CASE STUDY:

● ●

ANALY TICS

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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 with a hyper growth rate (1000%+/yr.) only 40% of all new Twitter users use it after one month, reports Neilsen. Twitter loses users faster than it gains them. And that is unsustainable.

CASE

STUDY: ANALY TICS

MAY 2009 N O . 1008

THE CHALLENGE

The company launched a mobile application that integrates with a contact 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.

COMPANY: UN

DISCLOSED

START UP

INDUSTRY: CONSUMER

MOBILE &

SOCIAL

MEDIA

PROFILE: PRE-RE VENUE &

FUNDED

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. They showed more than 7,000 people signed-up in 10 months. Sign-ups spiked with favorable press on TechCrunch and other sites. Yet, some reports showed a misleadingly rosy picture. The graph above mis-titled “Total Users Per Day” really showed cumulative sign-ups; including people who never used the product. The graph below shows a slow and steady increase in unique users each week. While it shows information useful to see “user” growth, it ignores that only 55% of sign-ups converted to use the service even once. And that masked a big problem in conversion and adoption of “tryers” to “buyers,” and 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.

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User Funnel: Detail of Usage

3,129

1,460

1,720

0 500 1,000 1,500 2,000 2,500 3,000 3,500

Repeat

1-Time

SignUp Never Use

Unconverted5Lost4Lapsed3Early Lapsed2Nearly Lapsed1Current

User Funnel: SignUps to Conversion to Repeat User

1,809

3,129

2,078

7,016100.0%

44.6%

29.6%

25.8%

(500) 500 1,500 2,500 3,500 4,500 5,500 6,500 7,500

Repeat User

1-Time User

SignedUp, NeverUsed

Total Sign Ups

THE SOLUTION

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 potential revenue streams, market opportunities or product benefits.

Using acquisition tactics, mostly promotions like blog mentions on TechCrunch, and word of mouth referrals, usage reports showed 7,000+ sign-ups in 10 months and 150 to 600 monthly users. We analyzed the conversion rate of sign-ups to users and frequency of use. Our analysis revealed the reports hid that only 45% of all sign-ups every tried the product, 30% used it once, and only 26% used 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%

With these charts we built a lead funnel per 1,000 Sign Ups to predict use and defections. Of every 1,000 Sign Ups, 446 Never Convert to use service, and 554

Convert to use it (24 Current, 78

Lapsed, 453 Lost). Of those who Convert, 296 = 1-Time (10

Current, 41 Lapsed, 245 Lost); 258=Repeat (14 Current, 37

Lapsed, 208 Lost).

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.

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1 Source: Neilsen, Twitter Quitters 4-28-09

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.

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.

2 Source: Neilsen, Twitter Quitters 4-28-09

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 develop the product, identify a market, and make a go/no go decision.

THE RESULT: ANALY SIS. INSI GHT. ACTION!

Results: After using this insight to identify loyal users and what they value, the company signed several Fortune 1,000 clients as early customers. It interviewed the 4% recent users. As a result, it identified new features and benefits they would buy, and that they shared common traits that represented, a targetable customer profile. And it changed; upgrading its data and report mindset to an analysis, insight and action process to find new opportunities and forecast sales.

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.

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