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