Marketing Analysis (A Level) (Cambridge (CIE) A Level Business): Flashcards

There is no past sales data, so it is hard to estimate how price changes will affect demand for a product that has not been sold before.

False.

PED can change as customer preferences or incomes change. Demand may be inelastic in the short term but more elastic in the long term as customers adjust.

PED does not consider how rivals respond to a price change. If rivals also cut their prices, demand may stay unchanged.

PED only looks at price and demand, but factors such as income, trends and marketing also affect sales, so actual demand may differ from the prediction.

Income elasticity of demand (YED) measures the responsiveness of demand for a product to a change in income levels.

YED = % change in quantity demanded ÷ % change in income

A normal good is a good where demand rises when income increases.

False.

Demand for a luxury good increases more than proportionally as income increases, so its YED is greater than 1.

A good with a negative YED value is an inferior good. Demand for it falls when income rises and rises when income falls.

A YED between 0 and 1 shows a necessity good. Demand changes only slightly when income changes, so it is income inelastic.

Quantity demanded falls by 40% and income falls by 10%, so YED = −40% ÷ −10% = 4.

True.

Wages usually fall in a recession, so demand for inferior goods rises while demand for luxury goods falls.

As incomes fall, demand for products with a high positive YED may fall sharply, so lower prices or discounts may be needed to maintain sales.

Necessities have a low YED close to zero, so demand is less sensitive to income and stays fairly stable when incomes fall.

A business can expand capacity for products with a positive YED when incomes are rising, and for inferior goods when incomes are falling, so it can meet the expected change in demand.

It is hard to know when or by how much incomes will rise or fall, so demand forecasts based on YED may be wrong.

False.

Different customer groups react differently, because not everyone's income changes in the same way. Average YED data may include groups the business does not target.

YED may be misleading in the short term because customers often delay spending after a change in income.

A product seen as a luxury today might become a necessity later. For example, smartphones were once luxuries but are now widely considered essential.

Promotional elasticity of demand measures the responsiveness of demand for a product to a change in the amount of money spent on promoting it.

Promotional elasticity of demand = % change in quantity demanded ÷ % change in promotional spending

It helps decide whether extra promotional spending is worthwhile, the effect on demand of cutting promotional spending and the best use of promotional budgets.

It is expressed as a ratio, such as 1.2 : 1, showing the percentage change in demand for each 1% change in promotional spending.

Promotional spending rises by 25%, so promotional elasticity of demand = 30% ÷ 25% = 1.2 : 1.

False.

A value below 1 means demand is unresponsive (inelastic), so demand rises by a smaller percentage than spending. Extra spending is unlikely to be worthwhile.

When promotional elasticity of demand is less than 1, reducing promotional spending may be an effective way to cut costs.

It shows that demand is responsive (elastic), because the percentage change in demand is greater than the percentage change in promotional spending.

True.

Demand is responsive to promotional spending, so a cut could cause a significant fall in sales and may be best avoided.

It should usually focus spending on the option with the highest promotional elasticity of demand, as this is most likely to increase sales.

Promotional elasticity of demand does not take into account the cost and affordability of each option, so a cheaper combination of options may achieve better results.

Some promotions aim to build brand strength or customer loyalty, which may not show up in short-term changes in demand.

False.

When several products are promoted at once, it is hard to separate how much of the change in demand came from one campaign.

Changes in taste, word-of-mouth and competitor actions can all influence sales during a promotion.

Extra product lines spread risk across different market segments, so the business is less reliant on one product.

True.

Launching fresh products regularly can improve brand image, for example by showing a business's commitment to sustainability.

New technology unlocks features that were impossible before, which can prompt ideas for new products.

Ideas are compared and some are dropped, with the decision based mainly on costs and likely demand, using forecast sales, market share and cost-benefit analysis.

Developing a prototype allows the operations department to see how a product can be manufactured and fix any production problems.

A test launch is when a product is sold on a small scale to a limited market to see how well it sells before an expensive full launch.

True.

Digital products such as apps and software run beta versions to test them with users before the full launch.

Staff in sales, customer service or production often notice problems or spot opportunities for new products.

A supplier might offer new materials or production methods that help create a new product.

False.

Looking at what rivals are offering can inspire new ideas or reveal gaps in the market.

Surveys, focus groups and trend analysis help a business understand customer needs and spot new opportunities.

It allows the business to keep up with changes in customer preferences and technology, so it can stay ahead of rivals.

New products can open up new markets or increase sales, helping the business grow in size and profits.

Offering fresh and useful products shows customers that the business listens and adapts to their needs, which strengthens the brand.

A sales forecast is a prediction of future sales revenues based on past sales figures.

They help determine how many staff are needed, how much inventory is required and whether capacity needs to be expanded or reduced.

True.

Sales forecasts are an important tool to support planning, and they improve the validity of cash flow forecasts.

Raw sales data can be volatile, making patterns hard to spot, so time series analysis smooths the data to make future sales easier to predict.

A three-month moving average is found by dividing each three-month moving total by 3.

The three sales figures add up to 37,451, so the three-month moving average is 37,451 ÷ 3 = 12,484, placed alongside February.

It should have as many data points above the line as below it, adjusted up or down if there is more than one outlier on one side.

The line of best fit is extended (extrapolated) beyond the existing data to predict future sales values.

Qualitative sales forecasting is predicting future sales using non-numerical data, such as expert opinions, market experience or customer feedback.

A sales force estimate is a forecast made by sales staff based on their direct interaction with customers.

False.

Qualitative forecasting is usually less accurate, because it is subjective and prone to bias and is not based on numerical data.

It is especially useful when a business launches a new product with no past sales data, or enters a new market.

It helps plan recruitment, training and staff scheduling, and avoids overstaffing when lower demand is forecast.

It ensures the right amount is produced to meet expected demand, helping to prevent overproduction or shortages of inventory.