Difference in Difference Out Marketing Analytics Questions

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I'm working on a data analytics multi-part question and need an explanation and answer to help me learn.

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Part 2: Difference in Difference out 2-4

Requires solid understanding of marketing analytics using excel, understanding results, and writing about them according to the homework case prompt

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Marketing Analytics Case 2 Steam promotions & pricing This case has two parts. The first part asks you to evaluate the results of an A/B test designed to lure inactive gamers back to the platform. The second part asks you to evaluate the efficacy of promoting a game via e-mail. Note: This case is fictional Part 1: A/B Testing [40 points] Dataset: SteamAB.xlsx Temporary discounts get customers to buy games and engage with the platform. Recently, Steam decided it wanted to test whether price discounts offered to inactive users could get them to return to the platform and stick with it. They targeted two segments: Segment 1 – “Losing Interest”: People whose activity playing Steam has dropped below three hours per week over the last two months. Segment 2 – “Inactive”: People who have not played Steam for two to four months, but who have not uninstalled Steam. For each segment, 30,000 accounts were randomly selected. Two different “treatment” e-mails were sent to 10,000 accounts each (with the remaining 10K used as a control group): Incentive A – 10% Discount: Congratulations! A 10% discount coupon has been credited to your account for use on any game! Incentive B – 20% Discount: Congratulations! A 20% discount coupon has been credited to your account for use on any game! Over the next two months, purchase and game play data was collected for each of the accounts. Your boss has asked you to examine the data and report back on which incentive(s) should be used for each segment (if any) going forward, and why. Consider factors that may be diagnostic of potential customer lifetime value, in addition to short-term profitability. Part 2: Difference-in-Differences [60 points] Dataset: SteamDD.xlsx In the past, Steam has also e-mailed existing customers to promote games from time to time. These e-mails are merely informative—no discount is attached. All customers received these e-mails; A/B tests were not run. As a consequence, we must identify the impact of these e-mails using a difference-in-differences approach. You’ve been provided data for a two-week period. At the start of the second week, e-mail promotions were sent out for some games but not others (identified by the “Promotion” variable). Your task is as follows: (1) Run a basic difference-in-difference (using a treatment group dummy, treatment period dummy, and their interaction – no other variables for now) to estimate the impact of the promotional e-mails. Report your regression results [10 points]. Interpret your interaction parameter estimate within the context of this case – what does the difference-in-difference mean here? [10 points] Note that video games that receive the promotion are in the treatment group; other games are in the control group. Week 1 is the control period, week 2 is the treatment period. The data are not currently formatted to run a regression; you will need to re-organize the data to do so. Think carefully about how to re-arrange the data. (2) Once this is completed, examine the other variables in the dataset. How might you improve your regression by adding control variables? Explain why the variables you add should matter (examining the data by generating plots or summary statistics will help here) [10 points]. (3) Now run a regression with the variables you discussed in the previous question. Explore different functional forms for your control variables, and report your final specification [15 points]. (4) Your estimate for the difference-in-difference parameter (the causal effect of the promotion) will be slightly difference in your regression from question (3) than your regression from question (1), but it won’t be much different. This lack of meaningful difference occurs in spite of the fact that many of your controls were strongly statistically significant, and your r-squared increased considerably. Why is that the case? [5 points] (5) Your regression results from part (1) should have shown a non-significant effect for the treatment group. In this context, this should not be surprising. Explain the interpretation of the treatment group parameter in this setting, and why it is not surprising that our estimate for it was zero. [3 points] (6) Our treatment group was all games that received a promotion, and our control group was all games that did not. Is there a better way to conduct our analyses in this setting? Explain your rationale, and run your proposed analyses to verify that it works better (i.e., that you learn something more than you did with the previous analyses). [7 points] Segment Group Number of people In group Who used gift Who purchased games Still playing eight weeks later Usage data Total games bought (8 weeks) Total hours played Financial Data Total price of games sold Cost of promotion redemption Control Losing Interest 10% Disc 20% Disc Control Inactive 10,000 n/a 765 650 10,000 921 921 775 10,000 1,137 1,137 893 10,000 n/a 287 159 1629 22750 1839 25355 2133 31027 399 3784 $38,561 n/a $48,506 $2,531 $55,391 $9,432 $7,738 n/a Inactive 10% Disc 20% Disc 10,000 277 277 201 10,000 468 468 213 425 5100 611 6234 $9,365 $1,335 $12,451 $4,352
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