Customer Acquisition: The Ultimate Guide to Turbocharging Your Digital Marketing Engine

Growth Strategy
0 min read
February 20, 2021
Kenneth Shen
Chief Executive Officer

Here at Half Past Nine, we have a unique perspective on building sustainable digital marketing strategies for highly accelerated customer acquisition.

Just like a meticulously engineered supercar, combining performance-driven design with cutting edge technology and superior execution leaves mediocre digital marketing plans fading in the dust.

With so many moving parts in any robust digital customer acquisition strategy, there’s no substitute for having the most talented engineers. It takes a creative mind with technical knowledge and unrelenting analytical obsession to get extraordinary results.

The aim? A customer acquisition machine that responsively handles any curve the road throws at it. Curious? Let’s pop the hood and take a look at the main components that make a best-in-class digital marketing and customer acquisition strategy.

Customer Acquisition example analogy to a car
How we view digital customer acquisition. Sketch credit: Johhny

The Basics of Customer Acquisition

All marketing success starts with the product. A good product is just like a car chassis - the strong foundation on which everything else sits. 

So, before you even begin, has your team created or invested in a product that’s fit for purpose? How will your customers feel once they’ve purchased it? Is it able to meet, or perhaps even exceed, your customers needs and expectations?

Here’s a few key elements you’ll need to successfully build a strategy upon:

Product-market Fit - Get the fit right between your products and your market niches so you can create or capture viable and sustainable market demand. Have you done your market research first?

Product Traits & Value - Make sure the product delivers the features your customers need, and has a clear value proposition that’s competitive within your markets. Have you adequately tested and collected feedback on your product?

UI/UX - Ensure your customers have a superior user experience (UX) with your product as far as possible, delivering the benefits they expect to receive. How do customers rate their overall experience with your product? How loyal are they? Make sure to keep track of customer feedback continually.

A digital customer acquisition strategy that can harness market demand while building on the momentum of positive customer experiences is the goal. Your product fit or design will be an iterative process. However, make sure you’re already far enough along with the basics before it’s go-time to execute a sleek digital marketing strategy.

Your Marketing Process - Before we begin, make sure you have a robust process in place for building a digital marketing machine. This includes market research, identifying your channels, understanding your marketing funnels, and more. Read more about the marketing process behind building a digital marketing operation from the ground up.

Installing Your Digital Infrastructure 

Now you need a strong frame in place. Your marketing tech stack is the digital infrastructure that allows your wider acquisition strategy to take shape and function. It also gathers data about how well activities are performing, allowing for continuous fine tuning and optimization. 

Without the correct digital tools, a well-oiled and responsive customer acquisition machine simply isn’t possible!

Your Website 

Metaphorically, your website is the shiny body of the car. It should feature content and branding that catches your customers’ eyes and is easy to ‘digest’. Draw them in and create a desire to purchase that’s easily fulfilled with a quick checkout process. A well designed website is easy to navigate and works well for users on all stages of your customer journey.

A website can often be referred to as a Content Management System (CMS) - the platform used to build and edit your website. Your website needs to be easy for you to edit regularly and search engine optimized (SEO) in its technical set up. We’ll get into that a bit more later.

Some of the most popular CMS platforms include:

CRM 

A great Customer Relationship Management (CRM) system is a key cog in your digital infrastructure. It’s not just about capturing the right customer data, but having the functionality to then use that data to further your sales lead conversions (as well as up-sell and retention strategies). You’ll ideally have a CRM system that’s compatible with the rest of your tech stack to help deliver highly personalized, timely and effective acquisition tactics. Use what you know about your customers to build audience personas and understand your typical customer journey.

Some of the most popular CRM systems include:

Landing Page Builders 

It’s important to understanding where your customers are “landing” on your website, as well as which pages are most effective for driving value. It can be very beneficial to have specialized landing pages for your potential customers to convert, depending on where they have come from. 

For example, say a potential customer has clicked on a digital advert for one of your products. They’ll expect to land on a page where they can easily learn about that particular product, and purchase it if they wish. A good landing page will have a simple layout and very focused information that guides your new customer to complete the conversion action that you want them to. 

Conversion actions aren’t always around completing a sale though. For higher value purchases, you could provide more detailed content to drive your potential customer along into the next stage of your sales funnel. For example, by asking them to fill in a contact form in order to download an ebook. 

Landing pages are generally divided into form-fill pages and product pages. Either way, capturing your customer’s contact information will then allow you to continue nurturing the relationship.

If you really want to nail the digital lead generation and conversion process, rapid A/B split testing is a crucial feature for ongoing optimization.

Effective landing pages also use highly focussed keywords that are correlated with linking digital adverts, improving Cost-Per-Click (CPC) efficiency. Depending on the size of your website or product range, it might make more sense to do this on a landing page rather than an evergreen page on your website’s navigation menu.

Some of the most popular landing page builders include:

Retention (Email + SMS) 

An email or SMS marketing platform comes into play after you’ve captured those all important customer contact details and communication preferences. 

You’ve now got a direct line of communication that offers the most cost-effective digital marketing possible. It’s easier and much more cost-effective to convert a warm lead than it is to find a new one. It pays to give this method of content marketing the level of thought, investment and expertise that it deserves.

Here’s where a good CRM system that’s integrated with your email (or SMS) platform really helps. It will allow you to deliver highly personalized content at the right time for your customer’s individual journey, and track the results for optimization. 

Use these platforms to nurture and convert your leads using automated email marketing flows tied to specific customer scenarios and preferences. Build a lasting relationship using individually relevant and valuable content or offers.

Some of the most popular email and SMS systems include:

Analytics Tools 

Once you have your customer acquisition tactics running, it’s important to know how well the various elements are performing so you can optimize for effectiveness, cost efficiency and ROI. 

At Half Past Nine, we emphatically emphasize the need to have robust analytical tools and practices at the centre of your digital marketing strategy. Analytics is the crux of any modern digital marketing operation. 

Acting as the headlights, your historical data allows you to read the contours of the road to see where you’re currently at and make informed decisions about moving forward. All of your digital marketing activity needs to be properly tracked and measured in order to do this.

  • Website Analytics - Google Analytics or similar tools are used for measuring webpage performance, conversions, and drilling into your audience demographics and opportunities. You can understand how individual pages and the whole site are performing, as well as how customers are using your website.
  • Search Engine Marketing (SEM) Analytics - Tools like SEMRush are for undertaking any meaningful SEM research and execution, whether that’s search engine optimization (SEO) keywords or Pay-Per-Click (PPC) keywords.
  • In-platform Analytics - These are channel-specific advertising managers - such as those within Facebook, Google or LinkedIn - allowing you to track how well your digital advert content is performing on that particular platform.

Some of the most popular analytics platforms include:

Data Visualization 

Working in tandem with the data that your analytics platforms are capturing, there are numerous third party data visualization tools that you can use like power steering. 

Marketing dashboards are a visual means to quickly assess and easily communicate how digital campaigns are performing - whether it’s live A/B split testing or iterative campaign results. Decide on your advert or activity KPIs, then create corresponding dashboards that work with the relevant data insertions to populate charts summarizing performance.

In a dynamic and competitive consumer market places, dashboards are invaluable. Once set up, they’re great for assessing and comparing data at just a glance. You can instantly see how the ‘road’ of your customer journey is curving, allowing you to react quickly and adjust the ‘steering’ when you need to.

Some of the most popular data visualization tools include:

Paid Customer Acquisition 

Paid acquisition is digital advertising that either FINDS or CAPTURES product demand. Finding demand means displaying adverts to relevant target audiences who aren’t searching for your product, oftentimes, creating demand. Capturing existing demand means displaying adverts to target audiences who are already searching for your product at the time.

The two best-known platforms for finding and capturing demand are Facebook and Google respectively. Both platforms boast huge regular user bases and offer excellent advert targeting and reporting functionality. This makes them the most effective platforms currently available for these two types of lead generation.

Advertising on Facebook 

Facebook is used to FIND and create demand. That’s because people haven’t come onto Facebook with the aim of searching for specific products with a pre-existing intention to purchase (unless they are using Facebook Marketplace within the user community). However, this platform is still the best way to get your product or brand in front of new prospects. As we write this in early 2021, the Facebook Ads platform is being affected by Apple's new App Tracking Transparency Framework. Read more about it here.

It works by showing people adverts in their newsfeed while they’re scrolling. You get to choose who you show your adverts to with high-level targeting tools. Facebook then selects who to display the advert to at an individual level based on the data they have about them. 

Facebook adverts for customer acquisition work on a CPM model, which means Cost-Per-Mille, or cost per thousand impressions. So you pay based on the number of people who see your advert appear in their newsfeed. 

As an advertiser, you’ll be bidding for advert placements within your target audience against many other businesses, whether they are direct industry competitors or not. The price you’ll need to bid to get an advert shown isn’t fixed. Your timing, creative quality, bidding strategy, ad placement selection and volume of competing ads for your selected audience will all affect the final cost. You can set a campaign budget and length of time you want an advert to run for, and Facebook will manage the advert delivery as optimally as possible. 

There is an ‘unlimited’ amount of ad inventory on Facebook. That’s because you can continue showing it to as many people as you’re willing to pay for. Or you could also expand your audience by including wider audience demographics.

On Facebook, you’ll need to optimize the following ad components according to your lead generation or brand awareness objectives:

  • Good Creative Direction and Copy - Your advert copy and creative is really important when the audience has a very short-attention span amidst a competitive and distracting online environment. What gets your audience’s attention?
  • Effective Targeting - You need to know your audience characteristics to select those who are most likely to convert, and how best to advertise to them using Facebook’s pricing models and bidding options.
  • Prospecting + Remarketing Structure - This refers to how you break down your budget between finding new leads, versus remarketing to people who have previously clicked on your adverts. For sustainable growth, prospecting should see more investment than remarketing.
  • Optimized Relevance Ranking - The higher your ads are ranked by Facebook for their content quality, user engagement and conversion rate, the less you’ll need to bid for your ad to be shown. Facebook has to weigh up UX against revenue, not discouraging users with lots of irrelevant or low quality adverts.
Facebook ads manager alongside an ad example
An example of Facebook Ads & Ads Manager

Google Ads

Google can be used to CAPTURE existing demand with Search Engine Marketing (SEM). This means you advertise to people who are already using Google to research or purchase your type of product. We need these people to find your website in their search results so you can capture that existing demand.

On Google, people type in search queries to find specific content. Google takes the ‘keywords’ they’ve used within the search query and shows them search listings ranked in order of content relevancy, quality and ‘authority’.

Google Ads lets you show search listing adverts for queries relating to your type of product. It will link to your selected webpage for the specific search query keywords you’ve selected. Depending on how well your website ranks organically against all other websites, you’ll most likely need to use Google Ads to be found on the first page of results.

Google Ads for customer acquisition work on a PPC model, meaning you only pay when someone clicks on your advert, not just when they are shown it. 

Inventory on Google is more limited than large social platforms like Facebook. That’s because there’s only a certain amount of people searching for keywords related to your product at any given time. We call this ‘search volume’. 

On Google Ads, its important to understand and get the following components right:

  • Keyword Research - Ensure you’ve nailed all the keywords and search terms that potential customers use when searching for a product like yours, so you can capture as much of the available demand as possible.
  • Ad Copy - Use concise, relevant and persuasive messaging within the tight text characters limit, getting your listing as many clicks as possible in relation to the search volume.
  • Account Structure + Set Up - This becomes particularly crucial the more products and keywords you have to manage. Setting up your account and settings effectively will let you run more targeted ads with highly specified parameters, run A/B split testing, and gather clearly defined data sets that will allow you to understand variables in performance for optimizing individual adverts.
  • Continued Optimization - Work at keeping your advertising costs as low as possible and your conversions as high as possible. Google Ads charge less per click for ads that have a higher Quality Score. Improving the relevancy and authority of individual landing pages as well as your website is an important ad cost reduction and conversion optimization tactic. 

Develop landing pages as closely tied to specific keywords or search terms as possible, and make sure the calls to action are clear. It also helps to list negative keywords you don’t want to appear for, filtering out searches that don’t typically lead to conversion.

Google ads platform and a google ad example
The Google Ads Platform and a Text Search Ad

Organic Customer Acquisition 

Organic acquisition is essentially anything you haven’t paid for, at least in terms of promotion. Key organic customer acquisition strategies include website SEO (where people find your website in unpaid search results), social media, and lead nurturing via email.

Search Engine Optimization

Search engine optimization is the art of having your website ranked highly for relevant search keywords and terms. It combines content and technical elements, with a bit of time and effort required to build organic ranking performance.

Google (or any other search engine) assesses how well your content fits with search queries and keywords, the quality of your content, how well your website is technically optimized, and how much authority your website has. It then uses that data to rank the webpage listings it shows to users. The higher your website scores against competing websites, the higher it will sit in the organic (unpaid) search result listings. 

It’s often worth the effort, because search engine users are most likely to trust and click on organic search results than they are for paid search results. Here are the most important SEO factors you’ll need to look at:

  • Keywords - Are you using the Google keywords that people are searching for in your content? Google will prioritize webpages that use keywords their users are searching for. Create webpages optimized for specific keywords by including them as many times as possible throughout your webpage copy, including in the headings and page meta descriptions. 

Note, it’s important that keywords are used in a way that reads naturally and doesn’t compromise the quality of prose. This so that your content readability score remains high, which Google is also looking at! Google generally prefers focussed content optimized for specific keywords and high relevancy, as opposed to lots of competing keywords.

  • On Page SEO - On page SEO looks at the optimization of individual pages within your website. Firstly, your pages need to be well linked within a clear website navigation structure so Google can easily read your site. 

The readability and quality of your written content is scored, along with how long people stay on your webpage and navigate within your website. Additional scoring is awarded for users clicking on links, demonstrating their engagement with your content. You’ll also need headings properly tagged in a sensible structure, and pages that load quickly with compressed image files.

Your pages generally need to be user friendly, mobile friendly, and contain image alt text in order to score well. Google’s search algorithm can see how much effort and attention to detail you’ve put in, and absolutely rewards that.

  • Domain Authority + Backlinks - Google gives your website an overall domain authority score, which will impact how well your individual webpages rank in search results. There are over 40 factors that go into calculating your website’s score, with any new website starting at zero. You’ll need to put work into increasing this over time. 

The most important factor is backlinks, which prove that your content has ‘authority’ because of the volume of online references it gets. Google looks at how many other websites or social media posts link back to your content, and the quality (or authority) of those backlinks directing to your website. For example, a link from Forbes to your content would rank high in quality

Wordpress meta description example


Organic Social Media

Organic social media activity is when you post updates to your existing followers, which you obviously don’t need to pay for. Excellent! 

To expand your ‘reach’ organically, the aim is to have your existing followers engaging with and share your social content with their friends and connections, helping you to reach new people that you hadn’t previously. 

We all know these social channels, from Facebook and Instagram to YouTube, Reddit, Quora, NextDoor, Snapchat, and TikTok more recently. Many of these channels also allow you to advertise. However, you can still rapidly acquire new followers and customers without using adverts if you have a strong enough social media content strategy and get high engagement.

Organic social media marketing example

Email or SMS

We consider email or SMS to be an organic channel, excluding the subscription price for your chosen delivery platform itself of course. Email is mostly used for customer retention, but it can also be part of your new customer acquisition strategy. 

If you can capture contact details and communication opt in for sales leads who aren’t quite ready to buy, you’ll be able to nurture them towards a sales conversion. As mentioned already, this is the most cost effective content marketing available, and you can achieve really great results if you do it well. 

You’ll need to devise a suitable email content strategy - obviously without getting into the danger zone of spamming and driving up unsubscribe rates. Focus on content that delivers genuine value and incentive for your sales leads.

email marketing example klaviyo

Earned Media Customer Acquisition  

This type of customer acquisition could be either paid or organic. What it rests on is the strength of your brand, product quality or position as a thought leader. 

This works if you have carved out a reputation for your business and people want to talk about it positively without financial incentive. Or, people are happy to be associated with your business via paid promotion while maintaining their own reputation as a trustworthy and influential authority.

Public Relations

Public Relations is a large part of branding, storytelling and even SEO nowadays for generating those all important backlinks! It comes from understanding who the influential voices are in your space, and having them tell your story compellingly. That can be using various forms of organic and paid online media.

You’re aiming to position yourself as a thought leader for your niche, although this can often take a bit of time and effort with relationship building and raising awareness. 

At the end of the day, you need to have earned that right by creating or doing something that is actually compelling and worthy of talking about. The more unique, innovative, knowledgable or trend setting you can be seen to be, the better. 

Influencer Marketing

Influencer marketing is a great resource for expanding your brand’s reach to niche audiences. Influencers are a trusted source of information and keep followers up to date with the latest trends or products in their specialist area. An influencer does best if they are known really well for a very specific niche and are driven by their own personal passion on the topic.

This type of marketing usually involves paid promotion for products an influencer feels will be of interest for their social media followers. Maintaining relevancy and authority with followers is key for gathering and maintaining a big following, so your product to niche fit is so important when looking for influencers to successfully work with.

Product promotion by influencers is usually paid for. There’s big money to be made as an influencer today if the following is large enough. However, the possibility for organic influencer promotion is possible if your product is a must-have that’s really hot all of a sudden, or an influencer is still working on establishing their following. 

influencer marketing example side by side youtube

Getting Your Customer Acquisition Machine on the Road! 

So, we have now reached the finish line of our run-through on the main components in a lean n’ mean digital customer acquisition machine! I hope we’ve helped you visualize the art to setting up and running a holistic digital marketing strategy that’s build for sustained business growth. 

There are many moving parts that need to be individually and continually optimized while also working together in unison. That’s why you need a strong team acting as the tires to carry things forward. There’s a lot to do and get right.

Here at Half Past Nine, it’s what we live for. The art of great design followed by the thrill of the ride and outstanding results gives us such a rush. If you’re potentially interested in teaming up, then we’d love to hear from you. Get in touch and we’d be delighted to talk through your needs.

What To Read Next:

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

Read next

Unlock Revenue Growth With Data

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

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

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

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

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

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

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

The Impact of Not Using Accurate Attribution Reporting

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

The common consequences are:

Incomplete Customer Insights Cause Poor CX

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

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

Unprofitable Resource Allocation

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

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

Poor Revenue Growth and Limited Brand Equity

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

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

Understanding the Challenges for Accurate Attribution

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

On-platform attribution issues:

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

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

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

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

 1. Tracking Data Across Complete Journeys 

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

 2. Data Privacy 

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

 3. Offline Data Tracking 

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

 4. Data Quality and Completeness 

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

 5. Data Integration 

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

 6. Attribution Modeling Complexity 

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

Types of Attribution Data

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

There are 2 main sources of attribution data.

Attribution Data sources

1. Software-based Attribution Data

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

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

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

 2. Self-reported Attribution Data

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

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

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

Hybrid Attribution Data

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

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

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

Attribution modeling data

1. Event Data (What Happened?)

Event data typically includes:

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

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

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

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

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

2. Channel Data (Where Did It Happen?)

Channel source data typically includes:

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

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

Types of Attribution Models

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

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

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

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

 A model either uses: 

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

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

Last Touch (or Last Click) Attribution

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

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

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

Last touch attribution

First Touch (or First Click) Attribution

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

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

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

First touch attribution

Linear Attribution

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

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

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

Linear Attribution

Time Decay Attribution

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

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

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

Time Decay Attribution

U-Shaped (Position-Based) Attribution

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

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

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

U-shaped Attribution

W-Shaped Attribution

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

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

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

w-shaped Attribution

Data-Driven Attribution

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

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

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

Data-driven attribution solutions

Fully Customized Attribution Modeling

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

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

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

Fully Customized Attribution Modeling

Choosing Data and Models to Match Goals

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Step 1 - Clearly Define Your Goals

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

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

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

Step 2 - Identify All Your Data Sources

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

Here’s a quick checklist of data sources:

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

Notes:

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

Step 3 - Bring in the Necessary Data Capabilities

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

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

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

Step 4 - Chose + Activate Your Data Tools

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

There are 2 options here:

  • Off-the-shelf attribution software

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

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

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

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

  • Build your own custom modeling

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

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

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

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

Step 5 - Integrate Your Data Sources

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

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

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

 Step 6 - Test and Iterate

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

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

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

Incrementality is a complementary metric to lift.

Lift Test and Incrementality

The Main Takeaways

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

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

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

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

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

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

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

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

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

What To Read Next:

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

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

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

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

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

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

Building Personalized Customer Journey Architecture

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

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

 The 3 journey stages for customer acquisition are:

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

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

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

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

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

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

Personalization: The 3D customer journey

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

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

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

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

Learning to Read Behavioral “Tells”

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

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

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

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

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

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

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

Introducing Digital Intent Signals

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

We call these digital actions “intent signals”. 

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

Build customer journeys using intent signals

 

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

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

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

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

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

Types of Digital Intent Signals 

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

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

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

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

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

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

Turn customer segments into unique journeys

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

Content Engagement:

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

Search Behavior:

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

Social Media Engagement:

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

Ad Interaction:

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

Event Participation:

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

Website Interactions:

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

App Interactions:

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

Shopping Behavior:

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

Email Engagement:

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

Customer Support Interaction:

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

How to Use Digital Intent Signals to Inform Customer Journey Architecture

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

Data Collection and Analysis

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

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

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

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

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

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

Segmentation

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.

Personalization

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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