Business Process Analysis
Research and Design
The TAU event simulation flowchart displays the order of events in the app's new business process. It shows how different entities interact with each other, including activity and decision points.
The flowchart contains several entities to demonstrate this:
Customer: Represents individuals who download and use the TAU app.
App Registration: Signifies the process of customer registration and account creation within the app.
Data Collection: Refers to gathering athlete data through technologies such as the Beyond Pressure platform, human motion analysis, and other relevant sources.
Athlete Assessment: Represents the assessment of athletes' physical attributes, including body composition, muscle power, pressure step dynamics, and more, using the collected data.
Performance Analysis: Analyze the assessment results to gain insights into an athlete's current status and performance metrics.
Personalized Training Program: A customized training program is developed for each athlete based on the performance analysis.
Training Execution: Depicts the actual execution of the personalized training program by athletes.
Performance Monitoring: Involves continuous monitoring and tracking of athletes' performance during training sessions.
Feedback Loop: Allows for collecting feedback from athletes and coaches to refine the training program and improve future performance.
Evaluation: This represents the periodic evaluation of an athlete's progress and the effectiveness of the training program.
The flowchart shows how the TAU app's business process works, specifically how it collects, analyzes, and uses athletes' data to create personalized training programs. It also includes a continuous feedback loop to improve and optimize the training process.
It is an app, and athlete assessment services drive TAU's new business process. Their sales strategy uses data-driven insights, personalized marketing, and a seamless customer experience to increase sales and revenue growth. The critical components of their strategy include:
Customer Segmentation: Utilize data from athlete assessments and customer profiles to segment the target market based on demographics, sports preferences, performance levels, and training needs. It allows for tailored marketing and personalized offerings to different athlete segments.
Personalized Marketing Campaigns: Develop targeted marketing campaigns highlighting the benefits of TAU's athlete assessment services and the app's ability to provide customized training programs. Utilize data analytics to deliver personalized messages, recommendations, and promotional offers to athletes based on their assessment results and training goals.
Customer Relationship Management (CRM): Implement a CRM system to manage athlete data, track interactions, and provide personalized support. The CRM system will enable effective communication, follow-up, and customer retention strategies.
Partnerships and Collaborations: Forge strategic partnerships with sports organizations, coaches, and influencers to increase brand awareness, reach a wider audience, and gain credibility in the industry. Collaborate with fitness equipment manufacturers, sports brands, and training facilities to create joint marketing initiatives and cross-promotions.
Sales Analysis
Data from the existing and proposed models and the cost analysis were utilized to perform the sales analysis and assess the feasibility of the proposed concepts. The following analytical algorithms and metrics were employed:
Customer Acquisition Cost (CAC): Calculate the cost of acquiring each new customer by considering marketing expenses, app development costs, and customer onboarding efforts. CAC = Total Marketing and Development Costs / Number of New Customers.
Customer Lifetime Value (CLV): Determine the estimated revenue each customer generates throughout their engagement with TAU. CLV is derived by analyzing historical customer data, average purchase frequency, and average customer lifespan.
Conversion Rate Analysis: Assess the conversion rates at each stage of the sales funnel, including app downloads, registration, and purchase. Identify areas of improvement to optimize conversion rates and enhance customer engagement.
Revenue Growth Forecast: Utilize predictive modeling algorithms to forecast revenue growth based on historical sales data, market trends, and the expected increase in customer acquisition and retention.
Existing Model - Sales Analysis
Customer Acquisition Cost (CAC):
CAC = Total Marketing Cost / Number of New Customers
= $200,000 / 1,000
= $200 per customer
Customer Lifetime Value (CLV):
CLV = Revenue Per Customer - Cost Per Customer
= $2,300 - $200
= $2,100
Conversion Rate Analysis:
App Downloads: 10,000
App Registrations: 5,000
Purchases: 2,000
Conversion Rate (App Downloads to Registrations):
= Number of App Registrations / Number of App Downloads
= 5,000 / 10,000
= 0.5 or 50%
Conversion Rate (Registrations to Purchases):
= Number of Purchases / Number of App Registrations
= 2,000 / 5,000
= 0.4 or 40%
Revenue Growth Forecast:
Assuming a conservative revenue growth of 10%:
Revenue Growth = Existing Revenue * Revenue Growth Rate
= $2,000,000 * 0.10
= $200,000
Proposed Model - Sales Analysis
Customer Acquisition Cost (CAC):
CAC = Total Marketing Cost / Number of New Customers
= $250,000 / 1,000
= $250 per customer
Customer Lifetime Value (CLV):
CLV = Revenue Per Customer - Cost Per Customer
= $2,250 - $250
= $2,000
Conversion Rate Analysis:
App Downloads: 10,000
App Registrations: 5,000
Purchases: 2,500
Conversion Rate (App Downloads to Registrations):
= Number of App Registrations / Number of App Downloads
= 5,000 / 10,000
= 0.5 or 50%
Conversion Rate (Registrations to Purchases):
= Number of Purchases / Number of App Registrations
= 2,500 / 5,000
= 0.5 or 50%
Revenue Growth Forecast:
Assuming a conservative revenue growth of 10%:
Revenue Growth = Existing Revenue * Revenue Growth Rate
= $2,000,000 * 0.10
= $200,000
Feasibility and Probability of Success
The proposed model has a slightly higher Cost of Acquiring Customers (CAC) than the existing model, which means that the cost of getting new customers is slightly higher. Nonetheless, the proposed model has a higher Customer Lifetime Value (CLV), indicating a greater potential return on investment per customer.
Both models have the same conversion rate, with 50% of app registrations leading to purchases. It shows consistent customer engagement and conversion rates.
Assuming a revenue growth rate of 10%, both models have the potential for revenue growth. However, further data and analysis are necessary to assess the likelihood of success accurately.