Understanding your consumers with AI in market research

Today’s demands for greater speed and agility have put more pressure than ever on marketers and insights pros to not only keep up with consumer behavior but 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.

Advances in AI are changing the way brands gather and activate consumer insights

In this paper, we will explore how AI is bringing new solutions and new insights to enterprises and challenging the status quo.

In 2020, Forrester began covering this emerging market, and last year published its first wave evaluation of the top platform vendors, including Synthesio. Unlike traditional social listening vendors, 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. Now, 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. Add in a push from CEOs who now see artificial intelligence as the most industry-impactful technology - according to a recent Gartner survey - and we have the latest spark for driving AICI into many more organizations.

The future of consumer understanding requires human intelligence and AI

The path to understanding consumers in 2022 requires more than what old school social listening techniques can provide. 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. As Sandro Kaulartz, Chief Research Officer within Ipsos Social Intelligence Analytics service line puts it,

Machine intelligence does a great job in laying the foundations, helping us navigate between complex contextual layers – not easily available to the human eye. But human-machine teams unlock this potential - and also deliver scale, speed, and accuracy to have confidence in results.

Yet not all vendors in the space deliver the same flavor of AI-enabled Consumer Intelligence (AICI). As the market moves away from bespoke social listening tools towards centralized AICI platforms, Forrester has identified three categories of providers:

• Software platforms that provide self-service tech for simpler use cases. Legacy vendors like Brandwatch deliver SaaS solutions with out-of-the-box support for common data types and analytics and visualization capabilities that allow practitioners to launch their own studies.
• Hybrid providers who combine software and services to get the most out of consumer insights. Synthesio, along with the Ipsos Social Intelligence Analytics service line, 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.
• Service providers who deliver strategic, full-service support. Specialty firms like Black Swan Data have their roots in services and primarily deliver custom studies and prescriptive reports.

Schedule a demo of Synthesio's AI-enabled Consumer Intelligence suite today

AI supports users in a growing number of roles

Across brand, insights, and product teams.

According to Forrester, 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%). AI-enabled Consumer Intelligence (AICI) helps brands on their journey to profitable growth by providing its users with actionable insights that lead to better business decisions – and enables teams to move from reactive to predictive decision-making. One Synthesio client at a major alcoholic beverage company said, “[These insights] ensure that our marketing strategy – and all of our initiatives – are grounded in our consumers’ realities.” As AICI extends across the organization, we see four primary users:

AI in consumer and market insights

AI enabled consumer intelligence complements traditional market research techniques like surveys and customer interviews by providing access to real-time data, online signals, and predictive capabilities. It also helps bust through blockers by reducing the time it takes to get insights, improving data quality, identifying market changes sooner. For example, teams at global cosmetics manufacturer L’oréal use AI to analyze millions of online conversations, images, and videos from over 3,500 sources. The aim is 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. “the main idea is to make sure we can detect before the competition the trends of tomorrow” said charles besson, global social insights and ai director at l’oréal group.

AI for brand health and marcomms

These 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, PR efforts, campaigns, partnerships, and more. AI-enabled Consumer Intelligence (AICI) enables teams to embrace more data-driven approaches to marketing. Danone, a multinational food products company and Synthesio client, studied one of their target audiences – yogurt eaters - using social data to identify key preferences, interests, and hobbies. Oikos, a Danone brand, incorporated the findings into their campaigns and content marketing assets and increased reach by 20% and engagement by 35%.

AI for product and innovation

As product teams are pushed to become more customer-obsessed, they can 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. GSK partnered with Ipsos and Synthesio to identify future innovation spaces in the cold and flu management category. Applying AI algorithms to over 340,000 online mentions revealed unmet needs – and a wide range of userdeveloped solutions like “blocked nose rollers” and “antitussive cough stoppers.” James Sallows, Global Head of Transformation & Capability at GSK recently said, “The resourcefulness and innovation of consumers is a key fuel to drive our own innovation processes and this approach has helped us understand how leveraging social data can provide actionable, powerful insights on unmet needs and innovation opportunities.”

Read more about Counsumer Signals and Innovation.

AI for creative agencies

These teams can tap AI enabled onsumer insight to improve their clients’ brand awareness and boost ROI with better audience targeting, more relevant campaigns, and smarter sponsor/influencer partnerships. Plus, it provides performance metrics that agency teams can share with clients to prove their value. McCann Dakar used this approach when helping a mobile network operator client relaunch and rebrand in a new market. By collecting and analyzing thousands of online mentions related to the consumers’ mobile plans, they identified top pain points and the content types, formats, imagery, and tones that resonated in the local market. The insights resulted in a recordbreaking campaign; Petra Cvitkovic, Strategic Planner at McCann Dakar:

The strength of our project was in the combination of Synthesio’s social data and cultural knowledge. This made it possible for us to launch communications that everyone could identify with.

How can brands make the business case for AI?

While these users recognize the need for better consumer intelligence that will allow them to explore their markets, spot unmet needs, and mitigate brand health risk, many still need to make the case for a new solution. Yet, the business case for AI-enabled Consumer Intelligence (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. One senior Customer Insights pro and Synthesio client told us, “Discovering new consumer insights helped us pivot our offerings and boost revenue.” 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’s a novel concept. Yet for all groups, AI enabled consumer intelligence is a worthy investment.

AI delivers enterprise-wide value

Historically, social listening and monitoring have been used by marketers to keep an eye on what consumers are saying about the brand, get a pulse on general sentiment, and market more effectively. 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.

AI provides answers to critical business questions

Real-time consumer insights from social and non-social data like search, surveys, and other first party sources unlock exponential value and opportunities for insights pros, but intelligence is worthless unless tied to a specific business outcome. AI enabled Consumer Intellignece provides the necessary data sources, AL/ML algorithms, and frameworks to find trustworthy answers to questions like, “How is our brand perceived, and how are campaigns performing?”, “Which trends are impacting our market, and who influences them?”, and “What unmet consumer needs exist, and where are whitespace opportunities?” Hybrid providers - ones that combine tech and services - also help find answers by overlaying predictive models, cultural expertise, and industry knowledge to ensure data is interpreted within the right context.

AI speeds time to insights and consolidates currently siloed programs

According to Forrester, the top priority (50%) for marketing, IT, and line-of-business (LOB) pros is improving management of customer data i.e., building insights and linking data across the enterprise. AI enabled consumer intelligence programs cut across internal data silos in social listening, CRM, Voice of the Customer, customer feedback management, and more to create a single (accurate) source of truth - and reduce duplication of work across teams. Plus, a central, real-time view of consumers enables insights pros across the organization to be more confident in their decisions and take advantage of first-mover opportunities.

AI aligns to key business drivers

According to Gartner, 56% of CEOs say growth is top on their list of priorities this year - but growth will require understanding new markets, new audiences, and launching new offerings. Consumer-centricity is directly tied to revenue growth; it allows brands to retain customers (and thus retain revenue), increase sales with new products and services, and identify and attract new audiences. AI enabled consumer intelligence also helps brands reduce costs by enabling insights pros to measure, benchmark, and optimize their actions, and improves speed and enterprise agility by embracing a proactive approach that’s rooted in real-time, accurate data.

How individual brands adopt and roll out AI for consumer intelligence

It 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.

• Insights “Insourcers” lead the way with data expertise and advanced use cases. Among future-forward brands in sectors like CPG, entertainment, finance, and high tech, which have both the motivation to apply data differently and the digital skills to make it happen, we see the growth of central data hubs as the driver for AICI. As teams adapt self-service tools and export insights far and wide, they are investing in insights platforms and hungry to consume services and tech add-ons via APIs. These are the first movers, and firms to study and learn from for those who aren’t yet data wizards. They’re also more likely to embrace emerging AICI use cases like innovation sourcing and planning – and using predictive models to anticipate market trends.
• Insights “Outsourcers” are fast followers with broader insights initiatives. Many companies in consumer sectors like automotive, insurance, and retail are investing in corporate insights teams and are looking to move quickly to catch up to first movers. They are highly motivated to become more data-driven and have moved beyond traditional social listening use cases like brand health and crisis management to more proactive category and trend research - yet are still building out the digital skill sets to transform at scale. These companies are looking to providers like Ipsos and other agencies to manage their consumer analytics and serve up critical studies. On the tech side, they are looking at APIs, but are more likely to start with a hybrid AICI solution that bundles services and tech.
• Insights “Integrators” will elevate social or VoC programs to AI enabled consumer intelligence. Most traditional consumer goods, travel, and manufacturing companies (and even agencies) have been forced to “think digital” over the past two years and will envision their AICI efforts as an extension of these efforts. Often, they have more basic data needs to help them manage communications and track campaigns and competitors - and are focused on refreshing or connecting their marketing-centric social listening programs to other VoC or insights efforts – even while testing the waters of full-on AICI. Many of these firms need to focus on the business case for AICI to sustain and scale up these efforts.

As AI goes mainstream, how should brands prepare for the future?

AI enabled cousumer intelligence will continue to reshape how brands incorporate consumer insights into everyday decision-making and drive us towards a more complete, accurate, and predictive understanding of consumers. As Ipsos recently noted, many companies have already accelerated their experimentation with more and new sources of data and tools in search of higher productivity and consumer-centricity. To take action and unlock new growth opportunities in this next generation of consumer insights, brands should choose a platform provider that meets their current and future state needs.

In summary, understanding consumers in 2022 and beyond will require new AI-powered tools and new skills. The role of AI in insights, marketing, and innovation is only going to expand, so teams need to prepare now to:

• Coordinate and consolidate efforts around a single source of truth. Brands will continue to move away from multiple social listening, customer feedback, and VoC tools towards a single insights platform that not only reduces redundancy in tech spending and employee work but also creates a tech foundation they can build on over time. This trend will only accelerate as current events create more change and drive urgency for brands to track behaviors, brand perceptions, and reactions to social movements like boycotts and protests. Brands moving to AICI should look for a vendor that can ingest multiple types of internal and external data sources to create a central insights hub.
• Track emerging channels and data sources needed to get the full picture of consumers. As convergent channels and hybrid experiences continue to blur the lines between online and offline, siloed approaches that center around a singular source of data like social media or customer surveys will no longer suffice. Tomorrow’s platforms must support small and big data, and natively apply machine learning to clean, combine, analyze, and extract actionable insights – while automating as many manual steps as possible. For brands, this means choosing a partner with a strong roadmap for delivering AI/ML tools and enrichments – and support for both machine learning techniques like topic modeling16 and custom use-case focused frameworks.
• Choose a trusted partner that also brings data science and social intelligence expertise. 71% of CEOs said they expect labor/skills shortages to disrupt their business strategy within the next 12 months. As brands struggle to find qualified tech talent to support insights initiatives, they’ll lean on partners to fill gaps and support regional data collection and applications. This is where hybrid AICI providers will step in, to bring scale and substance – something which is only possible from integrated human-machine teams.