more consulting work product of mine.

1. Non-Sufficient Funds and FraudAnalysis & Recommendations Core Growth Strategy May 12, 2009

4. When redbox charges the customers card for the “passive buy”, the charge is declined.

5. The current billing process has been in place since March of 2009

6. When a customer swipes their credit card, redbox immediately verifies and charges $1/DVD for the first night rental.

7. After the DVD is returned, redbox charges $1 per night for each extra night (called Extra Night Charges).

9. Definition of NSF/Fraud 2008 Uncollected Charges For redbox, Non-sufficient Funds (NSF)/Fraud consists of uncollected charges due to credit card declines for: Gross Charges ( $M) % of Declined Transactions Extra Nights ($1/night) disks were returned 1 Passive Buys ($24/disk) disks were kept by customer 2 4

11. Extra Night Declines (END) include declined charges on extra nights for DVDs that were returned to redbox

12. Passive Buy Declines (PBD) include declined charges on unreturned DVDs, and therefore include some component of intentional theft$61.5 Extra Night Declines $32.8 Passive Buy Declines 5

14. Stricter Blacklist policies were implemented in Q2

15. Weekly Blacklist updates were implemented in Q3, resulting in a sharper decrease in PBDs in Q4

16. It is too early for a comparative measure for Q1 2009, since more than 60% of the collection period is remaining at the time of analysisDeclined Transactions as % of Total Revenue 9.7% 9.3% 8.7% 7.4% Extra Night Declines Passive Buy Declines 6

17. Passive Buy Declines: June ‘08 Snapshot # of Customers (1,000’s) Customers with Passive Buy Declines on June 2008 Rentals 31,000 customers had a PBD result from a June ’08 rental. These 31K are associated with 70K credit cards. 6,000 had a PBD on one of their other credit cards prior to 6/08, so are excluded from the “new” offender analysis. 5,000 were “1 and done”. The June PBD was their first and only transaction with redbox on any credit card. 9,000 had paid rentals prior to the June PBD, but never rented again on any credit card after the PBD in June. 11,000 continued to rent from redbox on one or more credit cards after their first PBD in June. A B C D E $120K declined charges in 6/08 $216K declined charges in 6/08 $264K declined charges in 6/08 7

18. Passive Buy Declines: June ‘08 Snapshot Although this group of customers had net positive revenue following the first PBD in June ‘08, termination of these customers and all of their associated credit cards immediately after the first PBD would have had a net positive impact on EBITDA Post-June 2008 Revenue Stream EBITDA Impact of Customer Termination Revenue ($1,000s) EBITDA Loss on Future Paid Rentals: Revenue EBITDA Margin EBITDA Impact $(635,000) 18% $(114,300) 11,000 continued to rent from redbox on one or more credit cards after their first PBD in June. E Total net future revenue = $390 EBITDA Gain on Preventing Unpaid Rentals: Revenue COGS + OP Exp EBITDA Impact $245,000 72% $176,400 $264K declined charges in June ‘08 $62,100 Net EBITDA Impact Note: In practice, the potential EBITDA gain is less than in this illustration because termination would occur after 90 days of collection on first PBD, so future gains would exclude rentals during this 90 day period 8

19. PBD Recommendations The total potential EBITDA impact of the recommended PBD solution is estimated to be ~$600,000-$800,000 per year Short Term Solution Longer Term Solution Change blacklist processing from blocking names, to blocking name/billing zip code combinations. Convert blacklist from a manual weekly procedure to an automated, nightly procedure; incorporating all of the manual changes suggested in “Short Term Solutions”. Build new charge validation logic to try to collect the $24 due on the previous PBD immediately when a customer with a PBD visits a kiosk. Allow rental if customer covers PBD + current rentals, otherwise decline at kiosk. Maintain blacklist on kiosk CPU for local processing (preventing off-line fraud) Continue analysis of the potential value of extending the collections period beyond 90 days Add names to the blacklist as soon as 90 day collection period is ended on 1st PBD ($24 in charges) on only 1 credit card. A high number of customers should be added to the black list under the current policy, but are not because their name is shared by other good customers. For these customers, we need to deactivate all of the credit cards that share their name and billing zip code. Work with field ops to change connectivity hardware at high-risk kiosks to prevent customers from committing off-line fraud. 9

20. Extra Night Declines: June ‘08 Snapshot 37% of the total END charges from June 2008 rentals were from rental visits with a basket size of 3 or more disks Avg. Declined Charges per Visit ($) Extra Night Declines from June 2008 Rentals June ’08 END 5 disks taken $29,000 $25 Total # END Visits # Credit Cards w/ END Total END Charges 89,000 73,000 $767,000 4 disks taken $51,000 $20 3 disks taken $15 2 disks taken Opportunity: Develop a risk profile to identify and limit the basket size for high risk customers $10 $207,000 1 disk taken $5 $265,000 $214,000 100,000 50,000 25,000 75,000 # of June END Rental Visits

21. END Recommendations The total potential EBITDA impact of the recommended END solution is estimated to be over $2M per year Short Term Solution Longer Term Solution Implement a credit risk matrix that assigns a risk score to each redbox account (credit card). Update rental systems software to restrict basket size per credit card based on the risk score. Note: Analysis to date has identified one specific risk matrix / basket size limit scenario that would have had a $2M EBITDA impact if implemented in 2008. Further optimization is possible with continued analysis. Continue analysis to determine if there is a benefit of implementing the risk matrix in (1) at a customer, rather than credit card level. Continue scenario analysis to determine whether immediately implementing more stringent basket size limits at specific high risk kiosks, or for all customers could have a positive EBITDA impact. 11

22. Appendix

23. EBITDA Impact of Preventing Uncollectibles EBITDA Impact of Fraud vs. Sales Example Business Model Product Cost $.60 Labor Cost $.15 Overhead Cost $.05 Sell Price $1.00 EBITDA Margin 20% 1 unit is stolen -$.80 1 unit is purchased +$.20 Customer A Customer B Customer C Units Stolen 1 2 1 Fraud EBITDA -$.80 -$1.60 -$.80 Units Purchased 5 2 4 EBITDA $1.00 $.40 $.80 Customer EBITDA $.20 -$1.20 $.0 -$1.00 Even though the net revenue from these 3 customers is +7, the net EBITDA impact is -1, and the company would want to implement policies to prevent these transactions from occurring 13

24. Passive Buy Declines 14 From arevenue perspective, the paid charges associated with customers that owe redbox for at least one passive buy appears to exceed the unpaid charges they continue to generate 2008 Paid vs. Unpaid Charges Customers with at least one PBD in 2008 Note: Unpaid charges excludes the first PBD

25. Customer Rollup Methodology - 1 Emails Names Redbox customers often have more than one credit or debit card they use for transactions. To date, there is no standard procedure on matching multiple accounts to a unique customer. This is a proposed methodology for determining how many accounts each redbox customer actually represents. There are three main variables that play a part in the account matching process: names, emails, and ZIP codes. ZIP Codes Cardholder name formats differ across accounts Emails are manually entered and could be incorrect or not truly exist ZIPs are manually entered but most customers believe they need to be correct for the transaction to be successful

26. “Dirty” data must be cleansed before processing can begin; matching exact account names is often difficult because different credit and debit cards use various card holder naming conventions; for example, a MasterCard transaction looks like “Jane Smith”, whereas a Visa transaction may look like “Smith Jane”. Process Outline I. Scrub the Data Filter out invalid ZIP Codes (eg “00000”) Alter email addresses (eg change j.smith@yaho.com to j.smith@yahoo.com) Remove very common names, null names, and non-names (eg “a gift for you”) II. Refine the Data Separate full account names into meaningful pieces Remove extraneous characters (eg extra spaces or slashes) Match names in “forward” order Match names in “backward” order III. Group the Data Please see the next slide for a detailed outline of the proposed matching process Customer Rollup Methodology - 2

27. Below is the proposed order in which matches could be made; if wanted, varying levels of matches may be awarded a higher or lower “score” based on completeness of the match Possible Point Allocation 1Full name or reverse refers to a match like “Jane Smith” or “Smith Jane” 2Due to the many variations on name formats, the first initial may actually be the first letter of the middle or last name; thus the last name may actually be a first or middle name Customer Rollup Methodology - 2