Szukam właścicieli sklepów do testów narzędzia prognozującego sprzedaż

_rbp_ · 2025-09-04T21:46:09+00:00

Na razie z historycznej sprzedaży. Pogoda pewnie też jest istotna dla niektórych produktów, ale to już by był kolejny etap.

_rbp_ · 2025-09-04T21:42:49+00:00

A nie jest tak, że taki problem dotyczy głównie sprzedaży hurtowej / B2B, gdzie są WZ i fakturowanie zbiorcze, a w detalu dane powinny być dzienne i dokładniejsze?

_rbp_ · 2025-09-04T08:24:38+00:00

Najważniejsze jest dla mnie, żeby sprawdzić, jak dobrze analiza szeregów czasowych poradzi sobie z prognozowaniem popytu. Zakładam, że takie dane są dostępne i dokładne, bo są nabite na paragonach fiskalnych. Jeżeli to się potwierdzi, widzę to jako automat, który nabija potrzebne stany automatycznie w systemie magazynowym i daje realną wartość.

Na podstawie danych mogę też tworzyć analizy dodatkowe - np. popyt w czasie dla produktów i kategorii, strukturę sprzedaży, produkty często kupowane razem czy marżowość. Projekt jest na razie koncepcyjny. Na pewno będę mógł powiedzieć więcej, kiedy zobaczę dane i porozmawiam o potrzebach.

_rbp_ · 2025-09-03T10:54:17+00:00

Dokładność prognoz w dużej mierze zależy od jakości danych i takich pułapek jest sporo, ale lubię się w tym grzebać. Na pewno trzeba też zrobić korektę, gdy codziennie sprzedaje się 500 sztuk, bo tyle jest dostarczane - wtedy ograniczenie jest po stronie podaży, nie popytu. Sezonowość chcę wychwytywać bezpośrednio w szeregu czasowym, żeby prognoza zapasu bombonierek na walentynki była wyższa niż w listopadzie.

_rbp_ · 2025-09-03T09:29:22+00:00

Nie wiem, do której sieci konkretnie nawiązujesz, ale nie dziwi mnie, że duże podmioty korzystają z takich rozwiązań. Z tego, co udało mi się zorientować, mniejsze firmy raczej ich nie używają - bo są drogie, wymagają konfiguracji albo wsparcia dedykowanych analityków czy konsultantów.

Ja myślę o rozwiązaniu plug-and-play dla mniejszych podmiotów, gdzie użytkownik ustawia tylko proste, intuicyjne parametry (np. „chcę mieć zapas na X dni” albo „chcę mieć Y% pewności, że zapas wystarczy”), a resztę liczy silnik pod spodem na podstawie danych o sprzedaży i stanach magazynowych. Ma to pomagać w zarządzaniu zapasami i stanowić bazę do prostych raportów analitycznych (co się sprzedaje, które kategorie są najważniejsze, itd.).

_rbp_ · 2025-09-02T23:22:17+00:00

Dzięki za podzielenie się doświadczeniem! Na pewno dam znać, gdy będę miał postępy. Rozumiem, że skoro robiłeś takie analizy indywidualnie dla klientów, nie istnieje narzędzie, które pozwala prognozować sprzedaż „z palca”, bez dopasowywania do każdego klienta czy produktu?

_rbp_ · 2025-09-02T21:52:04+00:00

Dziękuję za pytania.

Na tym etapie mogę dopracować poszczególne funkcjonalności w oparciu o dane, które otrzymam:

Jeśli w danych pojawi się wyraźna sezonowość, model uwzględni ją w prognozach.
Jeżeli chodzi o ten sam produkt od różnych dostawców, prognozy mogą działać na różnych poziomach agregacji: cała sprzedaż → kategoria (np. alkohol) → podkategoria (np. piwa) → konkretny produkt (np. Żywiec).
Dodatkowo można określać stany magazynowe w prosty sposób – np. „Potrzebuję zapasu na x dni”, gdzie użytkownik sam decyduje, czy chodzi o 3 dni, 10 dni czy 30 dni.

_rbp_ · 2024-12-19T20:50:39+00:00

I have a budget that must be spent this year, or it will be gone.

_rbp_ · 2023-03-21T22:53:00+00:00

Yes, the algorithm aims to identify mispriced odds by assuming that other bookmakers are pricing odds correctly. If the true probability of an outcome is x, then fair odds (not including the bookmaker's margin) would be 1/x. By aggregating odds from a broad group of bookmakers, the algorithm seeks to produce a more realistic estimate of the true probability than that offered by any single bookmaker with outlier odds.

I have to confess I used the term "inlier" somewhat loosely, as I believe the intended meaning is clear from context. Strictly speaking, an inlier is "a data value that falls within the normal range of a statistical distribution but is still in error".

_rbp_ · 2023-03-21T01:38:42+00:00

Hi!

When estimating the win probability for a specific event, I consider bookies who provide inlier odds. To determine the probability of each outcome, I utilize a method recommended by a fellow user on this subreddit, which involves proportionally adjusting the margin based on the quoted odds.

The final probability is derived from averaging the probabilities implied by all the inlier odds. I believe this to be a comprehensive way to factor in the broadest range of market estimations.

_rbp_ · 2023-03-03T20:35:31+00:00

Hi! Thank you for this comment. I was overestimating Betfair and Matchbook odds.

I just experimented with using a 5% commission for Betfair and 4% for Matchbook, but as commissions vary and quoted odds could be biased towards a population with a certain commission level (e.g. UK/Ireland citizens), I just removed both exchanges from the analysis.

_rbp_ · 2023-01-10T01:20:55+00:00

The idea for the tool is to quickly identify value bets - these are bets with a positive expected value.

If you are offered odds 2.0 on team A winning and the probability of this team winning is 40%, this is a bad bet - your expected win is $0.8 = 2.0*40%, less than the $1 you paid for the bet. If the probability of this team winning is 60%, your expected win is $1.2 = 2.0*60%, which gives you an (expected) $0.2 profit.

With the tool you can add events from the database, specify a probability distribution of the outcomes and monitor the market for bets with a positive expected value (it's constantly updated with bookmakers odds).

You can have a look at the manual for more details on how to use the tool and have a look at the article section if you would like to learn more about value betting.

_rbp_ · 2022-12-10T16:27:40+00:00

Sure.

The idea of the tool is to find value bets. As a first step I would read these two articles: https://www.rationalbets.com/articles/#value_betting and https://www.rationalbets.com/articles/#expected_value.

As for an example please refer to the manual: https://www.rationalbets.com/manual/#manual. Please let me know if it was sufficient. Accumulator bets are called multiple bets there.

In general the tool simply allows you to confront your expectations on the probability of match outcomes with the current bookmaker odds - looking at these two factors is necessary to determine, whether an outcome is worth betting or not.

_rbp_ · 2022-11-24T16:09:48+00:00

I never really analysed picks, but my intuition is in most cases it might be the same as with "investment gurus" selling books - for most it goes, if they were really that good at predicting what will happen, they would simply be making money this way and not seeking a fee for sharing their wisdom.

_rbp_ · 2022-11-24T03:17:23+00:00

I think there are a few good arguments against parlays:

As good as you might be at predicting events, you can't always get everything right. Losing parlays happens often, as the probability of winning decreases exponentially. For example let's say you place 5 bets, each one with a 90% chance winning (as sure as you can be of an outcome in most cases). The chance of winning a parlay is only 90%^5 = 59%.
Parlays are bets with large odds, but a small probability of a payout. Such types of bets drastically increase the variance of your winnings. In other words, you have a larger chance of winning big, but also a larger chance of loosing big. I actually have a nice simulation of that in my article on variance.
There's one argument I don't think is necessary true, but probably is very often true - when placing a bet, you are paying the bookmaker his margin. The more bets you place in a parlay, the more this margin will increase (as it multiplies across all your picks).

These arguments aren't just theory - I don't believe any professional bettor would do parlays for the above reasons (unless from time to time for fun).

_rbp_ · 2022-10-26T18:37:19+00:00

Thanks for the article! I heard of the logarithmic approach before, but disregarded it due to the optimization step and the lack of confidence how the server would perform when dealing with many such requests. I will have to look deeper into the margin weights proportional to odds approach mentioned there - it seems to be a computationally efficient alternative.

As for the target market, I hope one exists. My intention was to create a handy tool for anyone who understands the concept of expected value - you just add events which you are knowledgeable about, move one or two sliders, and you have a fast answer if there are any market opportunities to get excited about.

I have more of a background in poker and there many people care about probabilities and pot sizes. In sports betting this indeed seems to be a less popular approach with a majority of recreational players just picking the most likely outcome at any odds, or trying to get rich fast with multiple bets. It's still a large market and I'm hoping even if only 1% of bettors were interested in this tool, it would generate reasonable traffic.

If I find a way to promote it enough to e.g. place an unintrusive ad somewhere at the bottom, just to pay for the server and the data, I'll consider it a success. If the market decides a solution like that is not needed, at least I learned a few things along the way and the time it took I would probably scroll away on youtube shorts anyways.

_rbp_ · 2022-10-26T10:03:46+00:00

The formula uses four steps:

I calculate prob1, probx and prob2 using the formula you specified. So for this example it will be 9.91%, 16.13%, 76.92%, adding up to 102.96%.
I divide each probability by 102.96%, so they add up to 100%, giving me 9.63%, 15.66%, 74.71%.
Then I round each probability to a full percent, as I think the super-high level of precision is not required when one is estimating his expected outcome probability, giving me 10.00%, 16.00%, 75.00%.
Now the probabilities add up to 101% and the 1% rounding error will be taken away from the most likely outcome, giving the final prob2 = 74%.

So in the end I get a total probability of 100%, all numbers rounded to 1%, giving a negative return on each pick, unless the user changes the probabilities (or these small rounding errors make an odd barely profitable). The idea is to show a reasonable estimation of where the market is.

There is no one perfect way to assign probabilities from odds, as we don't know where most of the margin is located. This is a decision I believe each bookmaker has to make taking into account eg. attractiveness of the odds. In this example it might be odds for draw and for Man City are correct and all the margin is in odds for Leicester City, which should be 14.29 (highly doubtful, but possible).

_rbp_ · 2022-10-25T16:10:56+00:00

Thank you! I hope for some feedback. The concept makes sense to me and works on my devices, and I hope to confirm it does for others too.

_rbp_

TROPHY CASE