Connect with us

MARKETING

TikTok SEO: Understanding the TikTok Algorithm

Published

on

TikTok SEO: Understanding the TikTok Algorithm

The author’s views are entirely his or her own (excluding the unlikely event of hypnosis) and may not always reflect the views of Moz.

TikTok has quickly become a viral sensation, with millions of users across the globe spending hours scrolling through the app’s endless supply of videos. But for marketers, TikTok’s greatest asset lies in its algorithm.

In the first chapter of this series, we dug into the search behavior on TikTok and why it should matter to SEOs. In this article, we are going to cover the ins and outs of the TikTok algorithm, and how to leverage it to get more users looking at your brand’s content.

The principles behind the TikTok algorithm

Before we dig into the algorithm’s ranking factors, a bit of background.

In 2020, TikTok’s CEO Kevin Mayer published a manifesto on the importance of transparency for tech companies, especially when it comes to their content algorithms. Mayer committed to being more open than its competitors, indirectly challenging Meta and Google.

Advertisement

Luckily for us marketers, TikTok has kept its promise and has some solid documentation on how their algorithm works. In this article, I will be combining that information along with secondary sources and inference based on general social media principles.

Surfacing interesting topics

A few months ago, I was raving about TikTok to my partner. He is big on privacy and didn’t love the idea of joining the platform, but I convinced him.

The moment he joined the app, his feed was flooded with bikini-clad teenagers, crude physical “humor” and what I can just describe as a bunch of British guys acting very lad-y. All the platform knew about him is that he is young(-ish), male, and British.

The content TikTok was serving was based on his demographic data. The algorithm hadn’t had time to work its magic then, but when it did, he could hardly put down his phone.

TikTok collects data on how users interact with different videos. Based on this information, TikTok can determine a user’s interests and serve them related content.

TikTok uses the content of each video to understand what topic it pertains to. This is based on the use of hashtags, video descriptions, the TikTok sound used, and the textual spoken audio. Based on what we know about other platforms’ natural language processing capabilities, this is likely more effective in English than in other languages.

Advertisement

The platform gets better at tailoring this content for you as you engage with it, but it also bases its recommendations on demographic data such as gender, age, and location.

According to their privacy policy, TikTok adds “inferred information” to your profile, such as age-range, gender, and interests.

Knowing this, it would make sense that TikTok puts audiences into different interest cohorts. By connecting different topics by how closely related they are, TikTok should be able to surface topics you’re likely to enjoy, even if you’ve never engaged with them on the platform before.

Let’s see an example. I like interior design, so I’m likely into IKEA hacks, which means I’m likely into DIY. If I’m into home improvements, I’m likely into crafting. Boom, a cross-stitching video reached my feed, and I love it.

@tiktokswithtom Cross stitch 🤷‍♂️ #fyp #fypシ #foryou #crossstitch #crossstitchoftiktok ♬ Che La Luna – Louis Prima with Sam Butera & The Witnesses

Bursting the filter bubble effect

TikTok’s transparency policy came about after receiving some criticism around how their algorithm creates echo chambers that promote radicalization and the spread of misinformation. Now some platform representatives have spoken about how the platform is trying to prevent that.

Youtube and Facebook have come under fire for this before, but the truth is that any platform with a content discovery algorithm that relies on engagement is susceptible to creating echo chambers and promoting radicalization. Human psychology tells us that we’re more likely to engage with content that elicits a strong emotional reaction. This incentivizes content creators to promote content that makes us angry or afraid.

TikTok’s answer to the filter bubble effect has been somewhat simple: the platform will show you random content from time to time.

Advertisement

In order to avoid homogeneity of content, the app has started showing users content that they don’t usually engage with. This includes surfacing random hashtags, video aesthetics, sounds, and topics. The app tries to keep things fresh by avoiding content repetition, so you’re unlikely to see two videos by the same creator or using the same sound in a row.

Another interesting incorporation into the algorithm is showing you fresh content that has not had any engagement yet. If you’re a TikTok user, I’m sure you have noticed this.

Is this enough to prevent creating echo chambers? Probably not. Familiarity or the mere exposure effect will make you engage with the content you see most frequently, so there’s still a pretty high chance of developing echo chambers.

According to the teachings of one of my favorite psychology textbooks, we’d need to see about 50% of this random content on our feed to break the behavioral learning and bias towards what we already like. Obviously that would be against the business interests of most social media platforms, so it seems unlikely to happen.

With this background and context in mind, let’s dig into TikTok’s ranking factors.

TikTok ranking factors

As I mentioned above, this list of ranking factors is based on a mix of TikTok-confirmed features as well as unofficial sources and general social media practices.

Advertisement

1. Video engagement

One TikTok ranking factor is engagement, which includes likes and comments as well as watch time and profile visits. When a TikTok video has a high level of engagement, it means people are taking the time to interact and engage with the content.

This also includes replays, follows, bookmarks, and tagging a video as “not interested” (which affects your video negatively, of course). Engagement shows TikTok that the content is worth pushing out to more users, thereby helping it rank higher on TikTok’s algorithm.

Not all forms of engagement are created equal, of course. A comment or share are stronger engagement indicators than a like. We see this on TikTok’s documentation and it’s true in many other social media platforms too.

According to TikTok’s documentation, engagement is measured at video level, not at account level.

The profiles a user follows on TikTok also contribute to determining the user’s interest profile. Following gardening accounts indicates to the algorithm even further that you’re interested in gardening videos.

The follower count or the previous performance of an account doesn’t directly impact the rankings of their videos. However, having a high follower count can indirectly help your videos perform better, as it will expose them to more eyes through your followers. If your followers engage with your content, that engagement can help you reach bigger audiences.

Advertisement

This is a big shift from classic forms of social media marketing, were the previous performance of posts on a profile are thought to influence the reach that future posts will have.

2. Discover tab engagement

Another way in which TikTok determines a user’s potential interest in a video is by analyzing their interactions with TikTok content beyond just video. Searching, clicking on a hashtag, exploring a trending topic, or viewing videos from a specific sound will weigh towards the video recommendations that users receive on their For You feed.

3. The content of the videos

As an SEO, I can’t help but draw a parallel between on-page SEO and the TikTok ranking factors within the video content.

For the platform to be able to recommend videos of topics that you like, it needs to understand what each of the videos are about.

There are several elements within the uploaded videos that help the app understand what topic and emotional tone each video has. Let’s take a look at what those elements are:

  • The video’s visuals. According to their privacy policy, TikTok can “detect and collect characteristics and features about the video and audio recordings” by identifying objects, scenery, and what body parts are present in your video. This is used for content moderation and to power their recommendations algorithm.

  • The audio. The platform can process the “text of words spoken” within your videos to further understand what they’re about.

  • Text over the video. Using text over the video also contributes to that understanding of the content. Adding the text natively within the platform might provide a stronger signal, based on the way other content ranking algorithms work.

  • Title and hashtags. This is the OG signal for TikTok and it’s the one they’ve publicly discussed the most. The title and hashtags used in the video help tell TikTok what the video is about, but they can also influence rankings indirectly by affecting engagement and discovery.

  • TikTok sounds. The sound being used in a video is a ranking factor on its own, as it helps the platform understand a video’s content. But the biggest way in which sounds affect your content’s performance is jumping on a trend. Trending sounds get a ranking boost for a short while, since they can predict user engagement.

4. Content language

There are three language preferences you can set in your account: app language, preferred languages, and translation language. This should be pretty self-explanatory, but there is an interesting aspect to explore here.

Advertisement

You can select several preferred languages and TikTok prompts you to select the languages you understand. However, you can only select one language for your app and one for your automatic content translations. It would not surprise me if TikTok used those settings to establish which of your preferred languages is actually your favorite.

5. Device suitability

TikTok explains in their documentation that the user’s device matters in the videos that users get shown, but they have not specified exactly how.

According to TikTok, the information they receive about your device is anything from user agent, mobile carrier, time zone settings, model and operating system,and network type to screen resolution, battery state, or audio settings.

My guess is that older and slower devices get shown shorter and lighter videos more often, to prevent disrupting the user experience if the phone’s performance can’t keep up.

6. Creator locality

There is one line on TikTok’s official documentation that really caught my eye:

“A strong indicator of interest, such as whether a user finishes watching a longer video from beginning to end, would receive greater weight than a weak indicator, such as whether the video’s viewer and creator are both in the same country.”

Advertisement

There isn’t a lot of clarity about how location is used as a ranking factor, but we know it exists. We can understand that proximity between viewers and creators helps in ranking, but we don’t know at what level this is measured.

TikTok tracks user location through SIM card information, IP address, and, if you give your permission, GPS.

7. Ineligible content

TikTok has two ways of moderating content: removing it or making it ineligible to rank. These include your usual suspects such as violence, nudity, and hate speech, along with some others.

There are some interesting types of content that are ineligible to appear in the For You page:

  • Content uploaded by users under 16 — so don’t use your company’s actual age to make an account.

  • Content that includes QR codes — TikTok wants to know what you’re linking out to and get a piece of the cake if it’s a product recommendation.

  • Content that manipulates users into engaging with the video or user — all that “tap the screen twice to see something magical” stays on Instagram.

  • Duplicated content from TikTok or other platforms where the user doesn’t add any significant creative edits.

  • Dangerous stunts not performed by professionals.

  • Content that features tobacco.

8. Native content creation

I am pretty confident that building content using TikTok’s native tooling can help boost your content ranking. Other social media platforms tend to favor native content and native content creation in their algorithm, so it would make sense for TikTok to do the same. For the sake of transparency, this is just an educated guess and not an official ranking factor.

Instagram, for example, has improved their native video creation tools for Reels and Stories while demoting content with watermarks from other platforms. Facebook favors native video over Youtube embeds. LinkedIn favors posts without external links while offering a native blog platform.

Advertisement

TikTok’s own analysis shows that companies who used their native creative tools saw 14 times more engagement than those who didn’t.

There is an indirect mechanism that could lead to native TikTok videos performing better: the familiarity of users with the type of content the app can produce natively. Users are very quick to spot an overproduced video as an ad and will tend to engage with it a lot less. This blog post on TikTok for Business supports that theory, by telling brands: “don’t make ads, make TikToks”.

TL;DR

In conclusion, the TikTok algorithm aims to show you content you’ll find interesting while avoiding filter bubbles.

Based on the user’s interactions with the app, TikTok is able to suggest videos that the user might be interested in. This is done through analyzing likes, comments, watch time, replays, follows, and bookmarks. The app is also able to understand the content of the videos through visuals, audio, text, and hashtags. Additionally, TikTok takes into account the language preferences, device information, and locations of both the user and the creator when suggesting videos.

What unique strategies have you implemented to perform well on TikTok? Share with us @LidiaInfanteM and @Moz on Twitter, and be on the lookout for part three of this TikTok SEO series: how to rank in 2022.

Advertisement



Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address

MARKETING

The Current State of Google’s Search Generative Experience [What It Means for SEO in 2024]

Published

on

The Current State of Google's Search Generative Experience [What It Means for SEO in 2024]

SEO enthusiasts, known for naming algorithm updates after animals and embracing melodrama, find themselves in a landscape where the “adapt or die” mantra prevails. So when Google announced the launch of its Search Generative Experience (SGE) in May of 2023 at Google/IO, you can imagine the reaction was immense.

Although SGE has the potential to be a truly transformative force in the landscape, we’re still waiting for SGE to move out of the Google Labs Sandbox and integrate into standard search results. 

Curious about our current take on SGE and its potential impact on SEO in the future? Read on for more.

Decoding Google’s Defensive Move

In response to potential threats from competitors like ChatGPT, Bing, TikTok, Reddit, and Amazon, Google introduced SGE as a defensive maneuver. However, its initial beta release raised questions about its readiness and global deployment.

ChatGPT provided an existential threat that had the potential to eat into Google’s market share. When Bing started incorporating it into its search results, it was one of the most significant wins for Bing in a decade. In combination with threats from TikTok, Reddit, and Amazon, we see a more fractured search landscape less dominated by Google. Upon its launch, the expectation was that Google would push its SGE solution globally, impact most queries, and massively shake up organic search results and strategies to improve organic visibility.

Advertisement

Now, industry leaders are starting to question if Google is better off leaving SGE in the testing ground in Google labs. According to Google’s recent update, it appears that SGE will remain an opt-in experience in Google Labs (for at least the short term). If SGE was released, there could be a fundamental reset in understanding SEO. Everything from organic traffic to optimization tactics to tracking tools would need adjustments for the new experience. Therefore, the prospect of SGE staying in Google Labs is comforting if not entirely reliable. 

The ever-present option is that Google can change its mind at any point and push SGE out broadly as part of its standard search experience. For this reason, we see value in learning from our observations with SGE and continuing to stay on top of the experience.

SGE User Experience and Operational Challenges

If you’ve signed up for search labs and have been experimenting with SGE for a while, you know firsthand there are various issues that Google should address before rolling it out broadly to the public.

At a high level, these issues fall into two broad categories including user experience issues and operational issues.

Below are some significant issues we’ve come across, with Google making notable progress in addressing certain ones, while others still require improvement:

  • Load time – Too many AI-generated answers take longer to load than a user is willing to wait. Google recommends less than a 3-second load time to meet expectations. They’ll need to figure out how to consistently return results quickly if they want to see a higher adoption rate.
  • Layout – The SGE layout is massive. We believe any major rollout will be more streamlined to make it a less intrusive experience for users and allow more visibility for ads, and if we’re lucky, organic results. Unfortunately, there is still a decent chance that organic results will move below the fold, especially on mobile devices. Recently, Google has incorporated more results where users are prompted to generate the AI result if they’d like to see it. The hope is Google makes this the default in the event of a broad rollout where users can generate an AI result if they want one instead of assuming that’s what a user would like to see. 
  • Redundancy – The AI result duplicates features from the map pack and quick answer results. 
  • Attribution – Due to user feedback, Google includes sources on several of their AI-powered overviews where you can see relevant web pages if there is an arrow next to the result. Currently, the best way to appear as one of these relevant pages is to be one of the top-ranked results, which is convenient from an optimization standpoint. Changes to how attribution and sourcing are handled could heavily impact organic strategies. 

 

On the operational side, Google also faces significant hurdles to making SGE a viable product for its traditional search product. The biggest obstacle appears to be making the cost associated with the technology worth the business outcomes it provides. If this was a necessary investment to maintain market share, Google might be willing to eat the cost, but if their current position is relatively stable, Google doesn’t have much of an incentive to take on the additional cost burden of heavily leveraging generative AI while also presumably taking a hit to their ad revenue. Especially since slow user adoption doesn’t indicate this is something users are demanding at the moment.

Advertisement

While the current experience of SGE is including ads above the generative results now, the earliest iterations didn’t heavily feature sponsored ads. While they are now included, the current SGE layout would still significantly disrupt the ad experience we’re used to. During the Google I/O announcement, they made a statement to reassure advertisers they would be mindful of maintaining a distinct ad experience in search.  

“In this new generative experience, Search ads will continue to appear in dedicated ad slots throughout the page. And we’ll continue to uphold our commitment to ads transparency and making sure ads are distinguishable from organic search results” – Elizabeth Reid, VP, Search at Google

Google is trying to thread a delicate needle here of staying on the cutting edge with their search features, while trying not to upset their advertisers and needlessly hinder their own revenue stream. Roger Montti details more of the operational issues in a recent article digging into the surprising reasons SGE is stuck in Google Labs.

He lists three big problems that need to be solved before SGE will be integrated into the foreground of search:

  1. Large Language Models being inadequate as an information retrieval system
  2. The inefficiency and cost of transformer architecture
  3. Hallucinating (providing inaccurate answers)

 

Until SGE provides more user value and checks more boxes on the business sense side, the traditional search experience is here to stay. Unfortunately, we don’t know when or if Google will ever feel confident they’ve addressed all of these concerns, so we’ll need to stay prepared for change.

Experts Chime in on Search Generative Experience

Our team has been actively engaging with SGE, here’s a closer look at their thoughts and opinions on the experience so far:

Advertisement

“With SGE still in its early stages, I’ve noticed consistent changes in how the generative results are produced and weaved naturally into the SERPs. Because of this, I feel it is imperative to stay on top of these on-going changes to ensure we can continue to educate our clients on what to expect when SGE is officially incorporated into our everyday lives. Although an official launch date is currently unknown, I believe proactively testing various prompt types and recording our learnings is important to prepare our clients for this next evolution of Google search.” – Jon Pagano, SEO Sr. Specialist at Tinuiti

“It’s been exciting to watch SGE grow through different variations over the last year, but like other AI solutions its potential still outweighs its functionality and usefulness. What’s interesting to see is that SGE doesn’t just cite its sources of information, but also provides an enhanced preview of each webpage referenced. This presents a unique organic opportunity where previously untouchable top 10 rankings are far more accessible to the average website. Time will tell what the top ranking factors for SGE are, but verifiable content with strong E-E-A-T signals will be imperative. –Kate Fischer, SEO Specialist at Tinuiti

“Traditionally, AI tools were very good at analytical tasks. With the rise of ChatGPT, users can have long-form, multi-question conversations not yet available in search results. When, not if, released, Google’s Generative Experience will transform how we view AI and search. Because there are so many unknowns, some of the most impactful ways we prepare our clients are to discover and develop SEO strategies that AI tools can’t directly disrupt, like mid to low funnel content.” – Brandon Miller, SEO Specialist at Tinuiti

“SGE is going to make a huge impact on the ecommerce industry by changing the way users interact with the search results. Improved shopping experience will allow users to compare products, price match, and read reviews in order to make it quicker and easier for a user to find the best deals and purchase. Although this leads to more competitive results, it also improves organic visibility and expands our product reach. It is more important than ever to ensure all elements of a page are uniquely and specifically optimized for search. With the SGE updates expected to continue to impact search results, the best way to stay ahead is by focusing on strong user focused content and detailed product page optimizations.”  – Kellie Daley, SEO Sr. Specialist at Tinuiti

Navigating the Clash of Trends

One of the most interesting aspects of the generative AI trend in search is that it appears to be in direct opposition to other recent trends.

Advertisement

One of the ways Google has historically evaluated the efficacy of its search ranking systems is through the manual review of quality raters. In their quality rater guidelines, raters were instructed to review for things like expertise, authority, and trustworthiness (EAT) in results to determine if Google results are providing users the information they deserve. 

In 2022, Google updated their search guidelines to include another ‘e’ in the form of experience (EEAT). In their words, Google wanted to better assess if the content a user was consuming was created by someone with, “a degree of experience, such as with actual use of a product, having actually visited a place or communicating what a person has experienced. There are some situations where really what you value most is content produced by someone who has firsthand, life experience on the topic at hand.” 

Generative AI results, while cutting-edge technology and wildly impressive in some cases, stand in direct opposition to the principles of E-E-A-T. That’s not to say that there’s no room for both in search, but Google will have to determine what it thinks users value more between these competing trends. The slow adoption of SGE could be an indication that a preference for human experience, expertise, authority, and trust is winning round one in this fight. 

Along these lines, Google is also diversifying its search results to cater to the format in which users get their information. This takes the form of their Perspectives Filter. Also announced at Google I/O 2023, the perspectives filter incorporates more video, image, and discussion board posts from places like TikTok, YouTube, Reddit, and Quora. Once again, this trend shows the emphasis and value searchers place on experience and perspective. Users value individual experience over the impersonal conveyance of information. AI will never have these two things, even if it can provide a convincing imitation.

The current iteration of SGE seems to go too far in dismissing these trends in favor of generative AI. It’s an interesting challenge Google faces. If they don’t determine the prevailing trend correctly, veering too far in one direction can push more market share to ChatGPT or platforms like YouTube and TikTok.

Final Thoughts

The range of outcomes remains broad and fascinating for SGE. We can see this developing in different ways, and prognostication offers little value, but it’s invaluable to know the potential outcomes and prepare for as many of them as possible.

Advertisement

It’s critical that you or your search agency be interacting and experimenting with SGE because:

  • The format and results will most likely continue to see significant changes
  • This space moves quickly and it’s easy to fall behind
  • Google may fix all of the issues with SGE and decide to push it live, changing the landscape of search overnight
  • SGE experiments could inform other AI elements incorporated into the search experience

 

Ultimately, optimizing for the specific SGE experience we see now is less important because we know it will inevitably continue changing. We see more value in recognizing the trends and problems Google is trying to solve with this technology. With how quickly this space moves, any specifics mentioned in this article could be outdated in a week. That’s why focusing on intention and process is important at this stage of the game.

By understanding the future needs and wants SGE is attempting to address, we can help you future-proof your search strategies as much as possible. To some extent we’re always at the whims of the algorithm, but by maintaining a user-centric approach, you can make your customers happy, regardless of how they find you.

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

MARKETING

Here’s Optimizely’s Automatic Sample Ratio Mismatch Detection

Published

on

Here's Optimizely’s Automatic Sample Ratio Mismatch Detection

Optimizely Experiment’s automatic sample ratio mismatch (SRM) detection delivers peace of mind to experimenters. It reduces a user’s exposure time to bad experiences by rapidly detecting any experiment deterioration.

This deterioration is caused by unexpected imbalances of visitors to a variation in an experiment. Most importantly, this auto SRM detection empowers product managers, marketers, engineers, and experimentation teams to confidently launch more experiments. 

How Optimizely Experiment’s stats engine and automatic sample rate mismatch detection work together

The sample ratio mismatch actslike the bouncer at the door who has a mechanical counter, checking guests’ tickets (users) and telling them which room they get to party in.

Stats engine is like the party host who is always checking the vibes (behavior) of the guests as people come into the room.

Advertisement

If SRM does its job right, then stats engine can confidently tell which party room is better and direct more traffic to the winning variation (the better party) sooner.

Why would I want Optimizely Experiment’s SRM detection?

It’s equally important to ensure Optimizely Experiment users know their experiment results are trustworthy and have the tools to understand what an imbalance can mean for their results and how to prevent it.

Uniquely, Optimizely Experiment goes further by combining the power of automatic visitor imbalance detection with an insightful experiment health indicator. This experiment health indicator plays double duty by letting our customers know when all is well and there is no imbalance present.

Then, when just-in-time insight is needed to protect your business decisions, Optimizely also delivers just-in-time alerts that help our customers recognize the severity of, diagnose, and recover from errors.

Why should I care about sample ratio mismatch (SRM)?

Just like a fever is a symptom of many illnesses, a SRM is a symptom of a variety of data quality issues. Ignoring a SRM without knowing the root cause may result in a bad feature appearing to be good and being shipped out to users, or vice versa. Finding an experiment with an unknown source of traffic imbalance lets you turn it off quickly and reduce the blast radius.

Then what is the connection between a “mismatch” and “sample ratio”?

When we get ready to launch an experiment, we assign a traffic split of users for Optimizely Experiment to distribute to each variation. We expect the assigned traffic split to reasonably match up with the actual traffic split in a live experiment. An experiment is exposed to an SRM imbalance when there is a statistically significant difference between the expected and the actual assigned traffic splits of visitors to an experiment’s variations.

Advertisement
1. A mismatch doesn’t mean an imperfect match

Remember: A bonified imbalance requires a statistically significant result of the difference in visitors. Don’t expect a picture-perfect, identical, exact match of the launch-day traffic split to your in-production traffic split. There will always be some ever-so-slight deviation.

Not every traffic disparity automatically signifies that an experiment is useless. Because Optimizely deeply values our customers’ time and energy, we developed a new statistical test that continuously monitors experiment results and detects harmful SRMs as early as possible. All while still controlling for crying wolf over false positives (AKA when we conclude there is a surprising difference between a test variation and the baseline when there is no real difference). 

2. Going under the hood of Optimizely Experiment’s SRM detection algorithm

Optimizely Experiment’s automatic SRM detection feature employs a sequential Bayesian multinomial test (say that 5 times fast!), named sequential sample ratio mismatch. Optimizely statisticians Michael Lindon and Alen Malek pioneered this method, and it is a new contribution to the field of Sequential Statistics. Optimizely Experiment’s sample ratio mismatch detection harmonizes sequential and Bayesian methodologies by continuously checking traffic counts and testing for any significant imbalance in a variation’s visitor counts. The algorithm’s construction is Bayesian inspired to account for an experiment’s optional stopping and continuation while delivering sequential guarantees of Type-I error probabilities.

3. Beware of chi-eap alternatives!

The most popular freely available SRM calculators employ the chi-square test. We highly recommend a careful review of the mechanics of chi-square testing. The main issue with the chi-squared method is that problems are discovered only after collecting all the data. This is arguably far too late and goes against why most clients want SRM detention in the first place. In our blog post “A better way to test for sample ratio mismatches (or why I don’t use a chi-squared test)”, we go deeper into chi-square mechanics and how what we built accounts for the gaps left behind by the alternatives.

Common causes of an SRM  

1. Redirects & Delays

A SRM usually results from some visitors closing out and leaving the page before the redirect finishes executing. Because we only send the decision events once they arrive on the page and Optimizely Experiment loads, we can’t count these visitors in our results page unless they return at some point and send an event to Optimizely Experiment.

A SRM can emerge in the case of anything that would cause Optimizely Experiment’s event calls to delay or not fire, such as variation code changes. It also occurs when redirect experiments shuttle visitors to a different domain. This occurrence is exacerbated by slow connection times.

Advertisement
2. Force-bucketing

If a user first gets bucketed in the experiment and then that decision is used to force-bucket them in a subsequent experiment, then the results of that subsequent experiment will become imbalanced.

Here’s an example:

Variation A provides a wildly different user experience than Variation B.

Visitors bucketed into Variation A have a great experience, and many of them continue to log in and land into the subsequent experiment where they’re force-bucketed into Variation A.

But, visitors who were bucketed into Variation B aren’t having a good experience. Only a few users log in and land into a subsequent experiment where they will be force-bucketed into Variation B.

Well, now you have many more visitors in Variation A than in Variation B.

Advertisement
3. Site has its own redirects

Some sites have their own redirects (for example, 301s) that, combined with our redirects, can result in a visitor landing on a page without the snippet. This causes pending decision events to get locked in localStorage and Optimizely Experiment never receives or counts them.

4. Hold/send events API calls are housed outside of the snippet

Some users include hold/send events in project JS. However, others include it in other scripts on the page, such as in vendor bundles or analytics tracking scripts. This represents another script that must be properly loaded for the decisions to fire appropriately. Implementation or loading rates may differ across variations, particularly in the case of redirects.

Interested?  

If you’re already an Optimizely Experiment customer and you’d like to learn more about how automatic SRM detection benefits your A/B tests, check out our knowledge base documentation:

For further details you can always reach out to your customer success manager but do take a moment to review our documentation first!

If you’re not a customer, get started with us here! 

And if you’d like to dig deeper into the engine that powers Optimizely experimentation, you can check out our page faster decisions you can trust for digital experimentation. 

Advertisement

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

MARKETING

How to Use Email Marketing Automation to Encourage SaaS Adoption

Published

on

How to Use Email Marketing Automation to Encourage SaaS Adoption

SaaS adoption refers to the process that earns your product a permanent place in your user’s workflow. This happens when you empower your audience to extract useful value from your solutions.

Email, a tried and tested communication tool, plays an essential role in helping brands relay their product’s value to their customers and educate them on how to make the most of it.

However, smaller teams might find themselves at a crossroads, balancing the need for personalized communication with the scale of their user base

Email marketing automation offers a practical solution by ensuring that each message is tailored and timely, yet sent out with minimal manual effort.

In this article, let’s look at five tips that will help you build robust email marketing automation that will motivate your audience to adopt your tool and make it a part of their daily lives.

Advertisement

1. Segment your audience

Audience segmentation is crucial for personalizing your emails, which in turn, can significantly boost SaaS product adoption. Remember, a message that resonates with one segment might not strike a chord with another.

The key to effective segmentation is understanding where each customer is in their journey. Are they new subscribers, active users, or perhaps at the brink of churning?

Here are some actionable steps to segment your audience effectively:

  1.  Analyze User Behavior: Look at how different users interact with your SaaS product. Are they frequent users, or do they log in sporadically? This insight can help you create segments like ‘active users’, ‘occasional users’, and ‘at-risk users’.
  2.  Utilize Sign-up Data: Leverage the information gathered during the sign-up process. This can include job roles, company size, or industry, which are excellent parameters for segmentation.
  3.  Monitor Engagement Levels: Keep an eye on how different segments interact with your emails. Are they opening, clicking, or ignoring your messages? This feedback will help you refine your segments and tailor your approach. Plus, consider setting up small business phone systems to enhance communication with your audience.

2. Create campaigns based on behavior

Sending behavior-based campaigns is pivotal in effective email marketing. By focusing on performance metrics such as open rates, click-through rates, and engagement times, you can gauge the effectiveness of your emails and adjust your strategy accordingly.

You can also use digital signage to entertain or make customers aware of something new – product or service, through a digital sign.

Different types of email campaigns serve various purposes:

  1. Educational Campaigns: These are designed to inform and enlighten your audience about their problem. They can include tips, best practices, and how-to guides. The goal here is to provide value and establish your brand as a thought leader in your industry.
  2. Interactive Campaigns: These campaigns encourage user engagement through surveys, quizzes, microblogging platforms, or feedback forms. They not only provide valuable insights into user preferences but also make the recipients feel heard and valued.
  3. Onboarding Campaigns: Targeted toward new users, these messages help them get the value they seek from your product as soon as possible. They can include step-by-step tutorials, video guides, or links to helpful resources.

4.Re-engagement Campaigns: Aimed at inactive users, these emails strive to reignite their interest in your SaaS product. They might include product updates, special offers, or reminders of the benefits they’re missing out on.

3. A/B test before deployment

Rather than pushing a new campaign to your entire audience as soon as you draft the emails, A/B testing helps you know whether your messages are any good.

Advertisement

Here are some best practices for A/B testing in email automation:

  1. Test One Variable at a Time: Whether it’s the subject line, email content, or call-to-action, change just one (or a couple) element per test. This clarity helps in pinpointing exactly what works and what doesn’t.
  2. Choose a Representative Sample: Ensure that the test group is a good mix of your target audience as a whole. This way, the results are more likely to reflect how your entire audience would react.
  3. Measure the Right Metrics: Depending on what you’re testing, focus on relevant metrics like open rates, click-through rates, or conversion rates. This will give you a clear picture of the impact of your changes. Along with these steps, it’s important to use an SPF checker to ensure your emails aren’t marked as spam and increase the deliverability rate.
  4. Use the Results to Inform Your Strategy: Once you have the results, don’t just stop at implementing the winning version. Analyze why it performed better and use these insights to inform your future campaigns.
  5. Don’t Rush the Process: Give your test enough time to gather significant data. Adopt comprehensive marketing reporting solutions that give you a clear picture of your campaigns’ efficacy.

4. Leverage email templates

When managing multiple email automation campaigns, each with potentially dozens of emails, the task of creating each one from scratch can be daunting. Not to mention, if you have multiple writers on board, there’s a risk of inconsistency in tone, style, and branding.

Email templates are your secret weapon for maintaining consistency and saving time. They provide a standardized framework that can be easily customized for different campaigns and purposes.

They are also a great way to communicate with your customers. Another way to communicate efficiently with your customer is through best small business phone systems, which is especially efficient when conveying information about your product or service.

Here’s a rundown of various types of templates you should consider having:

  1. Welcome: For greeting new subscribers or users. It should be warm, inviting, and informative, setting the tone for future communications.
  2. Educational Content: Used for sharing tips, guides, and resources. If you are making this template to introduce online GCSE physics tutor services that you provide, you should be clear, concise, and focused on delivering value in your template.
  3. Promotional: For announcing new features, offers, or services. It should be eye-catching and persuasive without being overly salesy.
  4. Feedback Request: Designed to solicit user feedback. This template should be engaging and make it easy for recipients to respond.
  5. Re-engagement: Aimed at rekindling interest among inactive users. It should be attention-grabbing and remind them of what they’re missing.
  6. Event Invitation: For webinars, workshops, or other events. This should be exciting and informative, providing all the necessary details.

5. Use a tool that works for you

Email is more than just a marketing platform; it’s a multifaceted tool that can drive customer engagement, support, and retention. Given its versatility, it’s crucial to choose the right email automation tool that aligns with your specific needs.

When selecting an email automation tool, consider these key features:

  1. Intuitive Interface: Even your non-technical team members should find it easy to use.
  2. Robust Segmentation Capabilities: The tool must offer advanced segmentation options to target your emails accurately.
  3. A/B Testing Functionality: Essential for optimizing your email campaigns.
  4. Integration with Other Tools: Look for a tool that integrates seamlessly with your CRM, analytics, and other marketing platforms. Additionally, integrating a multilingual translation support can further enhance the tool’s versatility, allowing you to reach a diverse audience with tailored content in their preferred languages.

Popular tools like Mailchimp and ActiveCampaign offer free trials which are great for brands to take these for a spin before making a choice.

Wrapping up

Leveraging email automation makes it easier for SaaS brands to market their solutions to their audience and ultimately increase adoption rates.

Advertisement

Segmenting audiences, creating messages based on their behavior, testing emails before setting campaigns live, utilizing templates for speed and consistency, and adopting a tool that you are comfortable working with are essential email marketing automation tips to help you get started on the right foot.

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

Trending

Follow by Email
RSS