There was another algorithm update since his original article, so we’ve weaved in other analyses as well.

Here are some tactical recommendations to increase visibility on X:

1. Optimize tweets for maximum engagement.

• Include images and videos (including GIFs) for a 2x boost in ranking. Having 3 or 4 images can boost it even further.
• Post in the same language as your followers to avoid a 90% penalty.
• Tweet about trending topics. These are promoted in the “For You” tab.
• Avoid using multiple hashtags, which can result in a 40% penalty.
• Eliminate misspellings/unknown words to prevent a 95% penalty
• Ask questions. Replies to tweets are one of the biggest boosters.
• Don’t link off platform. These get penalized.
• Avoid controversial (blacklisted) topics.
• Don’t be offensive.

2. Build quality relationships with followers.

• Reply to people who comment. An author replying is worth 2x someone else replying.
  • But, you can be penalized for engaging with low quality commenters—so don’t engage with ones in the “show more” or with low quality / offensive profiles.
• Collaborate with users and influencers in the same niche.
• Share targeted, valuable content tailored to your audience’s interests.
• Showcase your personality and brand voice to humanize your presence and make your tweets more relatable.

3. Leverage user factors to boost twitter presence.

• Consider subscribing to Twitter Blue for a 4x boost for people who follow you and a 2x boost for people who don’t follow you.
• Maintain a healthy followers-to-following ratio. You should follow no more than 60% of your number of followers.
• Improve your “TweepCred” score, similar to Google’s PageRank, by posting high-quality content, engaging with followers, and maintaining a positive reputation.
• Consider offering a premium subscription. Twitter/X makes money when people subscribe, so they’re incentivized to boost you.

Combine the above factors with high-quality content and consistent posting, and you’ll give your tweets a better chance of being seen and engaged by your target audience.

For deeper analyses check out this article and NFT_GOD’s tweets which are more recent (1 and 2). 

Yes, we need to be aware of what happens after a client is acquired, but the pricing and product have a lot to do with its success.

Could an enterprise startup become a product lead growth company in the digital ecosystem since its price and the sales process’s complexity might not provide meaningful experiences that “show and don’t tell”?

And it speaks to what I believe.

Data analysts’ job is to solve a problem. Adding context to the numbers is something that we should all do, no matter the request.

Nobody understands data better than you, the analyst.

That being said, I feel that we can fall into a trap easily, where we start to believe that data can provide the absolute truth.

This is false.

Data analytics, especially marketing analytics, is not accounting. The numbers, some of the times, are a bit fuzzy.

So, be sure to explain the limitations of data when presenting numbers to your stakeholders.

E.g., 1.000 visitors/month could mean that you could only track 1.000 visitors, not necessarily that these are all that landed on your pages.

Yes, you can get your message across faster, and at the same time, you will not cover a range of keywords. Most of the time, informational or navigational will not be part of paid campaigns since a budget will not cover all potential long-tail keywords.

Talking about budgets, nobody has an unlimited budget to cover all keywords. Therefore SEO is better at generating leads since the buyer journey starts with informational searches and this is where SEO shines at generating more leads even if they are in the learning phase.

Also with paid you might lose money before you find a campaign that will generate positive ROI.

It seems that the bigger the company, the more critical seniority is.
But is there a way for a new hire to become relevant to the company quickly?

The advantage of a new hire is precisely that. You are not aligned with the mindset of the company… yet. All the experience, vision, and understanding of your profession are worth more than competing with an existing employee with more years of experience inside the company.

So, as a new hire, you need to find colleagues with the biggest seniority and try to learn from them while bringing the “outside” perspective into discussions.

And this is a fine line, really hard to execute, especially getting the trust from the peers.

So, the way I did it is to plan the first 90 days right.

Step 1: try
Step 2: have you tried harder?
Step 3: when I doubt, follow steps 1 and 2.

Yes, this is learnable. I used to stop at step 1 or find excuses to procrastinate.

And if you want to develop a growth mindset, just build better corrective feedback loops.

People favor saying what they think you want to hear. It’s not their fault. Ensure the feedback loop includes negative feedback (or radical candor), even for things you are emotional about.

Nobody makes something great by giving another impactful presentation or building a one-pager deck.

Put yourself out there: fail -> keep learning -> grow.

A final note: for some of the things you want to learn, don’t rely on the current company to pay for knowledge or provide opportunities. Pay it out of your pocket and make the next company compensate for the new skills.

We usually refer to it as remote working, but this is a system for the individual worker. Distributed work is a design for the entire organization.

The base for distributed work is the autonomy to do the job. This also means that you can pick a schedule and the work is asynchronous.

Remote work, on the other hand, represents a small number of people that are not at the office but need to be available during office hours.
Most of the time, remote workers don’t have access to the same context, information, and social opportunities as on-site colleagues.

Ideally, the location should not be a factor in performance and participation. But for remote work to accommodate everybody, projects need to be documented so that the on-spot decision we make while at the office doesn’t need in-person meetings (or Webex calls for that matter).

Documenting work is just a way to help people pick up from where you left off. This way more people have visibility into goals and project status, the less they have to ask someone for it.

Also, communicating online is key. We already do this as Slack and Webex are part of our day to day activities even if we are at the office.

Working from home has a bad reputation because it implies you need to be home. But to help build a remote work environment location should not matter. When working remotely (or as a distributed company) we should benefit from an allowance to cover costs like working from hubs (if we choose to do so) or rent a meeting room if it is absolutely necessary to meet in person.

Similar to agile, distributed work needs a new social contract.
Co-locating is not key to deliver and perform, and remote work is possible, but expectations need to be set. On-demand, ad-hoc meetings are not always the key to success.

I succeeded because I failed more than you.

Let’s start with the history of A/B testing. It can be traced back to 1996, but the first tools started in 2003. Before this time cookies were not used, instead the logs were analyzed. The real democratization of split testing was in 2010 when VWO and Optimizely hit the market.

In 2020 the value of A/B testing is that it builds effectiveness. Similar to the health industry where randomize control tests are used to make decisions. Or even better, a systematic review of lots of RCT because just one experiment does not prove much.

Now we can all agree that A/B testing is valuable, but when to use it and more importantly how it should be applied is a different game. Let’s understand the 3 situation where split testing is valuable:

1. research – we are not looking for winners, but to identify which elements are making an impact:
   • leaving out elements on the web page and identify which ones are the ones show positive, negative, or neutral signals, therefore which ones are important.
   • fly-ins: research if for example, social prof notifications (x people bought in the last 24 hours) are having an impact.
2. optimize – like a deployment done by marketing (usually client-side) that hopefully it will be implemented. In this situation, we are just looking for wins.
3. deploy – instead of pushing live a new feature or other changes to the website, we can shift traffic to the changes and identify if it has a positive or neutral impact. In both cases, you should go live with the changes.

Planning A/B tests

The planning phase starts with one question: Do you have enough data to conduct A/B tests?

• ROAR model (Risk, Optimization, Automation, Re-think). If you have less than 1.000 conversions per month (transactions, leads, clicks), it will be hard to identify a winner. Still, if the conversions are not there, as mentioned in a previous chapter in the mini degree, you can run the A/B test as research.
• statistical Power: the likelihood that an experiment will detect an effect when there is an effect there to be detected and it depends on sample size, effect size, and significance level.
• Use calculators to determine if you have enough data.

The second phase of planning A/B tests is deciding on the KPI.

Another big part of the planning is the research to get insights to build A/B tests. I already know that doubling down on user research is 80% of the process. Most of the time use surveys and cohort analysis before expert opinions. The 6V research model can be used to generate user behavior insights:

• Value (CRO specialist, Data): What company values are important and relevant? What focus delivers most business impact in the short and long term.
• Versus (CRO specialist): What competitor analysis and market best practices can be found?
• View (Data &PSY): What insights can be found from web analytics and web behavior data?
• Validated: What insights are validated in previous experiments or analyses?
• Verified: What scientific research, insights, and models are available?
• Voice (Psy & UX): What insights can be taken from the voice of customer data such as surveys, feedback, and service contact?

The next step in the planning pillar is setting a hypothesis. If you want to get everyone aligned you need to describe a problem, propose a solution, and predict the outcome. This usually saves time on having discussions.

Here is how to write a proper hypothesis: If {i apply this}, then {this behavior change} will happen, among {this group}, because of {reason}.

The final step is to prioritize A/B tests. On top of the frameworks I already mentioned in a previous review, I learned about power determination (when using the PIPE framework) and the importance of unique visitors and the fact that they must have seen the test page before they converted.

Split test execution

Design, Develop, and QA your A/B test is the core of executing split tests. Each step is different depending on the size of the team but as far as I understand, the key is to just do it and keep an eye on the data streams.

Happy to see that for the chapter on configuring the A/B test tool used is Google Optimize. Here is what you need to configure to run a proper A/B test:

• create a variant called Default and one named Challenger in order to offer the same experience since we don’t control the tool.
• the next step is to add the JS script and send a GA event with the respective variant information to be tracked.
• run the experiment with the original variant traffic set to 100% for 2 weeks.
• next, change the original to 0 traffic and move it 50-50 to the Default and Challenger. This is a pre-test selection. After you finish the test, and have enough data, in the post-test just move back 100% of the traffic to the original.
• This is a better solution than to include visitors without cookies (new ones) and with cookie value > start test time.
• finally, use the analytics solution already implemented in the company and not the tool analytics.

User behavior is different on weekdays vs weekends. Therefore always consider as a time unit for the length of an experiment in weeks. Somewhere between 1 or 4 is enough to avoid dilutions but include full business cycles. Exception: when you run an experiment on logged in users. You can control better who see what and therefore you can run them as long as you want.

As the final step for executing A/B tests, monitoring is important and I am wondering if having live chat on the experiment pages.

Results of A/B testing

A/B test outcomes are closely related to statistics and deciding what to present to stakeholders. In my experience, data can be the weapon to shift decisions, but present it in a wired manner and everything goes down the toilet.

Therefore, what to present and what not to present is tricky because statistics are involved, but managers almost always care about how to make more money, therefore this is a key element in presenting the learnings. Also, there are some calculators that can help the business case for an A/B program. Doing a proper business case calculation of the program is important to show the value.

• https://abtestguide.com/calc/
• https://abtestguide.com/bayesian/
• https://abtestguide.com/fdr/

A/B testing and statistical significance

If you don’t know basic statistics then you can’t properly evaluate test results or even case studies of A/B testing. So the last chapter in the module is an overview of the statistical concepts that every digital marketer and certainly every CRO should know:

• Sampling – Populations, Parameters, & Statistics
• Mean, Variance, and Confidence intervals
• What statistical significance (p-value)is and isn’t
• Statistical Power
• Sample size and how to calculate it
• Regression To The Mean & Sampling Error
• 4 Statistics Traps to Look Out For

Conclusions

I really enjoyed the intro and outro of the A/B testing mastery chapter. Having context before diving into the core content helped me understand how to position A/B testing to a client or inside a company. It is just like data: without context, it might not make much sense.

This article is the sisth in a series of 12 reviews of studying Growth marketing Minidegree at CXL Institute. Follow the minidegree tag for the entire series.

In that case, you need to be the smartest person in the room.