When it comes to understanding trends, it’s far more about coming to grips with the movement of conversations versus snapshots in time. In a nutshell, that’s the difference between an analysis and a series of replicable analyses that are informed by your analytics. It may seem like splitting hairs, but the difference is monumental. That’s because if you misjudge an emerging trend, your competition will likely beat you to the dinner table. And no one wants that.
With that in mind, we’ll pit trend analytics versus trend analysis, with a focus on:
- The difference between trend analytics and trend analysis
- Why trend analyses often fail
- Trend analytics in action
And brands that don’t take trend analytics seriously are quickly getting left behind:
- The global data analytics market is projected to top $132B by 2026, with an estimated CAGR of 26.4%.
- When positioning your brand to engage with emerging trends, beating the competition to the punch is critical. It’s important to note that decision-making is 5x faster for businesses using data analytics for market intelligence.
- Brands are hungry for business intelligence. The overwhelming increase of unstructured data paves the way while brands scramble for data analytics tools with visualized data
On your journey to capture emerging trends, trend analytics forms the backbone of your strategic approach to long-term success. Let’s break it down.
Trend Analytics vs. Trend Analysis – What’s the Difference?
It might be helpful to envision it this way – let’s say that every layer of an onion is an individual trend analysis. That being the case, trend analytics would represent not only the whole onion but also the farm, soil, the truck that brought it to market as well as the vendor. It’s everything you have on hand.
Trend analysis tells the story of movement through social and news media. It’s the onion (and its layers) that are created from all that hard, foundational trend analytics work. Once you’ve spotted an area of interest within an emerging trend, you can follow it in great detail over time.
For instance, sustainability is a vast emerging trend with many sub-trends below the radar. Capturing it all is done with trend analytics. The actual analysis we pull from it is trend analysis. Here, we’ve tracked local and national news media conversations in the UK talking about single-use plastics over the past year. 71% of the articles mention banning it.
And even though this is a view for a year – it’s still one trend analysis. Adding to it is the key. Trend analytics is the process – it’s the raw data the powers the analysis.
Conversely, analysis is more singular in nature and doesn’t tell the whole story by itself. Merriam-Webster defines analysis as “a detailed examination of anything complex to understand its nature or to determine its essential features.”
But here’s the rub – to accurately spot and track emerging trends, your methodology must be consistent over time and intolerant of bias. If you are running a trend analysis sporadically while using different tools, search operators or filters, then your results measured over time aren’t trustworthy. Not at all.
Trend analytics is your cohesive methodology for ensuring that your study of trend movement is accurate. It’s all about the approach that ensures your individual trend analyses are structured exactly alike and run at intervals that best position your brand for leverage.
Let’s examine why that’s important – and why so many trend analyses fail.
Why Your Trend Analysis Failed
A trend analysis is critical to your brand to predict major shifts. As such, you should take care to ensure that each subsequent analysis is set up in precisely the same way. That’s where many go wrong with their trend analysis.
If you are trying to compare two analyses, but one is using a couple extra search operators, or one is filtering out reposts and the other isn’t – you’ve got accuracy problems. That’s known as a sampling error and should be avoided at all costs.
It’s a methodology problem, really. You want your parameters that you intend to use set in stone from the beginning, so you know how to structure each analysis going forward.
Systematic measurement errors are common problems analysts run into and can corrupt your trend analytics data. A systematic error is caused by miscalibration in your measurement tools. This, of course, is a dilemma when stitching together an analysis from disparate tools. Suppose one receives a software update with a bug that affects accuracy or causes friction between your data analytics tools. In that case, your accuracy could be on the chopping block.
This is one of the reasons that NetBase Quid leads innovation in the world of trend analytics. With two platforms working seamlessly under one roof, you never have to worry about cross-platform hiccups that spoil your measurements and invalidate your data.
Trend Analytics in Action
A solid methodology will help to ensure that your trend tracking journey is a success. Knowing that your trend analytics are built upon analyses that tell the same stories is paramount. Additionally, using tools you trust to work together every time you pick them up ensures your margin of error is low.
As you’re probably aware, within the booming Fintech world, cryptocurrency is an emerging trend that is humming along nicely – fueled by Bitcoin’s screaming rise in value this year.
As an example of trend analytics in action, we created two trend analyses with Bitcoin as the topic. To keep things simple, we sorted both for Reddit conversations which saw a nearly 5x increase in conversational volume year-over-year.
Both ran for one month ending May 14 and included the same search operators for each. In other words, each analysis was identical in every way.
Here’s last year’s Bitcoin conversation on Reddit, showing 178k mentions and net sentiment of 30% on a scale of -100 to 100. And in 2021, there is a colossal increase in volume for the same time period. Mentions have ramped up to over 765k, while net sentiment dropped some to 17%.
The point to be made here is that cloning our analysis structure gives us a series of analyses that we can trust. There are zero differences between data sources, configuration and tools. That way, we know that the intel we deduce from multiple analyses will yield actionable numbers that accurately express movement in the trends we want to monitor.
And a final note on your trend analytics. Be sure to explore the anomalies you uncover with each subsequent analysis – especially the spikes and dips. These could be indicators of changes in conversational direction.
For instance, the spike above on May 13 was when the Tesla CEO, Elon Musk, announced that the automaker would no longer accept Bitcoin (BTC) as payment. How significant are those implications to the crypto space? Is this a turning point of sorts for investor interest?
Those, of course, would be the type of questions we’d seek to measure as we revisit our trend analytics going forward. And in the meantime, we can zoom out into other areas of niche conversation and test the temperature across domains, authors and platforms.
For example, you’d think many retail investors might have cold feet with Bitcoin following Musk’s announcement – but markets don’t always follow logic. The following Discord commentary seems to be the general consensus for the time being.
In summary, trend analytics is growing in importance with every passing day. As more brands hone their skills, the window to be a first-mover on emerging trends will grow shorter and shorter.
Could your brand’s trend analytics game use an upgrade? Reach out for a demo, and we’ll put world-class data analytics tools at your fingertips for the next best thing to a crystal ball.