Complete Part C of the Strategic Marketing Plan.

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MKT/574 v1

Strategic Marketing Plan

MKT/574 v1

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Complete Part B of the Strategic Marketing Plan.

MKT/574

Professor Brent Duncan

17 Oct 2021

Complete Part B of the Strategic Marketing Plan.

Part B: Marketing Data Analysis

Internal Data

Evaluate internal sources of information available to you inside the organization and what information you will receive from each source. Identify 3-6 sources of internal data. Insert or remove rows as needed.

Source

What it Measures

Data

Potential Usage

Data derived from sales.

Weekly sales figures at retail establishments.

Sales at retail establishments are expressed in US dollars.

The information will be used to determine trends and budgets for the future.

Number of Clients.

The number of consumers that visit each shop every week.

A comparison of the number of consumers that came in each week and the number of goods they purchased.

This information will be used to identify the busiest days of the week when the most significant number of goods will be sold.

A satisfaction survey of customers.

How many consumers are overall pleased with their purchase?

Customers that participate in a survey will be able to give us information about their purchasing experience.

This information will help assess both success and possibilities in the future.

Budgeting

The total amount of money spent.

The total amount spent on buying goods, marketing, utilities, and other expenses.

The data will assist in calculating the appropriate amount of money to spend to maximize the likelihood of a positive return on investment.

Compensation

The amount of money spent on the wages of workers.

Money spent on compensating workers for staffing and performing duties on a project.

This information will assist in evaluating whether or not the business is using rightsizing in conjunction with the appropriate workforce for the job.

Secondary Data

Evaluate secondary data sources and the specific information you need from each source. Insert or remove rows as needed.

Source

What it Measures

Data

Potential Usage

Demographic data obtained via a census.

Age and gender distributions among different types of shoppers.

Provides information on the different types of consumers as well as their ages.

The information will help determine the most appropriate client to market products to.

An item's sales data and analytics

Technologies used to manage, analyze, and evaluate sales data will be developed.

Data on particular products and sales that have been targeted.

The data will be used to determine the viability and future of the item.

Polling results in information about product sales outside of the company.

A contrast of the sales of goods with those of other merchants.

Market share and revenues of businesses in a specific geographic region.

This data will determine where the company stands concerning its competitors (Scarisbrick‐Hauser, 2007).

USDA

It assists in keeping track of supplements and their sales.

Statistical information on the sales of supplements supplied by other businesses.

The data will aid in the identification of competitors and the expansion of the brand portfolio.

Primary Data

Evaluate primary data needs to create and evaluate the marketing plan. Insert or remove rows as needed.

Source

What it Measures

Data

Potential Usage

Discussions in small groups with a specific focus.

Concerns regarding product preferences, dislikes of products, and store design.

Insider information about product preferences, dislikes, and shop layouts were gleaned from insiders.

Data will assist the business in its search for new possibilities and marketing successes (Hallikainen et al., 2020).

Convenience store polls

What customers want and don't want.

Qualitative.

It helps in deciding what things should be included in marketing advertisements.

On-site sampling was done at several events.

Feedback on new items is solicited.

Feedback on individual goods, particularly new ones, is welcome.

It assists in determining what modifications should be made to the goods and which products are excellent and poor alternatives (Hallikainen et al., 2020).

Surveys are carried out online.

Clients' comments and suggestions.

Please provide feedback on which goods and marketing methods are most effective.

Customers' comments on their shopping experience and product use will be used to keep them digitally connected with the business, and the data will assist in doing so (Hallikainen et al., 2020).

Customer Relationship Management

Establish customer touchpoints and develop appropriate CRM events for customer acquisition, retention, and profitability. Insert or remove rows as needed.

CRM Touchpoint

Purpose & CRM Objective

Data

Potential Data Usage

Loyalty cards are used to reward customers (Gold, platinum, premium).

To collect information on clients, such as their name, age, and address, among other things (Kim & Kim, 2009).

Customers' shopping habits, as well as their email and postal addresses, are recorded.

It aids in identifying loyal consumers who often shop and the specific retail location from which they will make their purchases (Pradhan, 2019).

Online websites.

To assist customers in shopping online and receiving coupons.

When consumers buy online, they may take advantage of coupons that they can download and purchasing trends.

This will aid in identifying the coupons that are often downloaded, which will assist in identifying the purchases that the users will make (Pradhan, 2019).

Reviews and ratings on social media are becoming more common.

To increase the number of consumers and boost their loyalty to the goods (Kim & Kim, 2009).

Individuals' likes and dislikes, product preferences, and least liked products are all recorded by customers.

The information will assist in deciding which goods must be offered and which products must be removed from product lines (Pradhan, 2019).

References

Hallikainen, H., Savimäki, E., & Laukkanen, T. (2020). Fostering B2B sales with customer big data analytics. Industrial Marketing Management, 86, 90–98. https://doi.org/10.1016/j.indmarman.2019.12.005

Kim, H. S., & Kim, Y. G. (2009). A CRM performance measurement framework: Its development process and application. Industrial Marketing Management, 38(4), 477–489. https://doi.org/10.1016/j.indmarman.2008.04.008

Pradhan, S. K. (2019). Big Data, Analytics and Paradigm Shift in Marketing & Sales. The Management Accountant Journal, 54(5), 26. https://doi.org/10.33516/maj.v54i5.26-30p

Scarisbrick‐Hauser, A. (2007). Data analysis and profiling. Direct Marketing: An International Journal, 1(2), 114–116. https://doi.org/10.1108/17505930710756860

Copyright 2020 by University of Phoenix. All rights reserved.

Copyright 2020 by University of Phoenix. All rights reserved.