Viral Marketing on Facebook
(How does viral saturation affect active user count)
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Viral Marketing on Facebook
(How does viral saturation affect active user count)
As your app increases in usage and hits saturation, low retention rates can cause your app to "jump the shark" - here's a simplified model showing how it happens Andrew Chen (voodoo@gmail.com) http://andrewchen.typepad.com/ Jeremy Liew (jeremy@lightspeedvp.com) http://lsvp.wordpress.com/ 3/9/2008 Distributed for free under GNU Free Documentation License - http://www.gnu.org/copyleft/fdl.html If you have questions, anything to add, or want to generally chat, don't hesitate to reach me at my e-mail address, voodoo [at] gmail --Andrew Chen
Simplified viral marketing
invite conversion rate % avg invites initial user base carrying capacity 10% 8.00 3,000 100,000 time period 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 saturation % conversion % (adj) 0% 10.0% 3% 9.7% 5% 9.5% 9% 9.1% 16% 8.4% 27% 7.3% 43% 5.7% 62% 3.8% 81% 1.9% 93% 0.7% 98% 0.2% 100% 0.0% 100% 0.0% 100% 0.0% 100% 0.0% 100% 0.0% 100% 0.0% 100% 0.0% 100% 0.0% 100% 0.0%
(with reduced conversion % as userbase s
Cumulative users o
120,000
100,000
80,000 Total users
60,000
40,000
40,000
20,000
1 2 3 4 5 6 7
viral marketing model
conversion % as userbase saturates)
conversions / user 0.78 0.76 0.73 0.67 0.58 0.46 0.30 0.15 0.05 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
new users 2,328 4,035 6,789 10,835 15,763 19,580 18,784 12,255 4,953 1,320 285 58 12 2 0 0 0 0 0
cumulative users 3,000 5,328 9,363 16,153 26,987 42,751 62,330 81,114 93,369 98,322 99,642 99,927 99,985 99,997 99,999 100,000 100,000 100,000 100,000 100,000
Cumulative users over time
7
8
9
10 11 Time period
12
13
14
15
16
17
18
19
20
Act
retention rate % 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 active users 1 50% 3,000 2 60% 1,500 2,328 3 70% 900 1,164 4,035 4 80% 630 698 2,018 6,789 5 90% 504 489 1,211 3,395 10,835 6 95% 454 391 847 2,037 5,417 15,763
(wit
Time-period cohorts
3,000
3,828
6,099
10,135
16,433
24,910
Active users over time 40,000
35,000
30,000
Active users
25,000
20,000
15,000
10,000
5,000
1 2 3 4 5 6 7 8 9 10 11 12
Time period
Active user count model
(with viral and retention rates factored in)
7 96% 431 352 678 1,426 3,250 7,882 19,580 8 96% 414 334 610 1,141 2,275 4,729 9,790 18,784 9 96% 397 321 580 1,027 1,820 3,310 5,874 9,392 12,255 Time 10 96% 381 308 556 975 1,638 2,648 4,112 5,635 6,128 4,953 period 11 96% 366 296 534 936 1,556 2,383 3,289 3,945 3,677 2,476 1,320 12 96% 351 284 513 899 1,494 2,264 2,960 3,156 2,574 1,486 660 285 13 96% 337 273 492 863 1,434 2,174 2,812 2,840 2,059 1,040 396 143 58 14 96% 324 262 473 828 1,377 2,087 2,700 2,698 1,853 832 277 86 29 12
33,598
38,077
34,976
27,335
20,779
16,926
14,921
13,837
12
13
14
15
16
17
18
19
20
me period
15 96% 311 251 454 795 1,322 2,003 2,592 2,590 1,760 749 222 60 17 6 2
16 96% 298 241 436 763 1,269 1,923 2,488 2,487 1,690 711 200 48 12 3 1 0
17 96% 286 232 418 733 1,218 1,846 2,389 2,387 1,622 683 190 43 10 2 1 0 0
18 96% 275 222 401 704 1,169 1,772 2,293 2,292 1,557 656 182 41 9 2 0 0 0 0
19 96% 264 213 385 675 1,123 1,701 2,201 2,200 1,495 629 175 39 8 2 0 0 0 0 0
20 96% 253 205 370 648 1,078 1,633 2,113 2,112 1,435 604 168 38 8 2 0 0 0 0 0 0 10,668
13,135
12,572
12,061
11,576
11,113