Better Referral Program for Better Growth

REFERRING YOU A NEW MODEL FOR GROWTH

TIME BASED REFERRAL SYSTEM WEBINAR

WELCOME!

CURRENT REFERRAL PROGRAM MODEL

PROS

▸ WOM

▸ Scalable

▸ Tracking

▸ Directly related to CAC

▸ Dual sided


PROS

▸ WOM • one of the best forms of organic traffic there is, it’s low cost, and trackable with given technologies, so you can identify key influencers.

▸ Scalable • having a referral program is a scalable solution for growth, for example if you are doing inbound methods such as blogging, you would need more content, and thus

need to hire more content creators. or if you are doing outbound such as sales team, then you would need to hire more sales reps. However being a scalable solution like a referral program, all that is required is a well engineered solution deployed on a server and then the referral program will require O(1) extra employees in manpower.

▸ Tracking • having the data of your converted refers is fantastic, but also make sure you are tracking the total referrals sent, referrals opened, referrals landed, referrals signed up,

referrals converted, or other important user events. and also track the medium that these referrals are being conducted (i.e. facebook, email, txt msg, raven scrolls etc…)

▸ Directly related to CAC • your cost of acquisition for this particular channel can be calculated by the reward incentive that you give to your referrers/referrees, making it easy to determine

profitability or other related business decisions.

▸ Dual sided • one of the most important aspect is the focus on user experience/behavior psychology, taking away the feeling that users might believe they are taking advantage of

their friends by introducing rewards for both sides instead of just the referrer. Paying attention to user behavior psychology is utmost critical in designing a referral program.


CONS

▸ frigid incentive reward structure • this doesn’t leave enough room to raise perceived value. Perceived value is extremely important because lowering your perceived value

is HORRIBLE and anti referral best practices. When people refer products, they only want to refer you to product that’ll raise their own value in the eye of the recipient. which makes them unlikely to organically share your product if they perceive that you offer has inferior/lowly perceived value. things startups do that lowers perceive value, handing out discounts, lack of price comparison, low value, lack of anchor, focused on cost competitiveness.

▸ dual sided • dual sided is fantastic, but is there more to it? can we yield higher conversions of referrals?

▸ referral passivity

▸ discovery

▸ re-engage re-engage- re-engage


CONS

▸ referral passivity • Word of mouth referral method can be categorized as proactive or passive.

• proactive: people who are actively seeking out to maximize the amount of referral rewards• passive: people who are aware of your product but are not actively trying to refer people due to lack of awareness, lack of

initiative, lack of attractive incentive, lack of referee to product fit, etc….An effective Referral program needs to attempt to convert this passive cohort into proactive via raising awareness of the referral program, increase incentive attractiveness, break apathy inertia, and have a consistent message on brand value. Awareness is often done with on site notifications or email newsletters. In the case of emails, this leaves the possibility of your key message of raising awareness of your referral program have to cross the “gauntlet of attention distractors” to be read.

▸ discovery • referral programs that are not embedded into the product itself often need to be discovered by the user. if you have a referral

program, it needs to be advertised to your user base so that they become aware of it. Which means there is a possibility that a percentage of your user base may not have been reached, thus curbing your growth potential.

▸ re-engage re-engage- re-engage • even if you do make them aware, an effective referral program would need to constantly remind people of its existence, as long as

there is still contacts yet to be invited, there is still opportunity left on table for growth. This can be solved by using push notification and/or email reminders, but you have to be extremely careful to not cross the line and become a nuisance instead with spam.

• So could an alternative exist where we can directly imbed the referral into the product, by linking the usage of the product directly to the product’s growth? Thus solving the awareness problem while also increasing incentive attractiveness, break apathy inertia, an have a consistent brand value to better find referee to product fit?

LOREM_IPSUMINTRODUCING

NEW REFERRALINTRODUCING

NEW REFERRAL PROGRAM MODEL

KEY FEATURES

▸ ecosystem referral

▸ compound incentives

▸ time interval as main unit of currency

▸ All You Can Eat (AYCE)

▸ reimbursement instead of free trials

HOW ECOSYSTEM REFERRALS AND COMPOUNDED INCENTIVE WORK

CB

User

A D


B

User

A DPAID


B

User

A DPAID

+1N

+1N

where N is the unit of currency rewarded to the most adjacent referral


B

User

A DPAID

F G



B

User

A DPAID

F GPAID

+1N

+1N

+0.5N


+0.5N


User refer C C refer F Total per user

User 1N 0.5N 1.5N

Referral C 1N 1N 2N

Referral F NaN 1N +0.5N 1.5N

Total payout 2N 3N 5N

PAYOUT FORMULA/ACQUISITION COST SUM

Total payout for a chain of invites

d is depth of the chain in the referral tree at each level, m is the maximum depth(degree/level) of the chain

the multiply by 2 in the front is because double sided reward


WHAT ABOUT THE ENTIRE COST OF THE WHOLE REFERRAL ECOSYSTEM TREE ? NOT JUST THE CHAIN OF INVITES?


Total payout formula for entire tree

w is total number of node to child connection with single side reward

x is total number of node to grandchild connection with n/2single side reward

y is total number of node to great grandchild connection with single side reward

z is total number of node to great great grandchild connection with single side reward

ε is total number of node to grandchild connection with single side reward

.

.

.

*note: i is degree of connection


EXAMPLE




x is total number of node to grandchild connection with n/2single side reward




.

.

.



w = 1


w = 2


w = 3


w = 4


w = 5


w = 6


w = 7


w = 12




x is total number of node to grandchild connection with n/ single side reward




.

.

.



w = 12


w = 12x = 1


w = 12x = 2


w = 12x = 3


w = 12x = 4


w = 12x = 5


w = 12x = 6


w = 12x = 7


w = 12x = 8


w = 12x = 8w = 12x = 8


w = 12x = 8








.

.

.



w = 12x = 8


w = 12x = 8y = 1

w = 12x = 8y = 1


w = 12x = 8y = 1


w = 12x = 8y = 1

w = 12x = 8y = 2


w = 12x = 8y = 2


w = 12x = 8y = 2

w = 12x = 8y = 3


w = 12x = 8y = 3


w = 12x = 8y = 3

w = 12x = 8y = 4


w = 12x = 8y = 4


w = 12x = 8y = 4

w = 12x = 8y = 5


w = 12x = 8y = 5


w = 12x = 8y = 5

w = 12x = 8y = 5








.

.

.



w = 12x = 8y = 5


w = 12x = 8y = 5z = 1

w = 12x = 8y = 5z = 1


w = 12x = 8y = 5z = 1


w = 12x = 8y = 5z = 1

w = 12x = 8y = 5z = 2


w = 12x = 8y = 5z = 2


w = 12x = 8y = 5z = 2

w = 12x = 8y = 5z = 3


w = 12x = 8y = 5z = 3


w = 12x = 8y = 5z = 3

w = 12x = 8y = 5z = 4


w = 12x = 8y = 5z = 3

w = 12x = 8y = 5z = 4

w = 12x = 8y = 5z = 4


w = 12x = 8y = 5z = 4








.

.

.









.

.

.


= 4








.

.

.


= 4 == great great great grandchild








.

.

.


= 4 == great great great grandchild d = 4








.

.

.


= 4 == great great great grandchild d = 4 = n/16 single side reward


w = 12x = 8y = 5z = 4


w = 12x = 8y = 5z = 4

=0w = 12x = 8y = 5z = 4

=1


w = 12x = 8y = 5z = 4

=1


w = 12x = 8y = 5z = 4

=0w = 12x = 8y = 5z = 4

=2


w = 12x = 8y = 5z = 4

=0w = 12x = 8y = 5z = 4

=2w = 12x = 8y = 5z = 4

=2


y = 5 z = 4 =2w = 12 x = 8


Total Payout New referral model :


Total Payout New referral model :

Compare with the old traditional referral model


traditional total payout for a double sided incentive referral

where w is the number of referrals confirmed


so for our example you are looking at 12 confirmed referrals

2 ∗ 12 ∗n = 24n units of reward


Total Payout New Referral Model :


Total payout old referral model:


Total Payout New Referral Model :


Total payout old referral model:

new about 1.5 times more than old


but what is the growth curve of the total payout? is it going to spiral out of control?

am I going to bankrupt my product paying too much?




converges at a defined number, does not grow out of control





costs about 1.5 times more than traditional model





costs about 1.5 times more than traditional model

ONLYin worst case


best case worst case

no additional compound incentives

need to be paid

additional compound incentives need to be paid

and compounds for every additional node in chain


ECOSYSTEM REFERRAL ADVANTAGES

adds feedback loop for the referral program: referrers invite more people and build bigger reward network for all -> more referrals go out because bigger reward network -> more people join because more invites -> builds bigger reward network -> more invites ….

people who refer would benefit not only from their immediate friend but also each degree of additional connections as well

a system which benefits everyone symbiotically, each node’s edge will provide value for the root node, and subsequent node will provide value for the root node as well, thus increasing referral yield

thus making people more willing to invite in favor of not only benefitting yourself but also other friends and their friends, friend’s friends… make your friends look good among his/her peers

network benefits instead of just mutual benefits, potential to be bigger drive and better incentive to more invites and higher conversions

having ecosystem reward is stronger incentive for people to refer using the channel you provide, that way it’ll provide better metrics and tracking than when people use the generic word of mouth channel, which cause issues in attribution


COMPOUND INCENTIVE PROS

‣ more reward than double sided incentive for all participants

‣ potentially be greeted with rewards when logging back in, thus incentivizing frequent visits if reward deemed attractive enough, potentially raising engagement and/or retention

‣ the demographics of those who are the social connection rich group, will be better utilized

‣ variable reward - Nir Eyal

‣ making a good referral can yield passive residual rewards for no additional labor


KEY FEATURES





▸ reimbursement period

TIME INTERVAL AS MAIN CURRENCY

WHAT IS TIME INTERVAL AKA TIME CREDITS

▸ time interval as reward, tokens for unlimited spending in units of hours/days/months will be awarded depending upon actions converted for referral program provider’s business logic.


action business logicwhen a person you refer opens the invitation you sent.

increase open rate

when a person you refer clicks the link increase landing rate

when the cohort of people you refer converts at (5%) or more. (how to get this number? write longer detailed reasons why you think its best fit for them,

do not spam people, it will lower your rate, etc)

when a person you refer signs up for the service increase conversion rate

when a person you refer successfully implement the service increase on boarding success rate with goal at better retention, e.g. Facebook’s 10 friends in 14 days

when a person you refer to has signed up for the service, but have yet to successfully linked peripheral apis etc.

increase user experience satisfaction rate, e.g. your service could save X amount of server cost if your user uses dropbox instead of making you host

when a person you refer opens up the invite prompt when a person you refer starts selecting people to refer

breaks the apathy inertia

when a person you refer has been active for x period increase retention&engagement. use as a way for users to think before they invite. e.g. who amongst my friends is more likely to use this often. NOT a hack for metrics ! !

follow us on twitter/or whatever social media handle increase social media presence

when a person you refer has bought you a beer when a person you refer has become an employee hire

be creative!


BENEFITS

▸ having time interval aka time credits will give you power of unlimited spending as motivator

▸ All You Can Eat(AYCE) model, or BINGE as incentive/motivator, “percentage of users that binge watch shows is 70%” -Deloitte Survey in March 2016

▸ enables compounded and ecosystem reward

▸ more fluid transition between basic and premium plans, recurring to bill for time tokens


BENEFITS

▸ flexible in reward

▸ allows more influence on referral cycle

▸ loss aversion principle

once the user experiences the privilege and the access to the premium benefits during his usage of the time credit he earned, both loss aversion affect and gains kicks in, in this case the fear of losing premium privilege period subsidized by the business, “Some studies suggest that losses are as much as twice as psychologically powerful as gains”, while I don’t personally hold studies for intangible affects as universal truth in practice, but logically there is no wrong in implementing both loss version as well as gains effects in your incentive motivators. which would motivate privileged user to invite more people in order to not lose the privileges or decide to pay.


ALSO…

▸ since the data for each step of the referral cycle is already being tracked, organize and display these data to your users. Nurture them into pseudo growth marketers, give them the insights, the tutorials, the advice that are necessary so they can do a better job at what they are already doing, which referring your product.


KEY FEATURES





▸ reimbursement period

REIMBURSEMENT PERIOD

WHAT IS REIMBURSEMENT PERIOD

▸ all spending accrued during a specific interval of time will be reimbursed ( at a cost of $0 ) to the user

▸ subsidized by provider of the referral program and product

▸ equivalent to free trial financial wise


WHY

▸ have more control of the perceived value of your product

▸ building high perceive value is more important than charging the right price.

▸ removes the need for discount as incentive

▸ free is a form of discounting, and discounting is the fast lane to low perceived value


KEY FEATURES

▸ the amount spent displayed

▸ this allows the anchoring of perceived value

▸ the amount reimbursement shown

▸ micro unit pricing

▸ auto adjustment for the time that has the most spending

REIMBURSEMENT

AUTO ADJUSTMENT

▸ depending on your business

▸ charge by unit consumed? or charge by time credits?

▸ if by unit consumed then auto adjustment

▸ goal is to build trust, put their interest(save) over ours(profit)


WHAT FLEXIBILITY IN PRICING WITH REIMBURSEMENT CAN DO FOR ELEVATING PERCEIVED VALUE

▸ for example, a startup Arambay, has spent 50,000 micro units (in this case api requests) on its account.

▸ at the end of the month you display $10,000 value(at $0.2/request) in Arambay’s account dashboard, this $10,000 will be reimbursed so Arambay will receive no charge

▸ then when it’s time to start the next month billing cycle, give a notification that states “you’ve done an excellent job in using our service and sending us valuable data and giving us quality referrals to perfect our product. we’ve qualified you for a premium rate at $0.1/request” so next month for 50,000 requests it’ll cost only $0.1/request $5000, which is half of the original

▸ just like that , you maintained your brand perceived value as a premium product, and were still able to offer your loyal customer a discount without risking lowering perceived value. now the reception by Arambay will be similar to gaining admittance to an exclusive club

▸ where as if it was just a free trial. the sentiment received by the Aramabay would be something similar to “you have finished free trial, now it’s going to cost you $5000”, now that’ll feel like a cheap shot


OTHER BENEFITS OF REIMBURSEMENT + TIME CREDIT

▸ you can run endless pricing experiments without commitment or risk of price discrimination

▸ bad pr if you get caught split testing to charge different prices to same customer, you’ll get caught for price discrimination

▸ And it’s harder to iterate on pricing than other elements of your business. Once you set a price, coming down is usually easier than going up.


OTHER BENEFITS OF REIMBURSEMENT + TIME CREDIT

▸ leveraged to gain momentum into critical mass

▸ for startups with network effects, it is often ideal to reach critical mass as quickly as possible in order for network effect to function in full throttle. coordinating your big launches with a referral program that has compound incentives will motivate people to work harder to invite due to the residual rewards

▸ since you are also offering unlimited AYCE model, you can use that as a value proposition to generate buzz to build onto of your momentum to reach critical mass

▸ provide analytics

▸ find clusters, sub clusters or star nodes to identify demographics groups or individuals of certain cliques inside the referral graph

▸ identify the power referrers

▸ coordinate with other marketing efforts

IMPLEMENTATION

HOW TO IMPLEMENT

▸ product analysis

▸ current business model or planned business model

▸ cost limitations

▸ reward scalability

▸ network effect in place

▸ pick and choose features

IMPLEMENTATION

HOW TO IMPLEMENT CAVEATS

▸ some features have to be adjusted, 10k versus 10mil daily request is unsustainable

▸ ecosystem referral and compound incentive are fundamental

▸ AYCE only apply to certain services, where you know your spending ceiling is not easily reached, or there is network effect in place where you get exponential value from growth versus spending

▸ recommend at least network effect to use AYCE

REFERRAL HISTORY TABLE

username your reward their reward message

tutorial +1 days +1 days when a person you refer reads the invitation

tutorial +5 days +5 days when a person you refer visits our site

tutorial + 10 days +10 days when a person you refer signs up

tutorial +30 days +30 days when a person you refer purchased our product

[email protected] + 1 day + 1 day invitation sent

[email protected] +5 days +5 days website visited

patrickxie2016 +10 days + 10 days signed up as patrickxie2016

patrickxie2016 +30 days +30 days subscribed for our Premier Tier 1 plan

joey +15 days +15 days subscribed Tier 1 *courtesy of Patrickxie2016’s referral

monica +7.5 days +7.5 days subscribed Tier 1 *courtesy of joey’s referral

phoebe +3.25days +3.25 days subscribed Tier 1 *courtesy of monica’s referral

chandler +1.625 days +1.625 days subscribed Tier 1 *courtesy of phoebe’s referral

you + 5 days + 5 days twitter mention

you + 6 days + 6 days uploaded your data

you + 5 hours + 5 hours 3 month consecutive sign-in

mailto:[email protected]?subject=

mailto:[email protected]?subject=

EXAMPLE IMPLEMENTATION

INVENGINE.IO

▸ mvp stage, very very raw

▸ vision is a api/distributable engine inside apps to help apps grow

▸ by giving smart recommendation to the app’s invite more people page, we can the users in your apps exposed to more curated contacts, thus higher conversions, and higher lead counts.

EXAMPLE IMPLEMENTATION

INVENGINE.IO

▸ set up api, when apps access api, they trade in contacts data, after we calculate through data signals, such as invite history, the medium you are inviting on, the content type, keywords we will return you a list of contacts that we recommend in highest probability to low order

▸ api usage unlimited

▸ time interval credits are handed out based on ecosystem referrals

▸ when a client(A) refers a new client(B), A, and B will gain unlimited api data usage for 30 days

QUESTIONS?

contact info: [email protected]

checkout my startup’s mvp: invengine.io

interested in building a new referral model to enhance growth for your startup feel free to chat

mailto:[email protected]

Better Referral Program for Better Growth

Business

Transcript of Better Referral Program for Better Growth