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One of the biggest changes impacting SaaS companies today is the rise of customer success as a strategic function within these organizations. Where the original SaaS companies relied on annual and multi-year contracts to lock in customers and reduce churn, today’s best SaaS companies have adopted the “land and expand” approach. This approach has a much lower monetary (and psychological) barrier to entry, and presents more opportunities to grow the account via upsells. While sales still owns the “land” part of this approach, the “retain” and “expand” pieces have been largely handed off to customer success.

Customer success is a team of unicorns that know how to provide top-notch customer support while also keeping an eye out for upsell opportunities. They’re as comfortable reacting to a customer complaint as they are proactively offering advice and engaging in account expansion calls. And it’s this ability to expand accounts that is having a significant impact on the evolution of the SaaS revenue model.

As the SaaS revenue model evolves, so are the metrics that SaaS companies use to monitor their growth. After all, if the metrics you’re using don’t reveal the most accurate picture of your business, what’s the point? The rise of customer success impacts all three of of the most important SaaS metrics:

  • Churn: how many customers or revenue are you losing?
  • Lifetime Value (LTV): what’s a customer worth?
  • Customer Acquisition Cost (CAC): How much does it cost to acquire a customer?


Churn is a metric to help SaaS companies understand how good they are at keeping customers, but it’s not well understood. Most companies calculate churn as the Monthly Recurring Revenue (MRR) that cancels in any given month as a percent of the total MRR that they entered the month with. This is fine—it’s the classic way of measuring churn—but it’s not a one-size-fits-all metric.

If your company offers annual contracts, you may be measuring churn wrong. @kmehandru

This method of calculating churn becomes a problem for early-stage companies selling annual contracts. There are very real advantages to selling SaaS on an annual contract basis:

  1. It’s cash in the bank today
  2. Having a whole year to prove the value of a product is an advantage. We’ve repeatedly seen that total churn is lower among clients who sign an annual contract.

The downside is that the annual contract clouds what churn is meant to do: help SaaS companies understand how happy and engaged their customers are. Early-stage companies need this feedback loop to be as short as possible. In these cases, I prefer to use “discretionary churn”.

Discretionary churn is the amount of dollars churning in any month as a percent of total dollars eligible for churn. Think about it like this. In any given month you have a large percentage of your dollars that are tied into annual contracts, these contracts don’t have the opportunity to churn, even if they are unhappy/disengaged users of your product. You shouldn’t include this in your churn calculation because, realistically, you weren’t giving these customers a chance to get out of the product.

In this scenario, churn ends up looking very different. One example comes to mind of an early-stage SaaS company that quoted a churn rate of about 3% using the traditional method. This company used annual contracts and 60-70% of their deals were tied into annual contracts that didn’t have the opportunity to churn in any given month. When we looked at discretionary churn, this number was closer to 8%! The difference between the two is meaningfully relevant when calculating the lifetime value of a customer.

churn and annual contract

Discretionary churn turns this metric from a lagging indicator into a true barometer of how your customers perceive your product and service. Fix the product today, or expect a large number of customers to leave as soon as their contract is up. Once you’re at $5-$10 million Annual Recurring Revenue (ARR) you have to control that leaky bucket at the bottom or it will be a huge decelerator to your business.

Lifetime Value

LTV, for SaaS companies, can be calculated as average revenue per user (ARPU) divided by churn. We already talked about churn, the denominator in this equation. Now let’s look at the numerator.

If your SaaS business has upsells then your numerator is going to be heavily impacted by your customer success team. Traditionally, SaaS companies would measure ARPU as the contract value at signup. In a world where a large share of revenue was coming from annual contracts, this made sense. But that’s not the reality for many SaaS companies today. Today, monthly contracts and upsells are the norm, and measuring ARPU based on initial contract value doesn’t reflect reality.

Measuring ARPU based on initial contract value doesn’t reflect reality. @kmehandru

For companies with freemium products, I often advise them to optimize for LTV as opposed to some other metrics like free to paid conversion. While the conversion from free to paid is inherently desirable, it is a necessary but not sufficient condition to a long term profitable SaaS business. For example, a company could have two tiers of paid products (or two different products) where one has a very high churn rate relative to the other. There are cases where the conversion from free to paid could be accelerated by sales strategies/promotions but the business would end up with paying customers with a much shorter LTV in the long term. Understanding how acquisition strategies at the top of the funnel affect LTV at the bottom of the funnel is critical to high-growth SaaS companies.

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Customer Acquisition Cost

Of course, knowing what a customer is worth over the long-term is only step one. Knowing your LTV informs how much you can spend to acquire and service your customers — which brings us to CAC.

Because customer success is now playing a much larger role in generating revenue, it makes sense to include some of these costs in CAC. CAC, when used to calculate things like the LTV:CAC ratio, or the SaaS Magic Number, has typically been viewed as fully-loaded sales and marketing expense. But with a large portion of new recurring revenue coming from customer success, this needs updating.

If CAC is to reflect the true cost, we need to be honest with ourselves and say that a significant part of customer success’ job in the modern SaaS company is to acquire new revenue–via upsells, and via second-order revenue. Today I recommend that companies calculate CAC as fully-loaded sales and marketing plus 25-50% of customer success costs, depending on how responsibilities are split within the group.

CAC and customer success

If CAC is to reflect the true cost, we need to be honest with ourselves. @kmehandru

Putting these metrics to work

These metrics will vary for your business and they will certainly continue to evolve. The important thing to remember is that a metric should never exist in a vacuum. It’s tempting to want to look at your data in the best light and share that rose-colored view with investors, but it’s not in the long-term interests of the company.

Choose your metrics with care, tailor them to your company, create something that is highly actionable and meaningful. This might mean that you end up with a disturbingly high churn rate, but you’ll also have far more insight on how to fix it. And in the end, those are the best metrics any SaaS company can have.

Good companies have thus far focused on perfecting the “software” part of Software as a Service, however, the next generation of great companies will pay as much, if not, more attention to the “service” implied in SaaS and the accompanying metrics that are leading indicators of success. These companies understand that when a customer signs up for your product, that’s the beginning, not the end, of the relationship.


This is the third in a series of delicious analytics “recipes” that will make any data-driven professional’s mouth water.

Recipe: Product Analysis Pasta

Amazon, Netflix, and Pandora paved the way in cross product analysis. Their recommendation engines are big data at its best, comprised of finely-tuned algorithms that know what customers want before they even want it. These recommendation engines are powerful, but for most ecommerce companies, the cost is prohibitive.

Are you doing cross product analysis to increase customer lifetime value?

In this Product Analysis Pasta recipe, we’ll outline an approach to cross product analysis (also known as “affinity analysis” or “market basket analysis”) that will help you tap into the power of a recommendation engine, without the cost. This is a bare bones approach, but it works. One of our clients made an additional $12k in three days by implementing their findings from this approach.

Let’s get started!

Prep Work

Before you start, you need to first identify whether your product catalog is:

  • Narrow, but deep – for example, a company that only sells knitting-related products, including knitting needles, yarn, patterns, and kits.
  • Broad, but shallow – for example, a company that sells clothing for the whole family.

A company with a narrowly focused product line will want to do analysis at the item level. They’ll start by identifying their best-performing product and then find what items are most often purchased along with it. A company that sells a broad array of categories will be better served by analyzing what categories are frequently purchased with their top-performing category.

You also need to think about whether you want to look at your data on the order level or user level.

  1. Order level: Looking at your data on an order level will give you a good sense of how to boost your average order value by offering cross-sell and up-sell options.
  2. User Level: Looking at your data on a user level will show you how a product fits into the customer lifecycle. This analysis is particularly useful when combined with segmentation. For example, is your most popular item or category frequently sold to repeat purchasers? This could indicate that it’s a driver of loyalty, and you would want to promote it heavily to first-time buyers.

In this example, we’ll be looking at data the item level and the order level. The raw data will be different depending on the approach you choose, but the analysis will work exactly the same way.



Transaction Data

You’ll need a list of every order you have filled in the past 3 months, broken down by items purchased.

Get Cooking

Step 1: Measure Your Ingredients

To start setting up your product analysis calculation, you’ll first need to pull all of your Item and Order data from the past 3 months into an Excel spreadsheet.

Step 2: Find Your Top Selling Product

The next step is to filter your spreadsheet by only those orders that contain your top-selling item. You can find the formula for this, as well as the following steps, on this handy Google spreadsheet.

Step 3: Find What Products Are Most Often Bought With Your Most Popular Item

Once you identify what the top-selling product is, you’ll next want to look at which item is most frequently purchased with it. Calculate the total number of times that an order contained both your most popular item and each potential cross-sell item.

Step 4: Calculate Frequency of Cross-Sell Possibilities

Calculate the percentage of time that an order contained both your most popular item and each potential cross-sell item.

Step 5: Measure Popularity of Cross-sells

Calculate the total number of orders that contain each potential cross-sell item. This step will help provide some context by showing how popular each potential cross-sell item is overall.

Step 6: Calculate Frequency of Cross-sell Items

Finally, you’ll want to calculate the percentage of total orders that contain each potential cross-sell item, to get an overall sense of how frequently each one is purchased overall.

To start looking at cross-product analysis for your business, you can save a copy of the spreadsheet we created and do some experimentation with your own data.


Note: This kind of cross-product analysis can be a quick win to improve customer lifetime value, but it’s important to understand that product analysis is not the answer for all of your product-related issues. Unless your product is extremely unique in the marketplace, there are other factors that may be affecting your customers’ purchase patterns. What is your advantage in the market? If your customers buy a lot of one of your products but not another, it may just be that they’re finding a better deal or better customer service elsewhere.

Make Product Analysis Work For Your Business

Once you know what your data looks like, you can try one or more of following three marketing strategies:

1. Cross-selling

“Do you want fries with that?”

Cross-selling is offering a complementary product to your customers: like fries with a burger, or ping pong balls with chips.

Any kind of company can use cross-selling as a strategy to increase revenue. It’s not always as intuitive as grocery store examples, which is why using your data to be strategic about it is key.

Use your data to find the perfect-cross sells for your business.


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2. Up-selling

“Try our premium yarns for those special knitting projects”

Upselling is offering your customers a more expensive product than the product they are looking at. This works well for companies that have narrow, deep product lines. It can be challenging to get your customers to break their usual patterns and purchase higher-value items. Looking at your data can help you determine which customer segments would be most receptive, so that you can approach them strategically.

3. Sequencing

“Many of our customers also purchased…”

Sequencing is looking at the behavior of your best customers and using that information to guide other customers in the same direction. What do your best CLV customers buy overall? What did they buy in their first order? Second order? The behavior of your worst customers can all be very informative. If, for example, you find that you have a lot of one-time order customers, you’ll want to take a closer look. It may be a sign of a significant product quality issue. Sequencing is best for companies that have a lot of data about their product line.

When you’re trying to decide which of these three strategies to try, you should take into account whether your focus is on loyalty or acquisition:

  • Loyalty. If you sell knitting supplies, you likely have loyal customers who keep coming back. You have time to sell your customers more items and migrate them to your premium product lines, so you’ll want to focus on how best to do that through cross-selling and up-selling efforts.
  • Acquisition. If you sell mattresses, you’ll have very few repeat purchasers. You have one shot to sell to each customer, so you’ll want to focus on increasing average order value.

Gourmet Pasta Preparation

Using spreadsheets for some rough cross-product analysis on one or two products is doable. However, your results will be much more accurate if you’re able to do advanced calculations to take support, confidence, and lift into account:

  • Support is the probability that an order contains item X.
  • Confidence is the conditional probability that an order contains item Y, given that it already contained item X.
  • Lift is a statistical measure that shows whether the presence of X increases or decreases the likelihood of an order containing Y.

Doing these calculations for multiple products in a spreadsheet can be complicated and time-consuming, especially when you consider that you’ll need to update your data frequently. With RJMetrics, you’ll be able to set up your calculations for a number of products or categories just one time, and they’ll update automatically.

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There’s a scene in the movie “The Wolf of Wall Street” where Jordan Belfort, played by Leonardo DiCaprio, asks his friend to sell him a pen. In the movie, the friend answers with something about supply and demand.


The real Jordan Belfort, who the movie is based on, says the actual answer is different.

“The real answer is, before I’m even going to sell a pen to anybody, I need to know about the person, I want to know what their needs are, what kind of pens do they use, do they use a pen?”

This applies to any product. If you want people to visit your ecommerce store and buy from you, you need to know about them. You need to know their needs, goals, motivations and objections to buying your product. In short, you need to know your customer personas.

Your customer personas should look and sound like real people.

What is a customer persona?

Let’s clear a few things up first. A customer persona isn’t an actual person, and shouldn’t be based off of any one single customer. It’s a model that represents your customers, or a group of customers. The model may even look like and sound like a real person. If you have multiple customer segments, you can create a corresponding persona and assign fictional names to each.


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Secondly, while this model might be fictional, it’s definitely not derived from fiction. The model is constructed from real and complete data about your customers. It does not contain any conjecture or information about who you want your customers to be.

Typically, apart from a name, a persona contains information about customer demographics, such as age range, gender, employment, income and location. It also captures their goals, needs or wants, challenges and objections as related to your business.

You might want to add information about your customers’ online behaviors, like the sites they visit, their social network activity, how they found your site, and what they look for when buying online.


Finally, every aspect of your customer persona should have a purpose. For example, collecting information about their eating habits is useless unless your product is somehow related to food.

The point of the buyer persona is to give you an insight into the goals, motivations and behaviors of your customers. Each aspect of the persona should contribute to that in some way.

Here’s an example from Goodbye Crutches. It’s not very detailed, but it has essential information needed for them to market their products to this customer type:


Goodbye Crutches now knows it has to focus on online marketing and concentrate on spreading useful information and resources that will ease customers’ fears. They can’t directly push products onto consumers. They need to help them first before making the sale.

Developing Your Customer Personas: Qualitative Data

Most of the data you collect for your customer personas will have to come from qualitative research. That means communicating with your customers in some way and asking them questions about their goals and needs.

There are many ways you can do this:


Try to get customers to sit with you in the same room or video chat with you. This way you’ll also pick up on visual cues when your customers answer your questions. If in-person interviews aren’t possible, a phone call is a good alternative.

The best thing about a one-on-one interview is that you can change it if you need to. It becomes dynamic, like a conversation. So instead of asking questions that customers have already answered, you can dig deeper and find out more.

You can also cover a lot in one sitting because talking is the fastest mode of communication. Focus on subjective and open-ended questions, like finding out what drives them, what their objections to buying are, and how you can help them.


You can send questionnaires and surveys to customers after they subscribe. While not as effective as one-on-ones, it’s more convenient. It allows customers to reply when they have some free time.

You can also survey subscribers based on their buyer journey. For example, you might want to ask subscribers who haven’t bought anything what they are looking for and what’s holding them back from buying. For customers who just bought something, you can ask them why they bought from you and how your products satisfy their needs.

The problem with email is that people tend to ignore them until later, and most often they end up forgetting. To ensure that customers answer your surveys, you can do two things.

First, give them an incentive to answer your survey. Discounts and coupons are popular choices because they help customers save, and also make them shop with you again to take advantage of the deal.

Second, try not to make the survey too long. If customers see a long list of questions, they assume it’ll take a long time to answer and they’ll ignore it. Keep it short and don’t add too many open-ended questions.


On-site surveys, when done correctly, can provide some extremely relevant insights. They are presented to customers while they are browsing your site, so the timing is perfect.


You can trigger these surveys based on the page customers are looking at, or a number of other events. You can even trigger them as customers are leaving your site and ask them whether they found what they were looking for.

Because of their nature, pop-up surveys are best suited to multiple-choice questions. There’s not much real estate to play with if you pose open-ended questions.

Again, to incentivize visitors to answer your surveys, give them an on-the-spot discount.

Try to collect as many data points as you can. Remember, a persona doesn’t represent just one customer. After a certain point, you’ll find a pattern in your qualitative data.

A Real-Life Example

When Whirlpool started creating customer personas, they found four main personas and four secondary personas. The four main personas were purchasers under duress (someone who needed a refrigerator immediately), planned remodelers, new owners and owners with repair.

When conducting their interviews, they tried to understand the mindset that each customer had when visiting their website. It was important to find out what triggered them to visit the Whirlpool site and what kind of solution they were looking for.

All this information was included when they created the personas, along with ages, names, occupations and incomes. They also added pictures of real people to help their marketing visualize the personas.

You’ll notice that Whirlpool also used website statistics to help them with their persona creation. Let’s see why this is important.

Developing Your Customer Personas: Quantitative Data

While you can create customer personas solely through qualitative research, adding some quantitative data will help you strengthen them. After all, data doesn’t lie!


Another advantage is that you can collect way more information with quantitative data than you can with qualitative. While many customers may ignore your survey requests, a good analytics solution can help you track online behaviors of every single customer.

Your analytics solution will also help you close the loop with your personas. Qualitative data helps you create the personas, but quantitative data helps you make use of them. We’ll come to that in the next section, but for now here are some metrics to track:


Your analytics app can give you a much better view of customer demographics because of the number of data points it collects. Find out where your customers are coming from, what device or browser they use, and when they typically browse your store.

This means you don’t need to waste time asking customers where they are when you interview them. Use that time to find out the things that your analytics software can’t.

Traffic source

Your acquisition channels will help you understand why customers came to your site in the first place. Were they actively searching on Google or did they come from an ad? Did they see a review of your product online or did a friend refer them?

If they came via a search engine, you can also track the keywords they used to find you. This will give you an insight into what your customers value. Bargain hunters will use words like “cheap” while those who care about performance will use “best”.

During your qualitative research you might have noticed a pattern in the phrases and terms your customers use. Tying that in with your search engine data will show you which terms are most significant.

To develop customer personas, look for patterns in the phrases and terms your customers use.

Customer Journey

The customer journey through your site is important too. What pages do they visit and how long do they spend on each page? How your customers navigate your site tells a story and helps you differentiate between segments.

The segment that visits your “About” and “FAQ” pages are probably very early in the buyer cycle. They want to get to know your company better and aren’t ready to buy from you just yet.

The segment visiting your blog are now considering your products and want to find more information. They are a little further along the cycle, but still not quite ready to buy.

Finally, the group spending a lot of time on your product pages is probably ready to buy. Something might be holding them back, so look through your qualitative data to find out what it is.

The flow will also tell you where customers are leaving your site. Are they dropping out at the product page or another page? This can give you insights into how your customers behave at key conversion points.

Customer Lifetime Value

You should be tracking customer lifetime value already. It’s one of your most important metrics and can help you identify which are your most profitable personas. These are the people you want to spend more time crafting offers for, as they have a higher likelihood of converting.

Putting Your Customer Personas to Work

Now that you’re created your customer personas and boosted them with quantitative data, it’s time to actually put them to some use. At the end of the day, you want to increase conversion rates and make more sales.

Here are some applications of customer personas:


You can now craft a user experience on your site that speaks directly to each persona. No more generic content that makes customers feel like you don’t have the answers they’re looking for.

We’ve already seen how Whirlpool created their personas, so now it’s time to look at how they used them. Their problem was that their website was too generic, so they decided to redesign around their personas.

They created functionalities that catered to each persona. The duress purchaser needs to buy something quickly, so they implemented a filtering feature to help customers identify the best product for them in as little time as possible.

The remodeler, on the other hand, likes to take time in making a decision, so they created a showroom feature that allows customers to explore products at length. They also added a feature that allows remodelers to email products to other people involved in the remodeling.

The new redesign increased page-views by 42% and decreased bounce rates by 10%.

Product copy

When it comes to ecommerce conversion rates, product copy is one of the most important factors. It’s your sales pitch, the last thing a customer reads before clicking the “Buy Now” button.

While creating their personas, Leo Schachter Diamonds focused on the motivations and psychology behind their customers’ purchases. This helped them created targeted copy that answered questions their personas had.

This strategy helped them pre-empt any objections customers had to buying diamonds from them. Before this strategy was implemented, only 0.86% of all site visitors would go on to find a jeweler who sold Leo Diamonds. Now, because the copy solves their needs, 54% of them go on to find jewelers. That’s an increase of 5,500% in conversion rates!

Content Marketing

Now that you know your customers’ needs and requirements, you can create blog posts and other content that is relevant to them. This will attract more qualified leads with a higher chance of converting to customers.

After Skytap identified their personas, they were able to create targeted blog posts that impressed visitors. The relevancy of their content increased their organic search traffic by 55% and converted 124% more visitors into leads.

Collegis Education repurposed their content to create a targeted email campaign for subscribers. They segmented subscribers according to persona and sent them high-level content to help them solve their pain points. This resulted in an increase of 28% in open rates and 7% in conversion rates.


Your personas give you an idea of the language your customers use and the websites they frequent. Use this to optimize your PPC campaigns.

In Google AdWords, use the same keywords that customers use to make your ads more relevant. Your ad click-through rates will improve.

You can place your banner ads on websites that your customers visit. Again, use language that they identify with to increase your click-through rates.

Apple typically creates ads geared towards consumers, but as their products started gaining traction in businesses they wanted to include this new persona as well. In this iPad 2 ad, you can see them highlighting applications to view the stock market and make presentations.

A Tool to Understand Your Customers

If you find that consumers are not responding to your content or your offers, and that your website conversion rates are low, it means you don’t understand your customers. Use the customer persona as a tool to find out what their needs are and how you can better address them.

Have you started creating your customer personas? How many do you have?


One of the reasons why loyalty programs exist is to gather better data on customer behavior: if a customer is incentivized to identify themselves when they interact with our brand (presenting their loyalty card/info to collect rewards) we have a much clearer view of that customer’s interactions and purchases.

Which loyalty metrics are you tracking? Here are three you might be missing.

We have been a part of over 3,500 loyalty programs at Sweet Tooth. In that time we’ve used some cool metrics and gathered some pretty strong data to analyze; it’s a data nerd’s paradise! So when I knew I was writing a guest blog post for RJMetrics, I had to do it on data.

Here are three loyalty program metrics that most retailers won’t know, or use, but should. Where appropriate, I’ll include what we have found in our data from over 3,500 merchants.

Realized Customer Lifetime Value

Customer Lifetime Value (CLV) is the total profit (some use revenue) that a customer will generate over their entire lifetime. There are two ways to look at CLV:

  • Predicted CLV: How much profit you expect to come from a customer
  • Realized CLV: How much of the lifetime profit you have already made

Realized CLV is an extremely effective way to segment and target customers.

How to calculate it

To calculate realized CLV, take a given customer’s predicted CLV and divide by their actual lifetime profit. This will show how much of a customer’s lifetime value they have achieved, as a percent.


How to use it to improve your business

  • Use it to find your brand promoters. If a customer has realized close to 100% of a high predicted CLV, then it is very likely that they are happy with you. This is the perfect time to introduce a referral campaign. Remember, the total value that a customer brings isn’t just in profit that they directly generate; brand promoters can help find more high-value customers just like them.
  • Use it to measure the accuracy of predicted CLV. If a significant number of customers are regularly not realizing their predicted CLV, it means that the method being used to predict CLV is not accurate. Keep in mind that it takes time for a customer to realize their full CLV, so be sure to only analyze customers who have had sufficient time to do so.
  • Combine it with cohort analysis to improve your customer experience. When combined with cohort analysis, realized CLV can point you to cohorts of customers that likely won’t realize their full CLV potential. You can determine what is causing this low CLV, and improve the overall customer experience. The same can be said for a cohort of customers who have high CLVs.

How does your realized CLV compare to your predicted CLV?

How do you compare to other ecommerce retailers?

An average or range for this figure isn’t very useful, as this metric usually has a lot of variance. In general, retailers should aim for their customers to reach 90% of their (individually) predicted CLV. Note that if you’re using a general CLV metric for all of your customers (instead of a CLV for each customer or segment), then you should be aiming for approximately 70% accuracy.

A more practical way to measure realized CLV performance is to set intervals where customers should be at a certain realized CLV. For example, after a year we might set a target realized CLV of 30%.


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Breakage Rate

A loyalty program’s breakage rate is the ratio of points that do not get spent to the total number of points earned. Put simply: what percent of points aren’t spent?

Any retailer with a loyalty program can make use of this metric. But it can also be applied to other promotions as well. Any time you offer a customer some sort of discount or gift you can use a metric similar to breakage. The percent of unused abandoned cart coupons, the ratio of customers who didn’t accept a free gift during checkout, etc, are all useful situations for calculating breakage.

How to calculate it

To calculate breakage, find the total number of points that have not been spent, then divide this by the total number of points issued. This can be calculated for retailer’s lifetime, or during a particular time period. For example: the calendar year.


How to use it to improve your business

  • Determine the financial liability of loyalty points. Accountants will use breakage to determine the outstanding financial liability of points for a loyalty program, usually towards their fiscal year end. Accountants love breakage (free money!), but it often indicates that something is wrong with your loyalty program.


  • Use it to determine how engaged customers are with a loyalty program. If customers aren’t spending points to get rewards, then they are not likely to be active, loyal customers. It might also signal that your customers don’t know how to participate in the loyalty program.

How do you compare to other ecommerce retailers?

From our data, the average breakage rate is about 30%, but there is a ton of variance. Breakage will vary according to how frequently a customer visits or purchases, how engaging your program is, if you have point expiration, and if you have frequent communication with the customer, to name a few.

Average Time to First Spend

The average time to first spend is a measure of long it takes for customers to go from creating a new loyalty account to the first time they spend their loyalty points. This could be days, weeks, months, or (hopefully not) years.

The average time to spend metric is used to determine how long a customer takes to go through one “full cycle” of a rewards program. Basically, how long does it take for a customer to spend points and feel rewarded?

How long until your customers first start spending loyalty program points?

Knowing where a customer is in this rewards cycle is extremely useful for creating promotions and segmentation.

The average time to spend is directly related to a customer’s purchase frequency as well as what actions are rewarded. If a customer is making regular purchases, they’ll accrue points faster and will have a lower average time to spend. Similarly, if a loyalty program rewards customers for several actions, such as reviews or social sharing, then the customer will take less time to reach a spending threshold.

How to calculate it

First, calculate the time in between a loyalty account being created and its first point spending event. Calculate this on a per account basis. If you have a large customer base, use a sample size (make sure it’s a statistically significant sample size). Take the average of these and you’re done.


How to use it to improve your business

  • Optimize customer communications. If a customer is approximately 90% through their average time to spend, it is a great time to ask them to perform an action that will earn a reward. They’ll be more motivated than usual to earn points because they’re close to having enough points to earn a reward. You could send them an email letting them know that they receive points for referring their friends, or send them a coupon giving “double points” on their next purchase, or any action that you determine valuable. These campaigns are often our most successful, in terms of engagement. All of this is possible because we understand the average time to spend.

How do you compare to other ecommerce retailers?

From our data we see an average time of 92 days to spend points. Note that we also see a lot of variance in this number because there are a lot of variables that affect the average time to spend.

Loyalty & Data: Best Friends Forever

Like I said, one of the main reasons why loyalty programs were created was to give a customer an incentive to identify themselves at each interaction. This incentive comes in the form of collecting reward points.

Loyalty programs are great for developing happy, loyal customers that spend more and refer their friends, but don’t forget that loyalty programs are also great at giving data-driven retailers better data.

Facebook, Twitter, YouTube, Netflix, Instagram – companies that changed the habits of millions of people. How did they do it?

Last week Nir Eyal, author of Hooked: How to Build Habit Forming Products, joined us on a webinar to talk about his research and help us understand how these companies developed products that so effectively changed user behavior.

Nir started by outlining just how customer habits improve business performance:

  • Habits increase customer lifetime value
  • Habits provide greater pricing flexibility
  • Habits supercharge growth. “Hooked” users don’t churn
  • Habits improve a business’s defensibility. It’s hard to get someone to stop using a product that they use without thinking

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You may still be polishing off the last of the champagne and eggnog, but we’re already one week into the new year. It’s time to wake up from the food coma and get serious about growing your business in 2014.

Problem is, there are a million things you can do to grow your online business. You can write more content, better content, improve SEO, hire more employees, start an Instagram promotion, boost social sharing, do a publicity stunt, use PPC advertising, guest blogging, or get better at email marketing. There are so many things you can do, but what should you do? Where should you focus your energies in 2014 for maximum impact on the bottom line?

Fix Where You’re the Worst

Growing a business doesn’t happen from any single activity, it happens when you’re continually getting better and optimizing your business to deliver the best possible experience. Doing this requires shifting your thinking away from one-off tactics, and instead focusing on lifting conversion rates throughout the lifecycle.

The places where you will have the biggest impact are the areas in which you are currently the worst. You can’t just be good at turning strangers into visitors, you have to turn those visitors into users or customers, then turn those customers into repeat buyers. If you’re underperforming in one of these areas, your whole business will suffer.

How to Identify Where You are Underperforming


The acquisition stage is focused on one thing – get more eyeballs. Traffic is the lifeblood of any ecommerce store. You need eyeballs, and lots of them, to keep an ecommerce store growing.

Check this:

  • Time in business: If you’re a new store then this area will undoubtedly be a top priority, and it should be.
  • Traffic growth: If you’re not experiencing double digit traffic growth month-over-month then you have an acquisition problem. Keep in mind, if you’re looking at 2013 data to check the health of your acquisition strategy, be sure to exclude 2013 holiday data – it will muddle your averages.
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Recommendations for improvement:

  • Test your strategies: Every ecommerce marketer should be testing acquisition strategies weekly. If you’re struggling in this area, you’ll be well served by ramping that up to daily.
  • Don’t put all your eggs in the PPC basket: PPC is often a quick win for ecommerce, but it’s not cheap. Be sure not to ignore the slower-momentum channels like SEO and content marketing. These activities won’t have the immediate impact of PPC, but over time, the payoff is big. Particularly in the post-holiday slow down there’s additional opportunity to gain attention as plenty of ecommerce stores will be slowing their content production during this time.
  • Targeted landing pages: One very common mistake ecommerce marketers make is having ads that don’t point to targeted landing pages. This acquisition error is one that tends to have a negative impact on conversion rates. Make sure the customers’ experience stays consistent from ad to site.


Once you’ve got the eyeballs, it’s time to turn them into paying customers. Small, targeted tweaks to the conversion process can have a big impact on overall growth. If you use data wisely at this point you can get big results with much lower effort.

Check this:

  • Macro conversion rates: One big warning sign that you definitely do have a problem is if less than 3% of your visitors are turning into customers.
  • Micro conversion rates: Leading up to the big conversions there are often smaller conversions that happen along the way such as subscribing to your blog or newsletter, adding items to a wish list, or interacting with you on social media. These numbers don’t immediately translate to revenue growth, but steady improvement over time indicates a healthy ecommerce business.

Recommendations for improvement:

  • A/B test your face off: Make tools like Optimizely your best friend. Get smarter about how, where, and when you test.
  • Test visitor flows: Tools like Google Analytics will provide insight on your visitor flow so you can spot the areas where visitors are dropping off.
  • Revive abandoned carts: Abandoned shopping carts are a huge opportunity for online stores to boost conversion rates. Use your data to find the best time and approach for sending these follow-up emails to soon-to-be customers.


If you have a steady stream of new customers coming in, it’s time to look at how you keep them coming back.

Check this:

  • Repeat purchase rates: Repeat purchase rates vary dramatically depending on your business. If you’re selling computers, this number is likely very low, if you’re selling used books, it should be quite a bit higher. Benchmark your repeat purchase rates and use this as a barometer of your future success.
  • Churn rate: Churn is a vital health metric of your business. Even if customer acquisition is wonderful, high churn will destroy the success of your business.
  • Customer lifetime value: Your customers are not all created equal. If retention is low among customers with a high customer lifetime value, that’s a far bigger problem than if retention is low among your discount-chasing customers.

Recommendations for improvement:

  • Repeat purchase analysis: Use your data to find out where shoppers are dropping off. Is it between order 1 and order 2? What were the characteristics of the people who bought a second time and those who didn’t?
  • Churn analysis: Things like website difficulties or shipping delays can cause surges in churn. If you find that this was the case for you then go back to these customers and offer an explanation, a sincere apology, and an invitation to give your business another chance.
  • Test email marketing: Email marketing is a staple in the ecommerce marketing toolbox. You can drive repeat purchase rates by testing things like sending follow-up emails recommending items frequently purchased together, subject lines, and content.


If you’re younger than a year, don’t worry too much about reactivation. You will lose customers, but at this point it’s more important to focus on acquisition and conversion. Once you’re more established, it’s time to start looking at how to get those churned customers back.

Check this:

  • Win back rate: Your win back rate is a measure of customers who came back and remained customers. If this number is too low it can indicate a problem with your reactivation strategy.
  • Customer lifetime value: If you’ve lost a number of high lifetime value customers than this is the #1 place to start getting reactivation results.

Recommendations for improvement:

  • Win back analysis: There’s a tendency to approach reactivation with the discount hammer. This can lead to customers coming back once for the discount, only to never purchase again…again. Understanding why customers returned and turned into repeat customers will help you design a reactivation strategy with the offers and messaging to create repeat customers.
  • Retargeting: Most people think of retargeting as an acquisition strategy, but you have your customers’ cookies, go ahead and test using this for reactivation.

Test and Measure

Once you know your weak spot, it’s time for the fun stuff – testing new tactics and measuring the results. Remember: you don’t have to have all the answers up front. Just make sure you’re always trying new things and learning what works!

Shameless Plug

Want a marketing strategy grounded in data and aimed at fixing your business where it needs it most? Get in touch. We’ve got the tools and the people that can help you conduct the types of analysis described in this post.

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Your competitors are spending their time learning everything they can about their best customers. Why is figuring out this small group of customers just as important as attracting new customers? Read on to learn why your best customers are so valuable to your business:

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photo: flickr, plural

A holiday shopper is not your average customer. The rest of the year, you might be selling high-end kitchen supplies to self-taught chefs. Come November, your new customer could be a take-out addict – hard pressed to describe the difference between a braise and brine. Making the assumption that a holiday shopper will become a year-round customer sets ecommerce retailers up to overspend, underspend, or spend in the wrong places to acquire these shoppers. Before jumping in to capturing new holiday shoppers, make sure you first understand just what a holiday shopper is worth.

According to the NRF 36% of holiday shoppers decide where to shop based on discounts and sales. A separate study by Deloitte found that 71% of holiday shoppers expect free shipping and 47% expect free returns. Couple this with the fact that that advertising becomes more expensive and inboxes become more crowded, you are looking at substantially higher acquisition costs around the holidays.

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If you follow this blog on a regular basis, you know that we’re big believers in measuring customer lifetime value. Knowing your CLV is the key to effective marketing. If you know your customer lifetime value and your cost to acquire a customer, you know whether you have a profitable, scalable business or not. Segment these same numbers by customer acquisition source, channel, and ad placement, and you have a recipe for optimizing your marketing.

We’ve found that calculating customer lifetime value is one of the single biggest challenges digital marketers face. Companies tell us that they spend countless hours with SQL queries and spreadsheets or pay thousands of dollars to consultants.

So, we are releasing a free customer lifetime value calculator. Continue reading

looking at data

You have three groups of customers that require special attention. By creating contact lists for these groups you can send them tailored communications, such as rewards, promotions, and product updates. Then, just watch as the repeat purchases roll in.

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