FOR THE KNOWLEDGE
SEEKERS
Klaviyo Advanced Analytics - The Sauce
Aug 22, 2024
DATA
MARKETING
Don't just stare at your data - use it. Here's how with Klaviyo Advanced Analytics.
Have you noticed that eCommerce is really full of 3 letter-words?
e.g. CLV RFM CDP ROI CAC SKU AOV UTM CTR CPC CRO LTV KPI POS
I’m sure I missed a few. Which kind of drives the point. Let me know if any other ones deserve a mention here.
Today, I wanted to talk about two of my favourites on that list.
RFM - Recency, Frequency, Monetary Value
CLV - Customer Lifetime Value
I promise this is not a snooze fest and is packed full of actionable steps and real-life examples.
Let’s dive in.
______________________________
Preface
Now, I am aware that Klaviyo is not the only tool you can use to run these analyses for your brand. I am also mindful that this might not be the most affordable option out there.
However, Klaviyo has made it relatively easy for eCommerce managers to conduct relevant analyses and implement them swiftly and efficiently. Plus, chances are, you are also using Klaviyo already and have a robust segmentation and automation system set up.
We have taken Advanced Analytics for a spin and really like what we see.
Before we begin, I want to highlight that Klaviyo Advanced Analytics isn’t for everyone.
Who it’s not for:
If you’re a brand with less than 100k profiles
If you don’t have your basic flows under control and operating efficiently. (Don’t try to run before you walk.)
It is probably not ideal if you’re a brand generating less than $2M ARR (Pricing starts at $500/month for 100k profiles)
Who it is for:
Brands with over 100k profiles, ideally 150-200k+. (The larger the list size, typically the larger the impact.)
You’re proud of your systems within email as a channel and are looking to unlock further growth.
Generating at least $2M in ARR
______________________________
What are RFM segments & how to build them in Klaviyo
Recency, Frequency and Monetary Value are pretty self-explanatory.
By utilising data on how often, how recently, and how much your customers purchase and adding score weighting to each section, you can identify important customer cohorts and target them with the right messaging.
In Klaviyo, these cohorts are called:
Champions: Purchased recently, often, and spend the most money.
Loyal: Purchased often or recently, and spend a good amount.
Recent: Purchased recently, but not frequently.
Needs attention: Frequent customers who have not purchased for some time.
At risk: Frequent customers who spent a low or average amount but have not purchased for some time.
Inactive: Customers who do not purchase frequently and have not purchased in a long time.
Never purchased: Customers who have no purchases.
Now, when it comes to creating them, it is really easy. All you have to do is pick “Properties about someone” when creating a segment and choose their RFM segment. Klaviyo will automatically detect and populate these for you.
(Similar to creating any other segment.)
Tip:
Every business is different, and RFM weighting and settings will vary greatly among business types.
A supplement company with a relatively frequent repeat purchase rate will want to set a lower “Maximum days since purchase” period than a furniture company.
I recommend configuring these numbers based on your business vertical and model.
You can do this by clicking on the big “Customize” button under “Predictive models”.
Ok, now that you have created your RFM segments, let’s get to the fun part and look at an example of how you can use them to drive incremental revenue and repeat purchase rates.
______________________________
RFM Flows that will blow your socks off 🧦
Naturally, there are PLENTY of ways you can use RFM segments. I’ll outline a few here and let your imagination go wild with the rest.
Automate Messages when a valued customer becomes at risk
Let’s say that you have identified that many of your “Champion” customers are at risk of being inactive.
Create a date-triggered flow with profile filters to enter “Champion” customers in a multistep nurture when they enter “Needs Attention” or “At Risk”.
Recommendation:
Offer a special offer - high discount, early product access, BOGO - to incentivise a repeat purchase from a formerly monetarily very valuable customer.
By identifying “Champion” customers’ LTV’s, you can target them with higher-than-usual incentives to retain them as customers.
Retain Loyal Customers when they become a churn risk
Let’s say that your “Loyal” customers are disproportionately moving to “Needs Attention” over the past six months.
Add a repeat purchase nurture series based on the expected date of the next order. (Klaviyo predictive analytics.)
You can do this by creating a flow that counts down to the expected date each individual customer will re-order. The trigger for this date-based flow will be Klaviyo’s “Expected Date of Next Order”.
Afterwards, add a filter for “And has not been in this flow in the last 180 days” to ensure that a customer receives this nurture only once every six months.
Recommendation:
Offer a discount to incentivise that next purchase and prevent churn. Consider a lower discount than you might already offer in your regular win-back series. Or, include a personalised product feed that recommends products each customer might like based on Catalog Insights and past purchases.
These are only two examples of the myriad of possible improvements based on your business, insights, and situation. I hope the two examples I’ve shown you here today have helped you think creatively about what might be possible.
If you have a crazy idea and wonder whether it’s possible, contact our Klaviyo Master Partner experts for a no-obligation chat. (hello@atlasventures.co)
But wait - that’s not all! Now that we briefly touched on RFM segments let’s look at another powerful feature of Klaviyo’s Advanced Analytics.
Catalog Insights Using Catalog Insights to drive relevant and personalised product offers
Klaviyo’s Catalog Insights allows you to examine how your customers interact with your best-selling (or worst-selling) products in greater detail.
Let’s take an example.
In your product analysis, you find that your customers most often repurchase 12 days after purchasing your best-selling dress and that they are most likely to buy women’s active shorts next.
Instead of relying on your regular post-purchase flow, why not adjust it to accurately reflect re-purchase timing and include cross-promotion of your active shorts?
Or, how about running a one-time campaign for customers likely to buy soon with a bundle of these items?
Or, how about informing your merchandising team about these insights so that they can plan accordingly?
The opportunities you can uncover with product analytics are plenty. They can allow you to drive increased repeat purchases and higher AoVs all while providing your customers with an elevated UX.
I have not forgotten the paid acquisition readers. There’s something in here for you, too.
Let’s examine how you can utilise RFM Segments to supercharge your paid acquisition efforts.
______________________________
RFM Insights to increase return on ad spend
Finding new audiences through purchase-based lookalikes is a pretty good way to capture sales.
How about lookalikes of only your best customers?
You can combine your “Champion” and “Loyal” customer segments before exporting them and sending to Meta and Google to generate lookalike audiences.
Recommendations:
Don’t stop there. To get even more granular, combine your RFM groups with recent purchases, channel engagement, or web browsing behaviour.
How about combining RFM groups with demographic data to create hyper-targeted audiences of specific age, gender, location etc.?
Let’s look at another example.
You have implemented RFM segmentation and recently ran a successful campaign towards “At Risk” and “Needs Attention” customers.
Given that these segments consist of customers who aren’t very engaged yet have recently purchased from you through your email campaign, it’s probably not the best idea to spend ad dollars on them again so soon.
Consider excluding these audiences from your weekly ad campaigns so you aren’t spending ad dollars on an audience that just purchased (and isn’t likely to purchase again immediately).
You can use the median performance chart in RFM analysis to roughly identify when each group is likely to purchase again and can remove the exclusions accordingly.
______________________________
Understanding your customers and preventing needless churn has never been more critical.
Whether you’re nurturing at-risk champions, building incredibly personalised post-purchase flow or creating hyper-targeted paid acquisition campaigns, the possibilities are vast with Klaviyo’s Advanced Analytics.
I urge you not to be afraid of the endless possibilities and to approach it step by step.
Build out your segments, start analysing, and begin building block by block.
I hope that you have enjoyed reading and have found it helpful. As always, if you would like to have a no-obligation discovery chat with our team of Klaviyo experts, you can reach out to us at hello@atlasstudios.agency
Have you noticed that eCommerce is really full of 3 letter-words?
e.g. CLV RFM CDP ROI CAC SKU AOV UTM CTR CPC CRO LTV KPI POS
I’m sure I missed a few. Which kind of drives the point. Let me know if any other ones deserve a mention here.
Today, I wanted to talk about two of my favourites on that list.
RFM - Recency, Frequency, Monetary Value
CLV - Customer Lifetime Value
I promise this is not a snooze fest and is packed full of actionable steps and real-life examples.
Let’s dive in.
______________________________
Preface
Now, I am aware that Klaviyo is not the only tool you can use to run these analyses for your brand. I am also mindful that this might not be the most affordable option out there.
However, Klaviyo has made it relatively easy for eCommerce managers to conduct relevant analyses and implement them swiftly and efficiently. Plus, chances are, you are also using Klaviyo already and have a robust segmentation and automation system set up.
We have taken Advanced Analytics for a spin and really like what we see.
Before we begin, I want to highlight that Klaviyo Advanced Analytics isn’t for everyone.
Who it’s not for:
If you’re a brand with less than 100k profiles
If you don’t have your basic flows under control and operating efficiently. (Don’t try to run before you walk.)
It is probably not ideal if you’re a brand generating less than $2M ARR (Pricing starts at $500/month for 100k profiles)
Who it is for:
Brands with over 100k profiles, ideally 150-200k+. (The larger the list size, typically the larger the impact.)
You’re proud of your systems within email as a channel and are looking to unlock further growth.
Generating at least $2M in ARR
______________________________
What are RFM segments & how to build them in Klaviyo
Recency, Frequency and Monetary Value are pretty self-explanatory.
By utilising data on how often, how recently, and how much your customers purchase and adding score weighting to each section, you can identify important customer cohorts and target them with the right messaging.
In Klaviyo, these cohorts are called:
Champions: Purchased recently, often, and spend the most money.
Loyal: Purchased often or recently, and spend a good amount.
Recent: Purchased recently, but not frequently.
Needs attention: Frequent customers who have not purchased for some time.
At risk: Frequent customers who spent a low or average amount but have not purchased for some time.
Inactive: Customers who do not purchase frequently and have not purchased in a long time.
Never purchased: Customers who have no purchases.
Now, when it comes to creating them, it is really easy. All you have to do is pick “Properties about someone” when creating a segment and choose their RFM segment. Klaviyo will automatically detect and populate these for you.
(Similar to creating any other segment.)
Tip:
Every business is different, and RFM weighting and settings will vary greatly among business types.
A supplement company with a relatively frequent repeat purchase rate will want to set a lower “Maximum days since purchase” period than a furniture company.
I recommend configuring these numbers based on your business vertical and model.
You can do this by clicking on the big “Customize” button under “Predictive models”.
Ok, now that you have created your RFM segments, let’s get to the fun part and look at an example of how you can use them to drive incremental revenue and repeat purchase rates.
______________________________
RFM Flows that will blow your socks off 🧦
Naturally, there are PLENTY of ways you can use RFM segments. I’ll outline a few here and let your imagination go wild with the rest.
Automate Messages when a valued customer becomes at risk
Let’s say that you have identified that many of your “Champion” customers are at risk of being inactive.
Create a date-triggered flow with profile filters to enter “Champion” customers in a multistep nurture when they enter “Needs Attention” or “At Risk”.
Recommendation:
Offer a special offer - high discount, early product access, BOGO - to incentivise a repeat purchase from a formerly monetarily very valuable customer.
By identifying “Champion” customers’ LTV’s, you can target them with higher-than-usual incentives to retain them as customers.
Retain Loyal Customers when they become a churn risk
Let’s say that your “Loyal” customers are disproportionately moving to “Needs Attention” over the past six months.
Add a repeat purchase nurture series based on the expected date of the next order. (Klaviyo predictive analytics.)
You can do this by creating a flow that counts down to the expected date each individual customer will re-order. The trigger for this date-based flow will be Klaviyo’s “Expected Date of Next Order”.
Afterwards, add a filter for “And has not been in this flow in the last 180 days” to ensure that a customer receives this nurture only once every six months.
Recommendation:
Offer a discount to incentivise that next purchase and prevent churn. Consider a lower discount than you might already offer in your regular win-back series. Or, include a personalised product feed that recommends products each customer might like based on Catalog Insights and past purchases.
These are only two examples of the myriad of possible improvements based on your business, insights, and situation. I hope the two examples I’ve shown you here today have helped you think creatively about what might be possible.
If you have a crazy idea and wonder whether it’s possible, contact our Klaviyo Master Partner experts for a no-obligation chat. (hello@atlasventures.co)
But wait - that’s not all! Now that we briefly touched on RFM segments let’s look at another powerful feature of Klaviyo’s Advanced Analytics.
Catalog Insights Using Catalog Insights to drive relevant and personalised product offers
Klaviyo’s Catalog Insights allows you to examine how your customers interact with your best-selling (or worst-selling) products in greater detail.
Let’s take an example.
In your product analysis, you find that your customers most often repurchase 12 days after purchasing your best-selling dress and that they are most likely to buy women’s active shorts next.
Instead of relying on your regular post-purchase flow, why not adjust it to accurately reflect re-purchase timing and include cross-promotion of your active shorts?
Or, how about running a one-time campaign for customers likely to buy soon with a bundle of these items?
Or, how about informing your merchandising team about these insights so that they can plan accordingly?
The opportunities you can uncover with product analytics are plenty. They can allow you to drive increased repeat purchases and higher AoVs all while providing your customers with an elevated UX.
I have not forgotten the paid acquisition readers. There’s something in here for you, too.
Let’s examine how you can utilise RFM Segments to supercharge your paid acquisition efforts.
______________________________
RFM Insights to increase return on ad spend
Finding new audiences through purchase-based lookalikes is a pretty good way to capture sales.
How about lookalikes of only your best customers?
You can combine your “Champion” and “Loyal” customer segments before exporting them and sending to Meta and Google to generate lookalike audiences.
Recommendations:
Don’t stop there. To get even more granular, combine your RFM groups with recent purchases, channel engagement, or web browsing behaviour.
How about combining RFM groups with demographic data to create hyper-targeted audiences of specific age, gender, location etc.?
Let’s look at another example.
You have implemented RFM segmentation and recently ran a successful campaign towards “At Risk” and “Needs Attention” customers.
Given that these segments consist of customers who aren’t very engaged yet have recently purchased from you through your email campaign, it’s probably not the best idea to spend ad dollars on them again so soon.
Consider excluding these audiences from your weekly ad campaigns so you aren’t spending ad dollars on an audience that just purchased (and isn’t likely to purchase again immediately).
You can use the median performance chart in RFM analysis to roughly identify when each group is likely to purchase again and can remove the exclusions accordingly.
______________________________
Understanding your customers and preventing needless churn has never been more critical.
Whether you’re nurturing at-risk champions, building incredibly personalised post-purchase flow or creating hyper-targeted paid acquisition campaigns, the possibilities are vast with Klaviyo’s Advanced Analytics.
I urge you not to be afraid of the endless possibilities and to approach it step by step.
Build out your segments, start analysing, and begin building block by block.
I hope that you have enjoyed reading and have found it helpful. As always, if you would like to have a no-obligation discovery chat with our team of Klaviyo experts, you can reach out to us at hello@atlasstudios.agency
