A few days back upon submitting my email for an interesting looking upcoming product’s waitlist, I had a feeling of deja-vu. I felt I have done this at least 5-7 times over the last few months but if I were to try I can’t recall even a single name out of all those products I signed up to be in the waitlist for.
One of the most recent products I signed up to try for.
Herein lies the problem and that’s what bugged me.
What’s the point of signing up for a waitlist if it’d take you forever to release your product or approve my request?
What’s worse, you’ll have to keep guessing what happened to your invite or the product you signed up for.
Did the startup shut up before releasing or were they just collecting email ids?
I guess we’ve all been there. Now, let’s try to think what’s an ideal way to approach this.
What would the user be interested in?
As a user, my main concerns in this context (with varying priority as time goes on) would be
What is happening with my invite?
What is happening with the product while I wait for my invite?
Why should I still bother about your product ?
How could these concerns be handled?
Let’s divide this into two parts:
Invites are being released to users in waitlist – If uses on the waitlist have started getting invites for the product, you can share the details on invites being sent out (how many per day/week/month) and basis my waitlist rank, when should I expect to get an invite and if there’s something I can do to expedite this process.
Just to keep up the excitement, you could also add some reviews by users who’ve gotten access or any great PR/product reviews you are getting.
Invites haven’t started going out to users in the waitlist – This means, the product is not ready to be released to the waitlist yet, which means either you didn’t plan this properly or ran into unexpected delays.
Either ways, the odds of users losing interest on your product at this stage are really high (unless of course if you are building hey.com, or will launch a stellar product that people will be more than happy to get to use the product and nothing else would matter.
The first thing in this case that needs to happen is trying to get a handle on expected date on which you can release the invites. Depending on how far into the future, this date is, you can plan a cadence of communication. Communication plan for 1 month will be very different from one for 6 months.
Few things you can keep in mind while designing a communication plan for users in waitlist for delayed product release.
Product Release Date and Communication Cadence – How often will you send the communication.
Product Progress – Updates on how the product is shaping up. Details on this process (technical challenges or product decisions etc can resonate with users with appreciation for these things).
User Involvement – If you are up for it, you can actually have deeper engagement with the waitlist by having them part of user research interviews or beta testing programs
Closing Thoughts:
Start accepting waitlist when product is nearing the launch date.
Whether you are rolling out the invites or not, keep updating users on waitlist regularly (waitlist updates or product progress).
Give option to users to change the frequency of your updates or unsubscribe.
If possible, engage with users and build deeper relationships with them.
The trigger for this post was this interesting tweet
How are Engagement and Retention related?
An inquiry into this question leads to a perspective on how to view engagement for your product. This perspective will help you decide your engagement and retention strategy effectively.
To dig deeper, let’s start by classifying various products into categories
Utility/Productivity
News & Entertainment (Video, Audio, Text)
Messaging, Social Media & Gaming
Transactional/E-commerce & Payments
I’ve tried to club all apps into these four broad categories for simplification. Now you have to identify in which category does your product fall under.
Let’s consider some examples mentioned in the discussion on the tweet above and map them to categories
By virtue of the category to which your product belongs there will be a potential engagement frequency for it.
Potential Engagement Frequency: Number of times a product can be used over a period of time.
* This frequency is for usage of the core product functionality, the same can be increased by adding more features.
Is High Engagement a necessary but not sufficient condition for Retention?
Not really. While Engagement is strongly correlated with Retention, neither is it necessary for nor does it guarantee Retention.
To understand why that’s the case let’s try to list down other Influencing Factors that impact engagement and retention across products
Needs – Jobs to be done
Trends – Shiny new things
Incentives – Offers, rewards and such
Distributionadvantage – Availability of good alternatives, network effects
If you look at any product from these two lenses (categories and influencing factors) you would be able to understand if/where does engagement results in retention and where it doesn’t.
Note: Retention is significantly dependent on the products ability to reliably deliver on the core needs and its stickiness
Utility/Productivity – For this category engagement could be a function of frequency of the need and retention would be a function of how much stickiness can the product build (through data, learning curve etc).
Some apps I use are Evernote, Myfitnesspal and Shazam. I’ve been using these three apps (with varying levels of engagement) for at least three years. While I use Evernote almost daily, I use Myfitness pal weekly when I’m tracking my weight and it’ll sit unused for months when I don’t track my weight.
In many such cases retention comes because the effort to learn how a new app isn’t worth the marginal improvement you’ll get.
However, in some cases where the the single player mode isn’t that strong, you’ll have to build engagement around other features to ensure top of mind recall and retention
News & Entertainment (Video, Audio, Text) – For this category engagement could be a good indicator of retention as users one used to a platform don’t tend to change as much as long as content quality is maintained and new content (user generated or otherwise) keeps getting added.
The exceptions could be some paradigm shifts (like storage to streaming or desktop to mobile) or loosening of distribution advantage (people might be engaging actively with proprietary apps on an OS but that could change quickly if other apps surface) but the shift would be gradual so it can be picked
Messaging, Social Media & Gaming – For this category engagement could be a function of trends and retention could be a function of trends, needs (vs wants) and distribution advantage. Games have the highest 30 day retention among all categories (so definitely more engagement). While Games are particularly prone to change of trend cycles, everyone is familiar about how network effects if working in other direction (or Eflactem’s Law as it’s called) can sink a social media company. More on this here.
One of the biggest examples was a viral apps called Yo. For those who don’t know about Yo, it was a one tap, one messaging app through which users could send a ‘Yo’ to their friends, who could then reply back with a guess what? (Yo). It had great engagement (over 1.2 mn DAUs at one time) for a few days/weeks that people used it but that engagement never translated into retention.
The task at hand for games is translate high engagement into stickiness by means such as virtual goods, avatars and such to avoid change of seasons churn and for social networks it is to improve engagement and try to convert that into network effects (while improving the product and increasing its scope for potential users)
The Rise and Fall of “Yo”
4. Transactional/E-commerce & Payments – For this category engagement could be a function of frequency of need and incentives and retention could be a function of consistency of experience. Here figuring out the ‘Potential Engagement Frequency‘ and % adoption from that is a good measure. For ex: Uber measures and tries to improve their share of airport rides (to departure airport and subsequently from the arrival airport).
For a flight or a hotel booking site say the average user does a transaction after every 3-6 months or more. One way to approach retention for such cases is to see what’s the average usage frequency of the user and if that’s increased or maintained. A drop in previous levels of engagement could be a sign of churn. Also, an drop in funnel conversion rates for a user should be worrying (For ex: Uber on basis of my rides data might think I take cabs once a week only but if I check for a ride more often but book only once a week, perhaps my potential engagement frequency is higher and they should try to improve my engagement per week.
Here an engagement (non incentive driven) is a good indicator of retention.
Another product that I like but use once a year or so is Cashify. The app does the job well but it’s not a frequent use case so they have to look at retention over a slightly long period as compared to say an Ecommerce store. They can however try to get their users who’ve finished a transaction to refer more users.
Key Takeaways:
Identify your product’s category its potential engagement frequency (PEF).
Understand which factors can influence engagement for your product. External (trends, wants, temporary distribution edge) or internal (needs, network effect, stickiness)
For Engagement: First focus on internal influencing factors (strengthening core use case experience, data moats, network effects etc) and build on them. In case of low PEF try to increase referrals.
For Retention: Divide users into cohorts basis their engagement wrt the PEF, monitor and encourage their engagement accordingly. Try to understand reasons for drop in engagement and fix them through the product as much as possible (product improvement over incentives)
This Point of view (categories and influencing factors) would help us in figuring out how to look at engagement and retention better. Would love to know what do you think?
Credits: Thanks to Navneet Singh for reading the draft and sharing feedback
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