The ability to reproduce success is something hard to evaluate with common built-in market intelligence tools cause usually you just look at the revenue curve and don't see "how old it's revenue source”. But is it possible to measure? And why does it even matter?
Psst, guys, if you don't need all that hassle about methods and limitations, just use this table of contents and jump to the fun part with results →
Theory
My basic premise is that reproducibility is crucial, and often, it's more important than last year's revenue or profit. Imagine 2 different kinds of companies:
- X published a giga-hit 5 years ago, still earns XX mills/month with it, yet had nothing distantly successful from then;
- Y released several grossing games in the past few years but earning half of what X does;
So, in case X, you have guys with much money, in another — guys with much less money, yet probably better knowing how to make them. Whom will you choose if you're a potential investor, partner, or employee? — I will guess it's Y.
But you may disagree :) actually, there was a long tread in my FB (in 🇷🇺), where Denis Shevchenko (CEO of GreenPixel) and Nikita Guk (CEO of Hoopsly) fairly pointed out where my premise wouldn't be true.
Anyway, I decided to do a small research on reproducibility and compare ~50 companies along this measure: all their hits scoring, release frequency & recency, dynamics and ranking changes over time, etc.
Methodology
My initial goal was just to compare several companies I'm interested in and make it as simple as possible — so don't expect a deep scientific approach. There is mostly third-grade arithmetic with some explanations, but I put it under toggles to make this text a bit easier.
Disclaimers
I think we are all smart enough to understand the limitations and "superficiality” nature of this approach, but I want to highlight a few major points:
- Already mentioned premise limitations (see above)
- Limitations of data I'm working with (see eg. under “Age and final formula” toggle): in case of more time and API access, I think it's important to include not only total values but also their dynamics into calculations. Also, I have relatively small data points to calibrate my “model” on (see “Calibrating and checking”).
- An important fact about the data sample for comparison: I didn't have the goal of analyzing the whole market or a specific segment; therefore, I've just included companies I'm personally interested in because of my subjective and biased reasons, and the same way I've excluded others. So, it's not “market top” or something like that, it's just some random companies comparison, nothing more. So, yeah, it might not even be close to a representative sample; thus, all conclusions we come up with might be incorrect. But you can create your own “market top” by using my drafts (see below) with the same approach or anyhow modified for your own purposes :)
- And the last one: don't take it personally, please. I can imagine someone would see my data and think: “wow, why is my company ranked so low?! the author must been biased and made this to make us look bad!”. Nope, I just didn't care. But if you are still feeling frustrated, just tell yourself (and your investors) that this analysis was made by a looser, who recently failed his own business, so no one should care about it 🤗
Results
At least we came to the fun and visual part:
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It's a list of 43 companies out of 53 analyzed, filtered by those who have 3 or more hits over the past 6 years (hit_score ≥ 0.1, see “threshold” above). This list is ranged by a sum of hit_score of all hits released in the last 6 years combined.
Headers:
- Y6…Y1: years ago as 12-month periods, where Y1 = 0 to 12 months from now; Y2 = 13-24 months, etc (there is today() function, so it's always relative and recalculated);
- HIT PER YEAR: total hits released in each 12-month period;
- HIT SCORE: sum of score for all hits released within this period.
Example:
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24 hits over the last 6 years (2+4+4+6+6+2), and 2 of those in the last 12 months. All hits combined provided a 91 hit score over 6y (largest within the sample, though it's in the 1st place), and 20.3 of those 91 came from hits released 12 months ago. Now it's easy, right?So, what interesting can we measure, or what insight can we get?
Global Tendencies
First, we can clearly see global tendencies and their impact, and here we clearly see how the market peaked 3-4 years ago but dropped for the last two years.
Y6 | Y5 | Y4 | Y3 | Y2 | Y1 | |
Total Score: | 221 | 126 | 181 | 160 | 123 | 89 |
Total Hits: | 80 | 137 | 187 | 225 | 180 | 86 |
Score per Comp: | 5.1 | 2.9 | 4.2 | 3.7 | 2.9 | 2.1 |
Hits per Comp: | 1.9 | 3.2 | 4.3 | 5.2 | 4.2 | 2.0 |
Score per Hit: | 3.4 | 1.1 | 1.6 | 0.8 | 1.4 | 1.7 |
Still, it might partly be explained by not having enough time for hits to grow, despite my formula already strongly pessimizing age.
Also, keep in mind that almost 20% of those companies just were started within the last 4-5 years, so they didn't have the option to contribute in the period of 5-6 years ago, which still somehow looks better than the last 12 and 24 months.
Another possible explanation is survivorship bias. Those relatively new companies that got to the list were those who drew my attention, which means they had at least something interesting in their portfolio; thus, it's reasonable to suggest that all similar calculations would tend to peak at the middle of the timeframe, regardless of the market state. But, new companies weight relatively low in total data, plus we see the same picture for top 20 companies:
Y6 | Y5 | Y4 | Y3 | Y2 | Y1 | |
Total Score: | 216 | 115 | 148 | 134 | 110 | 74 |
Total Hits: | 64 | 109 | 142 | 180 | 136 | 63 |
Score per Comp: | 9.8 | 5.2 | 6.7 | 6.1 | 5.0 | 3.4 |
Hits per Comp: | 2.9 | 5.0 | 6.5 | 8.2 | 6.2 | 2.9 |
Score per Hit: | 5.4 | 1.5 | 2.0 | 1.0 | 2.5 | 2.3 |
Also, a companies funnel per period:
Y6 | Y5 | Y4 | Y3 | Y2 | Y1 | |
Hits ≥1 | 25 | 26 | 33 | 37 | 33 | 25 |
Hits ≥3 | 13 | 17 | 19 | 17 | 19 | 11 |
Score ≥1 | 15 | 21 | 25 | 26 | 19 | 14 |
Score ≥3 | 11 | 10 | 18 | 16 | 9 | 7 |
Direct Comparison
Next interesting thing is the comparison of direct competitors, like classic publishers:
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Few things surprised me despite the fact I've worked at publishing since 2018, for eg:
- such low position of old and well-known companies like Tilting Point and Green Panda;
- the fast growth of Supercent and Supersonic (mb, I should've named my publishing as super-smth too?); Rollic and PlayDucky are also very impressive.
- sad fact that almost a third of the sample released ZERO hits for the last 12 months, including such giants as Voodoo;
- also it's possible (but not clearly) to see another confirmation of the hypercasual fall; because those who shifted to hybrids earlier than others clearly feel better for the last years (eg. SayGames, Homa, partially Supercent compared to Azur and VOODOO). Though, focus on classic HC doesn't bother MOONEE or Supersonic somehow (no idea about the first one, but there is a guess about Supersonic, which might use its advantage of close relations with ad networks).
Dynamics & Ranking
For a better understanding of dynamics and the latest changes, I've made sorting and filtering of companies based only on the last X year's performance with different min. hits requirement:
For more informative results, I've made a ranking comparison: all companies ranked by total score within the last 6 and 3 years (”New Era”), next we see the changes in their ranks among competitors (”New Era Rank Δ"):
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Actually, let us draw a line here: there are still a lot more or less reliable conclusions we may come to with this data, but if you are really interested in competitors, it should be more use of me to share my tools instead of opinions :)
DIY: gSheet Draft
To work with it just open and make yourself a copy (apps script attached). And yeah, it works really slow, too many data and calculations for gsheets :(
How to use it:
CompYoY
Main info by 12-month periods. Filter allow to choose date range (LastYears) that affect minimum hits variable and used for sorting companies.
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Comparison
Tab to draw changes in ranking within two selected date ranges.
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PubProfile
Tab to see detailed profile for the selected company: list of hits ranged by release day, graph of hit's score over time, and hits distribution over score intervals.
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How to add more companies
if you get to the bottom of this post: wow! I'm really impressed and glad to have you on my social networks! :) please, let me know about yourself in the comments (Li or FB) so I will know that all that posting wasn't a wasted effort 💕