What story is your marketing data telling you?
At Half Past Nine, we liken data analytics to the headlights of a car that is circuiting the ever-evolving road track of the customer journey. It allows you to see where you are and where you’re going so you don’t veer off track.
And if marketing dollars are the gasoline of sales performance, we want you get around your customer journey track as fuel efficiently as possible. That’s why it pays to put that effort in with your digital marketing analytics.
Digital marketing analytics can seem daunting when you have a number of active platforms in your marketing tech stack. There are a few things you’re contending with in order to optimize your analytics and decision-making processes:
- Accurate tracking of all digital marketing activity and consumer interactions.
- Interpreting data correctly to inform further decisions on customer acquisition and retention initiatives.
- Finding one source of truth in a sea of marketing analytics platforms.
In this article, we will overview setting up a digital analytics infrastructure and data tracking, and using analytics platforms to interpret your data accurately.
If you’re still looking at how to set up your wider marketing operation and process, learn more before diving into the technicalities of tracking and analyzing your marketing activity.
The Importance of Marketing Analytics & Data Integrity
Achieving accuracy and clarity around data is the billion-dollar conundrum for digital marketing in this era. Trustworthy data you can confidently use for making effective decisions that grow your business is the goal.
Understand Where Budget Is Spent - Your data should be able to provide a clear and detailed picture of where and how investments are made in your marketing. Reporting should allow you to drill down into specific business streams, audience segments, individual marketing channels, products, and so on.
Measure Results Accurately – It's crucial to understand what the result of investments are, directly tying spending back to measurable results. That could be bottom line revenue in resulting sales, or other marketing goals such as improved brand awareness in target audiences. Your KPIs should guide what kind of results you want and need to report on as a priority.
Improve ROI – High quality data gives you the ability to improve budget efficiency and ROI with precision by investing more in tactics that work best, and divesting in ones that don’t. Ultimately, good marketing data integrity allows you can scale your business and drive growth.
Once you have this data, it’s also important to consider the use of data visualization to communicate the information across your business clearly and quickly.
The true source of your data’s power is being able to make highly responsive and good quality decisions across your marketing operations and wider business. That won’t happen if people can’t easily understand what the data is saying.
Maintaining Marketing Data Integrity
Maintaining the integrity of marketing data can be a challenge in today’s digital environment. That’s down to the number of cross-channel touch-points brand typically have with users on a daily basis resulting in double attribution for conversions, coupled with the growing trend for enhanced user privacy.
Does your business have a process for assessing how well marketing data gives a complete and accurate picture? If not, we’d recommend introducing one, because data integrity does require some proactive management. Learn more about managing your marketing data integrity and hygiene.
Now, let’s cover setting up accurate data tracking, as it’s not possible to analyze data that you don’t have!
Capturing Digital Marketing Data
When capturing digital marketing and advertising data, it’s all about pixels, tracking tags, and tracking codes.
If you haven’t come across these yet, they are small and unique pieces of code that need to be added to your website. Provided by analytics and advertising platforms, it’s your responsibility to add them to your website. Either your website developer can help you, or if you have access to do it yourself, instructions are always provided.
The codes allow the platforms to follow users onto your website and track their subsequent activity. That’s how they know what a user has done after they have clicked on your website, and, for example, if an advert was responsible for a ‘conversion event’. First, you’ll need to set up the conversion events you want to be tracked. Depending on your business, that could include newsletter sign-ups, adding items to cart, making a purchase from your store, a form-fill, an account registration, a demo sign up or trial subscription, etc.
You’re typically going to need the following tracking-codes:
- Analytics Tags - Google Analytics is the biggest platform in the website analytics space, allowing you to see and report on all user activity (anonymized) on your website for free. Create an account and you’ll be provided with the Google Universal Analytics Tag to link your website. You could also add the newer version called Google Analytics 4, which links with your main Universal Analytics account. This newer extension provides advanced functionality that works for both websites and apps (with a new Firebase SDK code).
- Advertising Pixels - Any platform you use for digital advertising will provide you with pixel codes, allowing user clicks on adverts to be tracked onto your website. You’ll need to set up the conversion actions or user engagement ‘events’ you want the pixels to track and report on, depending on the goal of your adverts and your sales funnel. The most common pixels are the Facebook pixel and Google AdWords pixels, or Klaviyo pixel for email and SMS marketing.
- Other Tracking Codes - Some other codes you may find useful include Google Optimize for free A/B testing of website pages, the Hotjar Tracking Code for using visual tools such as heat-maps to see how people use your website.
Note that tracking codes can actually slow down your website if you have too many. That will impact your search engine marketing (SEM) performance and also detract from the user experience. It helps to either use a tag manager, or be selective about which advertising platforms work best for your audience so you can minimize the number of tracking codes on your site.
Google Tag Manager
Google Tag Manager lets you manage all of your tracking tags in one place. So rather than needing to add all your various tracking codes directly to your website, only the Google Tag Manager code goes into your site. We recommend using it - it’s an incredibly helpful free tool.
Not only does this tool stop your site being slowed down by too many tracking codes, it also has a great feature for Custom Events Tracking. That means you can track valuable user activity like clicks, scroll depth and form fills. This kind of data provides greater insight into how much user engagement and value that individual webpages are generating for your business.
UTM codes are slightly different. They are custom URLs that work with your analytics platform to track where your users last came from when they clicked onto your website. It involves adding a little bit of specially formatted text to the end of your webpage URLs.
Analytics platforms like Google Analytics or HubSpot use the URL tag parameters to report the exact location users came from, meaning you can see which individual pieces of marketing content were responsible. You can see how many users came from which source and medium, (E.g. Email, Email banner click), which campaign it was part of, right down to an individual social media post or link within an article.
It’s an invaluable tool for understanding how much traffic individual pieces of content or adverts drive to your site. You can use it to assess the effectiveness of entire marketing campaigns, channels, online PR coverage, right down to individual Pay-Per-Click (PPC) adverts, etc.
You’ll have to create these UTM codes yourself, depending on what you want to track and how you want to label the dimensions for unique pieces of content. URL builders are available to help you do this, such as Google’s URL builder. You can also find tools to help you keep track of all your codes too. Depending on how many you use, this can definitely be worthwhile.
Understanding Marketing Analytics Platforms
There are a number of marketing analytics platforms out there to choose from. All will require some degree of tailored set-up in terms of tracking conversion activity that’s specific to your website and marketing goals. It’s worthwhile investing this effort to fully utilize the analytical potential available to you.
We’re going to take a deeper dive into using website analytics, and then in-platform analytics. But there’s something we want to note first.
We’ve not found one analytics platform that’s able to offer a complete source of truth across digital marketing channels. Website data, in-platform advertising data (such as from Facebook), and email marketing data can’t all be captured by one single analytics platform.
On one hand, data is often treated as proprietary to the platforms providing the ad or email service, although you can usually export it manually. Companies like Facebook offer their own in-platform analytics tools that they’d like you to use. And on the other hand, nobody has developed an analytics platform that can capture all the different types of data from across the various marketing channels and platforms.
That means you’ll have a number of analytical platforms you need to extract data from to create combined reporting. You can work around this by using visualization tools and creating dashboards with multiple data inputs for a holistic overview and top-level reporting purposes. Read more about data visualization.
But for now, let’s get back to your website and in-platform analytics.
Google Analytics is still the king of website analytics platforms - an incredible product to have at your disposal for free.
Set up your Google Analytics account so it’s tailored to your own unique website and marketing goals. The most important data you’ll need to set up includes:
- Reporting views - if you want to separately analyze distinct business steams on your website (including language versions), if you have more than one website linked to the same Google Analytics property, or if you also have a customer app. These filtered views give you the ability to instantly view sliced data, making reporting quicker and easier.
- Traffic sources dimensions - to see exactly which entry points users have to your website. You can see source/medium data automatically, but as already discussed, you’ll need to use UTM codes for more detailed data. For example, clicks from specific Facebook ads.
- Custom events - to track user interaction and engagement with your website content. For example, call-to-action button clicks or link clicks, how many times a video was played, how long a video was played for, etc.
- Goals - to measure how often your users complete events that you consider to be conversions. For example, making an e-commerce purchase, downloading a sales brochure or submitting a contact form. After you have set up goals, you will have access to view conversion reports.
Pros of Google Analytics:
- Data is shown on a last-click basis by default, or the click that brought users to your site. You can accurately track advert Click-Through attribution if you use UTM codes.
Cons of Google Analytics:
- You don’t get the full attribution scope for the combined effect that multiple outbound marketing touch-points contribute towards website traffic and conversions. That’s because you only know when someone has clicked on an advert - not how many adverts they have seen to create familiarity with your brand or product before coming to your website.
However, do note that there are several attribution models you can use within conversion reports - you don’t just have to go by the default last click. For example, you could go by first interactions, or specific conversion paths using event metrics. However, none can give a complete view of attribution for every customer on their journey to a sale conversion.
Other Website Analytics Platforms
Google Analytics may be the biggest and best known website analytics platform, but that certainly doesn’t mean it’s the only one you should use.
Google Search Console is a free tool for diving into your organic search performance. It allows you to see and fix any indexing problems (things that stop Google from crawling your site) and request that Google re-index it when fixed. You can look into which search queries show your site in search results, how many clicks you get for those searches, review backlinks, mobile usability and more.
Bing Webmaster Tools is a surprisingly competitive (and also free) alternative to Google Analytics. It’s simpler and more user friendly, geared towards organic search engine optimization (SEO). The main platform offers additional functionality for SEO reports and recommendations, mobile-friendliness reports, plus research keywords and see inbound links analysis.
Third-party analytics is data that’s reported on within the platform where you’re deploying paid marketing activity. The most common are Google Ads Manager, Facebook Ads Manager, and email providers like MailChimp or Klaviyo.
Pros of Third-Party Analytics:
- The analytics functionality provided by advertising or email platforms offer valuable information designed to help you get better returns from the unique channel. It is specific to the type of activity that happens on the platform, whether that’s reporting on users engagement activity such as likes, shares, comments, retweets, followers gained or open rates, plus click-throughs and conversion attribution. Facebook, for example, will let you set up 8 types of conversion events that will be tracked and reported on across all of a user’s devices.
- Data is mostly available immediately, meaning you can optimize live campaigns, amend landing pages, and make cost-efficiency decisions quickly. It gives you much more control and responsiveness when you can monitor live results.
Cons of Third-Party Analytics:
- Advertising channels are competing against each other and want to demonstrate higher conversion attribution than the others, encouraging you to spend more of your ad budget with them. If you use more than one advertising platform, double-attribution can be an issue, meaning more than one advertising platform claims full attribution for a sales conversion on your website. The extent of over-estimation will depend on how many ads a user has seen or clicked across different platforms within the set attribution timeframe - called View-Through and Click-Through attribution respectively.
- The quality of advertising data declines by the day as more user privacy laws and tracking restrictions are introduced. Where users have opted not to be tracked, data modeling is used as a substitute, with its reporting delayed by a day or two. Facebook, as a prime example, now indicates in its ad reporting where modeled data is in use. Given the size of the user base that measures such as the Apple iOS 14 privacy update are impacting, the data accuracy for how effective your adverts are is hugely compromised.
You definitely can’t rest on your laurels and trust the in-platform analytics to manage the integrity of your reporting data for you. The best way forward for is a combination of using in-platform data with your own website analytics data.
The Bottom Line
In a fierce digital marketing landscape where competitive edge is driven by continually leveraging data with razor precision, it pays to fully utilize the many free data analytics tools available to you.
Our top tips are to honestly assess how well you currently report accurate ROI data across your marketing activity mix. Familiarize yourself with what data analytics functionality is available beyond what you’re currently using. Take no ROI data from paid marketing providers at face value. And make sure you really understand what the data is telling you so you can make decisions that drive market-leading results. Don’t get complacent, but continually keep your eye on things with regular reporting schedules and a continuous improvement loop.
As always, the team at Half Past Nine are here to support you should you need us! Data analytics & visualization is our passion. We’ll happily confess to being full-on data geeks who get a kick out of showing our clients how much extra revenue they’ve earned thanks to our obsession with all things analytics.
Feel free to get in touch anytime via email or our contact form. We’d be delighted to explore how we can help you harness the power of data analytics & visualization to turbocharge your marketing strategy and tactical delivery.
What To 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.
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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:
- 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.
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:
- The New Era of Personalization Explained; A Guide to Building Profitable Customer Journeys With Digital Intent Signals
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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)
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.
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.
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.
Here are the most common intent signals that can be tracked:
- 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.
- 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
- Clicking on digital ads.
- Video ads watch time.
- Clicking on retargeting ads
- Registering for webinars or online events.
- Participating in trade shows or conferences.
- Engaging in live Q&A sessions or forums.
- 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 downloads.
- App usage patterns and content engagement.
- Search queries.
- Abandoned carts.
- Registration or subscription.
- User reviews and ratings.
- 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.
- 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.
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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