Feature Highlight: Time Series Analytics

by Bryan Teo

Feature Highlight: Time Series Analytics

Why we added Time Series Analytics to Fairing

Use Case #1—Marketing Channels

Imagine this. Your business has invested heavily in specific campaigns like email and instagram advertisements for your marketing. The decision to do so was guided by your own knowledge and research and you’re hoping that it will pay off for your business.

However, you start to realize that it isn’t faring as well as you’d hoped. As a result of that, you install a post-purchase survey like Fairing to help you understand what is actually drawing customers to your store.

As you begin to collect more and more data, you find out that TikTok has been a significant contributor to your growth. This comes as a surprise because you haven’t invested in or considered using TikTok at all. But with your Fairing data, TikTok appears to be a must-have in your marketing mix.

With the increased marketing spend on TikTok, you now have to patiently wait to see if the switch was worthwhile. This is where Time Series Analytics becomes a core feature to unleash. With it, you will be able to monitor your survey responses over periods of time.

This gives you a macro level understanding of if and how your customers have heard about you, and how it has changed since you switched up your marketing mix. Use Time Series Analytics to track if switching your investments into TikTok has been worthwhile by observing if it trends upward in your response data.

Use Case #2—Market Positioning

Customers make their purchases for a reason. Be it for themselves, for their partners, or for a birthday gift, the list goes on.

Fairing data helps you understand what occasion your customers are making their purchases for. This is important to note as it allows you to understand your position in the market. Not only that, how your customers perceive your brand and product.

Asking your customers questions like “What is this purchase for?” can give you the data that you need.

Depending on whether or not you feel that the current position is optimal, this data can further aid you in repositioning to where you want to be.

This took place with Füm, a brand offering naturally flavored air from diffused plant extracts to help people break bad habits. They originally started as a breath help tool for athletes, fitness enthusiasts, and weight lifters. However, Füm started to find growing traction with customers and influencers who were using the product to curb bad habits.

With 3 months of runway left at one point, the business needed to find new channels for their marketing. Füm then stumbled into success with podcasting as an advertising medium, as well as other organic channels like YouTube and TikTok. While this was great, murky attribution made it difficult to pin down exactly which channels were driving the most revenue.

This is where Time Series Analytics in Fairing helped play a big role. Füm was able to compare their progress over time as they began to test messaging that positioned them as a "good habit builder" over a "health and fitness" device.

This allowed them to monitor any changes, and be certain of what was driving them the most revenue.

Fast forward to today, Füm has validated their repositioning, and is scaling rapidly.

Imagine you're a hypothetical soap brand. With Fairing, you find out that while the majority of your customers were buying it for themselves, a significant amount of your customers were buying it as gifts, too.

Discovering this information is critical because it tells you that there’s untapped potential in that area. As such, you create bundles or products aimed at the demographic buying them as gifts, and you track how well they're:

A) returning to purchase, and

B) purchasing for themselves.

Over time, the time series of that cohort shifts towards purchasing for themselves, on top of buying them as gifts. Re-positioning complete.

Use Case #3—Net Promoter Score

Another important use case for Time Series Analytics is with Net Promoter Score (NPS).

NPS is an important metric for any business. It’s a measure used to gauge customer loyalty, satisfaction, and enthusiasm with the company. This affects how happy your customers are with you, and how likely they are to promote it to others.

NPS can help predict your business growth. If your NPS is high or higher than industry average, you know that you have a healthy relationship with customers—which is especially important for brands that also have a subscription model. They are hence likely to act as evangelists for the brand, fuel word of mouth, and generate a positive growth cycle.

So, let’s say that using Fairing NPS questions you find that your NPS is lower than the industry average. And this wasn’t a surprise to you considering your business hadn’t paid much attention to it until now. As such, your business’ main focus now is to boost your NPS having understood your baseline score.

Having researched your competitors, you implement strategies to try to go above the industry average. You decide that you have too many detractors, and want to build your relationship with them and solve any issues they’ve faced. This means dedicating more resources to reaching out and providing solutions for your customers.

This solution takes time to show results. You build and nurture relationships with your customers over periods of time. As such, any improvements to your NPS takes time as well.

This is where Time Series Analytics is important as it allows you to view improvements to your NPS over time.

By consistently asking your customers “On a scale from 0 to 10, how likely are you to recommend our product/company to a friend or colleague?” you gather the data needed to be on top of your NPS. Compare your score periodically to verify if your strategy has worked.

Fairing’s NPS Reporting feature will also aid you in this process. It provides you with your NPS score, detractor, passive, promoter numbers and additional NPS specific metrics. This, coupled with Time Series Analytics, makes strategizing and analyzing your NPS efforts all the more easier.

Breaking down Time Series Analytics

Functionalities and features

A key aspect of Time Series Analytics is in its simplicity. The feature was built with ease of understanding and use in mind, so that users can manipulate the data and derive insights easily.

The feature is made up of a line graph of your top 5 responses charted over time. On the X-axis, it shows the ‘Response Percentage (%)’ and on the Y-axis, the order date associated with the response.

The graphs have been designed to automatically refresh every 30 minutes to provide you with the latest data. You then have the additional option to aggregate the data based on the time period you want—by Day, Week, or Month, or even a custom range within that limit.

There are 2 things to note about the default settings of the chart.

By default, the chart displays responses as a percent over time. Although, users will also have the option to switch the chart to show response count over time as well. This flexibility allows you to view your data from 2 different perspectives.

Secondly, the default state of the chart is such that your top 5 responses (by volume) are charted. Not to fret, as you have the option to chart any 5 (or any number up to 5) responses you want. This gives you the option of selecting the responses you want to compare. This ensures that you will be able to analyze the data that you want.

Why use Time Series Analytics with Fairing?

As demonstrated in the use cases above, the core benefit of Time Series Analytics is to enable your business to compare your progress easily. It helps you to chart your progress over time, comparing current performance to old.

Time Series Analytics was implemented to further improve the user experience. Not only that, but to also help you derive more actionable insights.

Maximize the benefits of Fairing with this feature. Read our docs here, or request a demo here.


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