This case study looks at the abandonment rate of a cell phone app and how we moved from the quantitative analysis of the abandonment rate to a qualitative analysis of why to action and strategy. 1008 Esq Unltd Case Study Analyt Rf Market Analytics V1.5.1

1. C ASE S TUDY:A NALY TICS
After Neilsen revealed TwitterÊs low 40% re-
use rate, one company realized that to survive
it had to do the same analysis
How data uncovers landmines that kill your business
Summary: When using free trials to launch a new product, it is
especially critical to determine if “tryers” will become “buyers.”
Segment “tryers” by recency and frequency to see if you have a
market or need to develop a new product. Why do this? Even
C ASE with a hyper growth rate (1000%+/yr.) only 40% of all new
Twitter users use it after one month, reports Neilsen. Twitter
S TUDY: loses users faster than it gains them. And that is unsustainable.
A NALY TICS
T H E C HALLENGE
M AY 2009 The company launched a mobile application that integrates with a contact
NO. 1008 manager through a website. (Details are masked to conceal its identity.) It used
a “freemium” model to launch, giving away the service for free. Many people
signed up to use the product, but the company had no revenue. And, the
company’s web usage data provided no analysis, insight or action steps.
So the challenge is to identify revenue streams for the company. Are there
markets that will use and pay for its product or service? What is the lead funnel
from sign-up to users, to repeat user, to frequent and recent user? Can we
forecast if users will become buyers?
Surface results looked favorable, when viewing website reports and graphs.
C O M PA N Y: They showed more than 7,000
UN people signed-up in 10 months.
DISCLOSED Sign-ups spiked with favorable press
S TAR T U P on TechCrunch and other sites. Yet,
some reports showed a misleadingly
I N D U S T R Y: rosy picture. The graph above mis-
CONSUMER titled “Total Users Per Day” really showed cumulative sign-ups; including
MOBILE & people who never used the product. The graph below shows a slow and steady
SOCIAL increase in unique users each week. While it shows information useful to see
MEDIA “user” growth, it ignores that only
55% of sign-ups converted to use the
PROFILE: service even once. And that masked a
PRE- big problem in conversion and
RE VENUE & adoption of “tryers” to “buyers,” and
FUNDED retention of potential “buyers.”
The company needed to understand sources of growth and emerging
characteristics of users, information critical to reaching real and informed
decisions. It needed analysis and insight into how likely someone is to go from
tryer to buyer. And it did not get that from web reports.
Bruce E. Segal ● 610-667-8188 ● BruceESegal@gmail.com

2. Page 2 of 3
T H E S OL UTION
So, the solution is to look beyond site reports. Do analysis. Segment users by
recency (time since last use) to measure adoption, retention and defection. See
if users make discernable patterns that reveal Number of SignUps and Conversions
potential revenue streams, market
opportunities or product benefits.
Repeat User
Using acquisition tactics, mostly 1,809 SignedUp,
promotions like blog mentions on 25.8% Never Used
1-Time User 3,129
TechCrunch and word of mouth 2,078 44.6%
referrals, the company reached 29.6%
7,000+ sign-ups in 10 months
adding 150 to 600 monthly. We
analyzed the conversion rate of sign-ups to
users and did a summary frequency analysis which revealed that the reports
masked that of all sign-ups–45% never use the product, 30% use it once, and
only 26% use it more than once. And this is a free product.
Then we analyzed recency rates.1 How recently a user last used a service or
product is a good predictor of future usage and retention rates. Recency analysis
revealed of those who sign-up, only 2.4% of them used it within the past 15 days
and 4.0% used it within the past month. In the best case, 45% of all sign-ups
stopped using the free product 2 months ago or longer and are “Lost.” In the
worst case, those same people comprise 80%+ of Users; a huge defection rate.
Recency Rates by 3 User Frequency Types
SignedUp 1-Time Repeat
Recency Count % of Tot Count % of Tot Count % of Tot Total Ct. Total %
1Current 0 0.0% 71 3.4% 97 5.4% 168 2.4%
2Nearly Lapsed 0 0.0% 45 2.2% 64 3.5% 109 1.6%
3Early Lapsed 0 0.0% 121 5.8% 73 4.0% 194 2.8%
4Lapsed 0 0.0% 121 5.8% 115 6.4% 236 3.4%
5Lost 0 0.0% 1,720 82.8% 1,460 80.7% 3,180 45.3%
Unconverted 3,129 100.0% 0.0% 0.0% 3,129 44.6%
Grand Total 3,129 100.0% 2,078 100.0% 1,809 100.0% 7,016 100.0%
To build a lead funnel we matched percents
in the two charts to forecast use and
3,200
2,800
defections. Of every 1,000 Sign Ups… 2,400
446 Never Convert to use service. 2,000 71
45
121
121
554 Convert to use service (24 Current, 78 Lapsed, 1,600 3,129 97
64
73
115
1Current
2Nearly Lapsed
453 Lost)
3Early Lapsed
1,200
4Lapsed
296=1-Time (10 Current, 41 Lapsed, 245 Lost);
5Lost
800 1,720 Unconvert ed
1,460
258=Repeat (14 Current, 37 Lapsed, 208 Lost).
400
0
0 SignUp
Never Use 1-Tim e
Repeat
1 Recency Levels: "1Current" = used in past 15 days. "2Nearly Lapsed" = used in past 16-30 days. "3Early Lapsed" = used
in past 31 to 45 days. "4Lapsed" = used in past 46 to 60 days. "5Lost" = used in past 61 days or more.
Bruce E. Segal ● 610-667-8188 ● BruceESegal@gmail.com
1008 ESQunltd Case Study Analyt RF Market Analytics v1.4.doc

3. Page 3 of 3
For every 1,000 Sign Ups, only 24 are Current and 453 are Lost (1-Timers and
Repeaters combined). An industry bench mark put this in perspective and
highlighted the urgency the company faced.
Neilsen analyzed recency rates for Twitter,
FaceBook and MySpace users. While not a
perfect benchmark, Neilsen’s data gives a
directional comparison. It found 60% of Twitter
users stop using it after one month. At that rate,
keeping only 40% of its users after one month,
there comes a time when there are not enough
new users to replace those who defect; it is
unsustainable. A 40% retention rate limits
Twitter to a maximum reach of 10% of the web. 1 Source: Neilsen, Twitter Quitters 4-28-09
In comparison, at the same growth stage both FaceBook and MySpace had
higher retention rates of about 70%. No matter
how we slice our data, if this product does not
raise its retention rate to 60% or more, then at
some time there are not enough new users to
acquire to replace defecting ones.
This analysis let the company ask users why
they use and like the product and non-users
why they stopped using it, or never used it after
sign-up. The answers gave the company the
quantitative and qualitative foundation to 2 Source: Neilsen, Twitter Quitters 4-28-09
develop the product, identify a market, and make a go/no go decision.
T H E R ESULT: A N A LY S I S . I N S I G H T. A C T I O N !
Results: Company found that the 4% of recent users shared common traits and
represented a potential customer profile. It talked to them and identified new
features and benefits they would buy. Then it signed several Fortune 1000
companies as early customers. And, going forward, the company advanced from
data and reports to analysis, insight and action; to identify revenue streams and
forecast if tryers will become buyers and make informed decisions.
Analysis: Even as a “freemium,” the product had low retention rate, only 4% of
sign-ups used the product within the past month, which is significantly lower
than benchmarks, Twitter, FaceBook and MySpace.
Insight and Actions: Used analysis to identify users and non-users. Asked the
45% of lapsed users why they stopped using product and if cause is fixable.
Asked the 4% of recent users why they use it what more do they want. User
feedback let it develop a saleable product and close first deals.
To learn how Bruce and E*S*Q unlimited can help you make business decisions
based on a quantitative foundation and avoid company-killing-mistakes, call
Bruce at 610-667-8188 or e-mail BrucESegal@gmail.com.
Bruce E. Segal ● 610-667-8188 ● BruceESegal@gmail.com
1008 ESQunltd Case Study Analyt RF Market Analytics v1.4.doc