Marketing Analytics & Insights

Marketing Analytics & Insights

1. For this question, please assume there is no pandemic!
Transport for London is the local government body responsible for most aspects of
the transport system in London. Its role is to implement the transport strategy and
to manage transport services across London.
TfL launched the Night Tube service on the London Underground in August 2016.
Currently, the Night Tube operates on Friday and Saturday nights across five lines
(the Central, Jubilee, Northern, Piccadilly and Victoria lines). TfL management
would consider expansion of the service to more lines and/or other nights of the
week, subject to consumer demand.
Some 1.34 billion passenger journeys are made each year on London Underground
(albeit with many individuals making multiple journeys), however there is no
sampling frame with full coverage of all distinct passengers available. Despite this
limitation, TfL management wants to conduct market research to assess consumer
appetite for an extension to the Night Tube service, which may have fare
implications. At present Night Tube fares are classified as off-peak, although a
special night-off-peak fare could be introduced, depending on passengers’ price
elasticities.
TfL has invited you to devise an appropriate sampling scheme (for example quota
sampling, simple random sampling, stratified sampling etc.) to research attitudes
towards developing the Night Tube. Propose the most suitable sampling method,
explaining your choice by describing the advantage(s), but also any disadvantage(s).
Your answer should also briefly address reasons for rejecting other sampling methods.
2. (a) When developing a Likert scale, which decisions need to be taken, and why?
(b) Explain why the sample mean and sample standard deviation are useful
descriptive statistics of measurable variables.
(c) When estimating means and proportions, how would you choose your sample
size?

3. When we make data-driven decisions there is always a risk we may be wrong.
In binary situations, we refer to false positives and false negatives. Explain the
difference(s) between these terms and provide a marketing example where:
(a) a false positive would be worse
(b) a false negative would be worse.
4. A national newspaper was interested in researching how effective paid-for advertising
was in its newspapers. The editors suspected the number of customer enquiries
resulting from advertisements depended on two key factors – the day of the week
(Monday through Friday), and the section of the newspaper where the advertisement
appeared (news, business and sports).
Analyse the selected SPSS output in Figure 1 (spread over the next two pages) and
discuss what conclusions can be drawn from the data. In your analysis, you may
wish to address at least the following.
• Describe the strength of the joint effect of the factors.
• Test the significance of the variables individually and the interaction between
them and interpret the results.
• How you would use the results to inform your pricing strategy for
paid-for advertisements in this national newspaper.

5. A mail-order company collected data on 250 customers and wanted to understand
current year spending amounts with multiple regression. Data were collected on the
following variables:
• Log(AmountSpent) – the dependent variable, the logarithm of annual
expenditure in £
• Age – age of the customer, in years
• Gender – gender of the customer, male = 1 and female = 0
• Salary – customer’s household salary, in £
• Married – customer’s marital status, married = 1 and otherwise = 0
• OwnHome – customer owns home outright = 1 and otherwise = 0.
Selected SPSS output is provided in Figure 2 (on the next page). Analyse the
regression results, making sure you first write out the regression model, including
any assumptions. In light of the regression results, propose any changes you would
make to the model, including any other explanatory variables which you would
consider using, or any you would remove from the existing model. Keep in mind the
company’s objective of understanding customer spending.

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