Final Flashcards
Forecasting
Estimating future conditions. Tools and techniques to estimate future conditions
Predictive Analytics
Estimating future conditions
Data Mining
Extracting patterns from data
Balanced Scorecard
Establishing and tracking strategy and operations metrics
Critical Success Factors
Defining and measuring business objectives
Product
Companies apply forecasting to tell them how many units to manufacture
Price
Organizations exercise forecasting to predict the break-even point for a given price
Place (Distribution)
In order to specify the type of distribution channel to use, we need to anticipate the volume of goods we expect to sell, and for that we need forecasting
Promotion
Companies apply forecasting for promotion, so they can select relevant media. For example, we would select direct marketing media for high volume products
Sales
Organizations forecast future sales so they can track actual sales with expected sales
Support
Companies need forecasting information so they can staff support centers with sufficient personnel to manage the expected number of customers
Sales forecasts
Predict the volume of products and services expectedd to be sold by a given organization for a given time period, generally one year.
Time Series
Studies sales history to date to extrapolate future sales
Causal Analysis
Which examines underlying causes to calculates future conditions, given certain inputs
Trial Rate
Uses market survey of initial trials of new products and services to predict future market share
Diffusion Models
Predict adoption rates of new products and serves by comparing their characteristics to pervious products and services
Degree of Accuracy
The first criterion to apply when deciding on which forecasting method to use is the degree of accuracy required. For example, the causal analysis approach lends a fairly high degree of accuracy to its estimates, because it examines the underlying causes driving sales trends
Availability of Data
The second criterion is the availability of data. Casual analysis methods require significant
amounts of historical data because the methods consider the impact of multiple factors. Diffusion models do not require a sales history to predict future sales.
Time Horizon
The third criterion is the time horizon for the sales forecast. For example, the validity of forecasts based on time series models erodes when extended time periods, such a multiple years. Causal analysis methods work better for long time horizons because the root causes it leverages are less likely to change over time. Nevertheless, all forecasting methods lose accuracy over long time periods
Life Cycle Stage
The fourth criterion is the position of the products or services in their life cycle. For example, diffusion models can work well for the introduction and early growth life cycle stages because they forecast adoption rates based on the adoption rates of past products and services with similar characteristics. By contrast, time series methods are best suited for the maturity stages in the product/service life cycle, when sales trends are more stable
Resources
The fifth criterion is the availability of time and money resources. For example, trial rate methods to predict market share for new products and services can lend higher accuracy than diffusion model methods. However, trial rate methods cost more (due to the cost of market surveys) and take longer (due to the time to conduct the surveys and analyze the results). Similarly, causal analysis methods can hold greater accuracy than time series methods, but require a much greater time to gather, analyze, and interpret the data.
Time Series Forecasting
One of the most popular approaches. This series approach determines the underlying trend over time, and continues that trend to predict future conditions. Marketers frequently use the time series approach to predict sales volumes expected for their organization for the coming year (or quarter), based on sales data for the past few years.
Time Series Forecasting - Advantages
- The only data required is a record of sales volume over time, which is some of the easiest data to gather.
- It is also intuitive
- The technique is useful as a “sanity check” to confirm the accuracy of more sophisticated forecasting methods.
- Biggest advantage is it’s ability to capture all of the underlying market drivers and forces.
Time Series Forecasting - Disadvantages
Dynamic market forces can cause trends to change. Therefore, we cannot state with certainty that trends affecting the sales volume will continue forever.
Linear Regression
Sales = (Intercept)+(Slope)*(Time, in periods)
Causal Analysis Forecasting Method
- Technical analysts concern themselves primarily with how stock prices fluctuate over time. In technical analysis, causal factors play a secondary role.
- Causal analysis seeks to find the underlying factors that explain behavior.
Causal Analysis Forecasting Method - Advantages
If we can show which variables drive sales growth, we have unlocked a powerful advantage.
Causal Analysis Forecasting Method - disadvantages
causal analysis takes more work to execute. We need to study multiple variables and determine their effect on sales.
Casual Forecasting Formula
Sales = (Intercept) + (Coefficient 1) * (Market Awareness) + (Coefficient 2) * (Number of Locations)
Time Rate Calculation
Time Rate = (Number of First-Time Purchasers or users in Period t) / (Population)
Repeat Rate Calculation
Repeat Rate = (Number of Repeat Purchaser orUsers in Period t) / (Number of first-Time Purchasers or Users in Period t-1)
Penetration
The total number of people who have purchased the product or service at a given time.
Penetration Calculation
Penetration in Period t = [Penetration in Period (t – 1)] * (Repeat Rate in Period t) + (Number of First-Time Purchasers or Users in Period t)
Projection of Sales
Projection of Sales in Period t = (Penetration in Period t) * (Average Frequency of Purchase) * (Average Units per Purchase)
The Trial Rate Calculation
Trial Rate = (Number of First-Time Purchasers or Users in Period t) / (Population)
Trial Volume Calculation
Total # of units we can expect to sell to the population over a given period.
- Trial Volume = (Population) * (Units per Purchase)
Intention to Buy Question
Questions obtain information about the respondent’s likelihood of purchasing dog grooming services from company
Awareness Questions
Questions asked about the respondent’s awareness of the company’s brand as compared to it’s competitors
Availability Questions
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To Calculate repeat volume
Repeat Buyers = (Trial Population) * (Repeat Rate)
To Calculate the sales volume repeat buyers generate,
Repeat Volume = (Repeat Buyers) * ( Repeat Unit Volume per customer) * (Repeat Occasions)
Diffusion Models Forecasting Method
- Forecast demand for fundamental new innovations
Innovators
Innovators are the first people to adopt an innovation. They tend to have a high tolerance of risk, allowing them to adopt innovations which may ultimately fail.
Early Adopters
Early adopters also adopt new innovations readily, but not as fast as innovators. Early adopters tend to hold a high degree of opinion leadership, fielded by their passion for new innovations
Early Majority
Early majority individuals adopt new innovations significantly more slowly than innovators and early adopters. They represent the beginning of the mass market adoption of the innovation
Late Majority
Late majority individuals hold a high degree of skepticism on new innovations. They adopt only after the majority of society has already decided to do so.
Laggards
Laggards are the last segment of individuals to adopt a new innovation. They actively dislike change and prefer instead to stick with established, traditional methods.
Diffusion Driven by Imitators
Imitators will play a larger role for innovations involving networks effects and infrastructure investments.
- The network effect refers to innovations requiring networks to function properly.
S-Curve: Imitator-Driven Adoption
Where adoption of products and services starts out very slowly (b/c imitators do not adopt until other do). But once adoption starts in earnest, growth proceeds rapidly. As adoption continues, growth slows down and adoption asymptotically approaches 100% of the innovation’s target market. The vast majority of adoptions follows this type of adoption profile.
Diffusion Driven by Innovators
Research shows that innovators play a greater role in some situations and cultures. Much of the role is based on supporting the innovators’ tolerance for risk, so they are in a better position to take the risks new innovations bring. In general, the following situations and cultures tend to favor innovators over imitators:
Creaming Pricing (Also Skimming)
Set prices high during the intro of a new product or servie. Only to target the top 1-5% of the market. People who will have a low sensitivity to price
Demand-Based Pricing
Set prices to maximize profit profit, based on consumer demand for the product or service.
More quantity = Lower cost and vice versa
Economics tells us that for most goods, quantity demanded increases as we decrease price, and vice versa. Economists call this relationship the demand function, plotted out as the demand curve.
Demand-Based Pricing Advantages
It’s an effective method to maximize long-term profit
Demand-Based Pricing disadvantages
Can be time consuming and expensive since they need to know what the demand is
Everyday low pricing
sets prices consistently low to attract price-sensitive customers and increase sales quantities. The technique avoids deep discounts and sales promotions.
Going Rate Pricing
Companies align their prices with those of competitors and adopt a so-called market price. Companies will charge nearly identical prices to similar goods.
Ex: Gas stations
