Your full guide to analyzing your web3 brand for better performance

How do you think YouTube, Netflix, Instagram, Facebook, Google and other sites always create personalized experiences for their users?

Yeah, you guessed right, it’s through the power of data. Data is king and the fuel behind the growth of many brands in today’s world.

Just like Jeff Bezos said, Data is the lifeblood of any organization. Now let’s go over how you can leverage data to supercharge your web3 brand in these 3 ways

Recognizing the current problem or situation
Compiling necessary the KPIs
Analyze the data to glean insights and make driven decision

Cool right?

Recognizing The Current Situation or Problem

There are times when the problem you want to solve will be standing tall like the sun while there are other times when you might not be sure of exactly what you want to solve or what you need to move to the next phase of your business

In both cases, asking the right questions can break the ice and help you find the root problem or the insight you need for the next phase.

Asking the right questions about your brand also helps light up innovative ideas that many businesses are thirsty for.

So how do you ask the right questions?

Ready? drum roll

You ask the right questions by applying the SMART formula. That means your questions must be specific, measurable, action-orientated, relevant and time-bound.

Let’s go over the SMART formula, shall we?

Specific: Specific questions are practical and tailored to a single topic. They are designed to address the problem

For example, if you own a web3 gaming brand, a specific question will be something like

Why don’t users like this custom skin feature in my game

Measurable: This deals with questions that can be measured and quantified.

For example, how many mobile downloads did we have in the last month?

Action-oriented: These questions help you get answers that can influence a change in your process or any aspect of your business.

For example: Do we have more mobile users than PC users? if yes, how can we optimize our game to get more mobile users?

Relevant: These kinds of questions hammer hard on the problem you are trying to solve.

For example: How often did users complain about our custom skin feature and what is it that they don’t like about it?

Time-bound: Questions that indicate the times studied and whose answers are relevant to your analysis

For Example: How many new users have we had in the last two years when we started the “operation game on”?

The next step after you have recognized the problem by asking SMART questions is to understand the problem you are trying to solve

Data analysis problems usually fall into one of the 6 categories below.

Six Common Problem Groupings

When you have gathered your data (we will talk about this in a second) you need to understand the problem before you can glean insights from it.

Knowing the category the problem falls under would make the analysis a seamless one. So here are the top six

Making Predictions
Identifying Themes
Categorizing Things
Finding Patterns
Discovering Connections
Spotting Something Unusual

Making Predictions

In this problem type, you leverage data to make predictions of happenings in the future.

For example
The number of engagements you will likely get on a giveaway post
Determining if a project will sell out or not

Categorizing Things

Here you look out for common features in your data and group them based on it.

You can analyze your community’s sentiment of an action you took by grouping their replies from surveys, comments and interviews by the specific phrases that were repeatedly used.

You could have groups like Excited, Interested, disappointed, Happy and the likes, based on their replies.

Spotting Something Unusual

In this problem category, you look out for data that is out of the norm.

For instance
when measuring your data and you see a very high spike in the number of your engagements in the last month, you would like to analyze further to see what caused that spike and how you can leverage it to boost your engagements

Identifying Themes,

Here you take categorization even further by organizing those groupings into larger concepts

For instance, in our example under “categorizing things”

You could have a category named “satisfied community members” and another named “unsatisfied community members”

Under the satisfied community members group, you can add subcategories like happy, excited, interested, and lovely… ( based on their replies)

Discovering Connections

This helps you find similar problems faced by complementary brands and then merge data to solve them.

To buttress this point, consider this example…

Let’s say Axie Infinity saw a drop in their number of active users, and they can’t seem to find what’s wrong after analyzing all their metrics.

If all the factions complimentary to the Axie Infinity brand sit and share data, they could just realize that the issue was from the inactiveness of several gaming guilds

And those gaming guilds were inactive because of the lack of scholarship. Voila! they could then go ahead to solve the problem

Finding Patterns

To find patterns, you leverage historical data to be informed of what happened in

the past and its probability of reoccurring

We can’t have a future without an understanding of the past

Many trading problems are under this category, the analysts in that field go to track historical data to see what caused the bull runs that happened in the past and

analyze the reason it happened and the circumstances surrounding it.

They then use those insights to prepare for the next bull run, guide their investments and buying decisions

Collect and Organize the Necessary Data

After you have crafted a problem statement and identified the category it falls under, the next step is to source out the data you need to analyze.

Take some time to think of the metrics that are in line with your brand goals and will help you find the answers to your SMART questions.

Gather these metrics and process them so it will be seamless to analyze and glean insights.

For starters you can easily get some of the required data from CRM (customer relationship management), social media channels and your website.

There are also publicly available data like industry reports and market research studies, you might also need to go a step further to manually collect data.

After highlighting all the data you need, organize it in a spreadsheet, database or available tools for seamless access and analysis

Analyze The Data to Glean Insights And Make Informed Decisions

Woohoo! This is the fun part.

See yourself as a detective with all the clues and puzzle pieces before you.

Questions play a crucial role at this point because you will see gaps and opportunities as you analyze and when you ask why, you will be amazed at what you find.

The beautiful thing is beyond solving the identified problem you would also glean insights that can help you make custom tweaks, create personalized experiences for your community members and take your brand to the next level.

Analysis like this can help you make

Effective Data-driven Collaborations
Provide a personalized experience for your community members
Employ data-driven engagement strategies
Deploy-driven marketing campaigns

Over time when you keep tracking the KPIs relevant to your brand, you will spot oblivious trends and patterns that you can tune to your advantage.

You should also note that all data analysis process begins with asking. Asking helps you understand the problem and glean insights from the data

With those insights, you can understand your customers better, provide personalized customer experiences, increase engagement and make better-informed decisions.

Cheers from my screen to yours!