## 4 Discussion
Thus, for comparative reasons, the method presented here is closer to the conjoint analysis-based methods. The suggested procedure focuses primarily on finding a product that will appeal to many customers. Hence, it has primarily the market share in focus. The conjoint-based methods solve the problem of maximizing the producer's profit (\"the seller's welfare problem\"), sometimes while maximizing at the same time the well-being of the buyers (\"the buyer's welfare problem\"), using observed, or estimated, utilities of individual levels of the product features (overall product welfares are then sums of the individual-feature utilities subject to some constraints). These optimization problems can theoretically be solved either by way of complete enumeration, calculating welfares of all within-production-limits conceivable product variants, or by sophisticated algorithms which look for a path to move closer to the optimal solution without the need to evaluate each product variant. The problem is that given a number of product features together with their different levels and different product portfolios, the total number of combinations to be evaluated increases exponentially even to hundreds of billions of combinations. This makes complete enumerations from the practical point of view difficult, or even impossible. The methods that try to avoid these problems are based on heuristics. Since the method proposed in this text centers on the market share, while the heuristic method focus on the producer's profit, their direct comparison is elusive, although the heuristic methods do not generally attain the optimal profit, rather a high profit, while for the proposed method, the following can be said: the procedure takes as the \"winning product design\" the design that is best by a large number of weights from the weight space; if we assume that this means that such a product variant will have a larger market share, it could also rank among the more profitable variants provided its profit margin is the same or higher than that of any other product variant. What if there is another product variant on the side of the producer, not considered by the method as the best one, because it is not the best by the _largest_ number of weights from the weights space, whose profit margin would be higher than that of the winning product variant, if it was produced, and in total, it would bring a higher profit? Theoretically, this can happen. On the other hand: (1) the winning variant, satisfying many customers, can be expected to have at least some of its attributes to be rather boosted (see the example), hence it will not be the cheapest to produce and so, setting a fixed profit margin, its price and the profit, as a percentage of the costs, will be rather higher than lower; (2) there will usually be more winning product variants with the same estimated market share (see the example), thus the producer can choose out of them the most profitable variant, bringing itself to or closer to the most profitable variant overall; and (3) it is difficult to justify the argumentation that there might be a variant with lower market share that could bring a higher total profit, especially if the resulting winning product variant is best according to an overwhelming number of weights (see the example) because, unless it is a market of high-end luxurious products, products that sell less (at least as estimated by the AHP model) can be expected to have less boosted features, thus cheaper production and smaller profit for a fixed profit margin, and additionally they will probably sell in smaller numbers than the product defined as the winning by the method. Thus, even though it is impossible to guarantee that the winning product is the most profitable one, it should bring the producer high profits rather than lower profits. Hence, in some sense, the heuristic methods and the method proposed here can be to an extent similarly oriented: to achieve higher profits. This argumentation makes them now a bit more comparable.