Product Selection in Internet Business, A fuzzy Approach ( B.K.Mohanty and B.Aouni,2009 )

1. Product Selection in Internet Business, A fuzzy Approach(B.K.Mohanty and B.Aouni,2009) Presented By Hasan Furqan Mkt.Nr: 241639

2. Agenda: • Product Selection • Product Selection Approaches • Fuzzy Logic • Fuzzy Representation of the Product Attributes • Similarity Measure • Solution Procedure • Conclusion

3. Product Selection • “It is the customer who determines what a business is” (Peter Drucker) • Marketing has the responsibility within a firm to reflect customers’ goals, needs and wants. • Tools like sales calls, surveys, focus groups, test markets, trial samples and statistical analysis of purchase data. • Results from these studies reveal opportunities for new designs and features as well as show recent failures

4. Product Selection • Companies have thousand, even millions of customers. • They all are somewhat different, with different preferences. • A customer evaluates all available products as a whole before ranking them according to his or her own preferences. • A customer’s preferences for the products and hence their rankings are implicit in his/her mind and are difficult to express explicitly.

5. Product Selection Approaches

6. Product Selection Approaches 1- Rules-Based Systems • Rules based systems work best when the important product attributes are quantifiable and the user needs are not extremely complicated • e.g. Yahoo! 2- Endorsement Systems • It works best when the product needs of consumers do not differ greatly. In this case a simple endorsement can be best. • Consumers need to know that a service provider is competent and has the best quality available.

7. Product Selection Approaches 3- Case Systems • The online system begins by surveying users about their product category preferences. • Searches and narrow their choices from thousands of possibilities to a few highly ranked alternatives. 4- Collaborative Filtering • Matches different users or it matches the customer requirement with the products’ attributes itself. • These individuals/system can then share recommendations and preferences about difficult to judged products and services.

8. Evolution of Fuzzy Logic • Conceived by LotfiZadeh (1965), a professor at the University of California at Berkley, • Presented not as a control methodology, but as a way of processing data by allowing partial set membership • A simple way to arrive at a definite conclusion based upon vague, ambiguous, or missing input information. • Early adopter were the Europeans and Japanese

9. Explanation of Fuzzy Logic • Customers like to have the fullest satisfaction on all the desired attributes of the products. • The product attributes are in general conflicting, and fuzzy in nature • A customer makes effort to satisfy most of the attributes • The customer attached some weights to the product attributes. • These underlying weights are implicit in customers’ mind.

10. Fuzzy Representation of the Product Attributes Normally a customer expresses his/her desire in multiple product attributes, which in general are fuzzy e.g.in a car purchasing problem (A,µA): Cost should be around US$20,000, (B, µB): Cost should be approximately US$22,000, (C, µc): Cost should be closer to US$23,000, (D, µD): Cost should be more or less US$25,000.

11. Fuzzy Representation of the Product Attributes • These fuzzy terms can be presented as fuzzy numbers as (A,µA) = (15, 20, 25), (B, µB) = (18, 22, 24), (C, µc) = (17, 23, 25) and (D, µD) = (22, 25, 27),

12. Similarity Measure • The degrees of similarity between the attribute levels of the available products on the Internet and the desired attribute levels of the customer • Let B1, B2, ….. ,Bmbe m products available on the internet. • Let these products be assessed in terms of the satisfaction levels of the k attributes. • We denotes the jth attribute of the ith product as Bij so the ith product in terms of the attributes is Bi = (Bi1,Bi2, ….. BiK)

13. Similarity Measure • Similarly we can have the customer’s desired levels of the product attributes as E = (E1, E2,….,Ek) • Ej(j= 1, 2, ….k) represent the customer’s desired level in the jth product. • We have assumed here that the product attributes are in the form of L-R fuzzy numbers, so we have • Ej= (Ejl,Ejn, Ejr) (for j= 1, 2, …..k) and Bij = (Bijl,Bijn,Bijr) (for i = 1,2,…..m and j = 1,2,…..,k

14. Similarity Measure • The similarities between the attribute level Ejand the product attributes Bij, in order to determine the satisfaction levels of the customers regarding the available products as Bij~ Ej(for i = 1,2,…..m and j = 1,2,…..,k) • We derive the degree of similarity between Ejand Bij TEj|Bij(i) = Max [Bij(x)] Here TEj|Bijrepresent the extent to which the customer satisfaction level in the jth attribute matches Bij

15. Similarity Measure • By varying over all products, for the attribute j and the customer’s preference on the jth attribute, (TEj|B1j, TEj|B2j,………., TEj|Bmj) • If we want to know that a customer may get a product with the desired attribute levels. This can be predicted by calculating the probability as given below Pf (Ej) = 1/m Ʃ TEj|Bij The term Pf (Ej) represent the probability of obtaining the customer’s preference level in the jth attribute Ej .

16. Similarity Measure • Since each TEj|Bijare fuzzy numbers their addition and hence the derived probabilities are again fuzzy numbers. Thus we have Pf(Ej) = (Pf (Ej)l, Pf (Ej)n, Pf (Ej)r) • The expected value of the jth attribute under the given market profile can be calculated as Expf(Ej) = [Pf (Ej)l x Ejl , Pf (Ej)n x Ejn, Pf (Ej)r x Ejr] • The membership function of the fuzzy set represents the flexibility behavior or compromising attitude of the customer while purchasing a product.

17. Solution Procedure • Generally, for a particular product one attribute may achieve to the fullest extent, another to some extent P (i) = Ʃ WjIi(j) • The product corresponding to the highest satisfaction level is selected as the best product. This can be obtained through the following equation: P = Max. [ P(1), P(2), …..,P(m)]

18. Conclusion: • Discuss different approaches adopted • Customers’ fuzzy behaviors and their expectations are modeled • Businesses on the Internet are expected to benefit from such a measure

19. Q & A