Market Launch: How To Determine If Your Freemium Phone App Has A Market?


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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

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Market Launch: How To Determine If Your Freemium Phone App Has A Market?

  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 ●
  2. 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 ● 1008 ESQunltd Case Study Analyt RF Market Analytics v1.4.doc
  3. 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 Bruce E. Segal ● 610-667-8188 ● 1008 ESQunltd Case Study Analyt RF Market Analytics v1.4.doc