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CLOTHES WASHER CONSUMERANALYSIS

TECHNICALAPPROACH

DECEMBER 15, 1998

ANALYSISAPPROACH The objective of this study is to determine which clothes washer features consumers value most and estimate how changes in these attributes resulting from an efficiency standard will affect clothes washer purchases and consumer utility. As discussed in this Technical Approach, focus group and conjoint analysis data will be used to estimate the probability of purchasing standard and high efficiency clothes washers. These purchase probabilities provide

# an intuitive context to evaluate clothes washer demand, since changes inequipment attributes will be expressed as a change in the likelihood of makinga clothes washerpurchase

.. The estimation of purchase probabilities and elasticities guides virtually every stage of the analysis. Focus groups are used to determine the most important clothes washer attributes. These attributes are presented to respondents during conjoint analysis sessions. Using conjoint analysis has the advantage of presenting different washer attributes simultaneously, which forces respondents to make tradeoffs across attributes as they would iftheywere actually purchasinga washer. The conjoint data is combined with actual market data to estimate the probability ofpurchasing a clothes washer and calculate purchase elasticities. A detailed description ofeach analysis stage is presented below.

PURCHASEDECISIONSTRUCTURE Exhibit 1 shows the two-step decision involved in purchasing a new clothes washer. The first decision is whether or not to enter the market and purchase a clothes washer; and is referred to here as the Purchase Probability. The second decision is, giventhata purchase is going to be made, which equipment option will be chosen. This is labeled the Equipment Choice Probability. The product of these two yields the Total Probability, whichis the probability thatany one equipmentoptionis purchased.

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### Exhibit1 Clothes WasherPurchase Decision Stages



## x

FOCUS GROUPS The primary purpose ofthe focus groups is to identify the key washercharacteristics to be used inthe conjoint analysis. The focus groups are conducted to develop a list of all equipment characteristics that influence on the decision to purchase a clothes washer. This is accomplished by havingthemoderator guidethe discussionto get anunsolicited list of important clothes washer attributes. Once the unaided list of attributes is obtained, the moderator will suggest any other attributes that were not volunteered initially for the group to discuss. Once the complete attribute list is determined,

.questions are asked to determine which attributes are considered the most importantin

selecting a specific clothes washer. The focus groups are also used to identify key population segments that should be included in the conjoint session sample frame. For example, it may be that attitudes toward washers vary substantially by house type or some other demographic segment. These differences will be evaluated after the focus groups are completedto determine if thesampleframe for the conjoint analysis should be stratified across these demographic segmentsas well. Focus groups will be conducted in five cities; Washington, DC, San Francisco, CA, Madison, WI, Miami, FL, Dallas, TX These cities were selected so that five different regions are represented inthefocus groups. Tenfocus groupswill beconducted,two in eachcity, with a total of approximately 100 focus group participants divided evenly across the ten sessions. Focus group participants will be recruited randomly by phone to ensurea mix ofdemographic types. Each respondentwill bepaid a $50 incentive for participatinginthefocus group.

Quantum Consulting Inc. 2 TechnicalApproachCONJOINTANALYSIS Conjointsession design Conjoint analysis involves having respondents sort through and rank cards that reflect different clothes washer equipment options. Each card describes a separate clothes washer based on several equipment characteristics such as price, energy and water savings, and axis orientation. The actual equipmentcharacteristics used in this analysis will be determined from the focus groups. Respondents rank the cards from most to least preferred. As discussed below, this ranking information is used to estimate the probability of purchasing different clothes washer options based on the equipment characteristics. The success ofconjoint sessions rely onmaking surethatrespondents are well informed as to whatis expected during the sessions as well as beingfamiliar with the equipment being evaluated. The moderator will explain that respondents should rank the set of cards from most preferred to least preferred. Photographs and additional information onhighefficiency clothes washers will be available ateach session. This will allow each respondent to become familiar with the high efficiency washers so that they can make informed decisions while doingthe conjoint analysis. FullProfile ConjointAnalysis The conjoint analysis will.utilize a full profile conjoint analysis, where respondents rank cards in the order of their preference. Past experiencel as well as existing literature indicates that the most successful conjoint designs limit each exercise to ranking 16 cards at a time with 4 to 6 features on each card. Including more cards or additional attributes tends to overwhelm respondents and results in less reliable data. To accommodate more features, such as the 8 to 12 mentioned in the original RFP, session participants may rank two different sets ofcards of4 to 6 characteristics each, with one or morefeatures, such as price, commonto bothtrades. This will allow the comparison oftwo separate models and allowfor more washer characteristics to be**evaluated~** The full profile conjoint design will utilize an orthogonal card design. The orthogonal design results in each of the card attributes being perfectly uncorrelated with the other attributes. This is critical to the analysis, as correlation across attributes results in a loss ofprecision and makes it difficult to estimate the importance that respondents place on each attribute. For example, consider the situation where price and rebate are two of the characteristics being evaluated and on each card rebate is 10 percent of the price. Since price and rebate are perfectly correlated, there is no way to determine from the data if a respondent is ranking the cards based on price or on rebate. For this reason, havinganorthogonally designed studyis essential. To address the likelihood of making a purchase, upon completing the rankings the respondents will be asked to determine those clothes washers that they would actually

Quantum Consulting Inc. 3 Technical Approachpurchase giventheir situation today. This is done by inserting a 'ParticipationCard' in the deck after the ranking is completed. Those cards ranked above the Partidpation Card are options that the respondent would purchase today while those below they would notpurchase. As discussed below, Participation Card placement will be used to estimatethe PurchasePropability. SampleDesign The conjoint sample will be designed to include a mix of people across age and income groups. Atleastpartofthe sample should contain people thathave recently purchased a newwasher orthatareplanningto in thenearfuture. These people aremostlikely to give answers that will match most closely with what they actually do in the market place. However, utilizing the Participation Card requires a sample ofrespondents who are not currently in the market for a clothes washer. Excluding these customers may bias the sample and ultimately make model predictions less accurate. Information obtained during the focus groups will also be used to determine if there should be any further sample stratificationfor the conjoint sessions. The conjoint sessions will be held in four different cities across the country: Washington, DC, San Francisco, CA, Madison, WI, and Dallas, TX. These cities have been chosen to mirror the focus groups sites and to ensure that a broad range of geographic regions are represented. Each session will _have 12 to 16 attendees with an average of 100 attendees for each city for a total of 400 conjoint session participants. Session participants will be recruited randomly by phone and will be paid a $50 incentivefor participation.

PURCHASEPROBABILITYCALCULATIONS As shown in Exhibit 2, the decision to purchase a new washer is divided into the purchase probability and the equipment choice probability. The results of the conjoint analysis will be. used to estimate both of these probabilities. Once the probabilities are estimated, the probabilities will be calibrated to the actual distribution of purchases observed in the market. Once calibrated, the estimate probabilities can be used to estimate how purchases of washer_s will change given changes in the equipment characteristics used in the conjoint analysis. This provides a real world context for the conjoint analysis results and provides a powerful tool for predicting future market behavior. The remainder ofthis documentdescribes howthe conjoint analysis data will be used to estimate purchase probabilities and calculate the elasticities associated with clothes washercharacteristics. : Exhibit 2 shows how the estimated probabilities will be combined with eligible market information to predict annual purchases. The first stage is multiplying the eligible market by the Purchase Probability to get the total number of washers purchased annually. Then, total purchases are multiplied by the Equipment .Purchase

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i Probabilities to get the number of annual purchases for each of the equipment options. This will give annual purchases for each of the standard and high efficiency washer options.

Exhibit2 Clothes WasherMarketPenetration,·ModelStru.cture

## Sig~e**~~ Ma~~** 

Purchase Probability The decision to make a clothes washer purchase is the Purchase Probability shown in Exhibit 2. This probability is estimated from the conjoint analysis data based on the placement of the Participation Card. Since the purchase choice variable is discrete, the model is estimated using a standard logit model specification. In this application, the dependent variable has a value of one for those cards placed above the Participation Card and a value ofzero for those below. The independentvariables are the values for the characteristics oneach card. Inequationform, this is givenby Purchase (0,1) =a+ LJB{Xijeii Where Purchase = 1 ifcardis ranked above theParticipationCard, 0 ifrankedbelow Xii=Value ofattributejoncardi Bj 

### =Coefficients to be estimated



### a 

## =Constantto beestimated

Quantum Consulting Inc. 5 TechnicalApproachSij = randomerrortermed assumedto be logistically distributed Using the estimation results from this purchase logit, the probability of making any equipmentpurchasesProb(Purchase) is givenby Prob(Purchase) = exp(J3'X) / (1 + exp(j3'X)) Equipment Choice Probability The second stage of the model is the choice of a specific equipment option given that a purchasewill definitelybe made, whichis the Equipment Choice Probability inExhibit 2. Using a slightly different version of the logit model, the rankings of the cards are regressed againstthe attributelevels on the cards. This has a different appearance than the standard conditional logit since the dependent variable is the actual ranking rather than a zero or one value.1 However, estimation ofthis model is conducted the same as with the more familiar logit specification. The equation to be estimated in this stage is givenby

WhereRanl<i**~** Theranking ofcard i Xii= Valuefor attributej fromcardi J3i =Coefficientstobe estimated Sij = Randomerrorterin assumedtobe logistically distributed Using these estimated results, a clothes washer choice set will be constructed that**re~ects** both standard and high efficiency equipment options. Using the logit density function, the probability of choosing any option n among M different equipment optionsgiventhata purchase**i~** madeis Prob(Equipn 

## I Purchase)= exp(f3'Xn) / }:M(exp(f3'Xm)) The Total Probability is thencalculatedbymultiplyingthePurchase Probabilityby the Eq1:1ipment Choice**Probability~** or Prob(Total) "."' Prob(Purchase) 

### *Prob(Equipn 

## I Purchase)

1 A more complete description of how ranked conjoint data can be analyzed using this logit specification is contained in "LogitModels for Sets ofRanked Items", Nicholos Christakis and Paul Allison, Sociological Methodology, Volume 24, 1996, pp. 199-228. ·

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Model Calibration· Both of the probabilities described above are determined using the conjoint analysis data. However, relying entirely on stated preference data can be risky, since there is always the chance that the decisions respondents make in a controlled experiment do notaccuratelyreflect whatthey would do inthe marketplace. To account for this, both estimated probabilities will be adjusted to match observed market data. The constant in the Purchase Probability will be adjusted so that the estimated probability of making a purchase matches the observed proportion in the population nationwide. Similarly, the Equipment Choice Probability will be adjusted so that the estimated, distribution across ·standard and high efficiency equipment options matches the actual distribution in the market place. This calibration adjusts the estimation results to account for unobserved customer characteristics that are not accounted for in the model. The result is a model that utilizes the tradeoff information obtained during the stated preference sessions while anchoring the model to observed marketbehavior. PURCHASESCENARIOS The estimated probabilities provide a variety. of options for estimating the effect of changes in equipment design due to an efficiency standard. In addition to elasticities, changes in purchases due to changes in equipment features will be estimated by examining how these changes affect the purchase probabilities. The analysis of purchase scenarios using both the elasticities and probability changes is discussed below. Probability Scenarios The probability estimates are used to estimate the change in purchases due to changes in clothes washer design resulting from an efficiency standard. To evaluate the effect these changes will have on purchases, the purchase probabilities will be calculated using the values for attributes of the original equipment before the standard. For example the pre-standard purchaseprobabilityis givenby Probi 

### =exp(13'X) / (1 + 13'X) Where**~** 

### =Estimated coefficients from the purchaselogit X 

### = Attribute values such as price, savings, axis orientation for the pre-standard equipmentdesign. This pre-standard purchase probability is compared with the post-standard purchase probability. The post-standard probability is calculated using the same formula with post-standardvalues for price, savings, and otherequipmentattributes

Quantum Consulting Inc. 7 TechnicalApproachProbi

### =exp(13'X) / (1 + 13'X) Where 

### p=Estimated coefficients from the purchase logit X

### = Attribute values such as price, savings, axis orientation for the post-standard equipmentdesign. · The effect of the standard can be shown by comparing the difference in purchase probabilitiesbefore and after the standard Effect ofStandard= (Probi

- Prob2)/ Probi. For example, if the pre-standard purchase probability is 0.15 and the post-standard purchase probability is 0.10 then the effect of the standard is (0.15

- 0.10)/0.15 or 0.33, which indicates a 33 percent reduction in the likelihood of purchasing this clothes washer. Since the adoption of an efficiency standard will likely increase the price of all clothes washer options, it will be useful to evaluate the effect of the standard on purchases while holding price constant. Holding price constant allows the evaluation of different equipment configurations that 

### will comply with the standard independent of price considerations. For example, suppose that there are two different equipment designs that comply with the new standard. The first option meets the standard utilizing a horizontal axis. The second option meets the standard using a vertical axis and no hot water option for the rinse and wash cycles. The same probability calculations can 

### be used to determine which equipment configuration will be more popular holding price constant ProbA1 = exp(f3'X) / (1 + exp(j3'X) Where

### f3 =Estimatedcoefficients from the logitpurchase model X =Pre-Standard equipment features (vertical axis) for choice A with the poststandardequipmentprice. The post-standard purchase probability for OptionAis givenby ·ProbA2 

### =exp(j3'X) / (1 + exp(j3'X) Where

## f3 = Estimated coeficients from thelogitpurchase_model X = Post-Standard equipment options (horizontal axis) with the post-standard equipmentprice. The same method is used to calculate pre-standard and post-standard purchase probabilitiesfor OptionBwithandwithoutthe hotwaterfeature:

Quantum Consulting Inc. 8 Technical Approach'i' ProbB1 = Purchase probability with**pre~standard** options (hot water) and post standardprice. ProbB2 =Purchase probability withpost-standard options (no hot water) and poststandardprice. The effect of the standard on these two different equipment options can be evaluated the same asbefore. Supposethatthe optionshave thefollowing probabilities: ProbA1 = 

### 0.20 ProbB1 = 

### 0.15 ProbA2 = 

### 0.10 ProbB2 = .010 The effect of the efficiency standard on Option 

### A is 

### (0.20

- 0.10) / 0.20 = 

### 0.50 or a 

### 50 percent reduction in the likelihood of making a purchase. Similarly, for Option B the change is a 33 percent reduction in the purchase probability. In this example, meeting the standard using the Option B equipment design utilizes more popular equipment features and willhave less ofa detrimental effect onpurchases. Elasticity Scenarios The sensitivity of purchases to changes in equipmentfeatures can also be evaluated by calculating the elasticity associated with each equipment characteristic. The purchase elasticities arecalculated usingtheprice andequipmentoptions aswell as the estimated purchaseprobabilities. For the elasticity withrespectto price, the elasticity is calculated by Price**Elasticity=(~ Prob/~**  Price)* (Price/ Prob) Where**~** Prob= Changeinpurchaseprobability dueto the change inprice**~** Price = Changeinpriceresultingfrom theefficiency standard Prob = Pre-standardpurchaseprobability Price= Pre-standard price. This number reflects how sensitive the purchase probability is to changes in price. In general, an elasticity with an absolute value of less than one is considered inelastic or insensitive to changes in price while an elasticity greater than one is elastic or sensitive to changesinprice. Elasticities can also be caiculated with any of the equipment options that were used in the conjoint analysis and the logit purchase model. For example, the sensitivity of the purchaseprobability to a horizontal versusvertical axis is determinedby

.

Quantum Consulting Inc. 9 Technical ApproachAxis**Elasticity=(~ Prob/~**  Axis)* Axis/ Prob Where**~** P = Change in the purchase probability due to a switch from a vertical to a horizontal axis**~** Axis = Chan_ge in axis orientation (denoted by a dummy variableinthemodel) Prob = Pre-standardpurchaseprobability Axis = Originalvertical axis orientation. By calculating the elasticities, the sensitivity of washer purchases to changes in each of the equipment attributes used in the purchase decision can be evaluated. However, each elasticity provides information on only one individual attribute while holding all other attributes constant. To evaluate the effect of changing several equipment attributes simultaneously, examining the change in overall purchase probabilities as described above is appropriate.

SUMMARY A combination of·focus groups and conjoint analysis will 

### be used to determine the clothes washer features that consumers value most and to estimate the effect that changes in these features due to an efficiency standard will have on clothes washer purchases. The attributes that are used in conjoint sessions will be determined from focus groups to ensure input from as broad a range of customers as possible. As described above, the conjoint analysis results will be used to estimate clothes washer_ purchaseprobabilities. The purchase probabilities will be used to evaluate how sensitive the purchase decision is to changes in the equipment attributes that are likely to be affected by an efficiency standard. The conjoint analysis and logit specification allows these issues to be addressed in a variety of ways. Purchase sensitivity will 

### be evaluated by calculating elasticities as well as looking at how purchase probabilities change with changes in equipmentfeatures dueto ari efficiency standard. Purchase scenarios willbeconducted allowing price to vary and with holding price constant. Holding price constant in the purchase scenarios provides an opportunity to evaluate changes in non-price attributes without having the change in price overwhelm the changes in other equipment features.

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