Talk:Marketing mix

Wiki Education Foundation-supported course assignment
This article was the subject of a Wiki Education Foundation-supported course assignment, between 30 October 2018 and 11 December 2018. Further details are available on the course page. Student editor(s): Tongxin Liu.

Above undated message substituted from Template:Dashboard.wikiedu.org assignment by PrimeBOT (talk) 00:36, 18 January 2022 (UTC)

Wiki Education Foundation-supported course assignment
This article was the subject of a Wiki Education Foundation-supported course assignment, between 4 October 2021 and 9 December 2021. Further details are available on the course page. Student editor(s): Sunnydayreading.

Above undated message substituted from Template:Dashboard.wikiedu.org assignment by PrimeBOT (talk) 03:27, 17 January 2022 (UTC)

Marketing mix
marketing mix is a general phrase used to describe the difference kind of choices organisation have to make in the whole process of bringing a product or service to the market. 105.112.41.48 (talk) 19:08, 12 December 2021 (UTC)

Section on computational difficulty of marketing mix recently removed: a volunteer to review its suitability?
User MrOllie has recently removed my contributions about the computational complexity of marketing mix arguing citation spam. I would like to ask a volunteer to review the relevance to this article of that computational difficulty section existing before it was reverted by MrOllie at 22:30, 20 February 2024 (UTC). EXPTIME-complete (talk) 00:03, 21 February 2024 (UTC)


 * I'm still new to more involved editing, but here is my review of this section's relevance.
 * Respectfully, I would agree with @MrOllie's choice to remove this section, albeit for different reasons, namely a COI based on your username @EXPTIME-complete and a referenced article of the same name EXPTIME-complete.
 * My reasons for agreeing with this deletion are that this added section appears too different and specific from the rest of the article's content, relying on heavy computer science and optimization knowledge. This contrasts the rather business, marketing, and advertising focus of the rest of the article on what marketing mix is.
 * I would also comment that the proportion of text to number of references seems to rely too much on a single reference. There does appear to be some other references regarding optimizing marketing mix here.
 * My overall suggestions and compromise for this are either two options:
 * keep status quo of the removed section OR
 * keep the section heading, but significantly reduce the section's text make the section text less technical WP:MTAU
 * I would also suggest creating a separate page on Marketing mix optimization with opportunities to expand on that single reference.
 * I'd welcome more opinions and suggestions before making any actions or changes.
 * Erictleung (talk) 18:24, 21 February 2024 (UTC)
 * We definitely should not be creating a spinoff article without much better sourcing. And to be clear, an editor with a conflict of interest such as EXPTIME-complete is just about the last person who should do something like that. MrOllie (talk) 18:26, 21 February 2024 (UTC)
 * Thanks for the quick and second opinion on the matter. And agreed on the conditional that the spinoff article not be made unless more and better sourcing is done first. Erictleung (talk) 18:35, 21 February 2024 (UTC)
 * @Erictleung Thank you for judging this case. The paper referenced in the proposed text has more citations than any other paper in the mentioned Google Scholar query, and seems to be the only one identifying the computational complexity of the problem itself (i.e. not the complexity of any specific algorithm for the problem) in that query or any other, so I think this justifies its presence in a section dealing with marketing mix optimization. I'd like to ask permission to write an alternative, shorter version including also some brief comments about at least another two or three papers on this topic written by unrelated authors. I'd post the potential text here in this conversation, and wait for your opinion as to whether I could include it in this entry.
 * (By the way, I don't understand why the coincidence of my nick with a computational complexity class is relevant. I chose it long ago because I'm an enthusiast of computational complexity, on which EXPTIME-completeness is a very important concept invented 50 years ago ---it's kind of the "simplest" class of decidable problems which cannot be solved in polynomial time. I have never written anything on the EXPTIME-complete entry, I don't know any of the contributors of that entry, and no paper from the same institution as the one referenced in the proposed text is mentioned there. It's like liking biology and calling yourself DNA or Cell or something.) EXPTIME-complete (talk) 19:04, 22 February 2024 (UTC)
 * I have written a new version of the text, please tell me if if is acceptable.
 * Let me insist that I have not invented the 50-year old concept of EXPTIME-completeness (I wish I had!), but if the part of the last sentence mentioning it were a problem, then it could be removed.EXPTIME-complete (talk) 01:31, 23 February 2024 (UTC)
 * Thanks for your efforts to include this topic on marketing mix. The rewrite is much better, but I still feel it contains too much jargon, without context, that is only talked about in this section of the article. Plus, the last sentence appears to be a run-on sentence.
 * Additionally, as @MrOllie has stated, we should be "citing other reliable secondary sources such as review articles that were written by other researchers in your field and that are already highly cited in the literature". These references, although are relevant to the section topic, do appear to be primary sources, each proposing a new solution to the problem of marketing mix optimization. Are there any review article, short paper, or related that would fit this description?
 * Also, my understanding of why you were marked for a COI violation because of your username was incorrect. It was more than you were repeatedly citing the same authors, like Ismael Rodríguez, across multiple pages. I believe doing so in so little time across subjects is suspicious in the eyes of editors. As similar situation has occurred to me as well, I empathize with your frustration.
 * In sum, I would like to see some secondary sources added to your rewrite describing this problem for this section to be included. @MrOllie may have other suggestions for you as well. Cheers. Erictleung (talk) 15:39, 26 February 2024 (UTC)
 * Thank you very much! Yes, I also think that's what @MrOllie asked for, so I'll look for bibliography on marketing mix algorithms coming from secondary sources. I don't know if any survey on this particular topic has ever been written ---but if there isn't any, then I guess nothing about this topic can be written in Wikipedia about it until there is one. Nothing to complain about if this is the standard for all topics, I guess. EXPTIME-complete (talk) 23:42, 26 February 2024 (UTC)

This is the new version I'm proposing:

Marketing mix computational optimization and complexity
Several algorithms for marketing mix optimization have been proposed, including algorithms dealing with different versions of the problem. Given a set of $$n$$ customers, each one with a maximum price they are willing to pay, the problem of designing a new product with $$d$$ qualities and a price such that the benefit is maximized admits $$ O(n(log n)^{d+1}) $$ time approximation algorithms. The allocation of a market budget so that the benefit is maximized has been tackled with a geometric programming algorithm in a non-linear model depending on variables such as relative advertising expenditure, relative in-store promotion, relative price, and relative customer service. A game-theoretical approach for planning of optimal marketing-mix strategies in dynamic competitive markets has been adopted as well. The problem of deciding the locations of several competitive facilities so that the market share captured by the franchise as a whole has been solved with several heuristics. Given some customer profiles, the valuations they give to each potential product attribute, the attributes of the products sold by the other producers, and the attributes each producer can give to its products, the problem of deciding the attributes of the product to maximize the number of customers who will prefer it is Poly-APX-complete; and the problem of finding a strategy such that, for any strategy of the other producers, the product will always reach some minimum average number of customers over some period of time is EXPTIME-complete, which makes both problems intractable.

In order to ease the comparison, this is the previous version:

Difficulty of computational methods
Automatically selecting the attributes of a product (in any category, i.e. product, promotion, etc.) to maximize the number of customers preferring the resulting product is a computationally intractable problem. Given some customer profiles (i.e., customers sharing some features such as e.g. gender, age, income, etc.), the valuations they give to each potential product attribute (e.g. females aged 35–45 give a 3 out of 5 valuation to "it is green"; males aged 25–35 give 4/5 to "it can be paid in installments"; etc.), the attributes of the products sold by the other producers, and the attributes each producer can give to its products, the problem of deciding the attributes of our product to maximize the number of customers who will prefer it is Poly-APX-complete. This implies that, under the standard computational assumption, no efficient algorithm can guarantee that the ratio between the number of customers preferring the product returned by the algorithm and the number of customers that would prefer the actual optimal product will always reach some constant, for any constant. Moreover, the problem of finding a strategy such that, for any strategy of the other producers, our product will always reach some minimum average number of customers over some period of time is an EXPTIME-complete problem, meaning that it cannot be efficiently solved. However, heuristic (sub-optimal) solutions to these problems can be found by means of genetic algorithms, particle swarm optimization methods, or minimax algorithms.

EXPTIME-complete (talk) 09:01, 21 February 2024 (UTC)