Consumer insights are rocket fuel for businesses that struggle to pinpoint exactly what customers want and need, and why they buy. Even when businesses do manage to access these insights, interpreting them in an accurate and impactful way is always prone to human error. Could AI-powered consumer intelligence fill the gaps?
Why Traditional Market Research is no Longer Enough
Traditionally, market research has hinged on statistics to analyze people’s opinions. It worked better than no research at all, but it never was a perfect science.
Conducting market research has always been associated with broad-scope, time-consuming projects. Market research offers deeper insights into the consumer psyche, which is why companies are so reliant on it to guide high-stakes projects like product launches and rebrands.
But the costs and lengthy timelines of traditional research methods aren’t feasible for smaller-scale decisions and insights. The results are often laser-focused on a specific area of the business and generally not shared or used across the enterprise, thereby reducing the overall value that organizations gain from their research investments.
Then there’s the fact that humans are complex beings, given our range of emotions, experiences, beliefs, perceptions, and knowledge. It’s just not easy to pinpoint a specific sentiment based on statistics alone.
As marketing technology continues to expand, artificial intelligence is earning its place by offering consumer insights that take these fine nuances into account. By working in real time and offering ongoing insights into numerous topics and questions, AI enabled research can cause usability to increase, costs and timelines to decrease, and overall value to skyrocket.
Here’s how AI is helping to redefine market research and consumer insights as we once knew them.
1. Market Research is Faster, Cheaper, and More Accessible
Think about all of the pieces of consumer insights you’ve collected over the course of a single campaign. What do you think is faster: hiring a team of experts to review, sort, analyze, and compile these insights, or letting an algorithm do it for you?
Machine learning algorithms are trained to review large quantities of data in very short time frames. Natural Language Processing algorithms (a kind of AI) can make sense of huge volumes of social media posts and other unstructured data in a fraction of the time it would take a human to analyze all of that content, making it quicker and easier to uncover useful insights
Linkfluence’s Radarly uses up-to-the-minute data to deliver instant consumer intelligence for daily decision-making. It looks at a range of sources, from social media to online review sites, so even if you are not proactively conducting a market research project, real-time consumer insights are still at your fingertips.
What’s more, AI tools get “smarter” over time as they continue to collect and analyze data. The longer your tools are in use, the more valuable they become due to their data breadth and history. Users can identify trends over the long term and see how consumer insights and preferences change over time.
Hiring a team of analysts to do these same jobs is expensive and can be unsustainable for many businesses. Traditional market research is so costly that it prices many smaller and medium-sized companies out of the possibility altogether. With lower costs, more companies can take advantage of consumer insights and level the playing field with their larger competitors.
2. AI Brings Context to Consumer Insights
Context is everything in the field of consumer insights. Reading plain vanilla statements doesn’t always indicate the sentiments behind a consumer’s thoughts. Deciding the meaning behind a person’s words has long been the job of a human analyst, but this also means that the interpretation may be prone to human error.
For example, two people can hear the same statement and take away two different meanings. Their perceptions are often based on their own feelings and experiences, which makes it harder to decide how to use feedback appropriately.
AI brings context to consumer insights by analyzing tons of data points in real time and finding the why behind words. It looks for repetitions of keywords and brand mentions so you can find common themes in customer feedback and make better use of your data.
3. AI Expands Market Research Formats
As AI continues to change the dynamics of market research, it’s also paving a path for new research formats.
Facial sentiment analysis is a prime example. Webcams are increasingly being used to pair a user’s facial expressions with their verbal or written responses during surveys. The webcam and AI software tracks users’ eye movements, blinks, and reactions (all of which are referred to as “micro-expressions), then uses facial expression models to analyze the results. IT documents any changes of expression while watching an advertisement to get a clearer idea of a user’s engagement and response, as well as to discover how focused the user is on the content being shown.
It’s a step up from the traditional Q&As (e.g., How did you feel while watching this ad?), plus it reduces the need for a human analyst to process the data. What’s more, brands can carry out surveys using facial sentiment analysis or similar AI-driven consumer insights at a much larger scale.
4. In-House Data Becomes More Valuable
Brands already sitting on a mountain of valuable data can make better use of their existing consumer insights with AI tools. Historically, one of the biggest challenges in tapping into in-house data is simply being able to process it and connect the dots in a timely and efficient way.
AI bridges these gaps by using machine learning to find connections between data points that companies might not be looking for or realize exist. That’s the beauty of machine learning: the more data it ingests, the “smarter” it becomes and, therefore, the more value it can create.
For example, the telecom industry is in the midst of a major transformation with its 5G rollout. Because it already has an existing widespread infrastructure and customer base, a telecom company likely has the data it needs to decide where to make the biggest upgrade investments based on an expected ROI.
However, searching through this data manually can take months. In past upgrades, telecoms would rely on market research to identify the markets that were most in need and most likely to make the biggest impact on ROI.
With AI, market selection for upgrades happens much faster and decisions are still based on the same data that a costly, time-consuming market research project would have delivered. Telecoms can turn AI consumer intelligence into a competitive advantage by being able to address needs and opportunities quickly and secure a bigger piece of the market.
5. Timely, Relevant Communications Yield Better Outcomes
It’s a busy, noisy world for the average consumer. Studies show that an individual comes into contact with anywhere from 6,000 to 10,000 brand impressions per day. Many experts suggest the problem is only getting worse.
This is bad news for brands that need to break through their audience’s “ad blindness” in a way that’s valuable and not intrusive. While this is easier said than done, AI is helping to make it more of a reality.
The key to getting inside a consumer’s inner zone is to connect with them using timely, relevant messages. Your message needs to be something they care about, and it should be delivered at a time the customer needs it. Without both of these building blocks, it’s going to be much harder for brand messaging to be something other than ad noise.
AI models are being deployed to learn more about consumers’ habits and find the best time to send specific messages. For example, if a person is most engaged on their smartphone in the morning, then you might send a text message feedback survey in the morning hours.
6. Supply Chains Become More Robust
In the sales and marketing pipeline, the supply chain is far removed from market research. But using AI-driven consumer insights in the research phase can have a ripple effect that extends all the way through a brand’s supply chain.
That’s because AI supports predictive analytics to find out how consumers will respond to a certain offer, product, or service. Brands can anticipate needs and behaviors of consumers and plan accordingly. For example, if predictive analytics detects a potential spike in interest of a certain product, brands can plan on ordering more of that product to avoid back orders and shipping delays.
Throughout 2020 and 2021, we’ve seen what happens when gaps in the supply chain go unaddressed. During the pandemic, companies were depleting months’ worth of stock within days or weeks, with no way to quickly replenish their warehouses.
Predictive analytics may help to signal small and extreme scenarios alike so that businesses can adjust their ordering and warehousing strategies and shift alongside consumer preferences.
The Future of AI Enabled Consumer Intelligence
While a handful of researchers believe that AI may be a threat to the market research industry, the overwhelming majority believe it offers opportunities to add value. Already, AI enabled consumer intelligence is helping companies to redefine their approach to data collection and act on their findings. Given its lower costs, shorter deployment timelines, and less reliance on human resources (a particular important aspect in light of the ongoing labor shortage), AI is proving to be more than just a passing trend.
Linkfluence is helping to shape the future of AI enabled consumer intelligence today by pairing AI with market research expertise. Going beyond social listening, our technology continuously collects real-time insights across multiple global channels. We’re putting insights into your hands whenever and however you need them.
Just how far can AI consumer intelligence take your brand? Request a demo today to find out.