Predicting Consumer Trends with Social Listening

Look like an Oracle with our guide to predictive trend spotting. Understand how to read, segment, and analyze social data.

Social listening isn’t just for performance or analyzing likes and shares or looking at what happened and reporting on past actions. You can take that data and use it in a myriad of ways to predict what can or may occur. And with the right tools and research approach, you can do so precisely.

Read more below or download the full report.

Change Your Mindset About What a Social Analytics Tool Can and Should Be

With the correct data, your team can peel back the curtain and unveil how consumers or groups might behave based on how they talk online. You can leverage this data to understand how much product you will need based on the conversations around a new product launch or pinpoint the next trend amongst a group like moms or Gen Z.

No tarot cards necessary here. You just need a social listening platform capable of intuitively processing the billions of online conversations across the social media and online universe, then displaying insights in a meaningful and actionable way.
Playbook example

Use Intent-Based Conversation to Predict Market Need

Just log in to any social media platform, and you’ll know this: consumers love to sound off on their plans, or hopes, to get their hands on a trendy new product or the latest release from their favorite brand. Maybe it’s a new movie trailer that launched or a shoe influencer rocking new kicks that gets them fired up. Whatever drives the excitement, the fantastic thing about this instinctive draw to share intent online is that you can analyze them to predict trends. Using forward-thinking to trend social posts, consumers will tell you exactly what they bought or can’t wait to buy.

Example Analysis:
It’s so much easier to see data in action. For an insightful example, we looked at conversations expressing purchase intent about bike products and compared it over time to actual sales.

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Analyst’s Perspective: Purchase Intent-related conversation about biking increased 65% when comparing May of 2019 to May of 2020. When analyzing the sales data a month later - June 2019 and June 2020, there was an increase in sales of 63%. In this example, the purchase intent conversation actualized in the following month. Social data was able to predict a sizable increase in demand for bikes/biking equipment.

Practical Applications:
• A Streaming app like Netflix or Hulu could understand what types of content or genres will be in demand.
• An emerging music app could understand potential download intent for another audio category like podcasts.
• A shoe company like Nike could understand future changes in demand for a competitor and why.
Playbook example

Develop Product Lifecycle Indicators

Social media trends oracle 101: To correctly identify a potential trend worth watching, you must first understand the life cycle. Consumer trends typically develop in three distinct stages on social media.

Introduction -- where a breakthrough happens -- such as a new product emerges, or a viral video takes off.This is your early trend notifier and could hinge on an audience on a specific social platform (for instance, millennial moms on Pinterest).

Growth -- where a large subsection of the consumer base adopts and/or promotes the trend online-- the amount of conversation will start to expand and spread out to other channels at this stage.

Maturation -- where the trend begins to plateau or teters off and/or becomes mainstream - or possibly falls out of favor for something else (we do have the attention spans of goldfish, after all). You can spot a trend maturing ahead of time: post volume will decline, often on the social channel that drove the initial introduction phase. Knowing this, you can follow online conversations to identify trends.

Here, we look at how the conversation about essential oils evolved on social media, from earlyrise to mainstream adoption.

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Analyst’s Perspective: The overall conversation about essential oils peaks around 2018 then starts to drop. If you only take total social post data, one could assume that the essential oil trend is declining. However, Grandview Research estimates a 30% increase in valuation of the essential oil market. Understanding broader context (through ongoing social listening queries and outside data) is critical here. Instead of a declining trend, essential oils went from a buzzword to a known topic talked about online in different ways.Essential oils are now commonly added to commercial products, and just the oil type is mentioned, such as lavender oil or tea tree oil.

Practical Applications:
• A food brand like General Mills or Lean Cuisine can see which healthy ingredients are about to catch fire with target buyers.
• A fast-casual restaurant brand like Chipotle or Starbucks could predict the next big foodie trend, like avocado or cauliflower rice.
• An “athleisure” brand like LuluLemon could follow conversations to see which products are catching on with workout enthusiasts and which are falling out of favor.
Playbook example

Leverage Social Data to Forecast Sales

Who knew you could be a sales “meteorologist,” but it’s true! By combining your current sales data with social conversational data, and consistently tracking it, you can accurately forecast potential trends. So yes, using social listening to predict potential sales can be done. But it is a nuanced exercise. The researcher could misread the data or accidentally take outlier data points as sure-bet predictors. Not all blips on the radar mean there’s a storm. What you want is to make sure you don’t make outright assumptions based on one analysis or data point, but instead to constantly keep track of the data so you can unveil trends worth watching.

Using social listening analysis of intent conversations allowed us to pinpoint how online post volume correlates with sales in gaming. As the sales rank of a game increases, so does its social volume.

UPS TikTok

Analyst’s Perspective: Close attention to social listening data here shows its predictive prowess. The social post volume of Nintendo games grows logarithmically with selling rate rank. Moreover, measuring the Purchase Intent and Playing Intent themes within the conversation enables identifying outliers that weaken the predictive model’s fit. If a game receives a higher than normal percentage of Purchase Intent conversation but a low or relatively average Playing Intent percentage of conversation, it is likely to rank higher than its overall volume would predict. (A high PI % and playing % effectively normalize each other to indicate multiple posts from sources).

Practical Applications:
• The movie industry can predict the amount of the theaters are needed for each movie based on the conversation about that release.
• Based on the hype of a product before launch, will you have enough supply to meet demand.
• Predict the success (in relation to others in your category) of products popularly talked about on social media such as home decor products or supplements.
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