eCommerce Advertising KPIs: The Advanced Metrics You Should be Tracking

Growth Intelligence
0 min read
May 11, 2021
Jenner Kearns
Chief Delivery Officer

In the competitive world of eCommerce, a strong digital advertising strategy isn’t really optional. I’m guessing you already advertise on at least a couple of the best-known ad platforms. But do you really know exactly what results you get from your ad spending? Or how to improve on it?

We're going to cover a number of advanced advertising KPI metrics, and I’ll provide insights about how to use them. Some of them are specifically for eCommerce, however, many of these advertising KPIs are applicable for other industry verticals too.

Perhaps you’re wondering why you aren’t getting the increased sales results you expected from your ads. Or maybe you’re thinking about increasing your investment but don’t know what approach would be best? Whichever scenario you’re in, measuring and analyzing the right advertising KPIs is always essential. I’m afraid a quick look at the ROAS metrics provided by your ad platform isn’t going to cut it!

From our experience, we’ve pulled together the most insightful advertising KPIs you should be tracking. They’re going to help you decide how much you should spend, where you should spend it, and more clearly assess the return on ad spend.

Bear in mind that tracking advertising KPIs isn’t a passive process - just having the metrics isn’t enough. You need to sit down and look at them across time and in tandem with each other, making sure that you’re drawing the right conclusions. You should be benchmarking your advertising campaign KPIs against each other, along with split testing and published industry benchmarks.

Revenue KPIs

Revenue KPIs assess the monetary return on your advertising spend, determining its effectiveness with immediate financial results.

Transactional Revenue

Transactional revenue is the money you receive from your product sales. It includes the deduction of any discounts, coupons, or credits from loyalty programs that you’ve awarded.

what is transactional revenue

Unlike gross revenue, net revenue, or profit, it doesn’t take into account product returns, operating costs, or tax. It also excludes non-operating revenue that doesn’t come from your core business activities (for example interest, reoccurring asset rental fees, advertising fees, or dividends).

Transactional revenue is the best metric to use when assessing the effectiveness of advertising spend. It removes the noise from returns or tax that can convolute the data and distort the true story surrounding what you really want to know…ad spend ROI. 

Use transactional revenue to see the true and direct results of advertising spend on your sales performance.

Return on Ad Spend (ROAS)

Return on Ad Spend is the universal metric for measuring advertising ROI:

return on ad spend roas formula

For example, say you spent $2,000 on an online advertising campaign, and campaign tracking showed that the campaign resulted in revenue of $10,000. 

ROAS = $10,000 (revenue) ÷ $2,000 (ad spend) = $5. 

You could also express that as the ratio 5:1, or 500%.

ROAS is usually assessed at all levels; the company level, the campaign level, and the ad set level. This lets you: 

  • Compare and optimize individual ad sets within campaigns
  • Compare separate ad campaigns
  • See overall performance for total ad spend on your chosen ad platforms

ROAS is typically measured in two windows: 

1-Day Click ROAS 

This looks at the revenue generated by an advertisement within 24 hours of customers clicking on it. 

The biggest advertising platforms will optimize your ad and also provide ROAS data when you use their tracking pixel and set your conversion goals. Facebook and Google are best-known for this.

You can optimize for 1-Day Click results when setting up a campaign. Facebook would optimize the display of your advertisement according to your 1-Day click preference and selected conversion goal, such as completing a purchase. Its functionality selects specific target audience users and factors like placement and time of the day in order to achieve your goals.

1-Day Click ROAS is the best representation of how advertising platforms and adverts directly drive incremental revenue for your business. 

7-Day Click + 1-Day View ROAS 

This looks at the revenue generated by customers within 7 days of them clicking on an advert, or within 1 day of seeing it but without clicking. 

This the best representation of how effectively advertising platforms are driving both revenue + brand awareness for your business.

Marketing Efficiency Ratio (MER)

The Marketing Efficiency Ratio is an assessment of total sales revenue against total ad spending:

marketing efficiency ratio mer formula

You’re not looking at just the revenue that can be directly attributed to specific advertisements, but all transactional revenue. It will help you get a feel for the wider impact of advertising efficiency across ALL CHANNELS. It’s most helpful for companies heavily driven by advertising and is the best representation of the overall effectiveness of advertising spend. 

MER stands as a more reliable metric than ROAS. ROAS is reported by ad platforms using their tracking pixel. (When a user sees or clicks on an ad then makes a purchase on your website within a defined period of time, ad platforms attribute it to the ads on their platform.) Ad platforms try to maximize the ROAS they report and can double count a sale against more than one ad. They also can’t account for the impact of other ad campaigns that you may be running without their platform over the same period.

When working with brands, our team often uses this as our most important north star. It helps us understand if we should push harder with existing strategies, allocate investment elsewhere, or if we need to reel back and refocus. 

Ad Spend + Revenue by Channel

Break down your revenue results and ad spend by individual marketing channels, and compare them as a ratio:

revenue results to ad spend by channel

By marketing channels, I mean social media, search engine marketing (SEM) or email, etc. This information will help you see which marketing channels are most profitable to invest in.

You can also do this for your customer segments, assessing how valuable they are in terms of revenue generation. For example, is more revenue (or Average Order Value) generated from advertising that targets males or females, specific locations, or certain age groups?

Advertising KPIs

ecommerce advertising kpis

These advertising KPIs will help you understand how much you can profitably spend on your paid customer acquisition.

Blended-CAC (Paid)

Customer Acquisition Cost (CAC) is the average total cost you’ve spent to capture new customers:

calculate customer acquisition cost

For example, if your quarterly advertising spend across all channels was $200 and you acquired 100 new customers in the same quarter, your CAC is $2.00.

This lets you see the cost of gaining customers across ALL CHANNELS, and answers the question ‘How much am I actually paying to acquire a customer for my brand?’. 

One of the first things we do with a new client is look at their blended CAC goals, assessing where the breakeven CAC lies. We use this to see whether their paid media channels are hitting their goals and if the business is achieving profitability in its current state.

Bear in mind that if you use this metric over early campaign stages or short-term periods, it may not take into account the result of marketing activities that you won’t see instant results from. Brand awareness takes time to grow with multiple brand touchpoints.

Average Order Value (AOV)

Average Order Value is a self-explanatory and simple calculation:

average order value for ecommerce

It shows you how much customers spend on each order on average. This is an important metric - it drives a lot of other calculations. If your AOV is high, you can spend more on advertising to acquire each order, or visa versa. 

AOV can be increased through tactics like up-selling, cross-sales, new products, more expensive products, and bundling discount options.

Depending on your specific business, you’re likely to have different AOVs for distinct customer segments.

Lifetime Value (LTV)

Lifetime Value refers to how much a customer can be expected to spend over the relationship lifespan with your business:

lifetime value for ecommerce customers

It can also be referred to as Customer Lifetime Value (CLV). Knowing your customers’ average number of purchases across an Average Customer Lifespan (ACL) are key components in calculating it. 

When you have a reliable LTV estimate, you’ll better understand how much you can justify spending on acquiring customers based on the total value they represent - not just the first sale.

Of course, consumer behavior can be extremely variable and depend on the type of product. If applicable, you can calculate LTV for distinct customer cohorts with similar behavior to improve reliability. However, using this metric will still provide a helpful guideline when deciding how much to invest in paid customer acquisition.

Cash Multiplier

Cash Multiplier is a 60-Day assessment of customer value:

cash multiplier ecommerce kpi

However, you could also use 30, 90, or 120 day periods, and so on.

It’s essentially a short-term version of LTV that assesses the revenue value of your individual customer. As it’s challenging to actualize the entire ‘Lifetime Value’ of customers, using the CM is extremely valuable and necessary for generating cash flow predictions for the business, and making responsive ad spend decisions in the short term.

Understanding your Cash Multiplier will lead to better decisions surrounding budgeting, forecasting, inventory, and many other facets of operational decision-making within an organization.  

It’s also advised to apply this metric to your individual customer segments if possible.

New vs. Returning Customers 

Tracking new and returning customer numbers is a basic yet important audience segmentation. It lets you see the results of your new customer acquisition tactics in a given period, plus you can analyze how effectively you retain customers. 

Your optimal new vs. return customer split should be part of your business objectives based on your business model, product type, and customer retention goals. 

You may also be able to better estimate revenue flows based on new vs. returning customer numbers and how they behave. For example, do new customers tend to have higher AOVs, or vice versa?

A related metric is Repeat Purchase Rate (RPR), which is the percentage of customers who return to purchase from your business again. ‘Churn’, or customer attrition, is another complementary metric. It's either the number or percentage (rate) of customers who stop reordering. Churn Rate = (No. of Customers Lost ÷ Total Customers at End of Period) x 100. So if your churn rate is 50%, half of your customers don’t make another purchase.

Organic vs. Paid Customers

Track the ratio of your sales conversions by organic traffic vs. paid traffic sources to see how your investments in those channels contribute to overall revenue. In essence, in order to maximize growth, it’s critical to ensure organic or non-paid traffic is continuing to grow alongside paid traffic to meet your profitability goals. You can learn more about organic and paid traffic here.

It’s also valuable to analyze the conversion rates associated with each segment. Organic traffic tends to bring in higher-quality leads since people are searching by specific keywords with intent. Paid traffic sources can generate higher volumes of leads, but compare the paid conversion rates against organic conversion rates to sense check traffic quality. 

You’re likely to see a correlation between your new vs. return customer ratios against organic vs. paid customer ratios. Repeat customers are more likely to have organic or non-paid entry points onto your website. However, successful paid acquisition tactics will also boost brand awareness and organic performance over time.

Conversion KPIs

The rate that customers convert, aka make purchases on your website, can tell you about the effectiveness of your ad targeting, sales funnel design, and elements in your marketing mix (products, pricing, etc.). Split testing will help you narrow down variables for optimization.

Website Conversion Rates

Your website conversion rate is:

website conversion rate formula

This shows you the percentage of website sessions where a purchase is made. You could also have other desired actions that you class as a conversion, such as signing up for your newsletter.

This isn’t tied to ad spending or specific campaigns - this looks at the total conversion rates for your website from all sources. 

Low website conversion rates would indicate that there is friction on your website, sales funnel, or marketing mix that needs to be improved in order to drive more conversions.

Campaign Conversion Rate

Campaign conversion rate is the number of website visitors who make a purchase after clicking on all ads within a campaign:

ecommerce campaign conversion rate

If 100 people clicked on your ad and 10 of those users converted, your campaign’s conversion rate would be 10%. 

Compare your conversion rates at the ad set, campaign, and channel level to see which generates the most sales. You should also compare campaign conversion rates to your overall website conversion rates as a traffic quality sense check.

Low campaign conversion rates against overall website conversion rates could indicate that you have ad content, audience targeting or sales funnel elements that need to be adjusted.

Cost Per Add to Cart 

Cost Per Add to Cart is a sales funnel metric:

cost per add to cart for ecommerce business

This metric assesses adds to cart rather than the number of individual customers, giving you the average ad cost you’re paying per intended product purchase.

A similar metric is Cost Per Action (CPA), which will tell you the average cost of each desired action that you wish your customers to take. It could be watching a video, subscribing to your newsletter, downloading an ebook, etc.

Using metrics like CPATC or CPA offer early indicators of demand that allow you to understand if your strategy heading in the right direction. 

Cost Per Unique Checkout Initiated

Cost Per Unique Checkout Initiated is another sales funnel metric:

cost per unique checkout initiated formula

Unique checkouts initiated count the ad cost you’ve paid on average to acquire each customer with high intent to purchase.

It avoids double-counting people who initiate more than one checkout during their session and doesn’t indicate the volume or value of products that customers intend to purchase. If you have a smooth and easy checkout process, this cost should be close to the cost per purchase. 

Reasons people drop out of completing purchases include high shipping costs, long delivery times, or lack of a preferred payment method.

Cost Per Purchase 

At the endpoint of the sales funnel, Cost Per Purchase looks at the average ad cost you’ve paid to acquire a customer who completed a sale:

ecommerce cost per purchase calculate

This excludes data from abandoned carts where the payment wasn’t completed, showing you how much you spent on average for each order. Unlike CAC, this metric assesses individual ads or campaigns, rather than total ad spend against all acquired customers in a period.

If you only pay on the basis of customers acquired, it’s called a Cost Per Acquisition pricing model.

You’re not looking at the value or volume of the sales here, but just the effectiveness of an ad at generating volumes of converting traffic for the price you paid for it. You can compare it to the Cost Per Unique Checkout Initiated to see if you’re losing customers and ROAS at the final stage of the sales funnel.

A low Cost Per Purchase indicates an ad that generates high-quality, converting traffic for the price you’re paying. That could be because the price of the ad is low, or because a high proportion of people who are shown the ad click on it and make a purchase. You can clarify which with Cost-Per-Click and Conversion Rate metrics.

There’s a similar metric called Advertising Cost of Spend:

ecommerce advertising cost of spend metric

It shows you what percentage of every dollar earned by an ad/campaign was spent on the fees, e.g. 25% of each dollar generated went into paying for the ad.

Creative KPIs

creative kpis for ecommerce businesses

Assess the creative effectiveness of your ads with these KPIs and use them to optimize ad conversion rates. Creative elements include imagery, color, font, messaging and wording, placement of call to action buttons, sizing and placement, etc.

Use split testing to narrow down the performance of creative design elements.

Click-Through Rate (CTR) 

Click Through Rate is the ratio of ad clicks against how many times your ad was shown (impressions):

how to calculate click through rate ctr for ecommerce

A high Click Through Rate indicates a well-designed and well-targeted ad - a high proportion of your targeted audience are clicking on the ad when they’re shown it; which proves critical in driving traffic to your website.

3-Second View Through Rate 

The 3-Second View Through Rate tells you what percentage of people were shown an ad and viewed it for at least 3 seconds:

3 second view through rate formula

A high 3-Sec VTR indicates an ad that’s interesting/engaging for the target audience - they aren’t immediately scrolling past it without stopping to look.

Average Watch Time 

The Average Watch Time tells you how long people watched your video on average: It shows the average second mark at which viewers stopped watching. 

Needless to say, the longer the Average Watch Time is, the more engaging and well-targeted your video content is. This can also provide helpful information in understanding what part of a video ad needs to be iterated on to engage prospects better.

Ad Conversion Rates 

The Ad Conversion Rate looks at how many people who clicked on an ad made a purchase:

ad conversion rate for ecommerce

You could also look at this metric with View-Through Attribution in addition to clicks, or include other desired conversion actions such as signing up to a newsletter or adding items to the cart. 

Higher ad conversion rates indicate an ad that’s successfully attracting high-quality traffic and driving them to take the desire action.

Channel KPIs

ecommerce channel kpis

Understand how your chosen ad channels and platforms drive value with these KPIs.

Audience Size

Some platforms have bigger audiences than others. And each platform tends to attract specific audience demographics, offering better reach within the population of that demographic group.

The more of your target audience that a platform reaches, the more ad placements will be available, and the easier it is to ramp up ad frequency. 

Frequency is the average number of times your ad is served to each person in your target audience within a relevant time period. As your campaign spends more money, it serves more impressions, and the frequency increases.

Read our guide to where to advertise online and learn more about ad platform audience sizes and demographics.

Revenue by Traffic Source 

Track the revenue generated through ads by individual platforms in order to understand how they are contributing to overall revenue. You can also analyze by channels, such as social media or search engine marketing.

Compare average ROAS, which is revenue generated ÷ ad spend, to see which ad platforms are overall most profitable to continue investing in.

Search Impression Share

Search Impression Share refers to the percentage of relevant keyword searches that your ad appeared on. 

This can be an important factor in determining whether to spend more money on a search campaign. 

For example, if you are only showing up on 10% of searches, you’ll need to increase your budget and potentially the maximum price you’re willing to pay per click or impression in order to become more discoverable.

Cost Per Click (CPC)

The Cost Per Click is the actual price you pay for each click in your Pay Per Click (PPC) ad campaigns. That means you only pay each time someone clicks on your advertisement.

This incentivizes ad platforms to only show your ad to people whose data says are most likely to click on your ad. The higher the price you are willing to pay per click, the more times your advertisement is likely to be shown. This pricing model is best for generating sales leads.

If one ad platform has a higher CPC than another, how does it correspond to the revenue that it’s creating? Consider the audience reach, engagement metrics, and KPIs such as ROAS to help you decide how much to spend on platforms with higher CPC prices.

Cost Per Mille (CPM)

Cost Per Mille means cost per thousand impressions (views). If you choose a CPM pricing model for your advert then you’ll pay for every thousand people it’s shown to, whether they click on it or not.

CPM pricing models are best used for brand awareness campaigns and cost less than CPC adverts. They are optimized to get your ads in front of as many people in your target audience as possible. 

They usually achieve a higher share of available impressions since the ad platform is guaranteed revenue each time they show the ad. 

Attribution KPIs

ecommerce attribution kpis

Attribution reporting helps you understand how your individual advertisements are performing in terms of driving sales. Users are tracked after clicking or viewing an ad, so any purchases made within set time periods can be ‘attributed’ to your adverts.

View-Through Attribution

View-Through Attribution occurs when a purchase is made by a customer who saw one of your advertisements within a specified time period. 

It’s based on the premise of brand awareness, where the advertisment has influenced them into making a purchase. 

It is typically tracked by ad platforms within 1, 7, and 28-day time periods. (Facebook no longer offers 28-day attribution reporting.)

The higher the View-Through Attribution, the more effective ads are at increasing brand awareness that drives sales within a short time period, but also may indicate that the specific channel is not the main contributor to the purchase. This important to watch when optimizing ad campaigns towards better ROAS.

Click-Through Attribution  

Click-Through Attribution refers to when a purchase is made by a customer who clicked on one of your adverts within a specified time period. 

It’s also typically tracked by ad platforms within 1, 7, and 28-day time periods.

The higher the Click-Through Attribution, the more effective an advert is at directly driving sales revenue.

Landing Page KPIs

ecommerce landing page kpis

Once your adverts have gotten potential customers onto your linked landing pages, these KPIs will help you understand how effective the webpages are in driving conversion rates.

The quality of your landing pages is assessed by ad platforms like Google, and it will drive up your CPC or CPM prices if they’re low quality.

Page Load Speed 

Page Load Speed refers to how many seconds a webpage takes to load on the screen. 

Although not all factors affecting load speed for individual customers are under your control, lots of high-resolution images that take up internet bandwidth are one of the biggest avoidable causes of slow loading speeds.

How fast a page loads is directly correlated with the conversion rate. Online users have short attention spans and high expectations for website performance, so if your pages are frustratingly slow to load, your customers will quickly navigate away.

Conversion Rate (Lead Generation)

The conversion rate of your landing pages is the percentage of visitors who make a purchase:

calculate conversion rate for lead generation

Here, you’re assessing the conversion rates of individual lead gen landing pages linked from adverts, rather than the conversion rates for your website as a whole. 

Understanding the conversion rates of landing pages is important to optimize their effectiveness for driving sales conversion. Use split testing on your landing pages to optimize variable elements and increase conversion performance.

Form-Fills Started to Form-Fills Completed 

Look at the number of form-fills started vs the number of forms that are completed to understand how many sales leads are dropping out of your funnel.

If the drop-out rate is high, it’s likely you’re asking too many questions or making the process too laborious for users. 

Online users are increasingly unwilling to share their personal data, with privacy and data security front of mind. You may need to build the relationship and level of trust with your customers first and provide better justification as to why you are asking for data and how you will use it.

Adds to Cart

How many adds to cart do your landing pages generate? You can look at total adds to cart, adds to cart divided by total sessions, and adds to cart divided by unique users to see how many items each visitor wants to purchase on average.

A low number of ads to cart could mean people aren’t seeing what they expect to when they reach your landing page, the product price may be higher than they’re willing to pay, not enough information is provided (e.g. poor product descriptions & imagery), or the page isn’t visually well-designed and user-friendly for making quick purchases.

Checkout Initiated (eCommmerce) 

From your landing pages, how many checkouts are initiated in total? You can either measure this by checkouts initiated directly from the landing page or when the landing page was a customer’s entry point but they navigated to other pages before checking out.

To assess sales funnel retention, compare how many checkouts are initiated vs. total carts created, and checkouts initiated vs. checkouts completed.

Higher checkout rates indicate higher quality traffic and also tell about the ease of your checkout process. 

If you want to easily compare your chosen ad platforms in terms of their traffic quality and sales conversion rates (assuming you use more than one ad platform at a time), create separate landing pages for each one.

Cost Per Land  

Cost Per Land looks at the average cost for each person who came to your landing page:

ecommerce kpi cost per land

It can also be called Cost Per Lead. This tells you how much you’re spending for each sales lead.

Scroll Depth

An engagement metric, scroll depth tells you how far down your landing page your users scroll on average. 

The higher the scroll depth metric, the further down the page your users are going. This indicates that users are engaged with the content. A low scroll depth means people are leaving the page before seeing most of the content on it.

If you have low scroll depths, assess how your content could be improved to meet the needs and interests of your audience, made more user-friendly and readable, or, consider if the linked advert copy is potentially misleading.

Bounce Rate

An engagement metric, bounce rate looks at how many people navigate away from your landing page without clicking on anything on the page. 

High bounce rates means users aren’t interested in the content on the page, and they don’t navigate any further within your website.

Time on Page 

Another important engagement metric, time on page tells you how many seconds your visitors spend on average on each webpage. 

You can assume that the higher the time on page, the more engaging and useful your visitors find your content, whether it’s reading information or viewing products.


If you have a handle on these advertising KPIs across each of the areas we’ve listed, you’ll feel confident in spending your advertising budget knowing what you’re getting in return. It’ll take the guesswork and stress out of it, giving you more peace of mind as well as revenue growth.

A key part of the process is being able to automate data extraction and reporting so you can produce regular advertising KPI reports without time consuming effort, freeing you up for the all-important analysis.  

It's also essential to keep track of the benchmark KPIs for your industry. At Half Past Nine, we aggregate client data and leverage private partners for this. But there are some helpful online resources to reference, such as these quarterly KPI benchmarks from Adroll.

If you need support setting up data capture processes, producing real-time dashboards or creating an overall ad strategy, Half Past Nine specializes in enabling ambitious growth targets through demand generation. Get in touch anytime!

What To Read Next:

Kenneth Shen
Chief Executive Officer
Brenden Delarua
Sr. Paid Media Strategist
Jenner Kearns
Chief Delivery Officer
Jenner Kearns
Chief Delivery Officer
Jenner Kearns
Chief Delivery Officer
Kenneth Shen
Chief Executive Officer
Kenneth Shen
Chief Executive Officer
Kenneth Shen
Chief Executive Officer
Kenneth Shen
Chief Executive Officer
Jenner Kearns
Chief Delivery Officer
Kenneth Shen
Chief Executive Officer
Jenner Kearns
Chief Delivery Officer
Jenner Kearns
Chief Delivery Officer
Jenner Kearns
Chief Delivery Officer
Jenner Kearns
Chief Delivery Officer
Kenneth Shen
Chief Executive Officer
Jenner Kearns
Chief Delivery Officer
Kenneth Shen
Chief Executive Officer
Kenneth Shen
Chief Executive Officer
Isla Bruce
Head of Content
Isla Bruce
Head of Content
Isla Bruce
Head of Content
Jenner Kearns
Chief Delivery Officer
Isla Bruce
Head of Content
Kenneth Shen
Chief Executive Officer
Isla Bruce
Head of Content
Isla Bruce
Head of Content
Isla Bruce
Head of Content
Kenneth Shen
Chief Executive Officer
Isla Bruce
Head of Content

Read next

Unlock Revenue Growth With Data

Knowing where to invest marketing budget to increase contribution margin and overall revenue growth is the #1 pressing challenge for any marketing or growth leader.

As multichannel complexity and media budgets grow, attribution becomes one of those topics we really can’t ignore.

To truly understand the most valuable customer journey design, relying on default attribution reporting within ad platforms or Google Analytics just doesn’t cut it. In fact, it can even do more harm than good due to misattribution and double attribution - a big problem with these uncensored (and self-serving) tools. 

The trouble with free on-platform attribution reporting (like Facebook or Google Analytics) is that they are siloed walled gardens that work in isolation with their own limited data sets. Your most powerful and valuable attribution analysis needs to cover everything, directly tied back to revenue results. 

Without a proactive attribution strategy that connects all your customer journey and conversion data, optimized customer journey design will remain an elusive mystery. Highly influential channels like dark social or offline interactions are often underestimated or completely missing, while non-profitable campaigns are over-indexed. 

The difference in business results can easily stack up to millions of dollars in wasted budget and lost opportunities - especially where larger paid media budgets are involved. 

 Let’s explore how marketers can master attribution to start hitting revenue targets with much greater confidence and certainty.

The Impact of Not Using Accurate Attribution Reporting

The impact of not using attribution reporting - or using it poorly - is worth understanding. It can have multiple negative impacts on decision-making and overall business performance.

The common consequences are:

Incomplete Customer Insights Cause Poor CX

An incomplete understanding of customer preferences, motivations, and pain points hinders the ability to tailor marketing strategies to effectively engage and convert customers.

This can result in a lack of adequate content personalization and a poorer customer experience (CX), meaning your brand gets overlooked in favor of others by potential customers.

Unprofitable Resource Allocation

Struggling to accurately identify the marketing channels, campaigns, or touchpoints that are driving conversions or desired outcomes results in less effective use of resources.

For example, over-investing in underperforming channels, and underinvesting in high-impact touchpoints, wasting budget in the process.

Poor Revenue Growth and Limited Brand Equity

Incorrect assumptions about the impact of specific touchpoints or channels results in suboptimal marketing performance and missed opportunities.

If marketing efforts fail to engage and convert customers effectively over time, the business can suffer from stunted revenue growth, also putting a cap on brand equity.

Understanding the Challenges for Accurate Attribution

Marketers can’t fully rely on free attribution solutions for the insights they need to drive significant optimizations. Results can be significantly misleading when solely using free on-platform attribution reporting.

On-platform attribution issues:

  • No Cross-Channel Visibility - On-platform attribution doesn't have full visibility into the performance of other channels, or wider customer journey outside of their own ecosystem, acting as walled gardens. This limited view can make it difficult to understand the true impact of each channel on conversions and ROI (or ROAS). 
  • Double Attribution - When using multiple platforms, there's a risk of double attribution - where more than one platform takes credit for the same conversion. This overlapping attribution may cause businesses to overestimate the performance of certain channels or campaigns, and consequent overinvestment stunts overall marketing ROI.
  • Inconsistent Attribution Methods - Different platforms apply different attribution rules, leading to inconsistencies in how they assign credit to various touchpoints. This inconsistency can make it challenging to accurately compare the performance of different marketing channels or campaigns.
  • Tracking Limitations - With increasing data privacy regulations, third-party platforms may face challenges in accurately tracking user behavior across channels. A custom attribution model can help overcome some of these limitations by incorporating first-party data and other tracking methods.
  • Lack of Customization - On-platform attribution reporting may not be tailored to your specific needs, goals, and marketing strategy. A custom attribution model, on the other hand, can be designed to accurately reflect a business's unique customer journey, allowing for more precise insights into the performance of each marketing channel and campaign.

There is a compelling case for marketers to invest in their own customized attribution solutions. Especially when paid media investments start becoming more significant.

However, accurate attribution modeling isn’t one of the most straightforward tasks for a marketing department to tackle.

There are several hurdles to overcome to extract and benefit from the most valuable insights:

 1. Tracking Data Across Complete Journeys 

A typical user journey involves multiple devices, channels, platforms, and time breaks between visits, making it difficult to track a complete customer journey path from the first touchpoint to conversion. Cross-device tracking techniques are needed, such as device matching or probabilistic modeling.

 2. Data Privacy 

Tracking restrictions and cookie limitations can limit the ability to track customer interactions across marketing channels, sometimes requiring workarounds. Yet it's essential to adhere to data privacy regulations, maintain transparency and obtain appropriate consent from customers when collecting and utilizing their data.

 3. Offline Data Tracking 

Marketers may need to implement strategies such as unique identifiers, coupon codes, QR codes, or call tracking to link offline interactions to specific customers and attribute them properly. However, implementing and managing these tracking mechanisms may require additional resources and operational adjustments, including manual data entry from both customers and staff.

 4. Data Quality and Completeness 

Ensuring the accuracy and completeness of the data is crucial for building reliable attribution models. Marketers must establish data quality control measures, address data gaps, and perform regular data validation to maintain the integrity of the data.

 5. Data Integration 

Integrating data from various sources, both online and offline, can be complex. Offline data sources such as in-store purchases, call center interactions, or direct responses may not be easily captured and linked to other digital data. Marketers need to develop data integration processes to build a unified view of complete customer journeys.

 6. Attribution Modeling Complexity 

Choosing the best-fit modeling approach for marketing goals, and accounting for multiple touchpoints both online and offline, adds complexity to attribution modeling. Marketers need to understand statistical models that can properly attribute credit to different touchpoints based on their real impact on conversions. This requires analytical expertise, plus the budget for necessary data tools as marketing complexity grows.

Types of Attribution Data

Data attribution models are nothing without the data that you feed into them.

There are 2 main sources of attribution data.

Attribution Data sources

1. Software-based Attribution Data

This utilizes digital tracking tools, such as analytics platforms or marketing automation software, to track and record user interactions and automatically attribute conversion actions to specific marketing touchpoints.

Pros - It provides objective and granular data on user interactions and conversions, and enables real-time tracking and analysis of customer journeys. The reliance on voluntary self-reporting and subjective recall is reduced.

Cons - Aside from missing touchpoints that are not digital or easily trackable by software, it can be complex to implement and require technical expertise. You’ll need the right tracking set up for accurate data and reliable insights, and the analytics tools.

 2. Self-reported Attribution Data

This is data collected directly from your customers and leads, who share information about the touchpoints that influenced their decision-making process. It’s usually collected via an online form or survey but can also be collected in direct conversation with customer-facing staff and then recorded in a CRM.

Pros - It allows for qualitative data collection, using direct insights from the individuals themselves to capture subjective factors and nuances that software-reported attribution may miss, such as offline interactions or word-of-mouth referrals.

Cons - It relies on individuals' willingness to provide information, and their memory and perception which may not always be accurate or complete. This type of data can be more time-consuming and resource-intensive to collect and analyze.

Hybrid Attribution Data

Combining both self-reported and software-based data sources into attribution modeling is what is known as hybrid modeling. It’s the ideal solution to mitigate the drawbacks of each data type, providing the most fully comprehensive understanding of your customers journeys.

Next, depending on your marketing activity and data tracking sophistication, you’re going to have some of the following types of data sets to work with.

The best way to categorize your data inputs is to split it into channel data and event data.

Attribution modeling data

1. Event Data (What Happened?)

Event data typically includes:

  • Conversion Data - Conversion data includes information about the desired actions taken by users, such as purchases, form submissions, or sign-ups. Conversion goals need to be set for each journey stage.
  • Behavioral Data – Any data related to customers' online behavior, such as organic website visits, page views, time spent on site, clicks, search queries, and interactions with specific content or features.
  • Clickstream Data – This is a record of each click a consumer makes while browsing online. Tracking all these actions can help brands form an accurate understanding of the most effective consumer journey design.
  • Ad Impressions and Clicks - Ad impressions combined with click data provides information on the number of times an ad was displayed to users, and the corresponding clicks made. This data helps gauge the effectiveness of specific ads.
  • CRM and First-Party Data - This data provides long-term insights into customer behavior and can include survey responses, purchase history, and any interactions with the brand. CRM data is necessary to link the direct impact on revenue generation and CLV.

Note, there are two common ways to give credit to touchpoints within a conversion sequence: post-click, or post-view.

Post-click Conversion Data - If attribution is done on a post-click (not necessarily last-click) basis, clicked touchpoints will get a part of the conversion credit as long as the action happens within the defined lookback window.

Post-view (or view-through) Conversion Data – Here, the content a user viewed (impressions) within the specified lookback window also gets part credit for a conversion. Most of the advertisers who advertise on multiple channels will have video and social media as part of the conversion journey. These channels usually are not driving clicks, but still contribute to outcomes. This data is more challenging to accurately collect.

The lookback window is how far back a conversion action is included, usually measured in days. So a 7-day lookback window would only include advert impressions or clicks 7 days before the customer converted. In low-cost eCommerce transactions where the selling cycle is short, the most relevant lookback window only might be 7 – 14 days. Whereas for more complex sales like business software, a lookback window of 60 days could be used.

2. Channel Data (Where Did It Happen?)

Channel source data typically includes:

  • Referral Data - This identifies the source that referred users to your website (or app). It can attribute from the high-level referral sources, such as search engines, social media or email, right down to the specific pieces of content.
  • Device and Platform Data – This gives information about the devices and platforms used by users during their customer journey. It allows marketers to track cross-device interactions and attribute conversions across different devices. Device data is also helpful for providing location information.
  • Offline Data - This gives information about customer interactions outside of digital channels, such as in-store purchases, phone calls, word of mouth, events, direct mail responses, etc. Offline data is typically captured through mechanisms like unique identifiers, coupon codes, or CRM systems.

An attribution model is essentially used to link these two types of data together to show which marketing touchpoints deliver the best results. 

Types of Attribution Models

There are several types of attribution models to feed your data into. Applying the right modeling for the goal or KPI is key.

A model essentially joins up your event data (what happened) to your channel data (where it happened) to show you the most profitable journey connections. 

The difference between attribution models is where they place most credit for achieving a desired conversion goal (like submitting a contact form, generating an MQL or closing a sale). Conversion goals should be set up for each stage of the customer journey to feed attribution analysis.

Multi-touch models attribute results to more than one touchpoint, allowing for the influence of consecutive touchpoints to be considered as part of a process that led to the final conversion.

 A model either uses: 

  • Rule-based methodology - Analyzes data in a completely static approach. 
  • Data-driven modeling - Typically uses AI and machine learning to help automatically customize multi-touch attribution based on the influence of touchpoints.
Type of Attribution model

Here’s a breakdown of the most common attribution models:

Last Touch (or Last Click) Attribution

This model assigns all the credit for a conversion to the last touchpoint (or channel) that the customer interacted with before making a purchase or completing the desired action.

When to use it? - To understand which touchpoints are most influential for prompting people to take the final step in completing a conversion goal (e.g., submitting a contact form or making a purchase).

Limitations? – Although easy to use and collect data for, it’s not a great stand-alone model for longer and more complex sales cycles where conversion still heavily relies on the preceding touchpoints, particularly in B2B.

Last touch attribution

First Touch (or First Click) Attribution

The first touchpoint (or channel) the customer engaged with receives 100% of the credit for the conversion.

When to use it? - To understand which early journey touchpoints are best at first reaching new audience members who will eventually convert.

Limitations? – It ignores the influence that mid to late journey touchpoints have for final conversion. Data accuracy can also be more difficult to assure depending on your data tracking methods and lookback window

First touch attribution

Linear Attribution

Equal credit is given to each touchpoint in the customer journey, recognizing the role of all channels in driving conversions.

When to use it? – To understand how touchpoints and journey architecture work together to nurture conversions over time, including cross-departmental touchpoints between marketing and sales for B2B.  

Limitations? – The data collection process is more intensive and may require cooperation with other departments to capture all touchpoints, taking time to implement fully. This may include qualitative touchpoints through manual data entry. Distributing credit evenly doesn’t account for which touchpoints have most influence.

Linear Attribution

Time Decay Attribution

More credit goes to touchpoints that occurred closer to the conversion event, with the assumption that recent interactions have a greater impact on the decision-making process.

When to use it? – For longer, more complex customer journeys where later touchpoints are most influential. Equally, it can be useful for very short sales cycles where decisions are made quickly and you want to see which touchpoints have immediate effect for impulse conversion.

Limitations? – The influence of earlier touchpoints for creating brand awareness or intent won’t be accounted for.

Time Decay Attribution

U-Shaped (Position-Based) Attribution

A higher percentage of credit goes to the first and last touchpoints in the customer journey, while the remaining credit is distributed evenly among the other touchpoints. It's based on the idea that the first and last interactions play a more significant role to create new leads and drive conversions.

When to use it? – When you want to understand which channels generate most new leads, and which drive most conversions.

Limitations? – Again, the influence of in-between touchpoints will not be fully understood, and it requires data collection to cover all touchpoints within the journey.

U-shaped Attribution

W-Shaped Attribution

Equal credit goes to three key touchpoints: the first interaction, the lead creation event (e.g., form submission), and the final conversion event. The remaining credit is divided among the other touchpoints.

When to use it? – To highlight the key journey milestones from early journey, mid journey and late journey.

Limitations? – The influence of intermediary touchpoints is not fully understood

w-shaped Attribution

Data-Driven Attribution

Data-driven models use advanced analytics, machine learning, or artificial intelligence to analyze customer journey data and assign credit to various touchpoints based on their estimated influence on conversions. This can be done by off-the-shelf software solutions specifically designed for marketing attribution. There are two widely accepted data-driven models for attribution: Shapley value model, and Markov chain model.

When to use it? – For more accurate full-journey attribution across multiple touchpoints, providing greater flexibility for integrating multichannel data silos and more balanced weighting criteria.

Limitations? - An attribution software subscription is required, some of which can be costly. How the algorithms are coded and applied is sometimes proprietary information that is not made fully clear or adaptable. Data sources still need to be set up and connected, including offline touchpoints. It can take months of work to fully set up and implement a data-driven model covering all marketing channels.

Data-driven attribution solutions

Fully Customized Attribution Modeling

Custom-built models are also data-driven, but can include as much complexity and adaptability as you’d like. They allow for full visibility and control of the combined data sets, rules and weighting in use. It allows the layering of many rules and granular data analysis so you can deeply understand and drive growth to a level that isn’t available any other way.

When to use it? - For larger media budgets where small adjustments see the $ results impacted by millions.

Limitations? - Fully customized attribution requires a specialist to implement because of the complicated algorithms and calculations, along specialized statistical software and coding. Like off-the-shelf data-driven solutions, it can take several months to fully implement.

Fully Customized Attribution Modeling

Choosing Data and Models to Match Goals

For multichannel marketing across the customer lifecycle, marketers will have several different goals and KPIs, so there isn’t a one-size fits all when it comes to using attribution modeling. 

For example, marketing goals will vary by campaign, but also business lifecycle stage. As a business matures and can afford to allocate more budget in demand creation, longer payback periods become feasible in the name of sustainable growth.

For the most accurate results, several rules and weighting criteria may need to be layered together. This requires an understanding of how to choose the most appropriate combination for each goal or data set.

Here are examples of how different goals could affect the overall approach for assessing attribution against KPIs:

Brand Awareness - The main objective is to build familiarity rather than immediate conversions, so attribution models that consider upper-funnel touchpoints using a longer lookback window are most helpful.

Conversion Rate Optimization - Last touch attribution can provide insights into the most influential touchpoints in driving conversions for any journey stage.

Customer Acquisition – With a focus on identifying marketing efforts that drive most new customers, attribution models that emphasize first touch and last touch before sale conversion are a good fit.

Customer Retention and CLV - Attribution models that consider multiple touchpoints over the customer lifecycle are best. Time-based attribution models such as linear or time-decay attribution can help identify touchpoints that contribute to CLV over time.

Cost Efficiency - Attribution modeling using cost-per-click (CPC) or cost-per-acquisition (CPA) data provides insights into the cost of acquiring customers through different channels.

Channel Optimization - Models like time-decay attribution or position-based attribution can help evaluate the effectiveness of various channels throughout the customer journey.

Return on Ad Spend (ROAS) - Attribution that uses revenue conversion data along with position-based or data-driven models are most suitable for calculating ROAS. These models can help isolate the impact of an advertising campaign against other touchpoints.

Customer Engagement - Attribution models fed with click data are most valuable. Models like engagement-based attribution or position-based attribution can help attribute credit to touchpoints that generate higher engagement levels.

Campaign or Event Success - Campaign-based attribution or event-based attribution allow marketers to filter conversion data specifically for the corresponding campaign (or event) identifier.

Demographic Targeting - Companies that target audience segments based on demographic data, such as geography, need to be able to filter customer event data for segment-based attribution.

Social Media Influence - Models using multi-touch attribution with social media weighting can help more accurately attribute conversions or engagements specifically to social channels.

Experimentation with attribution models will help you find the most suitable approach for each reporting use case.

A Step-by-Step Guide to Building Custom Attribution Models

A customized approach to attribution modeling allows hybrid data usage to give the most complete and accurate view of your marketing effectiveness. (Reminder - a hybrid approach combines multiple online and offline data sources, reducing the risk of misleading insights).

With customized approaches, you can get journey clarity at the individual level. For example, you could isolate a new customer to see that their first website visit was 9 months ago, and they were exposed to 37 ads across 5 platforms. You can also use heat map tools to confirm how channels work together in order to predict where prospects will go next, targeting content messaging accordingly.

Here are the 6 steps to create custom attribution reporting that will truly allow you to start optimizing your marketing investments:

Step 1 - Clearly Define Your Goals

Identify the specific objectives that your marketing efforts aim to achieve, such as increasing conversions, driving brand awareness, or improving customer retention. They can be different for each channel or audience segment. As discussed, these goals will guide the rule options for your attribution model.

For each goal, decide what you consider to be a conversion for the journey stages, and whether you will need to include post-view data in addition to post-click data. The type of conversion is important, so you’ll want to identify the conversion events to look at for each specific goal, including the lookback window that will be most relevant.

Attributing marketing activity to revenue is the ultimate aim – this will give you the most powerful information to improve ROI and drive growth.

Step 2 - Identify All Your Data Sources

Start with accurately and consistently collecting all the data you possibly can for all customer interactions across all your active channels and platforms. You’ll need to UTM tag every link that matters, and have tracking pixels installed for all active marketing platforms.

Here’s a quick checklist of data sources:

  • Website
  • Social media (organic)
  • Paid media campaigns
  • Email marketing
  • CRM system and revenue data
  • Customer feedback
  • Call tracking
  • Offline touchpoints
  • Third-party data providers


  • Self-reported attribution is most valuable when free text only.
  • B2B buying decisions usually involve multiple people, so it’s better to track the customer journey at the account level instead by combining individual user data.

Step 3 - Bring in the Necessary Data Capabilities

Marketers need to have a deep understanding of marketing concepts and principles to be able to set up effective attribution models and make data-driven decisions.

You will need access to strong data analysis skills to be able to set up, manage and interpret the data for customized attribution models. Some technical knowledge is required to select, set up and configure attribution software tools, integrating them with existing data sources and systems. Knowledge of statistics is also necessary to understand, interpret and communicate the results of attribution models.

If in-house attribution data specialists are not in budget (or available), It can be more economical to use specialized data agencies to support you.

Step 4 - Chose + Activate Your Data Tools

Available resources are a big part of your consideration here. You’ll need to consider what is within means for your company in terms of ease of use, data integration capabilities and subscription cost.

There are 2 options here:

  • Off-the-shelf attribution software

There are several software tools available that can help marketers combine marketing attribution data from different sources.

Tools with in-built machine learning and AI are better suited to help you analyze and weigh the contribution of different touchpoints and channels in your custom hybrid attribution model. This will give you more accurate insights.

Google Analytics (or Campaign Manager 360) are the best known off-the-shelf providers. However, data integration from other sources can be much more of a challenge with GA. Some other off-the-shelf options which offer better data integration capabilities include Northbeam, Wisely, Adobe Analytics and Improvado. 

However, the drawbacks are that you’re still handing over power to a platform that uses its own proprietary algorithms, not always allowing complete visibility or flexibility in how rules are applied or data is weighted.

  • Build your own custom modeling

Depending on your resources, building custom modeling offers the greatest control and visibility of exactly how data is being weighted and analyzed for each scenario. 

If you’re doing this independently, you’ll need a data connector/warehouse solution to import and store your data from across your multichannel data sources. Custom coding and statistical tools can be utilized for advanced capabilities, allowing for layered algorithms and models tailored to any specific need or data set, including fully customized weighting criteria for data sets such as self-reported attribution.

The benefits over any other solution is the most accurate attribution possible, with completely granular insights depending on any criteria you’d like, allowing complete flexibility as variables such as channels, campaigns and customer or market dynamic shifts, and fully aligned for any goal you set.

With customized approaches, you can get journey clarity at the individual level. For example, you could isolate a new customer to see that their first website visit was 9 months ago, and they were exposed to 37 ads across 5 platforms. You can also use heat map tools to confirm how channels work together in order to predict where prospects will go next, targeting content messaging accordingly. 

Step 5 - Integrate Your Data Sources

Using your selected attribution tools, start collecting and integrating data from your multichannel sources.

This involves setting up data integrations between the attribution software and the data sources, whether through configuring API connections (recommended) or importing data files.

Automate the most relevant model-based analysis into dashboards, reporting on each of your specific marketing goals whether by revenue, channel, journey stage, customer segment, etc.

 Step 6 - Test and Iterate

Continuously test and refine your attribution model, adjusting the weights and methodologies as necessary. Monitor the performance of your model and make data-driven adjustments to improve its accuracy and effectiveness over time.

For example, data capture often relies on UTM tags, which requires links to be clicked before they are reported. This means some early-journey channels that rely on impressions rather than clicks (mainly social media and display advertising) will be underrepresented without qualitative self-reported data and weighting adjustments. Lift tests need to be run to help assess weighting criteria.

To test the influence of unclicked impressions, which is common for early-journey touchpoints and channels, you can use lift tests. Lift tests use test and control groups, only showing adverts to the test group. The difference in conversions between the two groups is known as lift, indicating the channel's real impact, and providing a helpful weighting metric. (Audience sample size and segment characteristics are important for statistically valid comparisons.)

Incrementality is a complementary metric to lift.

Lift Test and Incrementality

The Main Takeaways

Marketing attribution is critical to understand the impact of different touchpoints on customer behavior and conversions. 

While various simplistic attribution models exist, building customized data-driven models provides marketers with the greatest control and insight accuracy for their attribution analysis. This is essential to ramp up marketing spend with certainty of generating the required revenue results.

Custom data-driven attribution models offer several advantages over on-platform and Google Analytics reporting:

1. Report Against Goals - Marketers can tailor custom models to their specific business goals, customer behavior patterns, and available data sources. This level of customization enables a more accurate reflection of the complexities of the customer journey and the unique dynamics of the market.

2. Understand Touchpoint Influence Across Whole Journeys - Custom data-driven models empower marketers to attribute credit to touchpoints based on their true contribution to conversions, rather than relying on predefined rules or assumptions. And by integrating multiple (hybrid) data sources that include online and offline interactions, marketers can operate with a significant competitive advantage to drive growth forwards.

3. Allow Flexibility For Refinement - Custom models also provide the flexibility to adapt and refine the attribution process as the business evolves. You can more easily incorporate new data sources, update algorithms, and fine-tune attribution rules to ensure the model remains aligned with changing market dynamics and marketing activities.

Implementing a custom data-driven attribution model requires robust data integration and advanced analytical capabilities. However, the benefits of improved accuracy, granular insights, and informed decision-making make the investment worthwhile, potentially adding millions of dollars of additional annual growth. Particularly where larger advertising budgets are involved. 

By leveraging the power of custom attribution modeling, marketers can achieve industry-leading business outcomes.

If you need any support scoping, setting up or managing your attribution analytics, the team at Half Past Nine are here to help. We live and breathe marketing data! Just reach out.

What To Read Next:

Imagine a future where paid media actually adds real and welcomed value in people’s lives.

Where the information someone needs appears at exactly the right time to help them find what they want. Or while they’re browsing, learn about something that they weren’t aware could solve a pressing need. 

And in the process, brands spend less money putting content in front of people who don’t want or need it, radically driving up the profitability of media spend to deliver maximized revenue growth. 

This future is possible, even without third-party cookies. It will be built on a mindset shift, where the rigid parameters of the sales funnel are no longer paramount, and dynamic customer journeys become the north star. 

Where as marketers, we can cater to real people who don’t behave in linear ways, with empathetic understanding of what their goals might be and providing real value when it’s wanted.

If your goal is to improve customer engagement and fuel new revenue growth, this article is for you. Let’s explore how to build highly profitable customer journeys using digital intent signals.

Building Personalized Customer Journey Architecture

 Our job as marketers is to get the right touchpoints and messaging in the right place to progress our prospects from first introduction to converted and loyal customers.

 The basics of the customer journey remain the same under the tried-and-true framework of Awareness > Interest > Consideration > Decision > Retention > Advocacy.

 The 3 journey stages for customer acquisition are:

  • Early Journey (creating awareness)
  • Mid Journey (nurturing interest and consideration)
  • Late journey (prompting action)
The sales + marketing funnel

However, customers can move through buying stages in very different timelines. They may regularly loop back to previous stages, with pauses in-between. In our digital era, journeys can be incredibly fragmented across devices and platforms, and many journeys are completely unique. 

The typical customer journey today is actually a 3-dimensional process that can shift in any direction, rather than a straight line from A to B. They can resemble pyramids, diamonds, or even hourglasses, rather than a linear funnel.

A linear sales-funnel philosophy fits with the old approach of the stereotypical sales-led company. It’s not that a sales-led approach isn’t right for any business - but an overemphasis on sales goals can cause counterproductive tactics. For example, immediately jumping to harassing prospects with unwanted phone calls or emails, or running a generic sales ad to the widest audience possible and having to pay above average CPM/CPC due to poor engagement.

That’s why the most successful approach to fueling revenue growth is a dynamic and responsive customer journey framework, rather than a funnel approach. 

It allows for the individual to engage with relevant content while on their own unique path, maximizing the number of conversion routes and potentials at any one point in time. 

Personalization: The 3D customer journey

Naturally, the simplicity (or complexity) of a typical journey will vary greatly by the value and importance of the purchase being made. 

For ecommerce brands, a customer could leap from awareness to an impulse purchase in the space of 5 minutes in the right circumstances. Or a B2B sale could take many months from initial touchpoint. (Learn more about the B2B customer journey and buying process.)

 Regardless of journey timeframes, marketers building any type of customer journey architecture will still need to understand:

  • What are the common challenges, needs, goals, and desires of each audience segment?
  • What channels and platforms have best reach for the target audience at the specific journey stages?
  • What corresponding messages will work best for each journey stage and platform?
  • How do cross-channel and platform touchpoints work together to facilitate complete journeys for each segment?

Learning to Read Behavioral “Tells”

How can brands really get to grip with personalization across platforms?

Firstly by recognizing that the old way of building a sales funnel - assuming everyone who enters it will behave the same way - doesn’t reflect reality. We can’t assume that all people in a target market will be relevant leads, use the same platforms, automatically be ready to consider buying after showing interest, or that their consideration process will always follow the same path.

It’s the equivalent of walking up to a colleague in the middle of a phone call and expecting them to answer your question immediately. Or approaching someone perusing the vegan section of a store to offer them a promotional ham sample, then continuing to follow them around after they’ve said “No thank you”.

The need for observation, active listening and empathy applies as much to marketing and sales activity as it does anywhere else in life. 

That’s where intent data comes in. Intent data is the marketers means of observing what people are doing, before we “decide” if and how to approach them. 

Using intent data to target users will outperform targeting by demographics alone. Users who show intent are typically closer to making a buying decision, making them high-quality leads. By targeting these users, businesses can increase the likelihood of conversions to generate quicker and higher ROI.

 And the more digital and mobile customers have become, the more helpful intent data they generate for us. Of course, it still depends on a brand’s ability to manage and analyze the data… But with a solid data strategy, brands can tap into intent data to engineer hockey-stick moments of sustainable growth.

Introducing Digital Intent Signals

Just like in life offline, the key is to observe people’s “body language” within their digital world, building a picture of what might be happening for them in the moment. 

We call these digital actions “intent signals”. 

Being able to read them allows us to connect with only the most relevant people, using tailored messages that are most likely to resonate in that particular moment.

Build customer journeys using intent signals


The majority of buyer journeys start with some type of intent. Although…, your ideal prospects might not always start out directly looking for your type of solution or product.

For example, a person Googles healthy meal recipes. Their goal is to improve their nutrition and lose weight. They aren’t looking for complete nutrition shakes. But if we were to reach the user with content highlighting the quick and easy benefits of complete nutrition shakes to improve health and lose weight, we’re far more likely to capture their attention and create intent to buy. 

These types of people with relevant but indirect intent may represent a large portion of your serviceable/addressable market.

Demand is much easier to create with the right message that talks to a pressing goal, at the exact time a person has that goal front of mind. It’s always the goal we need to understand and talk to.

And to be clear, intent signals aren’t KPIs or “vanity metrics”. We use intent signals to deduce intent, and then target or exclude people accordingly. Intent signals should actively inform real-time content targeting when used correctly.

Types of Digital Intent Signals 

The intent signals we can gather spans internal and external sources. It crosses organic and paid content, to owned and third-party platforms.

  • First-party Data – CRM, website, app and email data (learn more about first-party data)
  • Second-party Data – Audience interaction on non-owned channels (E.g. Facebook)
  • Third-party Data – Data Companies (E.g. Nielsen)

Some signals can be very overt. Especially at late journey stages, such as filling out a contact form or adding an item to the basket. Whereas other signals are less obvious, like running a Google search to learn about a related topic, or following a competitor’s social media account.

The type of intent signal can give you clues about a person's journey stage to build real-time customer segments. It’s helpful to identify which intent signals feature most prominently at each stage of your brand’s customer journey paths.

Split targeted signals up according to the campaign goals they fit with, whether that's demand capture (late journey) or demand creation (early journey) campaign goals.

For example, if a website visitor is behaving like a user that typically converts after another couple of weeks, you can target them with the right tone of nurturing content accordingly. But if you were targeting someone showing an interest in a competitor that hadn’t been included in your campaigns previously, you could show them content that introduces your brand with the comparative benefits of your brand/product/solution over the competitors. 

Turn customer segments into unique journeys

Here are the most common intent signals that can be tracked:

Content Engagement:

  • Reading or viewing content related to specific products or services.
  • Downloading or sharing content.
  • Commenting on or liking blog posts or social media content.
  • Subscribing to a blog, newsletter, or YouTube channel.

Search Behavior:

  • Searching relevant keywords.
  • Searching for reviews or comparisons related to a product or service.
  • Searching for the brand name or specific products.

Social Media Engagement:

  • Following or liking a brand's social media pages.
  • Engaging with posts by liking, commenting, or sharing.
  • Mentioning the brand in posts or comments.
  • Clicking on social media ads or sponsored content

Ad Interaction:

  • Clicking on digital ads.
  • Video ads watch time.
  • Clicking on retargeting ads

Event Participation:

  • Registering for webinars or online events.
  • Participating in trade shows or conferences.
  • Engaging in live Q&A sessions or forums.

Website Interactions:

  • Traffic source
  • Visiting a website multiple times (yours or competitors).
  • Spending a significant amount of time on the site or on specific pages.
  • Checking product pages or service descriptions.
  • Downloading content such as ebooks, whitepapers, or product brochures.
  • Returning to the website after a period of inactivity.
  • Using online tools, calculators, or configurators.
  • Completing quizzes or self-assessments.

App Interactions:

  • App downloads.
  • App usage patterns and content engagement.
  • Search queries.
  • Abandoned carts.
  • Registration or subscription.
  • User reviews and ratings.

Shopping Behavior:

  • Adding items to a shopping cart or wishlist.
  • Repeatedly viewing a specific product or service.
  • Starting but not completing a purchase process.
  • Checking the availability or location of a product.

Email Engagement:

  • Opening marketing emails.
  • Clicking on email links.
  • Responding to surveys or filling out forms.
  • Forwarding emails.

Customer Support Interaction:

  • Contacting sales.
  • Using live chat or chatbots.
  • Requesting a demo, quote, or more information.

How to Use Digital Intent Signals to Inform Customer Journey Architecture

The process for incorporating intent signals into real-time, personalized media targeting requires the following steps:

Data Collection and Analysis

The first step is to collect data on your audience’s behavior across your channels, including offline touchpoints where possible.

This data needs to be analyzed to identify patterns and understand what specific actions might indicate a user's intent to purchase or engage further. What are the main actions taken within journeys, and what conversion goals can help you qualify people at each stage?

Data tools such as connectors and warehouses will help you merge data from multichannel sources for more holistic understanding and analytical power, whether historical or predictive.

A note here on data collection. User tracking and targeting across multiple advertising platforms can be achieved through more than one method. This means that what a user does on one platform can be used to target them appropriately with relevant content on another platform via:

  • First-party Data - Advertisers can import their customer segments into an advertising platform using Customer Match targeting. This matches identifying information that customers have shared with the advertiser, such as an email address, to target specific ads to those customers, and also other people that behave like them (look-alike audiences). This allows advertisers to narrow in on the highest intent/value customers.
  • Cross-device Targeting - Also known as people-based marketing, this approach uses Device IDs or User IDs to anonymize user data while still allowing people to be targeted individually (without cookies), so advertisers can track and target a user across multiple devices. Pixels are used for this type of targeting.

A combination of these data collection methods will give brands the most precise targeting power and best results. 


Once you've identified key intent signals and conversion goals, you can segment your audience based on their behavior.

For instance, users who have abandoned their shopping carts might be in one segment, while users who have spent a significant amount of time on product pages might be in another.


Each segment will have different needs and will be at different stages of the customer journey.

Create personalized paid and organic content for each segment, addressing their specific goals or challenges, guiding them towards the next step in their journey with defined conversion goals for qualifying. Content that matches keywords and the audience’s language directly performs best.

Leverage Media Technology + Automation Tools

There are a number of built in AI and automation tools within the bigger ad platforms for marketers to take advantage of.

Setting campaign goals and conversion goals allow platforms like Google and Meta to automatically optimize targeting to achieve them. Dynamic ads can use AI and machine learning to improve their targeting and optimize ad copy tailored exactly to user search terms. And machine learning already drives real-time programmatic buying, where advertising inventory is bought and sold via an instantaneous auction.

There are various independent solutions that can be used for paid media targeting, such Blueshift and 6sense, including intent data for account-based marketing (ABM) needs.

Testing and Optimization

It's important to continually split test and optimize your campaign creatives and targeting based on performance.

Look at which intent signals are most predictive of conversion, and which types of content are most effective for each segment. Use this information to refine your targeting and personalization strategies. How you use attribution modeling is also a crucial part of your media optimization process.

Summing Up

Recognizing and leveraging customer intent signals in the creation of personalized customer journeys is not just a valuable strategy - it's a business imperative for advertisers seeking to drive revenue growth. 

As the advertising landscape becomes increasingly digital and competitive, the brands that will rise to the top are those that truly understand their customers, meeting them where they are and providing what they need at every stage of the journey. By harnessing the power of customer intent signals, marketers can enhance customer experiences, build stronger relationships, and ultimately, achieve sustainable revenue growth. 

This shift towards a more customer-centric approach rooted in data insights is not just the future of advertising; it is the present. 

If your team needs support gathering, analyzing and incorporating intent signal data into your media strategy, Half Past Nine would love nothing more than to help you realize their transformative power on your bottom line. It’s what we get out of bed for! Just get in touch.

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