AICI providers will step in, to bring scale and substance – something which is only possible from integrated human-machine teams.
**The author is the Chief Client Officer, Client Organization at Ipsos in Kenya
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