Marketing Analysis Part C- 1 page
Running Head: MARKETING DATA ANALYSIS 1
MARKETING DATA ANALYSIS 2
Marketing Data Analysis
Name
Institution
Date
Internal Data
|
Source |
What it measures |
Data |
Potential Usage |
|
Sales data |
Monthly sales for each product |
Monthly average sales for each product. Data can be segmented by business and consumer markets. |
Can be used for analyzing projecting trends and measuring effectiveness of a promotional strategy |
|
Profit and loss data |
Profit and loss for all products |
Profit and losses made by the organization for each month. This can be analyzed for each product. |
To identify products with the highest profit margins. To establish whether the organization is operating at a profit or loss (Eevi, 2020). |
|
Cash flow |
Monthly cashflow for all products |
Monthly cash inflow and outflow for each product. |
To establish the amount of cash that comes in and goes out with regard to each product |
|
Cost accounting information |
Cost of production, distribution, and marketing |
Monthly production, distribution, and marketing costs |
To establish the total cost of producing, distributing, and marketing a single unit of product. To establish whether the organization is spending more on products than how much the products are returning. To establish areas of improvement for efficiency and internal cost controls |
Secondary Data
|
Source |
What it measures |
Data |
Potential usage |
|
Retail store analytics |
The dollar value of sales |
Major players total sales |
Seasonal patterns market share analysis |
|
Statistics provided by the government |
Economic trends, pricing, trade activity, and regulation |
Inflation rate |
Provide insight in relation to the state of the economy pricing regulation. This can be important in ensuring compliance with revenant marketing regulations. |
|
Industry associations |
Industry trends and participating companies |
A list of participating companies and quarterly industry trends |
A good place to start when learning about a growing industry or when seeking information that would be available from an insider in the industry is to look to industry associations. Potential usage includes establishing where the industry is heading. Identifying competitors and any other relevant news affecting the industry. |
|
International agencies |
The information be used to measure and analyzes foreign market prospects for the organization |
The data can involve products imported or exported by specific targeted foreign markets |
This information is crucial in analyzing foreign markets that present the opportunity for Amazon to venture in. Specifically, the Asian and African markets present an opportunity that is yet to be fully exploited by the company. |
|
Published surveys |
Survey reports on customers |
Feedback obtained on products surveyed |
To understand what customers want and how products can be improved. |
Primary Data
|
Source |
What it measures |
Data |
Potential usage |
|
Focus group |
Group level satisfaction, motive product usage |
Qualitative |
Identify opportunities in the market and reactions of different market segments |
|
Interviews |
Level of customer satisfaction |
Quantitative and qualitative |
Improve level of customer satisfaction |
|
Key informants |
Customer habits |
qualitative |
Predicting the behavior of customers |
|
Questionnaires |
Customer experience |
qualitative |
Improving the customer shopping experience (Eevi, 2020) |
Customer Relationship Management
|
CRM touchpoint |
Purpose and objective |
Data |
Potential data usage |
|
Product reviews |
To collect customer feedback |
Comments and product ratings |
To establish what customers like and dislike hence improve the product. |
|
Customer profile information |
To collect basic information about customers |
Name, Email address, location, customer ID |
Tracking purchases made by customers (Anshari et al., 2019) |
|
Subscription renewals |
To identify and track loyal customers |
Customers preferences |
Target loyal customers with tailored offers |
|
Customer purchasing behavior |
To establish preferences and choices of customers |
Purchasing trends, items purchased |
Tracking the most preferred and purchased products |
References
Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94–101. https://doi.org/10.1016/j.aci.2018.05.004
Eevi, V. (2020). Factors affecting the success of AI campaigns in marketing : data perspective. https://jyx.jyu.fi/handle/123456789/71539
Running Head: MARKETING DATA ANALYSIS
1
Marketing Data Analysis
Name
Institution
Date
Running Head: MARKETING DATA ANALYSIS 1
Marketing Data Analysis
Name
Institution
Date