Article

7min read

3 Alternatives to Black Friday Promotions [+ Ethical Examples]

Black Friday – the time for sales and doorbusters and discounts – but what about the companies that are choosing not to participate this year? Rather than letting your site stay stagnant, you can still use this time to capitalize on the holiday traffic by testing new ideas for your site and gaining customer feedback in real-time.

There are many reasons why a company might opt to sit out of Black Friday sales. We’ve determined that most alternative Black Friday messaging falls into one of three major themes. These themes are:

  1. Fair Friday/Do Good Friday: Brands take the time during the busy shopping season to instead promote donations or partner charities to help drive awareness of issues while building brand trust and developing corporate social responsibility.
  2. Anti-Black Friday: Some brands choose to take not participating in Black Friday a step further by sharing messaging that challenges hyperconsumerism, promotes ethical consumption, or encourages a boycott of Black Friday. This might also take the form of a brand shutting down their e-commerce sales over the weekend in protest.
  3. Normal Friday: Certain brands, including many luxury retailers, have not historically offered sales and discounts during Black Friday. In lieu of promoting active sales, these brands might instead drive traffic to new collections and releases for the season. This can also be an extension of Black Friday protests by choosing to conduct business as usual over the fanfare of the shopping season. 

 

If you are considering an alternative Black Friday campaign this year, it is crucial to provide your visitors with clear communication about your campaign just like any other brand would do for their own sales and promotions. 

Experimenting with easy-to-understand messaging, banners spread throughout the customer journey, and pop-ins reminding visitors of your initiative can help reduce confusion and provide a consistent experience for your customers. 

Below, we have collected real-world examples of alternative Black Friday campaigns from AB Tasty clients and other brands who wanted to make the most out of the holiday traffic and gather important customer insights without compromising on their brand values.

Fair Friday / Do Good Friday Campaigns

L’Occitane

In addition to their Black Friday offers, the team at L’Occitane ran a “Give Back Friday” campaign in support of The Fred Hollows Foundation, a nonprofit organization focused on treating and preventing blindness and other vision problems. During the promotion period, $1 from every hand cream sold would be donated to the foundation.

Working within AB Tasty’s experimentation and personalization platform, custom code was created to support the campaign. The L’Occitane home page featured a large hero image along with a block of copy explaining the details of the promotion for customers. 

Transparency in both the foundation that would receive the funds and for how the funds would be used to benefit others helped boost engagement and buy-in.

B&B Hotels

The team at B&B Hotels: Spain & Portugal set up a campaign on Black Friday called “Green Friday” where 5% of bookings made on that day would go to support the reconstruction of La Palma Island after intense fires. 

They considered their users’ paths throughout the website when setting up their campaign. Clear messaging about the initiative was shared on the landing page and a reminder was displayed again at the booking stage to help push conversions. To reduce cart abandonment, an exit pop-in encouraged visitors to complete their booking before leaving the page.

 

Nature & Découvertes

As a part of their annual Fair Friday initiative, retailer Nature & Découvertes swapped out their standard hero image for one that highlighted their 2021 campaign for rewilding endangered habitats. 

Throughout the last week of November, Nature & Découvertes encouraged both online and in-person customers to donate to the initiative by rounding up their purchase amounts to the next dollar. The brand then matched the total amount of donations.

Beyond the hero image swap on their homepage, Nature & Découvertes also drove traffic to a landing page explaining the initiative in greater detail. This helped customers have confidence that the initiative was genuine and offered the brand an opportunity to be transparent about the impact donations would have on wildlife and conservation efforts.

Anti-Black Friday Campaigns

REI Co-op

Outdoor recreation retailer REI Co-op’s #OptOutside initiative is a great example of brands taking a stance against Black Friday that also aligns with their brand values. 

Instead of promoting a special sale or discount on Black Friday, REI Co-op chose to close their operations for their online store and drive visitors to learn more about the REI Cooperative Action Fund which works to make the outdoors more accessible for all communities. 

With operations paused for the day, REI Co-op made sure to communicate clearly that no orders would ship out on that day by displaying a banner at the top of their pages. The simple banner helped reduce confusion for visitors and helped build positive attention for the brand by educating visitors about the #OptOutside mission.

 

Normal Friday Campaigns

BackMarket

BackMarket, the renewed devices retailer, does not offer additional sales or discounts on their products. In order to still capture attention from Black Friday visitors, they created a campaign dubbed “Any Old Friday”. 

Taking inspiration from Black Friday doorbuster commercials, their landing page showed a new carousel image with copy that read “For an unlimited time only!”. 

This cheeky take on the usual sales quips was a great opportunity for BackMarket to have a fun and engaging holiday season campaign without the need for offering discounts or sales. 

 

 

Whether your brand wants to support an important cause or give visitors a break from Black Friday promotions, take advantage of increased traffic and drive engagement during the holiday shopping season with these creative alternatives to Black Friday.

On the fence about your Black Friday strategy? Get inspired by our e-book “How to Win Big During the E-Commerce Holiday Season”, featuring 30 experimentation and personalization ideas for Black Friday from AB Tasty clients around the world.

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Article

6min read

Statistics: What are Type 1 and Type 2 Errors?

Statistical hypothesis testing implies that no test is ever 100% certain: that’s because we rely on probabilities to experiment.

When online marketers and scientists run hypothesis tests, they’re both looking for statistically relevant results. This means that the results of their tests have to be true within a range of probabilities (typically 95%).

Even though hypothesis tests are meant to be reliable, there are two types of errors that can still occur.

These errors are known as type 1 and type 2 errors (or type i and type ii errors).

Let’s dive in and understand what type 1 and type 2 errors are and the difference between the two.

Type 1 and Type 2 Errors explained

Understanding Type I Errors

Type 1 errors – often assimilated with false positives – happen in hypothesis testing when the null hypothesis is true but rejected. The null hypothesis is a general statement or default position that there is no relationship between two measured phenomena.

Simply put, type 1 errors are “false positives” – they happen when the tester validates a statistically significant difference even though there isn’t one.

Source

Type 1 errors have a probability of  “α” correlated to the level of confidence that you set. A test with a 95% confidence level means that there is a 5% chance of getting a type 1 error.

Consequences of a Type 1 Error

Why do type 1 errors occur? Type 1 errors can happen due to bad luck (the 5% chance has played against you) or because you didn’t respect the test duration and sample size initially set for your experiment.

Consequently, a type 1 error will bring in a false positive. This means that you will wrongfully assume that your hypothesis testing has worked even though it hasn’t.

In real-life situations, this could potentially mean losing possible sales due to a faulty assumption caused by the test.

Related: Sample Size Calculator for A/B Testing

A Real-Life Example of a Type 1 Error

Let’s say that you want to increase conversions on a banner displayed on your website. For that to work out, you’ve planned on adding an image to see if it increases conversions or not.

You start your A/B test by running a control version (A) against your variation (B) that contains the image. After 5 days, variation (B) outperforms the control version by a staggering 25% increase in conversions with an 85% level of confidence.

You stop the test and implement the image in your banner. However, after a month, you noticed that your month-to-month conversions have actually decreased.

That’s because you’ve encountered a type 1 error: your variation didn’t actually beat your control version in the long run.

Related: Frequentist vs Bayesian Methods in A/B Testing

Want to avoid these types of errors during your digital experiments?

AB Tasty is an a/b testing tool embedded with AI and automation that allows you to quickly set up experiments, track insights via our dashboard, and determine which route will increase your revenue.

Understanding Type II Errors

In the same way that type 1 errors are commonly referred to as “false positives”, type 2 errors are referred to as “false negatives”.

Type 2 errors happen when you inaccurately assume that no winner has been declared between a control version and a variation although there actually is a winner.

In more statistically accurate terms, type 2 errors happen when the null hypothesis is false and you subsequently fail to reject it.

If the probability of making a type 1 error is determined by “α”, the probability of a type 2 error is “β”. Beta depends on the power of the test (i.e the probability of not committing a type 2 error, which is equal to 1-β).

There are 3 parameters that can affect the power of a test:

  • Your sample size (n)
  • The significance level of your test (α)
  • The “true” value of your tested parameter (read more here)

Consequences of a Type 2 Error

Similarly to type 1 errors, type 2 errors can lead to false assumptions and poor decision-making that can result in lost sales or decreased profits.

Moreover, getting a false negative (without realizing it) can discredit your conversion optimization efforts even though you could have proven your hypothesis. This can be a discouraging turn of events that could happen to any CRO expert and/or digital marketer.

A Real-Life Example of a Type 2 Error

Let’s say that you run an e-commerce store that sells cosmetic products for consumers. In an attempt to increase conversions, you have the idea to implement social proof messaging on your product pages, like NYX Professional Makeup.

Social Proof Beispiel NYXYou launch an A/B test to see if the variation (B) could outperform your control version (A).

After a week, you do not notice any difference in conversions: both versions seem to convert at the same rate and you start questioning your assumption. Three days later, you stop the test and keep your product page as it is.

At this point, you assume that adding social proof messaging to your store didn’t have any effect on conversions.

Two weeks later, you hear that a competitor had added social proof messages at the same time and observed tangible gains in conversions. You decide to re-run the test for a month in order to get more statistically relevant results based on an increased level of confidence (say 95%).

After a month – surprise – you discover positive gains in conversions for the variation (B). Adding social proof messages under the purchase buttons on your product pages has indeed brought your company more sales than the control version.

That’s right – your first test encountered a type 2 error!

Why are Type I and Type II Errors Important?

Type one and type two errors are errors that we may encounter on a daily basis. It’s important to understand these errors and the impact that they can have on your daily life.

With type 1 errors you are making an incorrect assumption and can lose time and resources. Type 2 errors can result in a missed opportunity to change, enhance, and innovate a project.

To avoid these errors, it’s important to pay close attention to the sample size and the significance level in each experiment.