ENS Marketing Performance Assessment Model

As stated, I am publishing a model of ENS’s dependence on external factors, aimed at assessing the relative effectiveness of marketing.

Here it is: GitHub - danchousz/ens_marketing_model: A model for assessing the effectiveness of Ethereum Name Service marketing, using data on external (general market) factors.

It should be noted that I am not a developer, but only transferred the logic of econometric programs to Python. I’ve also never published anything on GitHub, so the repository may be unfamiliar with standardization.


The operating principles of the model are described in the form of comments in the code itself.

The program outputs the following metrics:

Снимок экрана 2023-11-08 в 17.20.44

A high value of external influence means that success/failure over a certain period of time should not be associated with marketing. It is simply a derivative of market movements.

The high importance of marketing means that at a certain point in time, good decisions were made regarding local and global strategies.

The influence of autoregressive/other factors is a rather useless metric if taken out of context. Its high indicators can be described both as “yesterday’s success” and, for example, by a seasonal component, the analysis of which I provided in my research, but the data on which must be constantly updated and applied, including visual and contextual analysis, which is difficult to programmatically.

Such models are used everywhere in classical business, because it is simply impossible to draw a line between the effectiveness of one’s own efforts and external circumstances. It would be very unwise to attribute any sales growth to a particular marketing strategy and continue to invest money in its implementation without understanding that this growth is caused only by macroeconomic/sociodemographic factors or the market.

But the repertoire of classic companies also includes internal marketing metrics that make the analysis more accurate. In this case, the inaccuracy of the model can be explained by the author’s lack of such metrics.

The model is intended to be used by any entity involved in ENS marketing. But it is also worth noting its main drawback - it does not demarcate the efforts of different entities. If more than one organization is involved in marketing, you simply will not be able to determine which of them influenced the number of registrations.

Some variables in the model (data shift, critical z-score, number of neighbors) were entered manually, based on experiments conducted as part of the study. Their evaluation programmatically can lead to distorted results, since expert assessment is also required here. I admit that they will not be relevant forever and I will try to make changes whenever possible.

Open to comments.


Can see you’ve put a lot of effort into this @danch.quixote

It’s not so clear what the goal or suggested action is here though. The analysis also isn’t clear to me.

Taking a step back, I don’t think it’s a good fit for the ENS DAO to be taking responsibility for marketing creatives for ENS. It’s better that this creative activity be delegated out to skilled creatives.

What’s missing is a performance-based framework of sustainable funding for creative marketers in the community. Making this performance-based is key. The market decides who is effective and who isn’t.

We’ve been working on a proposed framework for this for some time. Already collected feedback on the concept from a number of smart people in the community. Will be sharing a temp check for it on the forums as soon as I can get the time to write it up.


I’m not suggesting anything at the moment.

I have never offered any of my creative skills, because it is not entirely clear how qualified creatives can work without a research base, in conditions of unknown problems. This is simply unprofessional.

ENS has already hired “qualified people” for marketing outsourcing, which ended in failure. If the result of your proposal is the hiring of another agency, I hope it will take a more systematic approach to marketing implementation.

Not suggesting anyone is hired. Instead suggesting a performance-based framework that is permissionless to join.

Data is super valuable, agreed. But the reality is that we don’t have access to very good or meaningful marketing data right now. Any approach based on data-analysis right now is working in the wrong order. Garbage in, garbage out. What we need is a framework that gives the proper incentives for performance-based innovation. This can then attract the efforts of talented marketers who know how to navigate uncertainty based on instincts / experience / skill.

I sincerely hope that you manage to create such a system, but I honestly don’t understand what you’re basing your assumptions on.

In my research, I showed that it is possible to work with existing data, despite its complete chaos.
And this is just a drop in the bucket. If I had the resources or access to data (which ENS actually has), I would be able to develop the most effective strategies. And believe me, I am not the most experienced analyst you know.

I don’t know a single talented marketer who relies solely on instinct. Your experience suggests that you are based on sound methods that are always based on data analysis.

It is worth remembering that third parties are financially responsible for their actions, and their goal is to make money one way or another, so they will always invest the majority of their efforts in attracting a solvent audience, without developing markets where ENS has a small presence.

Keep building on it, but don’t focus all your time on it. You might not be receiving the response you are looking for. If there is something specific that you are seeking, find alternatives.

I don’t know how many idea iterations that I have been through that were not :

  • ready for implementation
  • other systems worked better
  • didn’t receive the response or outcome that ( i ) wanted
  • ENS will utilize your contribution when ENS needs it.
    and in that time, when that time comes, the outcome you
    seek will be aligned with what you have.

Please still …

  • Build systems that automate data aggregation.
  • Maintain your interest with the system you create.
  • Continue to compile data over time.
  • It will be useful in the future.

but also remember that there isn’t a guarantee of anything. It took me a long time to realize that no matter what I did / do / will do, that ultimately the greater ENS ecosystem will decide in of itself what the ENS ecosystem needs. :slight_smile:

this is also advice to myself…

Hi @danch.quixote . Thanks for the effort but like @lightwalker.eth mentioned, I am a bit struggling to digest what the take away from these marketing related figures.

Could you summarise how and where you got the data about the marketing campaign info (I took a glance at the links you left but couldn’t get to the relevant point)? I have provided you page view of ENS sites and name registrations for your prior study but I don’t think I have provided any marketing campaign related info.

Also, do you have some data on other web3 companies to compare? Without the industry benchmark , it’s hard to say the metrics number is good or bad



Hi, Makoto!

Yes, all the information that I provided during this time is scattered around the forum and it’s probably worth putting it all together.

Here is the research itself, and the model that is presented in this topic is just a transfer of the logic of Chapter 2.1 into one program.

In this study, I use the data you shared to estimate how ENS is affected by the industry, and based on these assumptions, interpret the model results as an estimate of marketing effectiveness. Of course, this is far from a perfect approach. It does not quantitatively take into account the contribution of marketing as such, but only provides evidence from the opposite, like “Date n: external factors formed the number of registrations by 80%, which means the share of autoregressive factors and the importance of marketing at that time was 20%, their demarcation shows that the role of autoregressive factors – 12%, marketing – 8%.”

In order to conduct a comprehensive analysis, more metrics are needed. You can get them in three ways: through ENSLabs (they refused to provide them due to confidentiality), through professional tools (they cost money), or manually (which will take many times longer).

For me, spending more time and putting everything together manually is not a problem from a technical point of view. But it’s important to understand that I spent 400 hours, 10 weeks of work on the first study, took a risk, and it didn’t pay off. Because of this, I am now in a terrible situation, in the worst, I would say. I simply cannot take on any more risk right now, spend hundreds more volunteer hours, or spend my own money on professional tools; this is a matter of survival. I wanted to apply for funding directly from the DAO, but I couldn’t get enough public positive reviews, so I dropped it.

However, I still conducted superficial analyzes of various parts of the marketing component.

I found that the demographics of the ENS community are not diversified, which is not as relevant to the industry as a whole, which can be understood from data from Unstoppable Domains, Uniswap or Etherscan. But any practical conclusions should be drawn only by analyzing historical and detailed data. This requires a professional tool.

I also found that ENS has problems with organic search traffic, while its competitors do not. The path to solving this problem also lies through On-Page SEO, but for this I again need to have a full understanding of what problematic keys exist. This requires a professional tool.

As far as ENS campaign analysis/sentiment analysis is concerned, this is too much information to collect manually even for a superficial demo analysis. I need at least Twitter API v.2 to parse information. In any case, I will not have information about such important metrics as ROI, but even without them it will be possible to obtain extremely useful insights.

As you can see, I am an adherent of the approach that involves basing judgments on the characteristics of the industry, since it, of course, is new and unexplored. But I think it needs to be explored.