In the previous question, you were given: If an Outbound Sales Rep has a yearly quota of $400,000 in sales and an average deal size of $30,000, they are expected to sign approximately 13.33 customers per year to meet their quota. The approximate costs for a sales rep (all-in) are $300,000, which includes compensation (fixed and variable), additional marketing, back office, and engineering costs. If Dropbox’s Strategic Finance team spends its remaining budget on hiring Outbound Sales Reps, how many signed new customers could Dropbox expect in return for its investment?
The Case Analysis portion of this exam is worth 50 points in…
The Case Analysis portion of this exam is worth 50 points in total. You must read this case carefully to understand how cloud-based products (SaaS models) are sold when pursuing inbound and outbound sales model strategies. Focus on: Understanding what the SaaS model is and how it works The main differences between Dropbox’s inbound and outbound sales model Understanding how inbound and outbound sales models are evaluated to determine the best ROI decision. For questions 1-7, the following case assumptions apply (make sure you note these) Churn Rate of Monthly Plan (Annualized): 30% Churn Rate of Annual Plan: 10% Gross Margin: 90% Inbound Sales Mix: 80% Annual/20% Monthly Outbound Sales Mix: 100% Annual Average Seats per Deal: 10 seats (inbound), 250 seats (outbound) Inbound Costs per Ad Click: US$8 Inbound Ad Click to Purchase Conversion Rate: 1% Outbound Discount from List Price: 20% Outbound Quota per Sales Rep: $400,000 Outbound Cost per Sales Rep (“All In”): $300,000 Case Dropbox_Go to Market Sales Strategy.pdf
Which computational model is commonly used in language proce…
Which computational model is commonly used in language processing for representing word meaning?
How is antonymy defined in the context of language, and how…
How is antonymy defined in the context of language, and how is it typically identified in natural language processing (NLP)?
The tf-idf model uses sparse vectors based on nearby word co…
The tf-idf model uses sparse vectors based on nearby word counts, while Word2vec utilizes dense vectors created by training a classifier.
Why is it challenging to find examples of perfect synonymy i…
Why is it challenging to find examples of perfect synonymy in language?
Which of the following is true about closed and open class w…
Which of the following is true about closed and open class words?
What tool can help measure the connotation of a word in NLP?
What tool can help measure the connotation of a word in NLP?
A patient admitted for chest pain and shortness of breath ha…
A patient admitted for chest pain and shortness of breath has a productive pink frothy secretions. Breath sounds auscultated with fine inspiratory crackles and wheezing. The following vitals are as follows: HR – 120 bpm Frequency – 25 breaths/min Blood pressure – 95/75 mmHg SpO2 – 93% Chest radiograph shows cardiomegaly and prominent pulmonary vasculature. What should help this patient’s shortness of breath?
A patient has a V/Q ratio 1.2, which of the following pathop…
A patient has a V/Q ratio 1.2, which of the following pathophysiology is related? I. ARDS II. Asthma III. Pulmonary emboli IV. Emphysema