6 Best Practices for Successful B2B Multi-Touch Attribution

“Which marketing channels should we put more money into? Reduce or stop spending?”

If you’ve ever asked these questions (or are still asking them), multi-touch attribution is your best bet to getting the right answers; it provides you with meticulous details of how buyers are engaging with your business and which marketing touches are driving purchase journeys. Plus, it shows everyone in your organization how much revenue you are bringing to the table, which puts you in good standing with your investors and builds credibility in budgeting with your peers.

While 82% of CMOs report that their goals are aligned to revenue targets (Forrester Research), B2B marketers are struggling to quickly connect the dots along a lengthy and complex B2B purchase process to make decisions that optimize revenue. According to DemandGen, 58% of B2B companies said their current ability to analyze marketing performance “needs improvement” or worse and 48% of B2B companies say their biggest challenge is the “inability to track activity between specific buyer stages.”

To close this burgeoning gap, many marketers are stitching together various technologies, data and spreadsheets to attribute marketing spend to revenue. However, this creates a data and management nightmare, and exacerbates your ability to continuously optimize your marketing investments to revenue.

WARNING: Unlike B2C, the B2B buyer’s journey is much longer and more complex, requiring the right multi-touch attribution system to capture and correctly attribute marketing touches to revenue. So if you are seriously considering instrumenting your marketing attribution to revenue outcomes, here are 6 best practices to ensure you’re on the path to accurately attributing spend and optimizing programs.

1. Capture all ad and conversion data

When it comes to attribution insight to do their jobs, most senior B2B marketers and their teams feel they are in a constant state of “data chaos,” suffocating under a combination of web, channel and CRM data that have been stitched together with Excel to give them the information they need to do their jobs. And how actionable, timely, and trusted is the end result?

To get an accurate and timely view of how buyers are interacting with your touchpoints, customer acquisition cost and lifetime value, you need to integrate your ad and conversion data into a multi-touch attribution system of record.

This allows you to accurately allocate revenue and cost back to all your marketing touches and channels that contributed to a closed sale.  Without a multi-touch attribution system of record, your team is plagued with messy reports and spreadsheet sprawl, wasting time on non-value activities. To eliminate this attribution data chaos, senior marketers should look to three ‘must-have’ capabilities:

  1. Automatically capture all your ad and conversion sources using pre-integrated connectors to most or all of your internal and 3rd party sources, making it much easier and less costly to connect and maintain.
  2. Instantly collect buyer interactions outside of ad and conversion sources using tracking technologies such as UTM parameters, click redirects and other unique identifiers.
  3. Seamlessly access your marketing attribution connections and overall system using only a web browser (think true, not fake SaaS), enabling simple and quick set up and configuration to your needs.

By building your B2B multi-touch attribution system to these standards, you’ll have the foundation or “plumbing” in place that frees you from “data jail” to focus your energy on optimizing marketing spend to revenue.

2. Connect every touch across your buyer’s journey

Rather than trying to make sense from fragmented lists of marketing touches, best practice B2B multi-touch attribution automatically connects all your touches to your buyer’s journey. This means every touch that achieved its intended outcome is connected to the buyer throughout their entire journey and is assigned a revenue credit and cost, resulting in a significantly higher degree of attribution precision and giving marketers the trusted insight they need to optimize their budgets to revenue.

To illustrate the power of following your buyer’s journey, let’s consider a scenario for a technology buyer for ZZZ bank who is looking for business intelligence tools.


In the buyer’s effort to understand ZZZ’s challenges and explore solutions, he begins by googling the web and researching various sources. One of your blogs shows up in his search results and he clicks on it and learns new insights on tackling his company’s problems – he also checks out your banking solution and company web pages. A week later he digs further into an independent tech community site he discovered for business intelligence and sees one of your ads pop up for an e-book, fills out the form and downloads it. That same week he continues researching on the web and one of your paid search ads pops up offering an educational video on business intelligence for banking.


Feeling he has a good handle on ZZZ’s business intelligence pains and different solution options, the buyer begins to crystallize the capabilities they need to solve their challenges and the vendors they should consider. Impressed with your e-book and blog, he registers and attends one of your live webinars. Another week later he receives and clicks on one of your emails (that he consented to when he downloaded your e-book) featuring a new blog on business intelligence for banking and checks it out. A month later he attends a banking technology conference and drops by your booth and speaks with a product specialist to discuss his needs; he also watches a demo of your business intelligence product.


At this stage in the buyer’s journey, he has narrowed down ZZZ’s short list of vendors to further evaluate and recommend.  He recalls the great experience he had at your booth and check’s out your website again to read the case studies of other banks that have gone through a similar business intelligence journey and are successfully using your tools. Later in the week the buyer is favoring your company and registers to watch your on-demand demo. At this point, your sales rep reaches out and schedules a live personalized demo for the following week. Impressed with the live demo, the buyer signs on for a paid proof of value and, upon completion of it, validates their business case. Though the buyer continues checking out your website, he moves ahead with pricing and a purchase contract for your business intelligence tools.

In this scenario, attributing 100% of the revenue credit to a single touch ignores the influence of the other seven touches. Further, analyzing touches in parts of the funnel such as “lead conversion” or “opportunity creation” neglects the full story of how your marketing is working (or not). In both cases, the attribution insight is heavily biased and/or flawed, leading to guessing your way into allocating marketing spend to optimize revenue.  However, using a multi-touch attribution model such as a position-based type allows you to accurately allocate weighted credits to all touchpoints in your buyer’s journey. For example, as shown in the illustration below, if your business most values the touchpoint that introduced a buyer to your brand and the final touchpoint that influenced the deal, you can assign the revenue credit accordingly with the balance (50%) being assigned to the touches in the middle of your buyer’s journey.

A position-based multi-touch attribution model allows you “see the forest for the trees” – to automatically connect and assign (and easily refine) the revenue and associated cost of every touch across your buyers journey, making it ideal for longer, more complex B2B buyer’s journey. With this level of insight into what’s really influencing (or not) your buyer’s journey, you can make precise decisions to continuously rebalance your marketing spend to maximize revenue.

3. Use a cohort method to determine ROI

The entire business value for building a best practice multi-touch attribution capability is based on the ability to accurately (and quickly) quantify the influence each touch has on the buyer’s decision, enabling marketers to optimize their marketing to revenue. The better the fidelity of the ROI calculation, the greater the value it has for marketers.

In senior marketer terms, a cohort method simply allows you quantify the ROI of your paid programs (ads, events, etc.) based on the start date of your ad/campaign (the initial spend) and capture the subsequent stream of conversions to calculate ROI. Having this true ROI insight allows you accurately compare “apples-to-apples” of your programs and confidently rebalance your total spend to maximize revenue. The cohort method is ideal for multi-touch attribution in longer B2B buying journeys (which makes it hugely problematic in determining true ROI), providing precise ad/campaign ROI and Customer Lifetime Value (CLTV). According to Neil Patel in The Case for Cohort Analysis and Multi-Touch Attribution Analysis, “In B2B SaaS, there are two techniques that I feel are particularly important but not used widely enough – cohort analysis and multi-touch attribution analysis.”

In stark contrast to the level of precision using a cohort method ROI method, a non-cohort approach typically starts with a given conversion date (i.e., content download, ad click through) and “looks back” at random and changing time periods in an attempt to quantify the true ROI, leading to poor insight about the relative performance of your programs and channels. While this method may seem straight-forward, it’s deeply misleading. As a result, when you are trying optimize your spend among Google Ads or AdRoll as an example, you are using ROI information that is analogous to comparing “apples to oranges,” leading to erroneous conclusions and suboptimal decisions.

To put in place a best practice cohort method, senior marketers should look for flexibility that allows you to calculate ROI from the campaign start date and calculate the revenue against multiple dimensions depending on your business needs such as:

  • campaign/channel by source
  • any date up to current
  • various types of traffic

For example, you invest $10,000 each with Google Ads on January 1 and want to know how well the campaign has performed through September 30; a cohort method would automatically link conversion A for $40,000 and conversion B for $80,000 to your original investment and allocate a portion of the $120,000 revenue credit.  You can then compare this to LinkedIn ads using to same method. This eliminates the fuzzy math of a using non-cohort approach and gives you true insight as to what’s really converting (and not) to make better paid spend decisions.

4. Unify offline attribution

While offline marketing such as field events and conferences generally represent a large portion of B2B budgets, their conversion outcomes are often disconnected from online marketing efforts which can lead to flawed ROI conclusions and misguided budget allocations. As an example, a common practice for offline attribution is to collect and report on outcomes shortly after the event occurred. Success is usually based on “leads captured,” “deals piped,” and/or the proverbial, “hey, that was a great conference” with 100% revenue credit allocated for any wins that were captured under a traditional first or last touch system.

But B2B buyers who visited your booth frequently visit your site (direct URL) or Google your brand, and engage in other touches (i.e., registers for a webinar, downloads an e-book). And unlike the instant conversion associated with your online channels such as clicking on a retargeting ad, offline channels have what senior marketers call the “halo effect,” meaning the true value or conversions of events shows up over a much longer period of time. How are you supposed to be able to best allocate your marketing budget when you can’t accurately determine the true ROI of offline channels and/or compare offline channels like conferences with online channels?  You may be getting more revenue per dollar at conferences compared to paid ads, but you’d never know the true picture without unifying online/offline attribution.

Unifying your offline campaigns with online channels into a single multi-touch attribution system solves the attribution madness that plagues senior B2B marketers through the following capabilities:

  1. Automatically capture event cost and credit the stream of conversions (using a cohort method – see best practice #3) attributed to it over a longer period to account for the longer halo duration associated with offline channels.
  2. Flexibly assign ROI credit among offline and online with weighted touchpoints, allowing you to allocate the most accurate value of each touchpoint based on their relative impact on the whole customer journey from beginning to closed-won.

Senior B2B marketers will have a much more accurate understanding of the customer journey and marketing’s impact on it when their multi-touch attribution model takes these two capabilities into account. From branded search queries and on-demand webinar views to conference booth demos and field seminar receptions, you’ll have a single, holistic view of what’s driving conversions and revenue.

5. Track B2B account-based attribution

Today’s B2B buying journey often involves multiple people and roles – seldom will a single person click on an ad, visit your site, and purchase. B2B buyers are usually a team of people within the same account who have different buyer roles such as researcher or decision-maker depending on the buying stage. Hence, according to Martech Today, “the rise of account-based marketing has spurred a shift in how marketers think about accounts.”

Using our earlier scenario for a technology buyer for ZZZ bank who is looking for business intelligence tools, we illustrate below moving from a single buyer to multiple buyers in an account and their roles.

Best practice account-based multi-touch attribution enables you flexibly attribute the revenue and cost of all your successful marketing touches to account, resulting in complete visibility of the account-level journey and true account marketing ROI (especially account-based marketing [ABM] campaigns) and enabling you to fine tune your marketing investments to maximize revenue.

6. Make sure everything adds up

For senior B2B marketers who have ROI accountability for multiple channels and big budgets, errors adding up the right cost or assigning the correct revenue credit to touches can impair your optimization efforts and ruin your credibility among peers and board members. And with increasing complexity of the B2B buyer’s journey, current multi-touch attribution practices exacerbate this problem of duplicate and/or incorrect revenue credit.

Consider this scenario (simplified for illustrative purposes): a buyer clicks through on your LinkedIn ad and a week later he or she clicks through your Google retargeting and TechTarget content syndication offer, eventually buying your product for $50,000. And because these media programs aren’t integrated to one multi-touch attribution system, they each claim $50,000 in conversion credit. Sure you can manually override this for your presentation, but how painful is this problem with dozens and hundreds of programs running simultaneously, media prices changing constantly, and buyer journeys evolving continuously? And how would this impact your ability to demonstrate to executive and board members your ability to drive more outcomes with the same or less dollars?

To safeguard the fidelity of your attribution insights and preempt a costly and possibly embarrassing problem, senior marketers should build their attribution capability on the best B2B multi-touch attribution practices discussed earlier:

  1. Capture all ad and conversion data
  2. Connect every touch across your buyer’s journey
  3. Use a cohort method to determine ROI
  4. Unify offline attribution
  5. Track B2B account-based attribution

These are required to ensure you’re not duplicating attribution credit. The sixth best practice is to automatically and continuously check your calculations and allocations through multiple dimensions. In much the same way accounting is reconciling transactions, a best multi-touch attribution practice is to continuously (and proactively) reconcile revenue credit to your marketing touches. This preempts giving credit to channels/campaigns that had no role in the conversion to revenue, duplicating credit, and over/under (+/- 100%) assigning the total of all revenue credits for a given deal. In addition, you should have a complete audit trail that lets you drill down into every marketing touchpoint, channel, user, and account across your buyer’s journey.


According to Forrester Research’s What B2B Marketers Must Know and Do, “Crystallizing a view of the lengthy B2B purchase process and recontextualizing marketing activity as steps leading toward revenue, rather than disconnected conversions, allows B2B marketers to connect the dots along the path to won deals.” Hence, the linchpin of multi-touch attribution is the ability to precisely track and allocate credit to across all efforts that contributed to revenue.  Applying these six practices to your multi-touch attribution journey will eliminate the pain of your current attribution efforts and enable you to see what’s really working (and not) so you can focus your energy on optimizing your marketing to revenue.

Top 5 Marketing Attribution Trends for 2019

By Ryan Koonce

Investor demands, customer expectations and competitive intensity will drive adoption of marketing attribution technologies and new attribution models.

Although classic web analytics and marketing automation tools serve up point KPIs, marketers are grappling with the lack true attribution insight to optimize their spending to revenue. According to a 2017 research study by the Marketing Attribution Think Tank (MATT), which was spearheaded by marketing leaders from some of the world’s top brands including Unilever, Allstate and Bank of America, while “81% of marketers use CTR (click-through rate) more than any other metric…there is virtually no relationship between click-through and sales.”

The good news is that marketers are finally recognizing the need to evolve their attribution approach. The days of last-click, first-touch and CTRs are fading into the annals of advertising, and a new day of multi-touch attribution models is gaining momentum, where MATT’s study also showed “75% of respondent marketers said they are or will be using multi-touch attribution within 18 months.”  Companies large and small are using these advanced analytics to patrol online (and offline) marketing touches and connect them to revenue. They can quickly tally campaign and advertising costs, allocate revenue credits and collect new insights to show marketers what’s really working, what needs watching, and what needs to go.

The rise of multi-touch attribution is just one example of change, one of our marketing attribution trends for 2019 with the potential to drive significant evolution and deliver economic opportunity over the next five years. Our CMO, Yancy Oshita, shared his perspectives on the adoption of multi-touch attribution and other pivotal trends in an article published by Martech Advisor: Top 5 Marketing Attribution Trends for 2019. Having successfully scaled marketing and grown revenue at venture-backed and some of the best-known tech brands, Yancy combines his experience and passion in a plain-speaking way to offer his insights and practical guidance to senior marketing leaders in B2B and B2C industries. In the article, he expands on the following movements:

  1. Connecting Marketing to Revenue Outcomes
  2. Rise of Multi-Touch Attribution
  3. Performance Monitoring of Ad Vendors Comes of Age
  4. Shift to a Marketing Attribution System of Record
  5. Emerging Marketing Organizations and KPIs

As an advanced marketing attribution platform working with B2B and B2C clients, we’re always pushing ourselves to help marketers and advertisers use data to optimize their marketing to revenue. We believe the future will be characterized by smart attribution delivering increasingly insightful intelligence everywhere marketers connect with buyers. Still, most marketers are wrestling to act on attribution insights, indicating the challenge to take advantage of attribution intelligence goes beyond technology and includes process and culture. As Yancy predicts, next year we’ll see more companies tune their organization models and KPIs around the new insights offered by multi-touch attribution technologies.

Advanced marketing attribution is really about connecting marketing to revenue. It’s about bringing the science of marketing to a whole new level and organizations need to evolve with it to reap the enormous benefits. And unless CMOs are taking extreme ownership of revenue return on marketing, moving to advanced marketing attribution would likely lead to marginal success at best. As you grapple with “doing more with less” and “showing the money” to you’re your investors, the Top 5 Marketing Attribution Trends for 2019 in Martech Advisor is worth a read!

Attribution Quora Partnership | Attribution

By Ryan Koonce

I am thrilled to announce our partnership with Quora, the popular Q&A platform that connects people seeking knowledge with those who have it. Quora Ads is now integrated to Attribution’s platform, meaning that marketers and advertisers on Quora can easily link their account to view their true return on ad spend (ROAS) alongside their other paid networks such as Google, LinkedIn, and Facebook.

With a reach of over 300 million monthly visitors, Quora Ads provide marketers with a native, high-performance channel to connect and engage with their audiences. Advertisers can automatically serve ads in a way that is consistent and additive to Quora’s user experience and deliver a strong ROAS. By integrating Attribution to your Quora Ads account, you’ll receive actionable insights that enable you to optimize your budget to drive more conversions and generate the best revenue outcomes. Attribution automatically captures ad and conversion data and connects it to every marketing touch across the full buyer’s journey.

Quora Attribution Dashboard


Commenting on our new partnership, Quora’s GM & Head of Partnerships, Brendan L. Weitz, added, “We are excited to launch this integration to enable our customers to have greater insight into the value of marketing on Quora.”

Attribution provides marketers with single version of attribution truth in how paid channels are contributing to revenue. Using a patent pending multi-touch attribution approach and unique cohort-based ROAS method, Attribution quickly culls through how target audiences responded to various touchpoints and accurately assigns cost and revenue credit. These unique capabilities give you the true picture about what’s working, what’s not, and what is your current and forecasted return on marketing.  

We’re dedicated to giving our customers a variety of options when it comes to measuring their success on Quora. Our integration with Quora will give you a way to track the holistic ROAS and optimize your campaigns. Linking Attribution to your Quora Ads account to is easy. Just go to your Attribution account, visit Settings->Integrations and “Connect Quora”, and start receiving insights alongside all of your other paid social platforms.

If you are interested in integrating your Quora campaigns into Attribution and/or would like a demo of Attribution, please reach out to us at get.attributionapp.com/request-demo. If you are new to Quora Ads, you can get started at quora.com/business.

Attribution App is one of the first to join new Stripe partner program

Most people today don’t know that only three percent of GDP is online. That’s why we’re excited to join the Stripe Partner Program to increase internet commerce and help companies start, run, and scale their businesses more efficiently.

By joining the program, our mutual customers will now benefit from the combination of Attribution’s multi-touch attribution solution with Stripe’s seamless payments platform.

This means that our customers can now connect their Stripe account to Attribution, which can then automatically integrate standard Stripe data like when a charge is successful (event label: Charge Successful) or a Charge is captured (event label: Charge Captured).  These events can then be associated with the appropriate ad channels to calculate return on ad spend.

We believe that removing barriers to online commerce helps more new businesses get started, levels the playing field, and increases economic output and trade around the world, and we believe that democratizing attribution is a key for businesses to grow and thrive. Together with Stripe, our mission is to bring more commerce online and increase the GDP of the internet.

Read more about the Stripe Partner Program here: https://stripe.com/blog/stripe-partner-program

3 Remarketing Mistakes Everyone Makes

Remarketing is everywhere now. It’s so common that my grandmother complains about it, and people outside the marketing bubble make jokes about it. This is the slightly creepy reality of modern marketing, remarketing works so well that companies basically have to use it to stay competitive. So without further ado, here is a primer on remarketing without being annoying about it. Because let’s face it, nobody wants to see your banner ad 1,264 times.

1. Showing the same ad over and over.

There’s two ways to make this mistake. Either you start your remarketing campaign and only use your best ads, or you create a campaign with lots of ads and let Google or Facebook ‘tune’ the campaign by showing the top performing ads more often. This seems logical, because we’re used to running display ad campaigns where you can show the same ad millions of times before showing it to the same person twice, and nobody wants to use ads that perform worse on even rotation with the best ads.

The effect is showing the same ad to the same person dozens of times. You’re like the new friend who calls 12 times per day – every day – just to see what’s new. You’re like a sales assistant following someone out of a clothing store and into the mall, asking over and over again if they’d like to try something on. Make some new ads, and tone down the frequency, and put them on even rotation.

2. Continuing your remarketing after the purchase

You know what’s worse than an overly aggressive remarketing campaign? One that doesn’t stop marketing after you buy the product. This is the kind of thing that would never happen in the real world. It makes no sense to market this way, but let’s face it, remarketing technology is complicated. In a perfect world, your remarketing would turn off after a purchase, or switch to customer-success style ads that market add-ons and special features.

Unfortunately, this is harder than it should be. There’s the easy way and the hard way, and most of us take the easy way and assume people won’t mind being targeted after they purchase. I blame this on the ad platforms. Firing a conversion pixel should turn off remarketing by default. Until this happens, marketers who don’t have a deep understanding of both JavaScript tracking and their ad platform will just keep on targeting. I don’t blame the marketer, this should be easier than it is.

3. Not splitting credit between the original source and remarketing ad

Want to know the real reason remarketing is so popular? Remarketing takes all the credit. Most of us still use a last-click attribution model, and that model heavily favors remarketing. Giving all the credit to your remarketing campaign is like a football team rewarding the extra-point kicker for scoring a touchdown. It’s just wrong.

If you use remarketing without using attribution software, you are seeing a vastly inaccurate picture of your marketing efforts. Sure, you should be skeptical of the guy who builds attribution software saying “You need attribution software,” but seriously you need attribution software. Without it you’re flying blind.

So there’s the three main remarketing mistakes. If you used remarketing, you’ve either made these mistakes or you’re still making them. I didn’t write this article to scorn you. I wrote this article because I set up remarketing for Attribution last week and I suddenly realized why everyone makes these mistakes. Google and Facebook encourage you to only show the ads that work best and discourage even rotation which would freshen up the ads. They both have a concept of “Audiences” but there’s no shortcut to exclude someone who’s fired a conversion pixel.

Finally, Facebook makes it genuinely difficult to UTM tag ads for Attribution software – their ads editor literally broke when I used a UTM tagged URL. The long URL caused a field to overlap the submit button, and I had to use developer tools to re-organize the page until the submit button was visible.

I’m guessing that some of the retargeting-specific tools like AdRoll and Perfect Audience would make this easier, so in the next part of this series I’ll explore some advanced remarketing techniques. Let me know in the comments what you’d like me to try!

Attribution Modeling Explained

These days, customers find your product through a variety of marketing channels (ad platforms, partnerships, content, organic, etc.). It is important to understand how these channels work together in driving conversions. After all, the journey of the converted customer is the one we really care about, the one we want to promote and replicate. To understand this journey, we use multi-channel attribution modeling.

Let’s say the follow diagram represents your customer’s journey, how much credit does each channel deserve? Let’s explore several ways distribute the credit.

First Click Attribution

First click attribution gives 100% of the credit to the first touchpoint. In our example, display would get 100% of the credit.

First click attribution is useful for figuring out how customers original found your product, but doesn’t shed much light onto the conversion driving touchpoints.

First click attribution is akin to giving my first girlfriend 100% of the credit for me marrying my wife.

– Avinash Kaushik

Although I love this quote, it’s not accurate. First click attribution is actually akin to giving your friend that introduced you to your wife full credit for your marriage.

Last Click Attribution

Last click attribution gives 100% of the credit to the last touchpoint. In our example, remarketing would get 100% of the credit.

Last click or last interaction is the classic model used in many reporting tools. It’s only good for figuring out which touchpoints are driving the actual conversions, it completely ignores the rest of the referral touchpoints.

Linear Attribution

Linear attribution is the most basic way of dividing a conversion. It divides the credit equally among each of the referring touch points.

This model is useful when analyzing a conversion event that has long sales cycles, where all the touch points are important in building a brand image.

Time Decay Attribution

The time decay model is the most advanced model we provide. It divides credit to each filter based on the number of days before the conversion.

The calculation we use for this is:

y = 2-x/7

where x is the number of days the referral happened prior to the conversion. The 7 in the equation is the half-life. A touchpoint 7 days before a different touchpoint, will receive half the credit.

For example, a user visits your site from a Google display ad, a remarketing ad and then finally a social channel, with the following timeline:

Based on the equation above, we would split the credit up for each channel accordingly:

Display Remarketing Social




.453 .673 .906
22.29% 33.13% 44.58%

So, What Should I Use?

The truth of the matter is, there’s no silver bullet for modeling attribution. Our team has found the best way to get an accurate understanding, is to compare our numbers for each of the models. This is easy to do using the model selector in Attribution. Give it a try with our demo data.

The SaaS Calculator: How Much Should I Spend to Acquire a Customer?

SaaS companies typically spend money upfront to acquire customers, then have to wait many months before recurring revenue makes up for the initial cost to acquire. Revenue from a customer is defined equally by the length of time they stay and the size of their monthly payment. This is a common problem: you spend a lot of money upfront to acquire customers who are only valuable if they stay for a long time. We are betting on our future product with our current cash. Anyone building a SaaS company faces this problem, but if growth erodes profit, how do we separate the viable businesses from the bottomless money-pits?

In this post we will explore the industry benchmarks for acquiring customers. I’m going to do this using Brett Victor’s Tangle library. Drag the blue numbers left or right, and adjust them until they reflect your company.

Monthly Average Revenue Per User (ARPU)

So let’s assume a simple case – you have a software company with 100 customers, and you make, on average $3000 recurring revenue per month. This means you have $30.00 Average Revenue Per User every month. That’s relatively straightforward. There’s also expansion revenue but we will bundle that into churn for simplicity.

Average Revenue Per User: $30.00

Monthly Churn

Your churn is a measurement of how many customers leave your product. Let’s say that 90days ago you had $2000 monthly recurring revenue. If we ignore the new customers, those customers from 90 days ago now account for $1800 remaining monthly recurring revenue. This would mean you have 3.70% monthly churn and 45.06% yearly churn.[1]

Monthly Churn: 3.70%

Lifetime Value Calculation

We can divide 1 by the monthly churn rate to get an idea of how many months your customers would be expected to stay. So for our calculation, 1 / 3.70% monthly churn = 27.0 months of expected revenue. Earlier we calculated that the average customer spends $30.00 per month and now we see the average customer stays for 27.0 months. Multiply these two numbers together and we see that an average customer would have a $810 lifetime value.

Lifetime Value: $810

So that’s a simple calculation of lifetime value. Now that we know how much a customer is worth, we can look at how much you can sensibly spend to acquire one.

Spend Less than 1/3 of LTV

A simple and prevalent model is spending 1/3 of your customer lifetime value [2]. Using the numbers we calculated above, we would have a $810 LTV divided by 3 for a $270 Maximum Customer Acquisition Cost. The idea here is to reserve 2/3 or your gross revenue for product development, operating expenses, taxes and profit.

Spend less than $270

Which is 1/3 of a $810 customer lifetime value.

Spend less than 12 Months of Revenue

Another common way to think about this to spend a set number of months income on customer acquisition, usually 12 months or less [2]. So if your customers spend an average of $30.00 per month, you spend 12 months income or less on acquisition. This would give us a $360 maximum CAC. This model forces you to think of acquisition costs as money that you recoup over time, and it re-frames churn as lost money. This can have a profound effect on the way you think about churn.

Spend less than $360

Or one year’s income at $30.00 per month.

The SaaS Conundrum – Profitability is Elusive

If you spend 12 months income to acquire a customer, you are in the red for 12 months. Losing that customer any time in the first 12 months should be treated as a failure. In the last few years, we’ve seen an increase of SaaS companies offering discounts yearly pre-payment. This is for good reason, SaaS companies typically face a cash crunch as their growth accelerates. 12 months is a long time to wait for revenue to catch up with spending. Couple this cash crunch with the common expectation of exponential growth and you can see why startups are typically not profitable for many years.

Spending 1/3 of LTV and recouping the cost of acquisition within 12 months are just benchmarks. The reality is that you know your startup better than anyone. These benchmarks are meant to get you started but growth is unique for every SaaS business. Your model will grow in complexity as you learn more about your market and your best growth channels.

Get in touch in the comments if you have any feedback or additions.

[1] This is a simplified churn calculation, you should read Steven Noble’s Definition of churn , Jason Cohen’s post on the topic and Joel York’s analysis for more information. Churn changes as your product improves and competitors enter your market. Churn is a deep concept and building multi-year models around it is a risky way to run your business.

[2] Both the 12 month benchmark and the 1/3 of LTV benchmark come from from David Skok’s excellent article on Sass Metrics. They have become common benchmarks but every startup is unique. For a nuanced analysis of the topic take a look at David Kellog’s post on CAC ratio.

Apple: let’s solve iOS attribution for good

Apple recently announced that they will be releasing a new “Sources” tab in iTunes Connect. iTunes will now track the referring url and an optional campaign ID, passed in as a url param. This is a huge improvement, and it will work great for developers that build exclusively on iOS.

The problem is, iOS doesn’t dominate the market anymore. Most developers need to build for both iOS and Android. If you’re building on both platforms, having your data in an iTunes silo is hugely problematic. We’re going to explore how attribution works, why it’s a problem on iOS and what apple could do to solve it (hint: Android has already done it).

The problem with mobile attribution

If you sell things on a website, there are excellent ways to see what you’ve gained for all that effort you put into marketing. You can easily see where people came from. The web has HTTP referrers built right into the protocol, and in the cases where that doesn’t work you can use UTM tags or campaign-specific landing pages.

Knowing what works is critical. Even if you’ve built something people want, you still need to get their attention and show them what you’ve built. When resources are limited, dumping what little you have into the wrong channel or the wrong campaign can have real consequences. Knowing matters.

When you put something on an app store, you don’t have any say over the tracking data they give you. You don’t get to see where people came from or what params were appended to the url. It’s very difficult to know where you should spend the limited resources you have. Until recently, this was the plight of every app developer publishing to an app store.

How it works with Android and Google Play

Google has come up with a simple, open solution to this problem. They pass the params from the app store URL into the app when it is first launched. Developers can now see where their customers came from. It’s a simple solution that takes advantage of best practices on the web. It works for every ad platform, it doesn’t compromise our privacy, and it works for every use case we can imagine.

Apple’s closed garden approach

Apple’s solution looks similar on the surface, with one important exception. Apple passes the params to their own tracking system but doesn’t make them available to the developer. Let’s take a look at how it works:


This is a great start but without the raw params, developers can’t see where actual users came from. Without the ability to tie the data together, developers cannot do a few really critical things. Let’s explore what those things are and why they are important.

Here’s a scenario: you’re browsing Amazon.com on an iPhone looking at a fancy new coffee grinder. A message pops up, letting you know that Amazon has an app. You download the app, open the app, and get dumped into a blank slate. There is no reference to coffee grinders. “That’s foolish,” you say “shouldn’t Amazon know I was looking at porlex coffee grinders?”

Amazon doesn’t know because there is no (supported) way for developers to pass data through the App Store. If we could append params to the App Store links and load those params at app launch, we could simply pass through the porlex grinder product ID and load it when the app was first launched. Apple has done a great deal to support deep linking, but they’ve ignored the critically important first link.

Advantage to passing params: Discount links and app sales

Here’s another scenario: you have a successful company, and you’re launching an iOS app with in-app purchases. You want to offer a discount to you mailing list. Maybe you want to give them 50% off in app purchases. Great idea, but it’s not possible. There’s no (supported) way to identify which users came from your email blast on iOS.

Advantage to passing params: See ROI across devices

Let’s looks at a common scenario for people who sell the same app across multiple app stores. Maybe you sell a productivity app on iOS and Android. You’re in the middle of a Christmas promotion and you want to see how your campaign is doing on Twitter versus Facebook. There’s no way to merge the data, the iTunes half is locked in a silo.

iOS Attribution Approaches

As engineers, having a problem means we build a solution. The solution, in this case, is for ad networks to fingerprint every device that clicks an ad. This can be done using the Apple-regulated IDFA (ID For Avertisers) or by fingerprinting the device using a combination of browser information and the IP address.

Attribution Method: Using the ID For Advertisers (IFDA)

When you click an app ad on Facebook, your unique IDFA is stored on Facebook’s servers along with a reference to which ad was clicked. You include the Facebook SDK in your app so that your app can send Facebook the IDFA of every user that opens your app. When Facebook sees a match, they will respond with “yes, we sent them.” This is a very reliable method, because it’s based on a unique ID that rarely ever changes.

Limited platforms caveat: Because IDFA’s require an integration with each advertising platform, it is unlikely that you will find an SDK that is integrated with industry specific ad platforms, affiliate sites, or any custom ad deals you’ve made.

Waste caveat: Identifying users by IDFA requires requires an unruly amount of code. There are SDK’s on top of other SDK’s and any app advertising on more than one platform ends up with megabytes of unnecessary code. Spread across hundreds of apps on millions of phones, this adds up to colossal waste. A phone’s storage space should be reserved for music, photos, and code that provides value.

Privacy caveat: IDFA’s should not need to exist. This is a unique identifier – specific to your iPhone – that is the same across every app. This ID is passed around an entire ecosystem of analytics providers, ad networks, and individual apps. In many cases is is saved along with geographic information, photos, and other personal information. Unique ID’s don’t exist on desktops and they shouldn’t need to exist on phones. If we passed the params through the app store we could get rid of IDFA’s. That’s a huge win for privacy.

Attribution Method: Fingerprinted Redirect with URL Parameters

Tracking with UTM params is great. What if we could somehow pass an arbitrary list of params from the app store into our app? Well, you can. It’s just not as reliable. The way to do this is to set up a link redirect service, similar to bit.ly. Every time you link to the app store, it goes through this redirect service. When a request hits the redirect service, it stores the IP address of the request along with the user agent making the request and any other identifying information available. That information is used to create a semi-unique device fingerprint.

If the user downloads the app, that same fingerprint data is sent from the app on first launch. If a match is found, the params from the app store listing are passed back into the app, along with the referring URL. This can be used to identify anything you want to track about the source – including discount codes, affiliate markers, and cross-platform campaign names. This concept is further explained in Implementing Deferred Deep Linking on the URX blog and it has been productized by Tapstream. We’re working on a similar methodology at Attribution that we hope Apple will make obsolete.

Reliability caveat: Fingerprinting can break down if many people in the same place download an app at once. Imagine launching an iOS app at SXSW. When a throng of people with the same iPhone, running the same iOS, all download the app from the same cell towers, there’s no way to tell them apart. Compared to desktop computers, iPhones are less unique and hence they are harder to fingerprint. In most cases this is not problematic but it can break down at conferences and events.

There’s also a speed/accuracy tradeoff. Additional fingerprinting information is available via javascript, but loading an actual page and executing javascript code may noticeably slow the re-direct. Nobody wants that.

Apple: just pass us the params

All of this would just go away if Apple passed the params from App Store url’s into apps. No more privacy issues, no more heavy SDKs, and no more developers pulling their hair out. As a bonus, app developers would have the ability to privide a much better first launch experiences with deferred deep links. Everybody wins.

We can’t think of any reason why Apple would choose not to pass the params. Maybe an oversight, or maybe a conscious decision. Either way, we hope they change it.

Apple, please, Just pass the params.

Cross Device Attribution

More than ever, people are visiting your product from phones, tables, laptops and work computers.

Cross device tracking is going from an optional nicety to a necessary call. Identifying your users on each device is the key to correlating their sessions and properly attributing referral sources.

Identifying Users

Identifying users with a unique user ID is essential. When identify is called for a specific user, all previous and future events on that device will be associated with that user. The key here is that previous events are associated as well. This allows for properly tracking many different cross device scenarios, such as the following:

    • Marcus is browsing his news feed on his phone and clicks a post about your product. He’s interested, but doesn’t sign up right away.
    • Later that week, Marcus decides to checkout your product again, but this time visits your domain directly from his computer. He decides to sign up. When he does so, your software makes anidentify call with a unique user id.
  • In a couple days, Marcus logs in to your app from his phone. On login, your software makes an identify call with Marcus’s unique user id. All of his browsing history on this device is now properly tied to his account and that original click from his news feed will be properly attributed for his conversion.

This might seem like an edge case, but these days it’s too common to ignore.

How To

Properly tracking this scenario is simple:

  • Make an identify call on your client side software (JS, iOS, Android, etc) whenever a user signs up or logs in (and pass a unique user id)

In order to associate traits with your user, we recommend making an additional server side identify call. This way you can associate information that you might not have in your client software.

It’s sometimes common to call identify using the email address instead of a user ID. This is, of course, a problem when a user changes their email. Tracking with the ID is really the best practice.

Why Do I Have So Much Direct Traffic?

Direct traffic means that someone was familiar enough with your brand to go directly to to your site. That’s an accomplishment. The problem is, a lot of the traffic that being tracked at ‘direct’ may be referral traffic that wasn’t tracked.


The most common cause for dropped referrers is when a link goes from an https page to your http page. This isn’t a bug, in fact it’s in the W3C spec. W3C believes encrypted headers should stay encrypted, and passing them to an unencrypted page could cause a security leak (W3C Spec). So what’s a marketer to do? It’s simple, serve everything over https. It can be a little more overhead to set up but it means your tracking will work better and your visitors may even feel more secure being on a page with a little green lock.

Referrer Blocking

So https is part of the problem, but it’s not the whole story. Browsers, security software, and even links themselves can also block the referrer field from being sent. Some people really don’t like the fact that the HTTP spec sends referrers. Here’s a quote from a Microsoft forum:

What is wrong with Microsoft Internet Explorer developers since they do not understand that this is a security issue that needs to be addressed right god damn now?! I do not want referrer information leaked to god knows who when I am surfing the web in the privacy of my own home! Microsoft FIX THIS RIGHT NOW !!!!!!!!!!


That’s an extreme case but the fact is, some people don’t want you to see where they came from. That pressure has led to the development of extensions for every browser that block referrers, default settings in Norton and ZoneAlarm to block referrer and even an HTML5 spec for dropping rel=”noreferrer” directly into the link.

Emailed links, SMS links, App links, PDF links, etc.

Browsers are not the only things that can contain links. Traffic can come from outside the web, and generally that traffic will come through without referrer and get swept right into ‘direct’. There’s not much we can do about this, since the referrer field was never meant to track links outside the web.

Redirects and link shortners

Link shorteners can send along the referrer if they use a permanent 301 redirect. If they use a Javascript redirect or if they use a temporary 302 redirect, you’ll just see ‘direct’.

Solution: UTM Everything

UTM tags are really the only reliable way to track your traffic. As a rule, you should UTM tag every link you have the power to tag. This includes ads, links from social media, links from your email campaigns, links in your PDF e-books, links you text to your mother. Everything. All the links. Tag them all. The reason for this is that you control your UTM tags. There’s no settings, devices, or HTML specs that can disable them.