New Product Management exam 3
Competent product testing can do
a great deal to ensure that the product is well received, so that obtaining trial is often the more difficult problem
Consumers evaluate the
alternatives in the market by examining how much they offer on the various attributes and how critical each attribute is to them
Avaialbity and awareness number are provided by
another firm that is experienced in that part.
A decision to develop a product has to
be based on an estimate of the sales potential of the new product
Baseline are the
columns that are missing . Can be all zeros
Avoid situations where
combinations of attribute levels result in unrealsitic profiles
Attributes are
features and characteristics that define various aspects of a product or service.can be tangible and intangible
low trial can result
from poor advertising copy, unattractive packaging, a weak product concept, or inferior distribution (e.g., penetration, shelf positioning, facings)
a company is less likely to reformulate a product and retest if it
has failed in the eyes of many consumers
Profiles are
hypothetical products or services desired as bundles of attributes that each set of a particular level
ranking
is better than rating because it gives you more information. To invert ranks to utility points, you invert the ranking numbers so 1 become 5 You create an average for each row and column Then you add the averages that correspond. Like the medium row and column
Sales potential
is them completing the sales
Status quo affect
is when you are happy with what you have.
If the problem is low repeat,
it may take months for the product to fail (because trial may overshadow repeat as a cause for sales in the early stages of introduction), making the failure all the more noticeable when it finally occurs
Pretesting is
looking at response to different price levels. Involves combining the elements of the marketing mix but not putting it out in the market to test. Keeps things hidden and away from competitors. More like experimentation. Involves: input ▪ Physical product ▪ Ad copy ▪ Packaging ▪ Price ▪ Selling ▪ Channels of distribution strategy ▪ Service policies ▪ Ad and Promotion budget. You test the inputs jointly. Justified if ▪ Sufficiently accurate predictions can be achieved ▪ Timing is before large resources are committed ▪ Suggests improvements( can find out what went wrong and can fix it) ▪ Reasonable cost
Low market share for a new product may be the result
of low trial rates, low repurchase (repeat) rates, or both
Pretest marketing can
pinpoint problems early so that they can be corrected
Consumers look at
what they are gaining
Alternative Approaches Judgment and Past Product Experience
A reasonable pretest-market approach is to examine past experience to determine what characteristics of a new product determine its success ▪ A model is developed, which establishes relationships between critical factors and consumer response- usually using regression ▪ Advantages ▪ Once estimated, model can predict rapidly and at low cost ▪ Alternative plans can be tried and the best chosen ▪ Disadvantages ▪ Sensitive to expert judgments and past products used ▪ No underlying theory- hence not generalizable to other categories
Assessor Process
A sample of consumers are surveyed to measure product awareness, preferences, and purchase patterns ▪ Subjects are then shown advertising for a new product and its major competitors ▪ They are given a sum of money sufficient to purchase the new product, and then enter a simulated shopping facility where the new product and competitive brands are displayed ▪ Once they have had time to sample the product, the subjects are interviewed about their repeat purchase intentions ▪ Measures of consumer response and post-trial preferences are used to forecast the product's expected market share and to diagnose product problems. The long run market share of a new product = trial rate x repeat rate x usage rate index
Trial/Repeat Measurement
Accurate forecasts of long-run sales can be made if the pretest analysis can predict the percentage of consumers who will try the product (cumulative trial) and the percentage of those who will become repeat users (cumulative repeat) ▪ Advantage ▪ Based on direct response of the consumer to the new product ▪ Disadvantage ▪ May still not be representative of a test-market or a full-scale launch
Long Run Share of Purchases
An Approximation Method ▪ When products depend on repeated purchase (e.g., consumer packaged goods), the long run share probability must consider both trial and repeat rates ▪ Sales forecast P= N p a w a vT R, where ▪ aw = awareness of new brand ▪ a v = availability of new brand ▪ T = ultimate proportion of the target group who would try the product conditioned on awareness and availability ▪ R= long run share of purchases of the new product among those customers who try the product ▪ N p= number of customer purchases in category per period
Methods of estimating purchase potential: Intent Scale
An Example ▪ If you selected the Acura RDX as the most preferred car, which one of the following statements reflects how you feel about your choice?. Check one ▪ I definitely would buy the Acura RDX ▪ I probably would buy the Acura RDX ▪ I might buy the Acura RDX ▪ I probably would not buy the Acura RDX ▪ I definitely would not buy the Acura RDX
panel Measures
An approach to forecasting with a trial/repeat model is based on direct measures of trial and repeat in a sample of households ▪ Advantages ▪ Household panel approximates the actual product launch ▪ Disadvantages ▪ Need to run for a long duration ; high cost. Getting responses from the same people over time.
Analyzing Test Markets
Awareness, Intent,Search,Trial,Repeat
Attitude
Change Models Estimates behavior from consumer preferences ▪ Process ▪ Consumers' attitudes are measured for existing products ▪ After consumers use a new product, attitudes are measured for the new product ▪ Advantage ▪ Avoids lab effects ▪ Disadvantages ▪ Not direct measures and can affect accuracy More recent commercial pretest-market systems integrate laboratory preference change and dynamic trial/repeat models to make forecasts based on measures collected in UPC-documented or internet based consumer panels. Looking to see if there is a change in the attitude. Attitude leads to a choice.
Loss aversion leads to endowment effect:
Consumer overvalue benefits they posses relative to those they do not Hence, status quo bias: investments, automobiles, jobs Intensifies over time Worsens with increase in the number of alternatives
Lab Measurement
Consumers are recruited, exposed to the Ads, and given the opportunity to buy in a simulated retail store ▪ Basic Idea- Force exposure to the product and provide a realistic purchase choice environment( shop shelf) ▪ Success depends on minimizing bias or correcting any bias ▪ Advantages ▪ Rapid results ▪ Low cost ▪ Accurate ▪ Disadvantages ▪ Systemic biases due to unreal situation. You give them the product and they use the product and they give you a response. It is a simulated experiment. Can not be sure about eh accuracy of the results.
▪ Perceptual Maps
Define market gaps
Grouping customers based on the relative importance (or weights) they place on product attributes leads to
Demographic Segmentation
Preference Analysis
Design the best product that appeals to consumers
Experimentation
Emphasis of test markets now shifting to profits ▪ Diagnostic information from experimentation can help gauge profitability ▪ "Controlled Store" test marketing ▪ Product is forced in specific stores and only store advertising, promotion and display are used to build trial ▪ Focus on repeat sales and usage rates ▪ Can vary marketing mix elements (can add couponing and sampling in some stores) ▪ UPC scanners & Online data ▪ Trial, repeat and continuous store sales data are available
Companies and behavior change:
Endowment effect in reverse _ consumers think what they own is to much and don't want to give it up Executive bias in favor of new product. Overvalue innovation 3x3=9x meaning you the manager should think the product is 3x as valuable and you the manufacture should think of the product as 3x as valuable Low degree of behavior change and low degree of product change is an easy sell Low degree of behavior change and high degree of product change is smash hit High degree of behavior change and high degree of product change is long hauls High degree of behavior change and low degree of product change is sure failures.
Summary of Sales-Formation Models
Estimates of purchase potential are modified because of complexities that occur in new product introductions ▪ The first modification is for awareness and availability ▪ Purchase usually does not occur unless consumers know about the new product and can obtain it if they want ▪ The second modification is for dynamic effects ▪ Long-run sales are not obtained immediately ▪ Trial/repeat phenomena and diffusion phenomena modify sales estimates based on short-run observations
Methods of estimating purchase potential:Intent translation:
Evaluate the purchase potential of the product based on a concept or the actual product ▪ Intent scales; customers are simply asked to make a subjective estimate of their likelihood of buying the new concept ▪ Translate customer responses to estimates of probability ▪ Linear or nonlinear relationships could exist between actual and stated probabilities of purchase
Preference-Rank Order Transformation
Everyone does not select their first preference, some will select their second or even third preference product ▪ Preference-Rank translation assigns probabilities according to whether the new product is ranked first, second or third, and so on ▪ Example: For deodorants, 83 % of first preferences, 15 % of the second preferences and 2 % of the third preferences purchased the product ▪ By using this method, we can convert easily measured preference for concept descriptions directly into purchase estimates ▪ However, this method ignores the intensity of preference
GO/NO GO Analyses-Quantifying Risk and Expected Benefit
Expected Benefit- Expected level of profit discounted by the organization's target rate of return ▪ Risk- Standard deviation of the discounted profit ▪ GO if the probability of achieving the target ROI > = cutoff level X ▪ NO GO if the probability of achieving the target ROI < = another cutoff level Y
Conjont can be used to figure out what :
Features should we include in our products/services How much as consumers willing to pay for each feature of our product/service. How much would they be willing to pay extra for an improvement on an existing characteristic What marketshare should we expect to obtain if we launch product/service x How should we price our products/ services How do consumer differ in their preferences How do we segment What different products/services should we offer
Forecasting purchase potential: methods
Final success= function of actual purchase behavior ( final success is not merely a function of product preference) Purchase potential is experiment based and research based. Sales potential is when they actually end up buying your product and that they are aware of your product Sales potential is a fraction of the purchase potential To predict demand we need to estimate how preferences translate into actual product choice Two methods: Measurement based; an actual measurement of customer intention to buy ( as stated by the customer) Model based; a set measurement of preferences , intentions and actual product choice are made. These are then analyzed to develop a predictive analytical model.
GO/NO GO Analyses
GO - Go to full scale launch ▪ NO GO - Drop the product from further consideration ▪ ON GOING- Collect further data and attempt to improve the product
Useful frame work
Identify the extent of the problem: List what consumers are gaining List what consumers are losing Consumers undervalue by up to 3 times Executives overvalue by 3 times Hence the 3*#=9x Factor Solution Offer significant product benefits( creating value) Maximize value captured by accepting or minimizing resistance Accept : be patient, strive for 10x , eliminate the old ( headphone jack ) Minimize: provide compatibility with existing behavior, seek non users ( unendowed), find believers.
Summary of Purchase Potential Models
Intent Scales ▪ Direct measure ▪ Not exact, but provides a good indication of behavior ▪ Preference Rank Order Transformation ▪ Useful because it is based on multi-attributed preferences ▪ May miss extraneous events not modeled in the preferences ▪ Logit Model ▪ Sophisticated and accurate ▪ Used later in the design process ▪ Can update/confirm predictions made by earlier analyses Note : A Sound practice is to use multiple techniques to get the best estimates of purchase potential
Methods of estimating purchase potential
Intent translation: Intent scales( how likely are they going to buy the product Modified intentions( how likely are they going to buy based on the attributes) The probability scale
Replication of National Environment
Issues (demographics, size, mix, sales effort, media spending, etc...) ▪ GRP-Reach * Frequency ▪ Shortcuts- use mini cities to reduce cost ( at the risk of reducing reliability) ▪ Audit retail-store inventory changes and shipments from wholesalers ▪ Observe for 9-12 months and project to a national level.▪ Advantages- Simplicity , Low cost of analysis. Disadvantages -Anomalies that make projections inaccurate, Missed opportunities because of lack of diagnostic information
Logit Analysis
Logit Analysis establishes a mathematical relationship between relative preference and probability of purchase ▪ The technique uses intensity information contained in preference values to produce accurate estimates of purchase probabilities ▪ The input data for this analysis are: -- Measured preferences and -- Observed product choice ▪ The output of the logit model indicates, for each customer , the estimated probability that the customer would buy the product ▪ Thus, we are able to arrive at an estimate of how many consumers are likely to purchase the product ( For example, if the estimated probability for a customer is greater than 0.5, we may conclude the customer would buy. Conversely, if the estimated probability is less than 0.5, we conclude the customer would not buy the product)
Gains and losses:
Losses looms larger than gains. Loose of the same amount hurts you more than the gains.
Methods of estimating purchase potential :intent Translation-The 90-40-10 Rule
Market research among people who stated the Acura RDX is their most preferred car indicated that 10% of them would 'definitely' buy the Acura RDX, 20 % would 'probably' buy the Acura RDX, and 30% 'might' buy the Acura RDX ▪ A common rule of thumb used to estimate actual purchase behavior is the 90-40-10 rule ▪ It states that 90% of the 'definites', 40% of the 'probables' and 10% of the 'mights' will actually purchase the product, once it is available ▪ The overall estimate for the Acura RDX can be calculated as follows: (0.90) (0.10) + (0.40) (0.2) + (0.10) (0.3) = 20% ▪ Hence, we will conclude that only 20% of the consumers who prefer the Acura RDX will actually buy the product
Intent Translation: When several design options need to be evaluated
Modified Intentions ▪ Used to find the effect of design changes on the intention to buy ▪ Intent questions are asked for each design ▪ The limitation is that only a few designs can be tested
The ASSESSOR Model:
Objectives is to Predict the new brand's equilibrium or long-run market share ▪ Estimate the sources of the new brand's share- "cannibalization" of the firm's existing brand (s) and "draw" from competitors' brands ▪ Produce actionable diagnostic information for product improvement and for developing advertising copy and other creative materials ▪ Permit low-cost screening of selected elements of alternative marketing plans (advertising copy, price, and package design. Test marketing is an expensive way for a manufacturer to detect an unsuccessful product ▪an integrated modeling and measurement system provides management with predictive and diagnostic information about the sales potential of new packaged goods before test marketing begins.
GO/NO GO Analyses- Interpretation
Outcome 1- Test Market has predicted better forecasts and has identified sufficient improvements in the marketing-mix to move the product past the decision frontier ▪ Outcome 2 - Test Market has reduced uncertainty and improved the marketing-mix, but there is still too much risk in launching the product- Hence, consider further analysis ( not a GO) ▪ Outcome 3- Drop the product from further consideration
Pre-test Marketing- Strengths
PTM can reduce the cost of developing and introducing new products (i.e., less "finished" advertising, limited media expense, only small quantities of product needed ▪ PTM provides more timely data (weeks rather than months) and diagnostics to help improve the concept product and/or its marketing plan (e.g., packaging, positioning, advertising, pricing) ▪ Such research is easier to keep secret from competitors and/or minimizes their inference.The existence of models potentially allows "optimization" of certain aspects of the marketing mix ▪ By providing a framework for analysis, such models increase managerial understanding of (and involvement in) the new product introduction process ▪ They provide a way, for example, of making use of particular historical data (e.g., product category norms) and, later, more new product-specific data in a meaningful way
Pre-test Marketing- Weaknesses
PTM models do not address potential problems in implementing marketing decisions (e.g., trade acceptance or support, sales force acceptance, delays in manufacturing or delivery, etc.) ▪ Competitive reactions are essentially not considered; changes in economic conditions are ignored ▪ Some model parameterization is based on judgments that may not prove valid because, with new products, they may extend beyond the manager's direct experiences. STMs ( Simulated Test Markets) are unrealistic and unrepresentative and thus may lack validity ▪ PTM models are less applicable to minor line extensions and more difficult for "new-to-the-world" products or products that may be not prove valid because, with new products, they may be faddish or have irregular usage patterns ▪ It may be difficult to provide "go/no go" advice for products that can succeed with very low volumes or market shares ▪ PTM models are also less applicable when the new product is to be sold in outlets other than supermarkets or drug stores
People evaluate products:
Perceived value ( not actual value) Against a reference point ( a product they are familiar with ) In terms of gains ( improvement over reference product) and losses ( shortcomings ) Gains must outweigh losses by a factor between 2 and 3 before a bet becomes attractive
Diffusion of Innovation: Incorporating the Diffusion Effect while estimating Sales Potential:
Problem of innovators - imitators (Recall the Bass Model) -- Short run responses are from innovators -- Consequence; seriously underestimates the long run adoption level ▪ So, measured responses to new product concepts and physical products must be modified in cases where a diffusion of innovation process takes place ▪ An analogous product can be chosen and a Bass Model can be fitted to sales data ▪ The estimates from the Bass Model are then adjusted to reflect the differences between the new product and the analogous product originally used to estimate the Bass Model
The New-Product Introduction Process-the NEWS Model:
Promotion Advertising Media weight ,Brand Awareness, Trial, Initial Repeat purchase ( look at the volume), Continued Repeat purchase , Sales and Market share,Distribution. Model tells you how much money you will be spending. And what kind of promotion you will be running. First test for trial rate.
What companies do not anticipate :
Psychological costs Consumers overvalue benefits they posses relative to those they do not Marketers overvalue benefits of new products over the advantages of incumbent products. Consumer reject genuinely better products
Test Market Strategies
Replicate National Environment ▪ Experimentation ▪ Behavioral Model based Analysis Strategies vary due to the different emphases on risk reduction and diagnostic information
profiles
Select profiles in which the attributes have enough variations to be able to quantify the link between attributes and preferences and capture important tradeoffs. Need sufficient variation amongst the profiles. number of profiles should be large enough to allow for estimating all the partworth of some reliability but not too large in order to limit the burden impose on respondents
Probability of Trial Based on Customer Response to Intent Scales
The Jamieson and Bass study illustrates the average relationship between stated intentions and actual purchase probabilities during a period of six months ▪ However, the percentages should not be used blindly ▪ The percentages vary across product categories ▪ The six month period is likely to capture less than two-thirds of eventual product trial ▪ Any rule of thumb ▪ Should be used to obtain initial estimates only ▪ Should be based on studies of past products and managerial judgment
Panel Data Projection Methods
This first step forward analyzing a test market is to decompose total sales into trial and repeat as shown in the figure (see next slide) ▪ Trial - and - repeat measures are used for early projection of results ▪ Unfortunately, in many cases the sales rate drops because although trial is high, repeat purchase is low
From Purchase Potential to Sales Potential:
To accurately forecast sales as opposed to potential, we must modify the probability of purchase estimates by the probabilities that the consumers are aware of the product and it is available to them
In order to obtain the long run share of a repeatedly purchased product, we need to know the following: (Choose the best answer
Trial rate in the first six weeks after launch
People demand two to four times more compensation to give up products they already possess than they are willing to pay to obtain these items in the first place. (See "Eager Sellers , Stony Buyers: Understanding the Psychology of New Product Adoption" in the course reserves section)
True
Models of sales formation:
Use existing product Become aware of new product new product is available Buy new product
Convergent Measures and Model
Use more than one method in parallel and compare the results ▪ Advantage ▪ Greater accuracy ▪ Disadvantage ▪ Higher cost
Behavior changes entail cost :
What companies anticipate( economic switching cost) Transactions cost ( activation fees) Learning costs( self driving cars) Obsolescence costs ( hybrid cars)( something that you are using today will no longer be in use when you move from another product)
Forecasting Sales Potential: Decision areas are
Which features need to be updated in the next generation ▪ Which features can wait for succeeding generations ▪ How much effort should be allocated ▪ What is the price at which to sell the update
Test marketing is
rolling out the product in a small certain section , replicating what you would like to do in a national level.Big disadvantage of test marketing is cost and competitors will know what you plan to do .Test marketing gives you the response from a consumer in a real life setting.A major investment to acquire market information ▪ Reduction in risk and improvement in product is expected to offset the cost ▪ Decision areas ▪ To undertake a test market or not ▪ How to achieve the greatest ROI in the test market ▪ Go/no go with the final launch. Decision Making ▪ Assumptions ▪ Success is worth $ 10 million if not test marketed ▪ Success is worth $ 10.5 million after improvement by test market ▪ A failure loses $ 5 million ▪ A test market costs $ 1 million
Total value of product ( utitlity)=
sum of subvalues( partworth/utils) of its attribute levels to the individual
Baseline level
the level of each attribute for which the partworth is arbitrarily set to a value of 0. the baseline profile would be the profile for which all levels are set to 0
Low repeat often means that
the product itself has some major problems (at least relative to what advertising and promotion claimed)
purchase potential is
the that they are interested in the product and might buy it
Partworths
the utility of each attribute level. The utility of a profile is the sum of the partworths of the attribute levels in the profile
Utility
the value of a specific profile. utility= U(brand orgin)= u ( body style)+ u( engine type)+ u ( price)
Levels are
the values that each attribute may take
Diagnostics are
things that are going wrong
Limit the number attributes
to the 4-6 that are likely to be important to consumers and most informative to the company
Companies tend to
underestimate the psychological toll a new product could have on the consumer
To accurately forecast sales as opposed to potential,
we must modify the probability of purchase estimates by the probabilities that the consumers are aware of the product and it is available to them
The unendowned effect is
when they are not attached to a system like being attached to a mac
