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Have you ever wanted to have a unique fragrance? Do you think this will be a perfect gift for someone? Well, now you have the possibility to make your own fragrance! The company called Scentcrafters is an online retailer which offers producing a customized perfume according to your own preferences. So, here it how it works. You are able to mix up to five different scents. These can be already existing perfumes like for instance Guilty by Gucci or Tommy Girl, or you can pick an aroma like vanilla or lavender. If you are not sure which scents you would like to mix, then you can just describe the smell you would like to have, for instance “I want it to be fresh with some flower or fruit tones”. Following that, you can choose the recipe, which will be used to produce the scent, or you can create your own one. Apart from customizing your own scent, customers of the successful online company have the possibility to give a special name to their newly created perfume. What is more, the bottle can feature a whole personalized message as well, in order to make it even more special and unique. If you would like to have your own logo or picture printed on the bottle – this is no problem, Scentcrafters can do that, while resizing and adjusting your image in order for it to fit perfectly on the bottle. Furthermore, the company offers a great diversity of bottle designs available from which you can choose the one which you like the most, or the one which fits best with the idea behind the scent you are creating. Also there exists the possibility to change the colour of the liquid inside the bottle. This means that your perfume can be clear, pink, light blue or light yellow. The last step of the customization process is filling in your postal address and payment details so that your uniquely created scent can be shipped to your home.

This type of customer empowerment to create new products is one of the most effective ways in which companies can achieve positive effects with respect to customer satisfaction and the image, which companies have created for themselves. The article called “Customer Empowerment in New Product Development” by Fuchs and Schreier (2011) says that customer empowerment can lead to positive effects in three main factors. Firstly, it leads to increase in the levels of customer orientation, perceived by customers. Secondly, customer empowerment in product development results in more favourable corporate attitudes. And finally, it leads to stronger behavioural intentions.

In the case of Scentcrafers, the customer empowerment in new product development is in the core of the business model, therefore, all of its positive effects and risks should be carefully considered. The great opportunity for personalization and creating unique products are a good way to attract the customers who value and want customization. However, sometimes the making of scents requires some expertise since customers do not necessarily know which combinations will be successful and which will not. Moreover, the use of already known brand names as a component of the new mixtures can lead to some problems with patents and rights to use the brand name.

All in all, the idea behind Scentcrafters is very innovative and offers a great opportunity for customization. So, how will your new perfume be called?

Fuchs, C. and Schreier, M. (2011), Customer Empowerment in New Product Development. Journal of Product Innovation Management, 28: 17–32. doi: 10.1111/j.1540-5885.2010.00778.x

https://www.scentcrafters.com/home.php

In this blog post I am going to talk about the paper called “Recommendation Systems with Purchase Data” by Anand V. Bodapati (2008).

The majority of firms selling online utilize recommendation systems, which are a decision tool that tries to identify products that the customer is likely to buy, if the product is brought to their attention. They are usually based on analysing the previous purchase behaviour of the user and showing him/her the products that are the most likely to be bought. However, what such a mechanism does not account for is the fact that customers are likely to buy these products even without an explicit recommendation. Therefore, a recommendation may have a higher value if it suggests a product that the customer would not buy unless it is recommended. Following on that, the main proposition of the author is that recommendation systems should be based not on purchase probabilities but on the sensitivity of the purchase probabilities to the recommendation action.

The methodology of this academic article is highly technical and quantitative. Firstly, the author builds an econometric model that incorporates the role of the recommendation actions of the firm. Secondly, the proposed model is empirically tested using the purchase data of a real life e-commerce company. Then the performance of the proposed model is compared with the one of other benchmark models. The results indicate the superiority of the proposed model.

One of the biggest theoretical contributions of this article is that it suggests the idea that purchasing can be seen as the outcome of two separate factors – awareness and satisfaction. Awareness (A) can be defined as becoming aware of the product and its characteristics. At the same time, Satisfaction (S) involves evaluating the product and buying it, if its utility exceeds a certain threshold level. A consumer buys a product if both A and S occur. The consumer will not buy anything of he/she is unaware of the product, or he/she is aware but decided that the utility is not high enough. Furthermore, the next contribution of the article is showing that these two events can be separated and identified using existing datasets that companies have. These datasets are information about self-initiated purchase data and recommendation response data. Using all of that information, the research paper proposes a decision framework for recommendation system use.

The econometric model that is developed tells firms at what time they should show a product recommendation to the customer in order to optimize the beneficial outcome for the company. The equation takes into consideration the purchasing probability, the timing of the recommendation and the two factors affecting the purchasing decision – awareness (A) and satisfaction (S). The main idea is that recommendations should not increase the purchase probability for products that the customer is already willing to buy, but rather increase the incremental revenue for the company by suggesting items that are otherwise less likely to be bought.

Despite the few limitations of the research, it makes a valuable theoretical and practical contribution.