Many reputable researchers operate on the assumption that a very carefully selected sample of 1,000 individuals can tell you, within a small margin of error, what 200,000,000 people are thinking. If you’re making important business decisions based on that kind of research we’d like to remind you that 90% of the best polling entities in the world were wrong about the 2016 presidential election. After a year of self-examination, there are many theories and explanations as to why most of the people ‘in charge’ of the numbers got it wrong.
To their credit, there are always anomalies and special circumstances that pop up. But in business, no anomaly is likely to forgive an investment of 100MM in R&D. Or 50MM in media spend. Or even 10MM in a re-branding effort. We can do better. Instead of asking a small sample of people, why not ask everyone?
Ok, but who do we mean when we say ‘everyone’? We’re talking about anyone, around the world, who has posted publicly on social media, engaged in public online conversations, or is accounted for in enterprise-held data. To be even more specific, we’re talking about anyone who has ever publicly engaged on:
Proprietary Support Tickets
Other Proprietary Enterprise Data
That’s a lot of people. It’s trillions of posts, comments, tickets, opinions, conversations and images that add up to a mind boggling amount of information. Old-school research will raise its hand and say ‘yeah but look at the amount of unrelated material you’re dealing with. How can you find meaning among that amount of information?’.
Good question. And the answer is Artificial Intelligence. AI and machine learning make it possible for brands to analyze information and messaging from not just social media, but also online resources like blogs, forums, review sites and publications as well as any of your own proprietary data you would like to use. Support tickets, other customer care data, and sales data can all be analyzed alongside these online data sets for a much more comprehensive view of the consumer and their behaviors. This is how modern consumer insights are discovered, and these technologies have changed the landscape of research forever.
But the real question, once we’ve explained where the insights come from, is who can actually benefit from them.
Who Wants To Know? The Growing Demand for Better Consumer Insights
Before we take a closer look at the data, let’s take a look at the people. Not every business has all of these functions, but most organizations have several groups, all with different needs, that can benefit from the new science of online analysis.
Trained analysts who dive deep into the data sets and source insights and reports for others to consume
Business Line Managers
Business line managers who need insights quickly and easily for data-driven tactical and strategic decision-making
Executives who need dashboards and reports of aggregate data to inform strategy and report on progress against goals
What’s The Problem? Too Much…Too Little…Too Late…
That about sums up the problems businesses have with conventional research. For people who need to know, there are pain points at every level. It could be simply the time it takes for ordinary research to produce the desired results. Or the basic issue of relying solely on solicited data, instead of the unsolicited conversations available online. Some would characterize their problem as too much noise, and not enough signal. Or reports that are hard to digest and derive meaning from. Or a critical need for emotive information and measures of sentiment that are simply unavailable otherwise. Sound familiar? Let’s see what online consumer insights can do.
A New Kind of Solution – Bigger Data. Better Decisions. Faster.
When you need to know what’s going on in the marketplace, whether it’s about consumer sentiment, buying trends or where you stand among your competitors, the best answers come from the biggest body of evidence. The largest source of online data and AI-Powered Consumer Insights bring a lot to the table.
Everything Online and More. Needless to say the ‘library’ of online data is immense. And remember, we’re not only dealing with the online resources listed above, your own proprietary information can be included in the mix. Sales information, customer service data, all of your own enterprise-held data can be a part of the picture. All together this means that any query you make into public online data can be seen from, and augmented by, the perspective of your own business.
Trends. The social sphere has been indexed for years. In the case of Twitter specifically, Crimson Hexagon has the capacity to analyze tweets as far back as 2008. So when you need to understand trends, you’re dealing with years and years of online conversations instead of just a recent benchmark.
Universal. These online resources have no borders. Companies and their workforces tend to be global. That makes the availability of these world-wide online resource even more valuable. Any time in any language. No extra charge!
On Target Examples of Online Consumer Insights.
Here are a few questions reliably answered by the use of online consumer research powered by Artificial Intelligence.
1) When should a leading national coffee retailer release one of its signature seasonal products?
One of the US’ leading coffee companies wanted to explore when to launch one of its leading seasonal products, and when to run its greatest campaign push for the product, in relation to the launch date. Using a combination of enterprise sales data with social media data, the company determined that the best time to launch the campaign was three days prior to the product’s release in order to maximize social conversation and sales revenue. This is a great example of how AI-powered consumer insights can be generated from a mix of enterprise and online data to inform a brand’s decision-making process.
2) When did Airbnb become more popular than the biggest hotel chains?
As measured by the volume of social media conversation, Airbnb became more popular than Hilton and Marriott, two icons of the American hotel market, in July of 2014. And Airbnb’s lead is only growing. This is a great example of an insight that might be time consuming and expensive to discover without AI.
3) When was the turning point for the #BlackLivesMatter movement?
By measuring millions of posts, #BlackLivesMatter gained national attention in 2014 with the deaths of Michael Brown and Eric Garner. However, the movement didn’t reach viral status until July 2016 with the deaths of Alton Sterling and Philando Castile. At that point, the conversation tripled from its previous high point.
4) How does Toyota’s target market differ from that of Honda?
By analyzing the interests of Twitter users, brands can understand the unique audience affinities for their own audience and for those of any other brand. We see above that the Toyota audience is strongly interested in Star Trek, car racing, NASCAR, and entrepreneurship, and the Honda audience cares more about Lil Wayne, Boxing, and shoes/sneakers. Such audience insights from social data can help the Toyota or Honda brands to make decisions about where to buy advertisements, what messaging to use, and what target audience to go after given the audience interests of their brand vs. their competitor.
Better Answers and Beyond
There is an intuitive advantage to working with such a massive data store. This kind of online analysis is bound to produce consumer insights that reflect the thinking of millions of people and provide answers that are broad-based and reliable. But there is another aspect of online research that is equally valuable and sometimes overlooked. Working with data at this scale, Artificial Intelligence can reveal information that you weren’t even looking for: online consumer insights that become true inflection points for your business.