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How to Attract candidates, understand Application motivation, & measure student recruitment success

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How to Attract candidates, understand Application motivation, & measure student recruitment success

  1. 1. Challenging beliefs How to Attract candidates, understand application motivation, and measure recruitment success? Soumik Ganguly, Vice President, PaGaLGuY.com
  2. 2. Observations on current practices Mostly modelled on B2C marketing Product-life-cycle based design Historical-data based marketing plans & design Incorrect Measurability definitions HEM treated as a one-size-fits-all solution Data collected from over 2200 Business Schools and Universities through Interviews, campaign designs, consulting work, etc
  3. 3. Prevalent Behavior ● Show me the “target” demographics OR reach ● Show me the performance of last year (for every channel used) ● Compare what my competition is doing (Herd Behavior) ● Work back from the “deadlines” ( something like - last date of “sale”)
  4. 4. But what’s the final objective? ● Enrollment = fn(Generic Demand) x fn(Goodwill) x fn (Recruitment Funnels and Marketing) x fn(Purchasing power of applicants) ○ Control Factors: ■ Goodwill ■ Recruitment Funnels and Marketing
  5. 5. A new Design for Aspirants Acquisition Need to redefine and restart
  6. 6. Critical Components Deducing a formula for Aspirant-acquisition Understanding integration of key channels and its functionality Building new frameworks
  7. 7. Algos, econometrics, and reasoning Bring in some Math
  8. 8. The Aspirant Acquisition Formula E = i x D If we consider Engagement for all institutions "g", at time "t", the given equation can take a form of: Egt = igt x Dt (1) User acquisition can be defined as the product of a positive “engagement” and the “action” taken due to that engagement in a given channel. Therefore: User Acquisition, Ua = Egt x At ; Substituting the value of “E” from equation (1) = igt x Dt x At (2) Action - At = Qgt x Ppt (3) How do we define “Demand”? The “demand” for any program is a function of the program’s goodwill (that consists of ROI and other intrinsic factors), the program’s price, and the amount of communication or Advertisements available for the program over a given time “t”. Therefore Demand - Qgt = Qg (Wt , Pt , Adt ) (4) Substituting all Values, Ua = igt x Dt x Qg (Wt , Pt , Adt ) x Ppt Ua = igt x Dt x Qg (Wt , Pt , Adt ) x Ppt
  9. 9. Integration of Key Channels Result = fn(Action, Engagement) We can predict the probability of engagement of a particular platform at a certain time for a result "R". Mathematically, this would mean finding - P(PG) = Probability of student engaging with Pagalguy.com P(C) = Probability of student engaging over a call R = Result from the overall marketing system probability of engagement on Pagalguy, when there is a Result from the marketing system. This can be denoted as - P(PG | R) P(PG | R) = [P(PG) * P(R | PG)] / [ [P(PG) * P(R | PG)] X [P(C) * P(R | C)] ] P(R | PG) = Time x Activity x (1/Total number of options at a given time) = 4 x 5 x (1/50) [Considering 4 hours total time, 5 pages viewed each time, and 50 B-schools as option during his visits] = 0.2 P(R | C) = 0.1 x 10 x (1/100) [Considering 0.1 hours call time, 10 calls, and 100 b-schools calling] = 0.01 Now, The P(PG | R) = [0.4 x 0.2] / [ [0.4 x 0.2] + [0.2 x 0.01] ] = 0.08/0.082 = 0.97 Result = fn(Action, Engagement) Marketing team in this case is using 6 different channels, and given that there are two factors, the number of engagement-action sets will be 6 P2 (permutation), giving us about 30 such sets that will contribute to the overall results.
  10. 10. Key Data input Recruitment Demand Generation It is the %age or number of users with the right “intent” and “demographic” fitment available for a specific program in a given medium/channel at a given time RDG is measurable, can produce trends, and needs to be correlated to Application Numbers for each year
  11. 11. A Case for RDG ● Application numbers of Top 50 Business Schools/Programs in India for past 5 years ● Calculated the “Recruitment Demand Generation” for past 5 years ● Correlation Score of “0.4” ● High correlation, since multiple factors involved towards contributing to an Application
  12. 12. The Framework 4 Phases in the HEM process: Follow for every channel used in the design, and in the integration Discovery Networking Conversation Conversion Content Maps Content Integration strategies Different formats of presenting Info/content Building a micro community of aspirants, current candidates, and alumni. Repeat every year. Extensions of internal counselling systems Access systems Presence across high RDG and “Action” probability channels Research Prep Application Conversion You Aspirant
  13. 13. Maximizing Enrollments via HEM Ground Rules: ● Change base definitions of HEM design ● Upgrade from “Product-life-cycle” design to “Aspirant-life-cycle” design ● Account for “intent” and “demographics” Ad plans ● Calculate and correlate demand for your program across all channels/geographies to application trends in past years ● Design Integrated systems supported by data and analytics ● Practice zero-based marketing design each year

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