Switching to First-Party Data: It’s Time to Stop ‘Renting’ and Start Owning Customer Data

Growth Intelligence
0 min read
December 8, 2021
Jenner Kearns
Chief Delivery Officer

What do I mean by ‘it’s time to stop renting and start owning customer data’?

I’m talking about relying on advertising platforms to gather your audience and customer data via tracking pixels and cookies. In other words, using third-party data to generate your customer insights and targeting.

With the crackdown on data privacy, which keeps gathering momentum, the days of leaving your customer insights and targeting to third-party tracking pixels and cookies are well and truly over. 

Apple users now have to opt into being tracked across mobile apps with third-party cookies since the IOS 14.5 update and introduction of the App Tracking Transparency (ATT) framework. Google Chrome is removing all third-party cookies by 2022, and Safari and Firefox already block third-party cookies by default.

It means the quality of user data collected by advertising platforms is already in decline. With reporting more heavily based on data modeling, adverts become increasingly less personalized to users interests or needs. 

The impact is particularly hard for social platforms as they can’t rely on keywords like search engines can. If social media advertising is a part of your marketing strategy then your investment and ROI will be impacted. In fact, over the last couple of months particularly, businesses are reportedly seeing their Facebook ad costs skyrocket while revenue takes a nose-dive. That’s the unavoidable result that poorer ad targeting and engagement has.

twitter thread about ios14.5
twitter thread about ios14.5

Given the recent global crash of the entire suite of Facebook-owned apps, further questions are raised about fully outsourcing customer data collection and targeting to these companies. Can these apps really be depended upon?

There is a rather urgent need for advertisers to start improving customer data collection and better leveraging it for campaigns. Well, at least if they want to be able to accurately target customers while staying on the right side of regulations.

Is your team adequately aware of this seismic shift? Or is action being put off in order to deal with other ‘priorities’? If your team falls into either camp, you’ll find that you are increasingly in a losing battle when it comes to marketing and revenue results.

I know change and the additional responsibility can seem daunting, but there are very manageable steps you can take to successfully make the switch over to first-party data collection. Let me talk you through it, with SME business needs front of mind.

First vs. Third-Party Data

First-party data is any data that you collect directly from your customers. Second-party data is someone else's first-party data that they share with you. Third-party data is collected by a business that does not have a direct relationship with the user that the data comes from.

When it comes to online user data collection, first-party vs. third-party cookies is where the issue lies. 

Here’s a quick refresher on cookies, in case you need it.

First-party cookies are just a record created on a user’s browser to track their activity on web pages within your own website domain. Users are automatically opted-in to first-party cookies to enable your website’s functionality. Cookies store all of a user’s activity and retrieve it each time a person navigates onto another page of your website. First-party cookies can retain login and payment details, and deliver personalized experiences like remembering a customer’s name, language and generate similar product recommendations. 

Users can see, clear or block these cookies on their browser any time they want to, and they are not tracked beyond your own website domain. If users browse your website anonymously, such as with ‘incognito mode’ on Chrome, then cookie data won’t be gathered.

First-party cookies usually have an expiration date of around a couple of months. People tend to keep first-party cookies enabled since it makes the user experience more enjoyable, and they are generally stored for longer periods of time than third-party cookies.

A downside with cookies is that they track a browser, not a user. You user could be using more than one device or browser to visit your website. This can result in double-counting of users and missing data when tracking the customer journey.

Third-party cookies are generated and owned by a URL outside your website domain and function across multiple websites. They’re usually inserted into your users’ browsers by advertising platforms after you install their tracking pixel within your website code. They track users' activity across the internet over time to build user profiles based on actions and behaviors. Third-party cookies include remarketing adverts that target your previous website visitors, such as Google’s DoubleClick tracking cookie.

Although advertisers benefit from deep behavioral insights and accurate audience targeting tools, it’s understandable that this kind of data collection makes people feel uncomfortable. It can also collect Personally Identifiable Information (PII) that users don’t realize is being shared. For compliance with CCPA in the US and GDPR in Europe, among other international regulations, you shouldn’t track users with third-party tools unless they have opted in, nor share their data in ways they don’t know about. 

However, just like first-party cookies, users do have the option to clear or permanently disable any third-party cookie tracking within their browser settings.

VPN’s also impact first and third-party cookie data, not by blocking them, but by providing false location data and hiding the IP addresses that identify users. But you may still be able to identify VPN users if they are logged into a user account.

How Is Your First-Party Cookie Data Reported? 

google analytics data

The data from your first-party cookies is reported in your website analytics platform, with the tracking pixel provided by your analytics platform directing your website user data to its server.

Google Analytics is the best known in this space, and will provide anonymized data about your website users, which you can drill down to the user-ID level. Using User ID in Google Analytics is a solution for unification of sessions that an individual person has had across their devices. This will give you a more accurate user count while also allowing you to see the individual customer journey and navigation patterns within your website. However, you can’t attribute this data to a specific customer.

How Is Your User Data From Third-party Cookies Reported?

The advertising platform’s pixel you have installed on your website (thus requesting them to track your website visitors) will report data to you through their ad manager service. Different ad publishers offer reporting tools of different capabilities depending on the investment they have made.

The biggest ad providers are Google and Facebook, which as we know deliver a detailed level of reporting on ad engagement and subsequent behavior. However, issues like double-attribution can impact accuracy from third-party reporting. They also use the data gathered from your website visitors to allow you to target similar audiences or Lookalike Audiences on their platform, in affiliated publishing networks using programmatic ads.

How Do Tracking Pixels Fit In? 

The story is a little more complicated than just cookies, and pixels are where the real data-collecting power lies.

A tracking pixel is a piece of code inserted into your website code, and can be first or third-party owned. Traditionally, it’s a tiny 1×1-pixel graphic, transparent and invisible to users, which can be embedded in everything from banner ads to emails. The downloading of this invisible graphic onto a user’s server happens automatically, allowing user ‘events’ to be reported on. A request is sent to the host company’s server where the pixel image is stored, and this download request provides identifying information about the computer or device.

To summarize the difference, a tracking pixel delivers information to a server, while a cookie stores information in a user's browser so a server can read it again later. However, modern tracking pixels, or tracking script, is more advanced and can be used to trigger cookie creation. Unlike cookies, a tracking pixel cannot be independently deactivated by users. The pixel will capture data from every user visit, regardless of what browser cookie settings the user has activated. However, the data storage and utilization process can still be managed with customer consent.

facebook tracking pixel code

The Facebook tracking pixel, for example, has a crafty workaround to bypass third-party cookie opt-outs and blocking. It now uses your website visitors' first-party cookies by default, with their pixel instructing first-party cookie data to be sent back to Facebook’s servers. Facebook’s servers still gather user data from websites that users are visiting without third-party cookies, as long as the Facebook tracking pixel has been embedded on these websites and the default first-party cookie usage hasn’t been deactivated by the website owner.

Do be aware that if you use the Facebook tracking pixel and haven’t disabled its access to first-party cookies, any personal customer data collected on your website is also being shared with Facebook at your request. Here is more information on Facebook’s cookie policy, and their legal policies regarding Facebook Business Tools Data and Data Processing. Make sure to disclose to visitors that their data is being shared with Facebook.

Can First-party Data Target Users On Non-owned Channels? 

Yes, you can use your first-party data for audience targeting on ad platforms, better protecting your customers’ data while also improving targeting.

Ad platforms like Facebook gather users’ email addresses. When targeting specific users by creating custom audiences, and also when creating lookalike audiences, you can either manually upload CSVs of customer emails to create a custom audience, or upload data from a Customer Data Platform (CDP) with integration technology. You can also use your data to suppress customers who ads aren’t targeted at, such as existing customers.

example custom audience build

First-Party Data Types and Uses

Your first-party data can be used to better achieve the two processes most essential for customer acquisition and retention:

  • Campaign tracking - Assess how well a campaign or ad set is performing on a non-owned channel.
  • Targeting - Reach specific users with targeted or re-targeted messaging based on their past activity on both owned and non-owned channels.

The goal is to create highly personalized content that achieves its aim within well-designed customer journeys. Improving customer retention is particularly important for achieving business resiliency in these changing and rather turbulent times. 

To fully leverage the power of first-party data: 

  • Investigate how you could better utilize customer data you already have
  • Identify any additional customer data that would offer tangible value and which you can viably capture

You’ll need to weigh up whether this is information that customers will want to share without damaging trust in your brand while requesting it. But more on that shortly.

First, let’s have a quick look at the most important first-party data types and uses. This should help you identify any basic gaps in your current data capture and reporting processes.

First-Party Cookies or Pixels

Using tracking pixels is the most robust solution for getting to know the users and customers interacting with your online assets without the limitations of third-party cookies reducing the reliability and accuracy of your insights. Assuming your users have opted in of course, and aren’t blocking the download of images if your pixels are image-based rather than script.

Pixels can track digital ad impressions, web traffic, conversions, email opens, locations, devices, operating systems, browsers, IP addresses, among others. The analytics platform you choose will provide you with tracking pixel codes. Instructions for embedding them can be found on whichever marketing platform you’re using, or your developer can help you. 

The Google Analytics pixel is ubiquitous for tracking and reporting on website visitor activity, and Firebase for apps. Google Tag Manager works hand-in-hand to help you track the custom events you want to track using your various pixels and UTM codes for social, ads, emails, etc. A ‘free’ tool with Google Analytics capability is hard to argue with, and using in-house data warehouses and analytics capability to report on user data isn’t a task most SMEs have resources for.

Then there are CDPs, which will allow you to go further than a normal CRM by matching data to individual customers who have consented. They can automatically gather and unify customer data from all online content you embed a pixel on. For example, here’s how Blueconic’s CDP works with Google Analytics. I’ll discuss CDPs in more detail shortly.

You can embed tracking pixels on:

  • Website or landing pages
  • Owned apps
  • Adverts
  • Social media content
  • Email content

The uses for data from your owned online assets includes:

  • Tracking collective and individual online behaviors, including content engagement, session duration, ad conversions, etc. 
  • Creating audience segments based on behaviors and correlated characteristics
  • Tracking channel source of converting traffic to determine most effective use of marketing resource
  • Assessing the effectiveness of marketing content in the customer journey
  • Seeing where audiences drop off from your website, and identifying broken links, ineffective content or UX shortcomings
  • Re-targeting specific users, such as abandoned cart sessions
  • Using behavioral data, such as hovering over content like text or images, to enhance retargeting ads

CRM Database 

A CRM database is home for the Personally Identifiable Information (PII) you hold on customers. It can range from basic information such as a customer's name and business/job title, email address and location, to their purchase history, online interactions with your marketing content and survey data. This data can be collected both on and offline, and data input can be manual or through other platform integrations depending on setup. It’s typically shared between marketing, sales, account and support teams as a centralized database.

Its value lies in tracking the sales process or purchases, and collecting data to create segments of customers by specific behaviors or characteristics. The goal is to use this data to execute targeted email and advertising campaigns, and emails are particularly valuable. Email marketing has the highest conversion rates and provides a very high return on investment. Email also allows you to target (or suppress) specific customer audiences for ad campaigns on social platforms.

The uses for CRM data include:

  • Pulling together data from multiple sources into individual customer profiles
  • Identifying your most valuable customers and segments, or categorizing leads to prioritize BD activity (B2B)
  • Creating audience segments for targeted campaigns, such as identifying customers for up-selling, repeat purchases at defined time intervals, or re-engagement campaigns
  • Customization of marketing materials with personalized information
  • Exporting data to create lookalike or similar audiences for targeting customer acquisition ad campaigns

Social Media

For brands that are serious about growth or maintaining market share, generating regular social media content to engage customers is not really optional! Your audience engagement provides deeper insights into preferences, behaviors and segmentation. It’s the ideal medium to find new customers, build brand awareness and generate high levels of engagement with customers, keeping you front of mind. 

A wealth of customer insight can be leveraged across channels, platforms and campaigns, using social tracking metrics such as: 

  • Shares
  • Likes
  • Mentions
  • Hashtag usage
  • URL clicks
  • Keyword analysis
  • Comments
  • Sentiment

There are corresponding metrics to note in your website analytics from Social Media campaigns as a traffic source, such as:

  • New users
  • Conversions
  • Average session duration
  • Page views
  • Bounce rate

The uses for social media data includes:

  • Deeper audience insights and segmentation based on social media behavior, such as the platforms and content topics that your customers engage with most
  • Designing a highly targeted content strategy and tone of voice, so that customers feel a deep personal connection with your brand
  • Audience targeting for paid content, including channel and corresponding content
  • Assessing the best time of day to post, when your customers are most engaged and likely to purchase
  • Choosing hashtags with the most reach for your target audiences
  • Social listening, to see what your customers are interested in, who is talking about your brand and what they are saying

How to Collect and Implement First-Party Data

Collecting online behavioral data on your website, app or social media using first-party tracking  simply requires a pop-up or message allowing users to opt into or decline first-party cookies, and any other tracking methods you use.

When it comes to collecting PII, however, progressive profiling is key. This is slowly and purposefully requesting information as your relationship with the customer develops and trust is built. Only ask for data your business actually requires, and always make each data collection touchpoint as quick and easy as possible in terms of UX.

Customers are usually open to sharing data if you demonstrate trustworthiness, deliver value and better user experiences. Here are a few basic rules to follow to gather customer data successfully.

  1. Provide incentives - That can include detailing the benefits of an improved user experience, access to valuable gated content, e-newsletters, promotional discounts, or a loyalty program with rewards.
  1. Be upfront and honest to build trust - Give your reasons for collecting any data and be clear about how the data will be used. Share your privacy policies and how data is protected. Use the data responsibly - trust is hard to gain and easy to lose. Investing in brand awareness will help engender more familiarity and trust.
  1. Honor customers’ choice - Make it clear to customers that they always have the opportunity to opt-out or withdraw permission to collect or store their data, and ensure that the process is easy and user-friendly for the customer to execute if they choose. Respect your customers’ data and privacy by putting control in their hands and not breaking their trust. Consent Management Platforms can help you manage this process.

Let’s take a quick look at some considerations when it comes to your data infrastructure and tech stack, then finish with some tactics you can use for collecting customer data.

Tech Stack Capability

Although many articles will claim that first-party data is free, you still have to consider the cost of data platform licenses, plus staff or agency resources required to implement or manage data technologies and processes.

The process of integrating all sources of customer information from off and online sources can be referred to as first-party data on-boarding. Compiling your customer data sources is necessary to leverage its full value by building complete customer profiles that allow you create accurate segments for campaign targeting in real-time. The more automated you can make this data on-boarding process, the better.

It may be the case that optimized utilization of your existing Google Analytics account, modern CRM databases and other marketing platforms are sufficient for your businesses current needs and resources. You’ll still be able to gather valuable insights from first-party data, and run successful ad campaigns without relying on third-party tracking. For example, here’s the Facebook protocol for creating a Custom Audience using your own customer data.

However, speaking to SMEs that don’t have the luxury of their own in-house data specialists, bringing in a Customer Data Platform (CDP) offers greater potential. These platforms deliver more advanced customer data technology with greater automation that’s more aligned for a first-party data future. They can be integrated to all of your customers’ online touchpoints, and improve the match rate across data sources, eliminating data silos. This affords a unified customer view by consolidating data from all platforms into 360°-view customer profiles using consensually shared identifying information. You can still input offline data sources too. Crucially, they also allow customer data to be easily implemented for accurate campaign targeting in real-time across activated third-party platform integrations. 

CDPs are a real game-changer because they are user friendly for non-technical marketers, unlike traditional data warehouse tools used for these purposes. Your team will be able to run better coordinated, automated, and omni-channel customer journeys across paid media, your website, email and other chosen channels. All in less time without technical or developer support bottlenecks, and while providing greater customer data security and compliance.

For example, Segment is one of the best-rated CDPs currently available. It makes collecting data from multiple sources and then executing tasks like creating Facebook Lookalike Audiences or re-engaging cart abandoners quick and easy thanks to pre-built API integrations to other marketing platforms.

There are also a number of more evolved CRMs out there that integrate with advertising, messaging, email and other platforms. For example, check out the customer platforms that Facebook, Mailchimp or Sprout Social integrate with. There are plenty of options out there with audience segmentation and omni-channel integration functionality that any business can make full use of despite more modest budgets.

Customer Data Collection Tactics

We’ve covered automated online data collection, whether it’s from your website, app or social media. However, you still need to collect basic customer identifiers with customers' permission, and there’s also more you can learn from directly asking about preferences and interests. So let’s finish with tactics you can use for collecting data directly. 

Do remember the importance of content quality and offering genuine value or insight for customers in a value-based data exchange. A/B tests can be used to determine how much information your visitors are comfortable sharing. Also, remember your customer relationships are a marathon, not a sprint. Take your time building familiarity and trust.

On-boarding, Point of Sale, or Customer Service - Design a data collection strategy and train your staff how to use it. You may already have a formal on-boarding or support process that requires certain data to be collected. That could include asking in-store customers if they want to provide their email to receive an e-receipt and product offers. Consider if there’s any additional data that can be justifiably gathered at all points of contact, and ask staff to be consistent in recording data where it’s done manually. Clearly explain the value of the data to customer-facing staff.

Customer Accounts & Login Areas - For ecommerce sites, point out to users that creating an account to save their details for the next visit saves time, along with any other benefits you want to offer. Or, use gated content and resources to incentivize website visitors to register for a free account. Develop a content strategy for selectively locking the most popular or valuable content you provide. Make sure that registration and sign in is quick and easy, and enable universal login with social media or Google credentials if you can.

Memberships & Loyalty Programs - Offering discounts and benefits for signing up to a customer loyalty program or membership is a great way to capture contact info, preferences and behavioral data.

Lead Generation Form-Fills - Whether you have ‘lead magnets’ and assets sitting behind a form-fill, or just a regular contact form, add in a few simple, interest-based questions that will help you better tailor future content. These forms work well on landing pages in ad campaigns, and you should ask for the bare minimum information.

E-newsletters - Your sign-up process will naturally ask for an email, but you can learn more about customers by asking what they are interested in. If content generation resources allow, segment your newsletter into areas of topical interest during the sign-up process or in a contact preferences area so you can customize your newsletter to better engage your distinct audience segments.

Surveys & Quiz Flows - From website UX feedback to a novelty quiz for fun, a Customer Service follow-up survey or chat box surveys - people are familiar with the survey format and don’t usually mind a few quick and easy questions, particularly with multiple choice or rating scales.

Channel & Platform Diversification - This is an impactful means of gathering more audience reach and data, if you can find additional areas and platforms where your audience is spending time and making decisions. For example, your audience demographic on Pinterest may not be the same people within the same demographic on Instagram.

Partnerships - Join forces with a business or organization that has relationships with your target audience. Joint marketing or co-marketing partnerships using content, promotions and events can be very effective for collecting second and first-party data on potential new customers.

In-store, Office Locations, or Events - Asking customers to sign-in using their email for WIFI access is an option at physical locations, and you can use the sign-in landing page to promote content or any offers that would help collect additional information voluntarily. You could also use beacons, which are small wireless transmitters that communicate with nearby smart devices using Bluetooth. You can pull information about customer location and in-store browsing behavior, but you can also send push notifications or offers. Then there’s the plain old-fashioned way of speaking to people and asking for contact information, whether it’s to send an e-receipt, provide access to a membership or discounts program, or a BD follow up with more information.


Enhancing your first-party data strategy is invaluable for:

  1. Nailing down your buyer personas and dividing your segments more effectively
  2. Enhanced targeting and retargeting, with relevant ads strategically placed throughout the customer journey
  3. Developing engaging and personalized content that hits the mark at a given point in the customer journey 

This is key to both customer acquisition and retention.

You can do this more effectively without handing over the task of knowing your customers to third-parties, jeopardizing customer trust or potentially falling foul of country-specific regulations. With more accurate and in-depth first-party insights on customer behavior, it’s easier to deliver relevant ads and personalized content that improves brand awareness and customer loyalty.

Don’t be averse to taking your time collecting data and prioritizing trust first. Rushing your first-party data collection process could actually do more harm than good. After establishing your brand’s credibility and transparency with customers, they will be far more willing to share helpful information about their interests as they move along the customer journey.

I hope this run-down has left you feeling that owning your customer data is a manageable endeavor. There are plenty of affordable and user-friendly systems out there to help you successfully implement any improvements you wish to make when it comes to collecting, organizing, protecting and actioning first-party customer data. 

However, if your team could use support, Half Past Nine is always happy to have a conversion and discuss potential alignment. We have deep expertise in performance media, data leadership and infrastructure development, and we’re passionate about enabling ambitious businesses who are ready to grow!

Read more about our marketing data insights:

Marketing Data Analytics: How to Build Your Source of Truth

8 Marketing Data Issues That Might be Holding You Back & How to Fix Them

Marketing Data Analytics: Can You Trust Your Source of Truth

Marketing Data Visualization To Fully Leverage Your Sources of Truth

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.

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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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