Too Many Eggs in Your Social Listening Basket?

Sergio Oliveri |
 01/27/23 |
4 min read

Too Many Eggs in Your Social Listening Basket?

Understanding consumer conversation in any category is challenging. This challenge is compounded when brands need to capture insight about a specific topic that includes very general terms, as it requires analysts to sift through countless irrelevant mentions. We’ll see this dilemma in action below, as well as the trouble brands face when they make assumptions about consumer perception instead of allowing the data to guide them.

During a recent webinar on Getting to Know and Grow Your Audience by Analyzing Social Media Behavior with Dr. Nicole Olynk Widmar, Professor, Associate Head, and Graduate Program Chair, and Dr. Jinho Jung, Research Associate, Purdue University, our friends at Purdue shared an exciting search that revolved around their exploration of an everyday food item: eggs. How were people talking about them, and how could that conversation apply to food production? Be sure to tune into the webinar to hear more about what they found there!

From this and other conversations (which we’ll speak to below), we realized this egg search offered a great example of how challenging it is to find relevant information about a topic online. At first glance, it appears to be a straightforward concept, but trouble spots immediately arise.

Egg conversations can revolve around many things, from the price of eggs and how to cook them to financial nest eggs and Easter eggs:

It’s an interesting example of the importance of fine-tuning your social listening results to capture the intel that matters and appropriately plan for projected impacts on your market. Of course, depending on what you’re searching for, the “egg” conversation volume is very different, and so is the corresponding conversation.

Many brands take the conversation as a whole and misread the conversation tea leaves—which is almost as dangerous as making uninformed assumptions.

Fine-tuning results is only one ingredient in the social listening scramble, however. Once you have accurate data, it’s time to dig into the specifics to separate the messy brand assumptions (yolk) from the egg white, i.e., the actual consumer perception! And we’ll leave the egg analogy there.

Consumer Perception is Unpredictable

Generally speaking, brands often get consumer perception wrong due to research bias. It’s not all their fault, though, because consumers are incredibly unpredictable.

For example – during our webinar, we discussed consumer response to food and health risks. Specifically, we explored consumer responses to food recalls and diseases transmitted by mosquitos.

Dr. Widmar shared findings revealing that although something makes a big splash online and presents an ongoing risk, interest and conversation can seemingly inexplicably diminish over time.

She shared correlations found between various datasets, including an exciting relationship between recall data and CDC illness reports. People apparently perk up when related illness reports hit, but the recalls themselves have become noise to them, as so many are happening all the time.

This also applies to planning ahead. The supply chain challenges and shortages of baby formula last year offers a timely case in point.

Plenty of indicators available online would have (and should have) alerted distributors and consumers to the impending shortage. But, overall, there are lessons for food distributors and manufacturers willing to dig into the data.

And they need to explore market movements and underlying sentiment for ways to win consumer and retailer attention when required. And to maintain that momentum during times of crisis.

By itself, social media listening is messy and seems to offer little value. It comes with a side of slang, emojis, and imagery—and it’s easy to misappropriate tone too. There’s lots to keep in mind at once when exploring social data, and that’s bad news as it’s merely one data source in a digital world full of many.

Revealing Insight from a Combination of Data Sources

Companies often wonder how social listening applies to them when not everyone interacts online. But when it comes to sorting out consumer preferences, there’s no “one” way to capture insight from every audience. As we can see below, the evolution of audience insight finds us at a place where combining data sources is a must:

stages of social listening

And to do this requires ways to aggregate and analyze these many data sources quickly. This doesn’t mean it happens in a matter of weeks, but rather in minutes, with real-time analysis becoming table stakes.

But how does one manage such a thing? Having an Intelligence Connector that feeds intel (and underlying advanced AI-powered analytics) directly to a proprietary business intelligence platform is one way that’s becoming increasingly popular

The team at Purdue has been doing this to power its own complex analyses, in fact—and they shared several findings during the webinar demonstrating the power of this capability. For example, they shared that their investment in tracking brands and individual products has helped them overcome a massive learning curve, resulting in them now scraping data on commodities and general product categories to inform their investigations.

We’d love to tell you all about it, but they do it best on the Getting to Know and Grow Your Audience by Analyzing Social Media Behavior webinar – be sure to tune in to hear all about it! And reach out for a demo to see other ways our platform tools are helping researchers and brands like yours uncover critical intel that is disrupting and creating categories!

Premier social media analytics platform

Expand your social platform with LexisNexis news media

Power of social analytics for your entire team

Media analytics and market intelligence platform

Enrich your media analytics with social data

Social media benchmarking
and competitive intelligence

Data streams & custom KPIs for advanced data science

AI, Image Analytics, Reporting Tools & more

Out-of-the-box integration with other data sources