Market Potential and Sales Forecasting 10/3, 2017

Forecasts versus Potential Expectations Possibilities Firm/Brand Sales Forecast Sales Potential Category Market Forecast Market Potential

Major Uses of Potential Estimates To Make Entry/Exit Decision To Make Resource Level Decisions To Make Location and Other Resource Allocation Decisions To Set Objectives and Evaluate Performance As an Input to Forecast

New or Growing Product Potential Relative Advantage Compatibility Risk Role of Analogous Products

Mature Product Potential For a consumable product, repurchasing will be in proportion to the market need (if an industrial product) or usage rate (if a consumer product). For a durable product, repurchasing will occur to replace a worn-out product, to upgrade to get new features, or, importantly, to add an additional model.

Methods of Estimating Market and Sales Potential Analysis-Based Estimates Determine the potential buyers or users of the product Determine how many are in each potential group of buyers defined by step 1 Estimate the purchasing or usage rate Area Potential Breaking down total sales by area To use a weighted index Sales Potential

Analysis-Based Estimates During the 1990s, an average 4 million babies were born annually in the United States The average child goes through 7,800 diapers in the first 130 weeks of life (2.5 years) until toilet training, or 60 per week The annual market potential for disposable diapers is 28.7 billion [(2.3)(4 million) babies][60 diapers/week][52 weeks/year]

Area Potential An index approach for a hypothetical new copying system might be as follows: Bases: Percent population in the region (P) Percent schools in the region (S) Percent retail business in the region (RB) Percent banks in the region (B) Percent offices in the region (O) Percent warehouse in the region (WH) Percent manufacturing facilities in the region (MF) Percent other businesses in the region (XS) Percent other copier sales in the region (CS) Index = W1 P+W2 S+ W3 RB+W4 B+W5 O+W6 WH+W7 MF+W8 OB+W9 XS+W10 CS

Demand Analysis Demand for products or services can be measured at two levels aggregate demand: for an entire market or country company demand: represented by actual sales Both market and sales potential can be viewed as a filtering process (Robinson 1984) Potential need Felt need Potential demand Effective demand Market demand Sales potential

Analysis by Inference Market assessment by inference uses available facts about related products or other foreign markets as a basis for inferring the necessary information for the market under study Related products Related markets’ size Related environmental factors Analysis of demand patterns

Cross-Sectional Comparison The assumption that there is an analogy between the relationship of a factor and demand for a particular product or commodity in two countries Let XA = demand for product X in country A YA = factor that correlates with demand for product X in country A, data from country A XB = demand for product X in country B YB = factor that correlates with demand for product X in country A, data from country B If we assume that : XA/YA = XB/YB and if XA, YA, and YB are known, we can solve for XB as follows: XB = (XA)(YB)/YA

Displacing Time Displacing time is a useful method of market analysis when data are available for two markets at different levels of development The assumption that an analogy between markets exists in different time periods Let XA1 = demand for product X in country A during time period 1 YA1 = factor associated with demand for product X in country A during time period 1 XB2 = demand for product X in country B during time period 2 YB2 = factor or factors correlating with demand for product X in country A and data from country B for time period 2 If we assume that : XA1/YA1 = XB2/YB2 and if XA, YA, and YB are known, we can solve for XB as follows: XB2 = (XA1)(YB2)/YA1

Market Size Assessment When using market size estimates, keep the following rules in mind: Use several different methods. Don’t be misled by numbers. Don’t be misled by fancy methods. Do a sensitivity analysis by asking what-if questions. Look for interval estimates with a lower and upper limit rather than for point estimates.

Major Uses of Sales Forecasting To answer “what if” questions To help set budgets To provide a basis for a monitoring system To aid in production planning By financial analysts to value a company

Forecasting Methods (I) Judgment-Based Methods Naïve Extrapolation Sales Force Composite Jury of Expert Opinion Delphi Method

collection of responses collection of response collection of response Delphi Forecasting Questionnaire Data feed-in (Numerical & graph) Formulation of first round questionnaire Expert panel selection Distribution and collection of responses Statistical analysis Formulation of second round questionnaire Distribution and collection of response Edit relevant opinion Data requested for search, collect, edit Statistical analysis Distribution and collection of response Statistical analysis Final estimation and circulation

Forecasting Methods (II) Customer-Based Methods Market Testing Mall Intercept Surveys Focus Groups Product Concept Tests Market Surveys The top-two-boxes scores (the number of customers who state they will either definitely or probably buy the product) Purchase intentions and actual behavior

The Top-Two-Boxes Score The number of people who definitely would buy or probably would buy are usually combined and used as an indicator of group reaction Definitely would buy Probably would buy Might or might not buy Probably would not buy Definitely would not buy

Estimating the Quantity The quantity of the product expected to be sold during a time period is Q: Q = N  A  P N: the number of potential customers expected to make purchases during the time period A: the fraction of these potential customers or purchases for which the product is available and the customer is aware of the product P: the probability that the product is purchased if available and if the customer is aware of it. P = Cdefinitely  Fdefinitely + Cprobably  Fprobably

Forecasting Methods (III) Sales Extrapolation Methods Moving Averages Exponential Smoothing Regression Analysis

Forecasting Methods (IV) Model-Based Methods Regression Analysis Leading Indicators Econometric Models

The A-T-A-R Model The A-T-A-R concept (awareness-trial-availability-repeat) Diffusion of innovation How we forecast sales and profit on a new item

The A-T-A-R Model Profit = Unit sold * Profit per unit Unit sold = Number of buying units * Percentage who become aware of the product * Percentage who opt to try the product if they can get * Percentage of intended triers who can get the product (it is available to them) * Percentage of triers who like the item enough to repeat their purchase * Number of units that repeaters will buy in a year Profit per unit = Revenue per unit (unit list price less trade margins, promotional allowances. Freight, etc.) - Costs per unit (usually costs of goods sold per plus direct marketing costs) Therefore: Profits = Buying units * Percent aware * Percent trial * Percent availability * Percent repeat * Annual units bought * (Revenue per unit - Costs per unit)

A-T-A-R and the Market Testing Methods Sources of This Information Information Needed by the A-T-A-R Profit Forecasting Method Pseudo sale methods Controlled sale methods Full sale methods Number of market units Market Research studies Market Research studies Yes Awareness of the new product positioning claim (A) Ad agency provides it Ad agency provides it Yes Decides to try the item (T) Yes Yes Yes Distribution estimates provided by sales dept. Distribution estimates provided by sales dept. Is able to get a trial supply (A) Yes Likes it and wants more (R) Yes--in sales wave Team estimates Yes Units used per year Yes--est. Yes--est. Yes Profit per unit (price-cost) Price plan plus estimates from accounting on costs Price plan plus estimates from accounting on costs Yes Additional diagnostic info. Yes--a little Yes--more Yes--tons