3.12 - Market Research Data
The reliability of data collected in market research
Market research data, whether primary or secondary, can sometimes be unreliable, which affects how businesses use it for decisions.
Factors affecting the reliability of secondary data
Secondary data comes from existing sources like reports or online statistics.
It can be unreliable due to several issues:
- Outdated information - Data may no longer reflect current market conditions.
- Lack of specificity - It might not match the exact needs of the business.
- Incompleteness - Essential details could be missing.
- Unavailability - Some required data might not exist or be accessible.
Factors affecting the reliability of primary data
Primary data is collected directly by the business, such as through surveys.
Its unreliability often stems from bias:
- Sampling bias - Occurs when the sample does not represent the target population, leading to skewed results.
- Questionnaire bias - Happens when questions are worded to influence responses, not accurately capturing true opinions or behaviours.
- Response bias - Respondents may not answer truthfully, especially on sensitive topics like personal habits.
Analysing quantitative data using averages and dispersion
Quantitative data involves numerical information that can be measured and analysed statistically. Key tools include measures of central tendency (averages) and dispersion, which help businesses interpret trends and variability in data like sales figures or customer feedback scores.
Arithmetic mean
The arithmetic mean provides an average value by considering all data points.
Where:
- Sum of all values = Total when all data points are added
- Number of values = Count of data points in the set
Mode
The mode identifies the most common value in a dataset, useful for decisions like stock ordering. The mode is the value that appears most frequently. If multiple values appear equally often, there may be more than one mode.
Median
The median is the middle value in an ordered list, helping to avoid distortion from extreme values.
For an odd number of values:
For an even number of values, take the mean of the two middle values.
Evaluating measures of central tendency
Each average has strengths and weaknesses, depending on the data and its use in business:
-
Arithmetic mean:
- Uses - Helps predict sales for stock management or compare datasets like customer loyalty rates.
- Advantages - Incorporates every value; easy to understand.
- Disadvantages - Skewed by outliers; often not a whole number.
-
Mode:
- Uses - Guides decisions like stocking the most popular product size.
- Advantages - Simple to spot; always a whole number.
- Disadvantages - Overlooks most data; not suitable for advanced analysis; possible multiple modes.
-
Median:
- Uses - Useful in salary discussions or claims about performance rankings.
- Advantages - Not affected by extremes.
- Disadvantages - Harder to calculate with grouped data; approximated for even counts.
Range
Range shows the spread of data, highlighting consistency or variability.
Where:
- Highest value = Largest number in the set
- Lowest value = Smallest number in the set
A limitation is that extreme values can exaggerate the range.
Worked example - Calculating the arithmetic mean
A business surveys 6 customers on their spending in pounds: 35, 42, 38, 50, 45, 40. Calculate the arithmetic mean spending.
Step 1: Identify the values
- Values: 35, 42, 38, 50, 45, 40
- Number of values = 6
Step 2: Calculate the sum
Sum = 35 + 42 + 38 + 50 + 45 + 40 = 250
Step 3: Apply the formula
Worked example - Identifying the mode
A retailer records shoe sizes sold in a day: 6, 7, 7, 8, 6, 7, 9, 8. What is the mode?
Step 1: List the values and frequencies
- 6 appears twice
- 7 appears three times
- 8 appears twice
- 9 appears once
Step 2: Identify the highest frequency
The value 7 appears most often (three times).
Step 3: State the mode
Mode = 7
Worked example - Calculating the median
A company records daily website visits over 9 days: 105, 145, 115, 135, 125, 155, 120, 100, 165. Calculate the median.
Step 1: Order the values
Ordered list: 100, 105, 115, 120, 125, 135, 145, 155, 165
Step 2: Determine the position
Number of values = 9 (odd)
Step 3: Identify the median
The 5th value is 125.
Worked example - Calculating the range
A business tracks weekly sales in units: 300, 280, 320, 290, 310. Calculate the range.
Step 1: Identify the highest and lowest values
- Highest = 320
- Lowest = 280
Step 2: Apply the formula
Analysing qualitative data
Qualitative data deals with non-numerical information, such as opinions and attitudes, to understand consumer motivations. It cannot be processed with standard statistics but offers deep insights into customer preferences.
Key features of qualitative data analysis
- Focuses on reasons behind behaviours, like reactions to new products.
- Based on subjective elements like beliefs and feelings.
- Helps identify unmet needs for product development.
Coding qualitative responses
Coding involves grouping responses by themes:
- Assign labels to common words or phrases.
- Match coded data with demographics (e.g., age, location) to spot patterns.
- These relationships inform targeted marketing strategies.
Methods for presenting market research data
Effective presentation makes data easier to understand and reference. Different methods suit various data types and purposes.
Tables
Tables display exact figures in rows and columns, ideal for quick lookups of specific values.
Pie charts
Pie charts illustrate proportions of a whole.
Where:
- Value of section = Amount for one category
- Total value of all sections = Sum of all categories
Line graphs
Line graphs plot data points connected by lines to show trends over time, such as seasonal changes in sales.
Bar charts
Bar charts use bars of equal width but varying height or length to compare values, like market share across competitors.
Worked example - Calculating pie chart angles
A survey shows customer preferences: 50 for option A, 25 for B, 15 for C, 10 for D. Calculate the angle for option A.
Step 1: Identify the values
- Value for A = 50
- Total = 50 + 25 + 15 + 10 = 100