Product design. A product is a bundle of attribute levels or features that have utilities to customer (price is considered as attribute as well)

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1 Product design Working ssumption: Wht is product? A product is undle of ttriute levels or fetures tht hve utilities to customer (price is considered s ttriute s well) The mening of : Designing product Deciding nd setting the levels of the ttriutes. Performnce criteri ) Sles ) Revenues ) Profit Consider elsticity, demnd curve, csh flow-credit (strts up firms), quick cents v.s slow dollrs. Elements to consider ) Wht re the product ttriutes nd their levels ) Where is the product positioned in the perceptul mp nd where it should e positioned. ) Where re the competitor for the sme dimensions

2 The Conjoint Model Conjoint is compenstory multittriute model - it ssumes tht wekness on one ttriute cn e compensted for y strength in nother. It ssumes tht the utility or vlue for product cn e expressed s sum of utilities for its fetures or ttriutes. u ) u ( )... ( ttriutes utilities Working ssumptions: tilities cn e mesured y consumers overll evlution of products where customers mke trdeoffs mong ttriutes. Customers differs in their preferences nd the vlue they plce on different ttriutes. Estimtes of the utilities cn e used to mke mrket shre predictions out new products

3 An Illustrtive Exmple (Lehmnn, Gupt nd Steckel, 998) P. 54 Consider sitution of shopping noteook computer, the ttriutes under your considertion re (ignoring other ttriutes such s price, for clrity): ) Processing speed: mhz, mhz. ) Hrd drive: GB or GB ) Memory: MB or 64MB RAM There re 8 different comintions of noteook - defined s product profiles: Hrd drive GB 4GB Memory Memory MG 64MG MG 64MG processor mhz 4 mhz

4 Rnking the profiles Next, we cn sk customer to rnk or rte, the profiles. The tle elow present rnkings for hypotheticl customer, the profiles re coded s dummy vriles. P roduct P roces s or Hrd Memory Rnk P roduct P rofile Drive tility Not surprisingly, this customers prefers profile 8, It is the rnking for of other profiles tht revels the customer s preference for vrious ttriutes.

5 ncovering Attriute tilities from Overll tility Recll The conjoint ssumption: *Processor *Hrd Drive *Memory sing the dummy vriles from the tle in the lst slide for profiles -4 we cn write down 4 equtions with 4 unknowns (,,, ) nd solve them: ,,, re clled prt-worths, in prctice they re derived y mens of computer lgorithms (dummy vrile regression, MONANOVA, etc.)

6 ses of the Prt-Worths ) We cn estimte the reltive vlue our customer ttches to different ttriutes. In this exmple, processing speed ( 4) is vlued more thn hrd drive cpcity ( ) or memory ( ). In fct >. ) We cn use the prt-worths to forecst the preferences of this customer for other noteook computers. Note tht in the exmples we used only the first 4 profiles to compute the prt-worths. In order to estimte other profiles we hve to plug in the dummy vriles: ) We cn simulte the impct of new product introductions

7 Conjoint Simultion - The Motivtion Consider mrket with two existing rnds A nd B with the ttriutes (nd levels) specified in the tle elow (using dummy coding); A hs mhz (processor ) with GB (Hrd Drive ) nd 64MB (Memory ). Brnd B hs MHz processor, with 4GB hrd drive ut only MB of memory. Assume tht we wnt to introduce new noteook with mhz, GB nd 64MB. Brnd Processor Hrd Memory Drive A B Ne w Wht shre cn the new rnd otin? nd where this shre will come from?

8 Conjoint Simultion - The Principle From the prt-worths estimted erlier, we cn otin rtings or rnkings for ech product profile, including the new concept. For ech customer seprtely we cn determine his choice. The tle elow presents prt-worths nd rnd utilities for customers. For ech customer we cn ssign choice of rnd A, B or the new concept (ssuming choice rule). Prt-Worths Brnd tilities Brnd Chioce Customer A B New W/O New With New B B A A B B A New A A B B A New B B B New B B Before the introduction the mrket shre is expected to e: A.4, B.6. When the new rnd is introduced: A.,B.5, nd New.. In other words, the new rnd is expected to drw % of rnd A nd % of rnd B.

9 Assessing Reltive Importnce of Ech Attriute Reltive importnce of n ttriute utility rnge of tht ttriute divided y the sum of the utility rnges for ll ttriute. For exmple the reltive importnce of processing speed for customer is: 4 7 Reltive importnces for our customers re given elow; H rd C u sto m e r P ro ce sso r Drive M e m o ry 9% 57% 4% Customers, nd to some extent customer 8 hve similr preferences. This dt llows segmenttion (e.g., y using cluster nlysis), nd understnding the mrket structure.

10 Designing the conjoint study: Steps involved Select ttriutes relevnt to the product or service ctegory. Select levels for ech ttriute Develop the product undles to e evluted Otining dt from smple of respondents: Design the dt collection procedure. Select computtion method for otining prt-worth functions. Evluting product design options: Segment customer sed on their prt-worth functions Design mrket simultions. Evlute (nd select) choice rules. Estlish the est design for the product.

11 j R ikm M k K Computing the prt-worth functions (using dummy vrile regression) R K M k k m ikm x jkm ε prticulr product or concept included in the study design the rtings provided e respondent I for product j prt-worth ssocited with mth level of the kth ttriute numer of levels of ttriute k numers of ttriutes dummy vrile tht tke on vlue if mth level of the kth ttriute is present in product j nd otherwise x jkm error terms, ssumed to e norml distriution with zero men nd vrince for l i nd j ε σ ikm cn e rescled for more esy interprettion

12 The utility of product j to customer i: Conjoint results u k M k k m ikm x jkm Note tht product j cn e ny product tht cn e designed using the ttriutes nd levels in the study, including those tht were not included in the estimtion of the prt-worths in the former eqution. Design mrket simultion: A mjor reson for the wide use of conjoint nlysis is tht once prt-worths re estimted from representtive smple of respondents it is esy to sses the likely success of new product concept under vrious simulted mrket conditions. A typicl question is wht mrket shre would proposed new product e expected to chieve in mrket with severl specific existing competitors? To nswer this we hve to specify ll existing products s comintions of ttriutes nd their levels. Also we hve to select the choice rules tht trnsform prt-worths into product choices tht customers re most likely to mke.

13 Choice rules - mximum utility Mximum tility rule: under this rule we ssume tht ech customer chooses from ville lterntives the product tht provides the highest utility vlue, including new product concept under considertion. This choice rule is most pproprite for high involvement purchses such s crs, videos etc. There re two wys to compute the mrket shre ccording to this choice rule. We cn compute the numer of customers for whom tht product offers the highest utility nd dividing this figure y the numers of customers in the study. The second wy is weighting ech customer proility of purchsing ech lterntive y the reltive volume of purchses tht the customer mkes in the product ctegory: m I i j J I j i w p i i w p I - numer of customers prticipting in the study, J - The numer of product lterntives ville for the customer to choose from, m - mrket shre of product j, w i i the reltive volume of purchse mde y customer I, with the verge volume cross ll customers indexed to the vlue, p the proility tht customer I will choose product j on single purchse occsion

14 Choice rules - Shre of utility This rule is sed on the notion tht the higher utility of the product to the customer, the greter the proility tht he or she will choose tht product. Thus ech product gets shre of customer s purchses in proportion to its shre of the customer s preferences: p u J j u where u is the estimted utility of product j to customer i. We cn otin the mrket shre for product I y verging p cross customers. This choice rule is relevnt for low involvement frequently purchsed products, such s consumer pckged goods. This rule requires tht utilities e expressed s rtio scled numers.

15 Logit choice rule p j e u e u

16 Detiled (Clssic) Exmple - Household Clener. (Green nd Wind, 975) Spot removers (e.g., for crpets); the following ttriutes were nlyzed: Pckge design (A, B, C) Brnd Nmes (KR, Glory, Bissell) Price (.9$,.9$,.59$) Good Housekeeping sel (yes or no) Money ck gurntee (yes or no). For the xxxx8 possile profiles nd orthogonl design (8 profiles) ws selected. The design with one customer s rnking is presented elow: Pckge Brnd Price Sel Money Rnking Design Nme Bck A KR.9 No No A Glory.9 No Yes A Bissel.59 Yes No 7 B KR.9 Yes Yes B Glory.59 No No 4 B Bissel.9 No No C KR.59 No Yes C Glory.9 Yes No 7 C Bissel.9 No No 9 A KR.59 Yes No 8 A Glory.9 No Yes 8 A Bissel.9 No No 5 B KR.9 No No 4 B Glory.9 Yes No 6 B Bissel.59 No Yes 5 C KR.9 No No C Glory.59 No No 6 C Bissel.9 Yes Yes

17 Derivtion of the Attriute tilities Assuming no interctions the regression model ecomes: Rting B (pckge A) B (pckge B) B (KR) B 4 (Glory) B 5 (Price.9) B 6 (Price.9) B 7 (Sel) B 8 (Money ck) With the dummy coding: Pckge - A, B, C; Brnd nme - KR, Glory, Bissel; Price -.9$,.9$,.59$; Se; - No, Yes; Money ck - No, Yes. The dummy coding scheme is presented elow: P ck ge De sign Br nd P rice R nking A B KR Glory.9.9 Se l Mone y

18 Estimted Attriute tilities in Vrious methods Simple Attriute Sum Reorded MONANOVA Re gre ssion Re gre ssion P ck ge A B.5 8 C Br nd KR Glory.5. Bisse l.7.5 Price Se l Ye s No.5. M one y Ye s No.4. Constnt 4.8, R.98 Simple sums: Estimtion of the verge vlue of the dependent vrile for ech level of ech ttriute (e.g., Pckge A ppers in six profiles, the verge score is (684)/65.). This set is rescled to rnge of. - (y liner interpoltion - 5.., ) The regression suggests tht pckge design is importnt, with rnge of 8 (-4.5 to.5), s is the price (rnge of 7.67). Strong preference for pckge design B nd low price the money ck gurntee nd the sel re reltively unimportnt We cn now estimte ny comintion, for exmple: KR with pckge design B with sel, priced t,9 nd no money ck gurntee ( )

19 Exercise in Conjoint Anlysis - Designing frozen pizz (Mrketing Engineering P. 89) Assume tht frozen pizz cn e descried y comintion of ttriutes - type of crust, type of topping, mount of cheese nd its type, price nd other ttriutes. Suppose tht firm considers types of crust (thin, thick n pn), four types of toppings (veggie, pepperoni, susge nd pinepple), three types of cheese (mozzrell, ordinry, nd mixed cheese), quntity of cheese t three levels (regulr, doule nd extr), nd price t one of the three levels (Nis, 6Nis, nd 4Nis). The tle in the next pge enumertes 6 product undles tht form n orthogonl study. Rnk these profiles ccording to your own preference (tste), otin your own prt-worth function, discuss your preference s comes up from the nlysis, nd define the est design tht mtches your own choice of preference. How close is it?

20 An orthogonl design for the frozen pizz nlysis Product Crust Topping Type of Amount Price Perference undle # Cheese of cheese Nis (yours) Pn Pinepple Regulr Regulr 4 Thin Pinepple Mixed Extr 6 Thick Pinepple Mozzrell Doule 6 4 Thin Pinepple Mixed Doule 5 Pn Veggie Mixed Doule 6 6 Thin Veggie Regulr Doule 7 Thick Veggie Mixed Extr 4 8 Thin Veggie Mozzrell Regulr 6 9 Thick Pepperoni Mozzrell Extr Thin Pepperoni Mixed Regulr 6 Pn Pepperoni Regulr Doule 6 Thin Pepperoni Mixed Doule 4 Pn Susge Mixed Doule 6 4 Thin Susge Mozzrell Doule 4 5 Thick Susge Mixed Regulr 6 Thin Susge Regulr Extr 6

21 Segmenttion Why do we segment? When it is mostly importnt? A Definition Mrket Segmenttion is concerned with individul or intergroup differences in response to mrketing mix vriles. The mngeril presumption is tht if these response differences exist, cn e identified, re resonly stle over time nd the segments cn e efficiently reched the firm my increse its sles nd profits eyond those otined y ssuming mrket homogeneity. Du-Pont s Definition A group of customers nywhere long the distriution chin who hve common needs nd vlues - who will respond similrly to our offerings nd who re lrge enough to e strtegiclly importnt to our usiness.