Start with the funnel
Marketing a game is easier to judge when you break the journey into steps. A player rarely goes from never having heard of a game to buying it in one move. A simple funnel has four stages:
- Reach: people see the game, in a trailer, a post, a stream or a store listing.
- Visits: some of them open the game’s page.
- Wishlists or follows: some of those add it to a wishlist or follow it.
- Purchases: some of the people who saved the game later buy it.
Each step has a conversion rate, which is the number who moved to the next step divided by the number who reached the current one. Looking at each rate separately tells you where the leak is. A game with many visits and few wishlists has a different problem from a game with many wishlists and few purchases.
All numbers below are hypothetical and chosen only to make the arithmetic clear. They are not typical results for any real game.
A worked funnel
Imagine a small game with a storefront page. Over several months before launch:
- 40,000 people visit the page.
- 2,000 add it to a wishlist.
- At launch, 200 of those wishlisters buy it in the first period.
The rates:
- Visit to wishlist: 2,000 / 40,000 = 5 percent
- Wishlist conversion at launch: 200 / 2,000 = 10 percent
What does this tell us? On its own, little. The numbers only become informative when compared with something: the same game earlier, a similar project from the same team, or a change you made. If the page was redesigned and the visit-to-wishlist rate rose from 5 percent to 6 percent, that is a signal worth examining. Be careful here, because a small change on a small number of visits can be pure chance.
The wishlist is also a promise-in-waiting, not a sale. Some people save games they never intend to buy soon, and many wait for a discount. Treat wishlist conversion as one indicator among several, and avoid assuming that any particular number guarantees success.
Comparing two campaigns
Suppose a team tries two efforts and wants to know which worked better.
| Campaign A | Campaign B | |
|---|---|---|
| Visits sent to the page | 10,000 | 5,000 |
| Wishlists gained | 400 | 350 |
| Visit-to-wishlist rate | 4% | 7% |
Check: 400 / 10,000 = 0.04, and 350 / 5,000 = 0.07.
Campaign B has the better rate. Campaign A produced more wishlists in total. Neither fact alone settles which is better. If Campaign B’s audience was smaller but better matched to the game, the sensible conclusion might be to find more people like them. If Campaign A cost far less, its total might still be the better value. The right question is cost per wishlist, together with how many of those wishlisters eventually buy, which you can only learn later.
Rates also behave oddly at small sizes. Five hundred visits and 35 wishlists give 7 percent, but the real rate could easily be a point or two higher or lower. Wait for enough numbers before drawing conclusions.
The attribution problem
The hardest part of marketing analysis is deciding what caused a sale. A player may see a short video, hear a friend mention the game weeks later, read a review, and buy during a sale. Which of those “caused” the purchase? Usually all of them contributed, and the tools available cannot separate them cleanly.
Common pitfalls include:
- Last-click thinking. Crediting the final link someone clicked ignores everything that happened before it. A store link in a stream description may get credit for a decision made earlier.
- Timing coincidence. Wishlists often rise when a game is featured, discounted or discussed in social media communities, so a campaign that happened to run during that period looks better than it was.
- Cherry-picking the window. Choosing the days that make a campaign look good is easy and often unintentional. Decide the measurement period before the campaign starts.
- Ignoring the baseline. Some wishlists and sales arrive without any campaign. Compare against what a normal week looks like.
- Counting the same player twice. A person who follows the game on several platforms may be counted in each.
- Confusing correlation with cause. A spike after a post does not prove the post caused it.
Because of these limits, it is wise to treat marketing numbers as directional evidence. Change one thing at a time where possible, keep records, and be modest about conclusions.
What to track
A small team does not need a complex dashboard. A short list kept in a spreadsheet is enough:
- Visits to the game’s page, by source where the storefront provides it
- Wishlist additions per week
- Visit-to-wishlist rate
- Wishlist conversion after release
- Spend and time put into each effort
Record what you did and when, in plain words. A note such as “posted a short gameplay clip on Tuesday” is far more useful six months later than a memory.
Marketing and product are connected
Marketing cannot fully fix a weak page or an unclear game. If visitors do not wishlist, the cause may be a confusing capsule image, an unclear description or a trailer that does not show gameplay. A well-built store page is part of the funnel, not an afterthought. Likewise, early testing, whether through beta testing or open demos, produces both feedback and an audience, which makes it a form of marketing as well.
Finally, remember that outcomes are uncertain. Many well-made games sell modestly despite good marketing, and a few succeed with little. Good measurement will not guarantee sales, but it helps you learn from each effort and avoid spending on things that do not work.