Fill This Form To Receive Instant Help

Help in Homework
trustpilot ratings
google ratings


Homework answers / question archive / Areas of Evaluation - SQL (+Python/R if desired but not necessary) - Excel - Problem solving and structuring - Developing actionable or relevant insights from data - Presentation storytelling Read the Data Set details to familiarize yourself with the data you are being asked to analyze

Areas of Evaluation - SQL (+Python/R if desired but not necessary) - Excel - Problem solving and structuring - Developing actionable or relevant insights from data - Presentation storytelling Read the Data Set details to familiarize yourself with the data you are being asked to analyze

Computer Science

Areas of Evaluation

- SQL (+Python/R if desired but not necessary)

- Excel

- Problem solving and structuring

- Developing actionable or relevant insights from data

- Presentation storytelling

Read the Data Set details to familiarize yourself with the data you are being asked to analyze. Please keep in mind you may not need to use all data field for this exercise

Use SQL and Excel (as well as Python/R if desired but not necessary) to manipulate, analyze, and visualize the data

Work through the list of questions on this document & make sure you answer every question

Summarize and present finings in a presentation PowerPoint deck. We recommend having at least 5 pages to showcase your work, but we are open to seeing your work in any format or structure of your choice. There is no upper limit to the number of slides

Please include your presentation deck, a copy of your SQL codes (Python/R if appropriate), excel files, and any other deliverables that you wish to show

They will review all of your deliverables and may have questions about your work

- Data Field Legend

- Opportunity _ Created _Date: date of when the sales opportunity was created (MM/DD/YYYY format)

- Opportunity _ID: anonymized, internal ID assigned to the sales opportunity

- Account _Type: type of prospect/customer

- Territory: territory of prospect/customer

- Deal _Type: type of deal, which can be:

- “New Business” -> first-time customers

- “Up-sell" -> existing customers purchasing additional licenses

-  “Cross-sell" -> existing customers purchasing additional products

- Sales _Team: which sales team (internal or external) is working on the deal; data may be incomplete

- Freemium _User: prospects who started using free products

- Deal _Source: how the deal/opportunity was sourced; data may be incomplete

- #Schools: number of public or private schools that the prospect is buying for

- Student _Count: number of students the prospect is buying for

- Product _Feature: binary value that indicates “1” if the prospect is interested in using a premium product feature

- In-school _Devices: number of hardware devices that the prospect may be purchasing for — but the information is only somewhat useful as the prospect is buying software, not hardware; data may be incomplete

- Take-home _Devices: number of school hardware devices that students can take home — but the information is only somewhat useful as the prospect is buying software, not hardware; data may be incomplete

- Deal _Stage: status of the deal/opportunity

- “Closed Won" -> deals that were already won

- “Closed Lost” -> deals that were already lost

- “Stage X" -> indicates different stages of the sales cycle and ranges from Stage 0 to

Stage 5; indicates that deals are still ongoing

- Deal _Age: number of days that the deal/opportunity was in the sales cycle for

- Contract _Term: length of deal/opportunity

- E.g, if contract term = 3, that means the prospect is interested in buying a software subscription for 3 years

- Close _Date: date when the deal/opportunity was marked as “Closed Won” or “Closed Lost”

- For historical deals -> the close date will be < 03/29/2021

- For future deals (for forecasting) -> the close date will be 2 03/30/2021

- Forecast _Category: Sales Team's confidence level of winning the deal

- For open deals -> highest to lowest confidence level = {Commit, Optimistic, Pipeline,

Omit}

- Deals that are already closed won or closed lost will be indicated as “Closed”

- TCV_USD: Total Contract Value in USD. Total amount of revenue from the deal.

- ACV_USD: Annualized Contract Value in USD, equal to = {TCV} / {Contract_Term}

2. Please evaluate the historical sales efficiency by calculating the following metrics:

a. Please use any tools of your choice.

b. Win/loss ratio by quarter of ‘Close Date’

i. Win/loss ratio = (Won $ revenue) / (Won $ revenue + Lost $ revenue)

ii. E.g., if ‘Close Date’ is 1/1/2020 -> 1Q20 and if ‘Close Date’ is 6/30/2021 -> 2Q21

c. Please feel free to include any other sales efficiency metrics if desired but not necessary.

3. Please forecast future revenue for Q2-Q4 of 2021. You may need to use historical deals as training data to come up with a forecasting model and then apply your model findings to the ongoing deals to arrive at your forecast numbers.

a. Please use Excel only.

b. Please measure the forecast in both ACV ($) and TCV ($)

c. Please reference ‘Close Date’ as when the ongoing deals will be Won or Lost.

4. Please show us any other insights you may find, but not necessary.

Data Set

- High-level Description

- Anonymized csv version of a SaaS sales database, which includes historical deals (Won or Lost) data as well as ongoing deals (open pipeline) data from 2018-2021

- Basic customer and deal information included for in-depth analysis

- You may need to make assumptions or leave certain data records out of analyses where the data entries are missing but not necessary

- You may not need to use all data fields for your analyses

Option 1

Low Cost Option
Download this past answer in few clicks

32.99 USD

PURCHASE SOLUTION

Already member?


Option 2

Custom new solution created by our subject matter experts

GET A QUOTE