Customer Retention & Cohort Analysis | How VCs Calculate Customer Retention
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- Опубліковано 28 тра 2024
- We learn the REAL way to calculate customer retention in the startup ecosystem - cohort analysis. We cover everything from user retention to net dollar retention to customer lifetime value. Downloadable template included.
✅ Download the Excel template: bit.ly/cohort_reten_mlchmp
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In this video, we learn about analyzing customer retention using cohort analysis tables.
The advantage of this method is that it works for all business models (subscription & non-subscription) to give a very exact view into the recurring purchase behavior of customers.
Sections:
0:57 explanation of customer cohort analysis data structure
4:11 calculating customer retention by cohort
7:14 revenue cohorts & net revenue retention (and net dollar retention)
10:14 calculating cumulative lifetime revenue by cohort
12:00 customer lifetime revenue
13:49 calculating customer lifetime value by cohort & CAC: LTV ratio discussion
Cohortizing this data allows us to view the quantities and timing of recurring purchases. With this info, we can understand how much in marketing we can afford to spend in order to acquire one new customer (customer acquisition cost).
We cover how to calculate venture capital KPIs like net revenue retention (net dollar retention), as well as the customer lifetime value (total lifetime gross profit from one customer) using our cohorts!
Long story short, cohort analysis tables tell the most important story of a company - the recurring purchase behavior of customers. With this information, we can raise funding, invest in marketing, and scale with precision.
By the end of this video, you will understand how to read and build customer retention reports using cohort analysis like a pro - I guarantee it!
If you have questions - leave a comment below and I'll try to help. Cheers!
#customerretention #cohortanalysis #startups
Questions? Let me know in the comments happy to discuss.
🚀 Also, if you want to learn how to systematically scale your startup without ending up as one of the 90% of startups that fail, have a look at this ⇒ www.ericandrewsstartups.com/financeforstartups
Hi, can retention of the subsequent month be higher than the previous one?
@vladimirdemidov6163 yes that is called revenue expansion or 100%+ net dollar retention and is common in SaaS
@eric_andrews thank you for the answer!
@eric_andrews could you please tell me how we should calculate average life span of the user?
Oh man this was excellent. Clear and concise. Even helped me understand the level of granularity(daily, weekly, monthly) I should approach while calculating CLV over time. Signed up for the waiting list to your course too. Cheers!
Really glad to hear it Rahul. Awesome you are on the waiting list as well, cheers!
This is super helpful. Thanks Eric!
This is so helpful! Thank you so much Eric
Thank you so much Eric , great explanation !
Eric, this is extremely valuable to me. Thank you so much for sharing and explaining what Customer retention and Cohort Analysis is to me.
You are very welcome!
thank you so much. so clearly explained. your pace and tone of speaking was so apt
Really glad to hear it, thanks!
Oh after watching the video, I have to say that you opened up my thought process! I subbed and did the notification thingy!
Really appreciate that Harish!!! 🙏🙏 Cheers
Hey Eric,
This is a superb primer on customer cohort analysis. Wanted to understand this for the first time and your video was super helpful. Liked and Subscribed. Keep up the awesome content.
Appreciate that prem, really glad to hear it
This is GOLD, thanks for this!
So well done and explained succinctly. A lot of information, explained in a simple manner and totally got it!
Cheers Roni!
I did nt got it becuze me come from Pakistan so I can not able to write in a english way. But I guess it is interesting in your busniess surrounding the retain of your call or in customers. I am also mental handicappated and I like to retain custonres that are in good quality bus and I also show good capability of understandinfg month of the year 12 month -start with december is called 0. Bery good retention , bery attractive learning book.
Very instructive Eric, see you at the next step
man this video is such a saviour.
Very well explained...one of the best cohort explanation...thank you buddy...!!
my pleasure, cheers!
Very explicit, informative, and concise. Thank you, Eric a bunch!
Glad it was helpful!
This is the best video about cohort analysis I ever see. thank you very much for sharing.
You are very welcome!
Amazing explanation. Excelent insights! Thank you so much. I will definitely come back here to review the content!
THANK YOU! Clear and concise.
Glad it was helpful!
Thanks, your videos are excellent!
Hey Leon - good to see you in the comments again! Thanks for the support and glad it was helpful
Eric you're a lifesaver TY for this video!!!
loved it, watched the whole video, stopped, started to follow along several times. so helpful thanks!
Awesome to hear
The explanation was spot on! Thank you so much!
Glad it was helpful!
Dude, I am starting a new job next week and your content has been a huge help.
You got it!
Im not having understaing in this content. I am wonder why retain if is no gain or interst in youir product? Maybe you product is not qualitaive and so people is bye the product. Maybe you shall have make in another country your product so it doesnt spread such a negative vibes for audience.
Very nice analysis and very nicely explained! Thank you. Keep up
Thanks, will do!
Thank you
This is super helpful. It gave me full understanding of the most practical way to calculate the retention matrix
glad to hear it
Great stuff- thanks for sharing
Thank You!
Helped me a lot!!!
I'm really glad it was helpful Sarvottam
Brilliantly explained...
This was really helpful! Thanks Eric :)
Glad to hear it!
Im Brasilian. I love your videos! Congratulations, you are the best!
Muito obridado amigo! I'm so happy they are helpful for you 😁
Thank you, really useful and informative info
Glad to hear it Tony 👍👍
Great content, thanks for sharing
It is clever because he knows book. He know to apply the book, in all the circumstances and he also see the future for the retain customers.
Great video!! 👍
Thanks Hans, cheers!
Thank you. I'm trying to start my career in Digital Marketing and this is helpful.
You are very welcome!
great explanation !
Thank you for this interesting video, very helpful in my marketing courses
you are very welcome!
Very interesting indeed. So good to retain according to the Marketing Parametrs,.
Though there are soo many example of cohort analysis using Tableau, Python, no one explained how to read the cohort table. Thank you Eric.
Haha, yes I noticed that, that's why I made this video!! Interpreting is usually harder than calculating 😎
Hey what an amazing session &thanks.
Basically i have 2 years experience in raw business development like lead generation,market research, team handling , sales, customer success or relationship so my question on which kind of analysis i should focua as you mentioned in video can you let me know such kind of techniques please i am in genuine need .
Hi Eric, nice video. I have a question, what is the difference between calculating retention rate by cohort vs by formula ((E-N)/S)*100%? as many websites explain. I compare these two methods the results are quite high different. Thanks.
Thank you for this amazing and beautiful beneficial information ❤❤
You are very welcome!!
Eric Excellent!
Thanks Peter! Appreciate the comment. Cheers 😎
Gran contenido. Me ha encantado y lo recomendarè.
me alegra mucho!!
That's really helpful!
I'm glad!
Thank you so much!
You are very welcome
Mind=Blown!!
This is really helpful, thank you! This focuses on new customers and the balance of digital acquisition spend as it relates to a customer's time with you which is eye opening. Two questions, this is a rolling twelve month view point, is there any point in looking at a longer time period? And then, do you have any videos on the health of repeat customers? What is the right balance of new to existing, etc? Thanks again!
On your first question, use the longest time periods you have data for. If you have 5 years of data, use it. That will give you a lot more info to plan your marketing.
On your second question, the actual mix of new vs existing is completely irrelevant (if you are growing faster you'll have more new vs. slower, youll have less, so you can misinterpret that data easily), what matters is your LTV:CAC ratio which tells you how much money you'll make on a customer after marketing. If it's high grow as much as you can. Subscription business often have LTV:CAC ratios that are 5-10+, eComm in the 2-3 range (average ones), and marketplaces 1-3 starting out, and then 5-10+ later on.
Hi Eric! This was such a great video to have gone through. I watched it a multiple times and made my own excel sheet and that taught me a lot. Thanks a ton!
You are very welcome! Ya these cohorts are super powerful. I use them a lot to build models and understand businesses. Good luck!
Great video
Appreciate that Rupert!
Eric, thanks for the video. Various SAAS companies have different subscription plan - monthly, quarterly etc. How do we look at the retention rate? Also customers shifting from monthly to quarterly plan?
Monthly to quarterly plan: might need a separate report, but still connected with the net dollar retention table
Great insights.....
cheers!
Great video, thanks for sharing!
How do you manage this same information when you have a 30 day free trial?
mindblowing
Hi Eric, thank you for sharing such valuable content. On application basis how do evaluate if the customer purchased on Ecommerce marketplace instead of our own website and if it was 1st or repeat, since MP dont share Customer data.
Without a customer ID like email or a way to track them, there is no way to calculate customer retention. You need to know who your customers are to track them. If the platform itself doesn't give you a cohort report, then it is impossible because they hide the data.
Eric - thank you, this was super helpful! I was wondering, how would you typically go about interpreting monthly/annual retention from such analyses? Would you just take the average retention of all cohorts every single month and then do another average of those figures to get to an average monthly retention for the year?
Yes, it's a great question. Yes you could take an average (it's not totally incorrect) but it is still a pretty crude way of measuring it...here's why.
So these retention tables sometimes eliminate the idea of "monthly" retention in the way you are thinking about it. So if you have very stable retention over time and across customer lifetimes (ex: 5% of customers return per month 6 months into their lifetime, and 5% return 3 years into their lifetime), well then yes an average is probably fine.
But the issue is that usually retention behavior generally declines in a non-linear way, so taking averages of people in month 3 vs. year 3 of their lifetime ends up not telling you anything very useful because it eliminates the nuance of your customer ages (ex: 10% of customers are returning 3 months into their lifetime vs. 1% 3 years in). Averaging those numbers basically tells you nothing.
Once you have the cohortized data split out by acquisition month, the best way to look at "monthly" retention is to compare the most recent month of data (the last cell in each horizontal row) across all the cohorts by comparing it to the vertical column (so that would compare June 2023 retention in every single individual cohort across the month 5, month 6, month 7, etc) so you could see if you had above average or below average retention in each cohort & lifetime month. So just look at the entire cohort table without averaging or combining anything, it will tell you the story.
In terms of your retention, you would more want to be tracking your customer LTV over time (ex: wow look our oldest customers are purchasing 5 times not 4) so that you can calibrate your CAC to profitable customer acquisition. You might see that LTV is higher than you had previously estimated in your oldest cohorts because in June you had strong retention. That would be something to dig into.
By the way overall % repeat revenue and your forecast for it are super important and you can build that forecast accurately with your cohort table!
Anyway, hope that makes sense!
Hey Eric, use an if function to conditionally apply zero's or blanks to the cells below the diagonal for which months or sales hasn't happened.
That's a good idea I'll see if I can work that into my future cohorts
I belive he shall use names. Like January February March is better to understand for us how lovely it is around here to see the meanaing of retaining .
Awesome vid thanks Eric! How would one approach it if each customer bought in a different MRR?
Two different options. First would be to create a separate cohort analysis for each product / price point and split them apart. I've seen these built with a filter at the top to switch between them.
Second is to just use the net revenue retention cohort table which makes the price point sort of irrelevant and just shows you how well your business does at actually retaining total dollars.
Hope that helps
@@eric_andrews thanks Eric ! I just created two different pivots, one with MRR and the other with churn, then combined them! Love your vids awesome content !
@@eric_andrews Hello Eric! Could you help me in understanding the question please? I'd really like to understand a new scenario. Thank you!
Perfect
Wow fantastic
Glad it was helpful Tarak, Cheers!
Hi Eric, Awesome video! I found this after I was trying to solve a for similar problem statement. While my approach was quite similar, instead of entering data in what looks like right-angled triangles with the base on top in your case, I made triangles with the base at the bottom. Eg. When I want to say that 18 people purchased for the first time in Feb, instead of Cell D12 in your sheet, I write it in Cell E12 and so on and so forth. That way, to find the total number of people purchasing in Feb, I can simply add values in column E instead of going sideways. While the logic seems to be the same, is the basic practice I followed flawed or might fail for a different scenario?
That way works as well, it's mathematically the same. In your way, it's easier to total the months but harder to compare the cohorts. Mine is harder to total months but easier to compare cohorts. For me the cohort comparison is where I'm focusing, but however you want is fine, cheers 👍
Hey. Nicely explained. Can you suggest that the same CLV is applicable for those companies who businesses through dealers.
I would say yes I think it applies to any business that makes money and has customers that have the potential to pay them more than one time.
Thank you m8
Awesome
thanks 👏
Thanks
🙏
amazing
🙌
Great video, thank you!!
I've been wondering about Day 0/Month 0. If a customer joins later in the month they get less days to experience the platform. So should we instead use a rolling window from the time a customer joins? Like if they joined on Apr 21, their Month 0 will be till May 20. If so how will we still group them in Apr cohort?
Here's my thinking - yes, you could theoretically build the report. You could go even further to build rolling weekly cohorts, or even cohortize individual days. Need to draw the line somewhere.
I think over longer periods of time monthly just summarizes the information into an easier-to-understand analysis. "The May 2021 cohort had great retention over the first 18 months" vs. "the rolling date cohort of 18 months ago with the start and end date constantly changing had great retention", that second analysis is a little harder to deal with.
Hey Eric, I've two questions
1: How frequently should we calculate NRR and report to senior leadership?
2: How to calculate NRR for multi year contracts?
1 - ideally monthly, but at a bare minimum quarterly
2 - if you are looking at cohorts, reference the initial purchase month to see the NRR of your oldest cohorts. If you are tracking business-wide metrics, you can use YoY. Just be clear with definitiiitions when you are presenting metrics.
Erick, amazing video, definetly subscribing and learning from you in the future. I wanted to ask you what way do you calculate your recurring customers that are first-time buyers in the actual month? What is a way of tracking it that your expertise would recommend?
How do you know they are recurring? Are they subscription?
@@eric_andrews no, it is an allacarte business. I am managing to get the information, but it is hard to get only new customers and their recurring purchases on following months. Im working on it 🫡
I was about to keep asking but I found the solution! A tough one but its done. if youre interested I can share it with you. My business is allacarte, that is why its so difficult.Thank you for your response btw!@@eric_andrews
Great video Eric! If you wanted to continue this model into a multi-year scenario, would it simply be a matter of extending the X/Y axes from 0-11 to, say, 0-23, or 0-35, etc? You should be able to extend any given cohort out forever, no? For example, would it be feasible/practical for a new customer that arrived in an Aug-2018 cohort to map out to Feb of 2023?
Yes just extend it. Being able to see a 5 year wide cohort would be extremely interesting and would give you much more confidence about customer lifetime dynamics.
Hello Eric thank you for the excellent teaching! My question is: when calculating LTV, the direct cost 35% (Gross Margin is 65%), what's the relationship between CAC and 35% direct cost, will any overlap exist?
The direct costs and CAC don't have any relationship. The 65% profit is basically the profit that comes back to the company as gross margin when they sell the product. With that 65%, they need to do the marketing. So the idea is that the CAC should be at a minimum less than the 65% GM LTV so that you know you will be profitable on the customer lifetime AFTER marketing expenses (CAC). Does that make sense?
Excellent video! Here, shouldn't we consider the churn rate of each month?
Well, the issue is that monthly churn rates only apply to businesses that sell their products on a monthly subscription. And even those businesses usually have churn rates that vary a lot for a customer that is 1 month old vs. 12 months old. So using the same "churn" every month is highly inaccurate. In addition, most businesses are not subscription based, but still retain a lot of customers. For example a social network, or a marketplace, or a consulting business, or a restaurant, or an ecommerce store - churn doesn't apply to them. The cool thing about retention is that you can use it for all business models, including SaaS, and get really accurate models.
I was about to shit in pants, when I understood your parrotism churn rate. It is siefe on the cherry liquior. Depends what your parrotism converastion is about. It is always good to copy the wanted and not wanted.
Great video, thanks. One thing still not clear to me:
The model here is based around 12 months - but if 26% are retained in month 12, then we can assume some % will continue into month 13 and beyond.
So how would you think about Customer LTV beyond month 12. Would you project forward (starting with 26% and decreasing by X%/month) beyond the 12 months to get a full account of Customer LTV?
I think this should be done yearly because the next year can help you out to compare the different rates of spends.
Bro.. you are a gem I want to go more with you.. I want to grab good knowledge in business analyst with excel so it's a bit of a request to advise me from where I can learn more from you?
Thanks Nikhil!! If you're looking to get some broad background on finance / business / marketing, I'd recommend watching my 3 statement financial model, the finance case study, KPIs for digital marketing, and the startup metrics and KPIs video....once you watch those 4 I think you will understand a lot of different concepts and I think you can decide where you want to focus next (maybe more deep financial modeling or maybe more e-commerce strategy), just leave me another comment and I'll try to respond 👍
Hi Eric! I'm doing someting similar but also trying to figure out how to model this when given a conversion rate and retention rate for users that converted from free to paid users.
Same, just need to decide what is the conversion event that you start the cohort table, either conversion to free users, or conversion to paid. I personally might build the table with paid users and then just track the free => paid CVR separately
@EricAndrews1 This was the simplest and most impactful content i have come across till date. You are doing a phenomenal job. I just had one question in this case (monthly calculation) LTV will change each month how often do you recommend one can do this analysis in context to the product lifecycle. For eg. Hypercasual games can have a short product lifecycle etc. If you can shed some light on this it will be really helpful
Hey I appreciate the comment! For your question, I'm not 100% sure what the perfect metric is to measure if you are succeeding (% progress, completion of game, number of days active, etc), but obviously you are measuring lifetime in days not months. Search "power user curves by Andrew Chen" and you will see a very powerful way of measuring this type of the type of user activity I think you're talking about. Cheers
@@eric_andrews Supremely delighted will surely check this out. Again you are doing an amazing job🙌
@@kapilbonde3090 awesome thanks 👍
Great! Questions: Is the month 1, month 2 buyers, refer to the users purchased in the month or in/after the month? A problem I met is: some buyers came in March but didn’t do any purchases on month 1 then they came back in month 2. So, sometime the month 2 buyers could be higher than month 1.
Hi Eric, can you give some use cases how these cohort tables are used monthly mobile subscribers data? How different would the retention percentages be?
User retention is also cohortized, but very often is tracked using the DAU/MAU ratio or even better power user curves. I also have a video on that here: ua-cam.com/video/YxJFzfXk5DU/v-deo.htmlsi=o1u_YZ2qtfxlXwL0
Hi Eric, I am unable to download your excel template using the given link. Where can I find the right link? Thanks in advance.
Hi Eric! Thank you for this great tutorial! I'm sorry if I sound ignorant asking this, If I have a shop with products that aren't purchased on a monthly basis, like shoes or appliances does this approach work by quarters for instance? (excuse my English I hope you could understand the point I'm trying to get to)
Hey, absolutely this type of analysis works for your business! You can look at quarterly, the main idea is you want to understand how much a customer will buy after their first purchase. This analysis will help you understand how often they purchase the second, third time etc, and when they do it. Perfect English as well btw 👍
@@eric_andrews Thank you Eric I appreciate!
Hi Eric, nice video. What about we take LTV to CAC ratio as well? Can you make a separate video on it?
Yes I have lots - here are a few!
Unit economics for hardware, software, and e-commerce: ua-cam.com/video/AMKgcBzK7cg/v-deo.html
5 ways to increase your LTV: CAC ratio: ua-cam.com/video/rTP39v2s8dI/v-deo.html
SaaS startup unit economics journey: ua-cam.com/video/o9ufogwDrwc/v-deo.html
@@eric_andrews why aren't you taking retention while calculating LTV?
Since generally the formula of LTV is:
Customer Lifetime Value = (Customer Value* x Average Customer Lifespan)
*Customer Value = (Average Purchase Value x Average Number of Purchases)
@@muhammadmuneebkhanafridi154 these cohorts show the same information you are summarizing but with more detail
Hi Eric, thank you for this insightful video. I wanted to know if this same methodology is applicable to a telecom company's mobile subscribers data? Is this how companies would do?
It is applicable absolutely
@@eric_andrews how would we identify seasonality for monthly mobile subscribers then? Would it be like a sharp increase for a certain month every year?
Hi Eric, How do I sort my data to get the cohort table?
This was helpful , but need to know how to get to that table ❗
Hi Eric, This is very informative. Could you please tell me how this can be calculated for each segment and sub-segments of business? More of an excel question, than a business question I guess.
Data should be aggregated based on the customer ID and the month of the first time they purchased.
hi Eric, thank you for the video. and I've a question.
what's the different Customer Retention and Customer Stickiness?
Same thing
Hi Eric,
thanks for the great video.
I have one question: What do I do when my customers don't buy regularly?
For example it's possible that a customer makes an order in January and February, doesn't purchase anything in March but then decides to order again in April. How can I analyse this kind of behaviour?
Just look at the purchase stream cumulatively and watch how the lifetime revenue of the customer accumulates over time and with what timing. That's perfect data to cohortize.
@@eric_andrews thanks for the quick answer!
Eric, hi! Need your help, I'm new in marketing and get not easy tasks. I need to calculate average client lifetime (not value), and CAC. Data that I have (all per week, 44 weeks total): installs, active users, retention rate (in %), weekly revenue and revenue cohort. Which formulas do I need to calculate ACL and CAC?
Is this for an actual business? Or just a case study?
I would calculate CAC by looking at marketing spend / installs.
For customer lifetime I would take either your revenue cohort / month 0 users, or look at customer lifetime by taking 1 / (1-retention rate i.e. churn rate).
Here are some other videos of mind that might help you:
CAC calculation: ua-cam.com/video/8WChmQuTeN0/v-deo.html
Customer lifetime value: ua-cam.com/video/eHi875QuVcA/v-deo.html
User retention ratios: ua-cam.com/video/YxJFzfXk5DU/v-deo.html
thank you so much for the valuable content. Just a quick question, let's say an investor ask what is the retention rate of the business? From this cohort table, which is the representative one? Is it the cohort having the largest samples? (which is 26% in the net revenue cohort table)
Yes important question. If they ask, you can literally send them this table, and then specify how many times a typical customer buys over their lifetime (and the gross profit from that lifetime i.e. LTV). So, as a made up example: our typical customer buys 7 times over a 3 year period, CAC is $50, lifetime revenue is $256 and LTV is $174 and here is the cohort retention table. That is my much more instructive than "50%" which basically tells you nothing and barely makes sense
@@eric_andrews great! thanks again Eric.
@@user-kz4vb6bm5g happy to help
Eric, if you were trying to find the average customer retention at say, 4 months. Would it be the straight average of retention percentages at 4 months or would you use a weighted average, taking into consideration the size of each cohort?
Yeah I mean I think a waited average would probably make the most sense if it's not too hard to do
Hi Eric, how would you use this analysis to determine the customer churn rate? I am unsure if this is by taking the average across all the cohorts or how this is done.
Different data, just released a video on that here: ua-cam.com/video/fC_gLwyAvMo/v-deo.htmlsi=xv7FMMkBzS45Sm4E
Great stuff, am learning something from each video you make. The whole LTV and retention calculation is quite complex for a marketplace business. Perhaps a topic for your next video? Its not as straight forward as subscription where you have fixed formulas. @@eric_andrews
Thanks, where can I download excel file?
I really don't get the idea of this cohort and retention analysis. please can you break it and show your dataset. thanks
If you spell proper nouns with a capital letter , than maybe you could have understood from the first second.
Hi Eric, I would like to ask. How do we calculate Customer Lifetime Value by Cohort if we consider 'Promotion Voucher' variable separately from CAC (Marketing Expense)
Yes - very important question. I would include promos in your CAC. That is a marketing expense. For revenue just use your gross revenue (pre-promo), and then include promos in marketing for your breakdown. Alternatively, you could just reduce your revenue in the starting month by the promo amount, either one gets you the same math. Cheers
Next question, if to retain our customer we would be needing promotion and CRM initiatives, how do we consider this within our lifetime value vs CAC? How can we exactly do the calculation to those CRM initiatives and retention promotion factors?
Hi Eric, how do you summarize data in the first table if the period is for more than 1 year? Lets say you have customer purchase data for 3 years. Do you summarize all the first purchases in April 2020, April 2021 and April 2022 (in year 1, 2 and 3) as one?
No, you should just extend the table out wider and keep breaking every cohort apart my month.
@@eric_andrews thanks for clarifying Eric!
One more question, how do you go about getting the aggregate number of cohorts per month if you only have customer ID ( would you just use simple count formula via pivot table?).
Also, say I have customer sign up date and first date of purchase of each customer. how do I find the average time of first purchase? Thanks for helping out!
@@eric_andrewsdo you have a work through video of how you went from the raw data (showing each customer’s purchase date etc) to the cohort table you used in this video. Would appreciate if you don’t und explaining please. Thanks
Can this help in finding out the loyal customer for a service or a store ?
Yes, would work for any business.
Hey Eric! Thank you for creating this super video that is really helpful in my work today. But maybe can you help to create a video with sales related analysis? Thanks
My pleasure! What exactly do you mean by sales analysis?
Hey Bro, thanks a lot.. anyway, just want to ask about net revenue retention by cohort, how you find the $ number? cause i try to calculate number of customer x $50, the result are little bit different from yours. Thanks in advance
Hey...yep so you can't just multiply by $50, you need to use the actual revenue not an estimate. The reason is that net revenue per customer / month can change over time depending on discounts, pricing, refunds, etc....so better to use the real revenue than the total customers for the NDR. The rev numbers are in the spreadsheet you can just download in the description.
@@eric_andrews understood, thank you very much 😀
Bro,how to create this table,can you please explain me
Can retention tables have repeat customers who use the products multiple times during the day D0/D1? (Users playing mobile games) or should it be unique?
Ya that could work for sure. Could be unique or not unique, just would want to specify when presenting it. But the original D0 number of users should always of course be unique when you calculate the customer lifetime usage (or whatever metric you're measuring)
@@eric_andrews sounds good! I’m currently measuring the retention rate, so unique or not it doesn’t matter right?
@@sofianghazali6203 so it just depends on what you're measuring. If you're measuring the lifetime of the customer then probably would be based on unique because you care about time not frequency. But if you're measuring the lifetime activity or purchases for a customer then you would want to include all of their individual actions. Do you see what I mean? If you're just looking at retention then probably unique would make the most sense.
What if a customer of apr cohort didn’t buy in may but then come back subscribe again in June?.. how do calculate retention
No issue. They would just appear in the april cohort but in june, just like we showed in the video. That's why these cohort tables are great, they show inconsistent buying behavior really clearly