{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

S8MetricRelationshipAnalysis

# S8MetricRelationshipAnalysis - Metric Relationship Analysis...

This preview shows pages 1–3. Sign up to view the full content.

Metric Relationship Analysis In this spreadsheet we introduce you to ways of understanding relationships between supply and demand metrics. The first thing we must do is define a metric that measures the extent of a relationship. Think about the relationship between price (P) and quantity sold (Q). We know that as P is lowered Q increases. But how much do changes in price explain changes in quantity sold? How much does variance in P explain variance in Q? Is it 100% of the explanation? No, because we know that other variables such as the state of the economy also explain variance in Q. Could P explain 50% of Q? Possibly. It is this "relationship metric" called explained variance that we learn to think about in this worksheet. In the first worksheet below studying metric relationships, we look at the relationship between a brand's differentiation from its competition and its profit margins. We want you to learn to study the graph of the relationship and compute a correlation coefficient. The way you study the correlation between the metrics/measures is to compute a correlation coefficient that is called "r" for short. If r is close to 1 there exists an extremely high positive correlation between the measures. If r is close to zero there is no correlation. For example a correlation of 0.20 is a very weak positive relationship. If r is close to -1 there is an extremely high, inverse or negative relationship between the measures: when one is high, the other is low. A correlation coefficient of 0.7 when squared is 0.49 and this is the percentage of variance explained (49%). Please remember this way of describing a relationship: "the square of a correlation coefficient multiplied by 100 is the percentage of variance in one variable explained by variance in the other, and vice versa." A note about what percentage (%) means. 50% of something means half of something. 25% of something means one quarter of something. 0.81 of something is 81% of something. Below are presented three relationship studies where you will learn to understand graph analysis and explained variance analysis. Page 1 of 8 How to compute and see the relationships between marketing supply and demand metrics

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Is Product Differentiation Profitable? This relationship study is based on a report published by The Conference Board, New York, 1982, called Product Line Strategies. It studied 25 brands in packaged goods categories sold in supermarkets, and specifically, the relationship between consumer brand substitution (percentage of time brand is replaced by a substitute) which we call Sub% and the brand's net profit margin percentage which we call Margin%. The higher the margin%, the higher the brand's profit margin percentage. The theory of product differentiation says that the more you distance yourself from the substitution competition of rivals the greater your profit margins. You can test this proposition below. When Sub% is low, product differentiation is high so profit Margin% should be high. When Sub% is high, product differentiation is low so Margin% should be low.
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}