Building a profitable marketing machine in the complexity of today's digital landscape can feel overwhelming. Where do you start??
In this article I'm going to whisk you through the fundamental components, from setting goals and selecting marketing channels, to understanding your audience, your brand positioning and building a customer journey to nurture prospects into loyal clients.
By the end, you're going to be familiar with the foundations you need to grow a successful business through marketing!
(If your business is B2B, we also have a 4-part guide to B2B content marketing, starting with the customer journey all the way through to advanced distribution tactics. Start here with Chapter 1: The B2B Buying Process + Customer Journey)
The Marketing Process Starts with Setting The Right Goals
Let’s start with the fundamentals. Ask yourself, what are the goals of the company? Think about this holistically from the corporate point of view. Oftentimes ‘bringing 10,000 website visitors’ is not the right north star to guide a marketing process.
What’s Your North Star? Is the goal revenue? Is it leads or sales? Is it cement the position of a brand within the market? Make sure these goals are the guiding principles behind your marketing plan.
Examples of Good Marketing Goals
- “For Q4 this year, we’d like to beat last year's Q4 revenue by 25% whilst maintaining equal or higher marketing efficiency.”
- “Over the next year, we’d like to position Company X as the leader in the X space. This means that we should become a household name within our target audience, attract more leads, and have greater flexibility over client selection.”
How does Marketing Work with Other Business Functions?
It’s also critical to understand how exactly marketing intertwines with the rest of the organization. Marketing should work synergistically with product, sales, and other business functions. Oftentimes this dynamic changes depending on the type of company. The following are high-level examples of common dynamics that we’ve come across.
Business-To-Consumer Marketing (B2C)
For B2C companies, marketing should work hand in hand with the product team, and help to conduct customer research, audience analysis, and help the product team build the most fitting product for a market’s demands.
Marketing is also responsible for setting the 7 P’s of marketing and generating demand for the product.
Business-To-Business Marketing (B2B)
For many B2B companies marketing’s job is to position the company and fill and grease the sales pipeline.
Marketing attracts top-of-the-funnel leads, qualifies them to the definition of a marketing qualified lead (MQL), and passes them along to sales. Marketing often also helps drive awareness and education so that sales needs to do less ‘heavy lifting.’
Direct-To-Consumer Marketing (DTC)
For Direct-to-Consumer companies, the poster child of e-commerce, marketing arguably drives the entire organization. Marketing is responsible for not only demand generation but also revenue capture.
In a DTC company, the customer purchases directly from the company and thus there are no channels such as retail partners or brick and mortar to lean on for sales.
Types of Marketing Approaches
Before we dive too deep into this, it’s important to understand the different approaches to marketing. We will focus on a few here:
Inbound vs. Outbound Marketing
Simply put, inbound marketing is when you attract demand to come to you. You put things into the world like advertising, content, case studies, etc, and demand finds you. If you found this article on google and you click this button to inquire about our strategy services at Half Past Nine, that would be inbound demand capture.
We specialize in inbound marketing because we believe that it is the most scalable, sustainable, and agile approach for the brands that we work with. Advertising that’s winning can be scaled for more revenue, we can write more articles to capture more intent online, and these investments pay passive dividends to us and our clients without the need for extraordinary man-power.
Outbound marketing is when your marketing operation goes out and finds demand. If I were to cold-call you and ask you if you would like marketing strategy work, that would be outbound marketing.
There’s a fine line between outbound marketing and sales, and we usually make that distinction depending on if the lead is qualified to some degree (sales) or completely cold (marketing).
The Market Research Process
The process for conducting market research is highly dependent on the type of company, the stage of the company, and the stage of the industry. Some principles remain constant.
Understanding your Customer
The heart of any successful marketing operation is a deep understanding of your target customer.
For your product to succeed, it's critical to know with some degree of confidence:
- What type of person is in your target audience? (Demographics)
- What does your audience think, believe, feel? Are they environmentally friendly? Do they lean towards a political stance? What is their worldview? (Psychographics)
- How does your product fit into their lives?
- What benefits does your product provide to this specific person?
- Where does your target audience spend time online?
Always use your customer persona (sometimes called a customer archetype) as a guiding principle. It’s common for companies to have multiple personas if there are multiple distinct groups of people that are purchasers of the product.
If you’re in the business of B2B marketing things are slightly different. Audience definitions can be less psychographically oriented and more tangible, though we recommend always getting as specific as you possibly can. In B2B organizations, it is important to understand:
- What type of company is searching for our product or service?
- What type of person is searching for our product or service?
- What frustrations are they experiencing with their existing solution?
- What role, title, or position are they? What stage of their career are they in?
- What are the benefits of them becoming a customer or client? Will closing this deal benefit their career?
For example, we know that our target audience at Half Past Nine is heads of marketing or senior marketing executives at companies that are generating $2.5MM - $50MM in annual revenue. We work best with companies that are facing difficult challenges like hyper-growth targets, or companies who are looking to break into a new market categories or market segments. We break this down much deeper internally, but that is who our audience is in a few sentences.
Understanding your Product or Service
A common reaction to this headline is “understanding my product? What do you mean? I built this product, of course I understand my product?”
Too often, we see companies who are highly product-oriented come to us in search of marketing strategy, but they fundamentally do not understand their product. Now, I don’t mean this in the sense that they don’t know what their product is — I mean they don’t know how their product fits into the world.
Describe your product or service out loud right now. If you can’t do it in less than three sentences and highlight the main features and benefits in a way that’s compelling to someone who’s never met you or your product before, you might not know your product.
If this is you, take some time to think about the three core benefits that your product/service provides. Think about why your product/service is better than the competition, and why your product/service is unique.
This step is incredibly important, because it unlocks communications and branding strategies, and unlocks external messaging. It also is vital to referral, influencer, and affiliate strategies because if you can’t simply describe your product to the world how can you expect other people to?
Understanding Your Market Landscape
This last stage is more traditional market research. This is the phase where we look at competing companies, trends, externalities, challenges, and opportunities. The way we approach market landscape research in digital marketing is quite different from the way that an investment banker might conduct market research. We look at different metrics and different trends.
For example, we might look at Keyword Search Trends and Search Volume, to determine if there is an increasing demand for a product. We might use this data to understand the best time to launch a product or run a promotion as well.
In the example above, we can see that the Google searches containing the keyword "diet" spike on January 1st each year. If we dive in even deeper, we notice that they also spike on the first of each month, after significant holidays, and even mildly each Monday. This type of data can give us marketers important data on the seasonality and demand of a product or market.
We’ll also dive into competitor research on platforms like SEMRush, which let you dive into historic organic and paid traffic for almost anyone of your competitors. It’s not 100% accurate but it’s one of the best tools on the market. This example is domain research for www.allbirds.com, and we can see historical data on organic search traffic, paid search traffic, keywords, and more.
It’s also critical to understand the positioning (pricing and messaging) of your competitors. If one of your competitors is messaging a similar product that’s lower-priced but poorly marketed, it might create an opportunity for you to brand your product as a premium product and charge a higher price. The key here is to look for gaps in the market where your product could fit in and fill a void.
Externalities are outside factors that affect your market landscape. One externality that is obvious right now is COVID-19. COVID-19 had an immense impact on the strategy recommendations we’ve provided to our clients. At the beginning of COVID-19, during February and March, we recommended a marketing approach that was more conservative approach to marketing investments to preserve cash for an uncertain future. As COVID-19 began to unfold, some industries were decimated whilst others were elevated.
For any of our clients in a negatively impacted industry, without sizable cash flow to weather the storm, we shrunk operations to preserve cash. For companies in positively impacted industries such as e-commerce, we scaled ad budgets and ramped spending to capture the increase in demand.
This strategy proved highly successful, and many of our e-commerce clients are now seeing their best months in history.
Understanding the Marketing Funnel
The marketing funnel, simply put, is a way of thinking about how we attract people to our brand and move them along a process that begins with the first time they hear about us and ends with them becoming a strong advocate for the brand. As we move forward, we will refer back to terms that relate to the funnel.
Top of the Funnel (TOFU)
The top of the funnel refers to the segment of people who are within your target audience but with the least amount of familiarity with your category or brand. When reaching out to these people, it is like the first time they are hearing of your brand, and it may even be the first time they are hearing of your product category.
Oftentimes, people in the TOFU do not know why they need your product.
Middle of the Funnel (MOFU)
The middle of the funnel refers to the segment of people who have some amount of familiarity with the category or brand. These people may have already been searching around for a similar product or may have visited a competitor's website.
These people likely know why they need your product but need convincing as to why your product is better than the rest.
Bottom of the Funnel (BOFU)
The bottom of the funnel refers to the segment of people with the highest familiarity with your brand. They may have already visited your website or been on the receiving end of multiple advertisements from your brand.
These people are closest to becoming customers or clients.
Re-Engagement, Advocacy, and Support
There comes a point in the funnel, after a purchase, that there is an opportunity to turn a customer into a loyal advocate. Marketing does not end with a sale. Re-engaging past customers to increase their lifetime value by bringing them back for a repeat sale can be a very profitable initiative. Architecting a referral strategy to turn your loyal customers into independent marketers can also be incredibly rewarding.
Architecting your Funnel Strategy
The 10,000-foot view of funnel strategy is to always be filling your funnel with high-quality traffic, and strategically move them along this buying process to eventually become a purchaser and an advocate. In practice, it’s not always this simple.
It begins by drawing the lines for TOFU, MOFU, and BOFU traffic based on the parameters best fitting for your brand. Then, we want to think about how we will move people along this process: is it through advertising and remarketing, or maybe it’s through direct calls and emails? Maybe your brand is positioned in a market with a great deal of MOFU and BOFU intent already, and the priority would be to capture that before moving to TOFU traffic. Once this has been figured out, it’s time to drive traffic to your funnel and begin filling it with high-quality traffic and prospects.
Driving Traffic: An Overview of Organic, Paid, and Earned Traffic
Digital traffic can generally be broken down into three categories. Other agencies may do this differently but this is how we do it: Organic traffic, Paid traffic, and Referral traffic. For an in-depth dive of all of these subjects and much, much more, check out our Ultimate Guide to Customer Acquisition.
Organic traffic comes from:
- Your Branded Social Pages (Your Instagram, Youtube)
- Word of Mouth (Non-incentivized)
- SEO (Organic Search Results)
Organic traffic is traffic you work for. When you post on social media—and people come to your site because of it—that’s organic traffic. Some of the best brands in the world have massive organic followings, and these inevitably become incredibly valuable assets to the business.
Organic strategy can be difficult, especially if your brand is new or in a crowded space. Organic is also difficult because you need to get people to volunteer their attention. Unlike paid media where you are just paying to get eyeballs on your brand. For this reason, if organic is a channel you plan to heavily pursue, always keep in mind that people need a reason to follow you, and the best reasons are oftentimes only tangentially related to your product or service.
A strong organic following is one of the most valuable and sustainable marketing assets a brand can have. It’s now factored into company valuation models and growth models at major banks.
Paid Traffic (Advertising)
Paid traffic comes from:
- Advertising (Including but not limited to Social Media Advertising, and Search Engine Advertising)
- Paid Content Promotion
Paid traffic is exactly what it sounds like, traffic that you directly pay for. For most brands this equals advertising. Paid traffic is generally the quickest way to get revenue in the door, and for many consumer brands is the channel that drives the majority of both traffic and revenue.
With digital media and advanced advertising platforms, the targeting and testing capabilities of paid media are nearly endless. This means that a well crafted paid strategy always creates a night and day difference from a low-effort one. It's important to keep in mind that the barrier to entry for digital advertising today is incredibly low. This means that if you’ve had a bad experience with digital advertising it may be a problem with execution rather than channel.
With paid traffic, creative strategy, targeting, A/B testing, and budgeting are all critical components to ensuring that your brand makes money instead of losing it. We will dive more into this in an upcoming article. Shameless plug: subscribe to our mailing list at the bottom of this page to be the first to know.
Referral traffic comes from:
- Social Media Shares / Reposts
- Reviews / Unboxings
Earned traffic is traffic that you earn by doing or representing something worth recognizing. Usually, earned traffic comes as some form of recognition such as from a news outlet like Forbes, or someone reposting your content.
Today, we are seeing a lot of this traffic become highly monetized, meaning that many of these channels are becoming pay-to-play. We see this happening in influencer channels and some parts of the public relations world as well. This is not true for all earned media though, as brands with compelling stories do get free recognition though it is becoming increasingly difficult to do so.
Organic Traffic: Understanding Social, SEO, Email
When it comes to organic traffic, the channels are very much the same as paid channels but with a different marketing philosophy and strategy. The key difference between paid and organic strategy is that on organic channels, people are volunteering their attention to the brand. Don’t abuse this privilege.
When thinking about organic social it's always important to keep one question in mind: why should people follow you?
Is it because you have a sustainable brand that advocates eco-friendly and sustainability posts? Or does your brand strongly advocate for another cause? Maybe your brand started from mom and pop roots with super relatable founders and their social channels are a glimpse into their behind the scenes life. Either way, always remember that your social pages need to have personality, relatability, and a reason for people to follow you.
Organic social channels have a large amount of overlap with the paid channels listed above. The following are the most commonly used channels for organic, though some work better than others.
- Facebook (Groups, Communities)
- LinkedIn (Personal)
- LinkedIn (Company)
- Facebook (Pages)
Our most recommended organic channels are Instagram, Youtube, and Twitter. These channels show your organic posts to a large percentage of your following and still have the potential for viral growth. The channels best suited for your brand will always depend on your brand’s specific creative type, messaging, and audience.
Unfortunately, LinkedIn (corporate) and Facebook Page marketing have dropped off our list of recommended organic channels. In recent years, we’ve seen Facebook Page organic reach drop significantly, and now we predominantly recommend paid Facebook, with some effectiveness from other Facebook tools such as Facebook Messenger and Facebook Groups. A similar phenomenon is observed in LinkedIn Company posts, though we still do believe in their importance in brand credibility to prospects for B2B companies.
Coming Soon: Email, SEO, and Content Marketing
Paid Advertising: Selecting Your Channels
Selecting the right digital marketing channels for your business can make or break your marketing campaign. In today’s day and age, you have a lot of options for where to put your money. It’s extremely important to select the correct channels for your product or service. Here, we will go through the major channels and the types of brands that succeed on each channel.
Advertising on Facebook / Instagram
We will bundle Facebook and Instagram together since they are both managed by the Facebook Business Manager and advertising on them is mechanically the same. Facebook has the largest audience of any social media advertising platform, with 2.7 billion monthly active users on Facebook and around 1 billion on Instagram.
So many active users mean two things:
- Your target audience is probably on Facebook / Instagram
- You better be really good at finding your target audience on Facebook / Instagram
Without a robust Facebook advertising strategy, your message can get lost in the noise. Contrary, a well crafted and managed Facebook strategy can be one of the most profitable and sustainable channels for a brand.
Facebook may be the right choice for your brand if:
- Your product is highly visual
- Your audience is easily targeted on Facebook
- You rely on demand creation as opposed to demand capture (people don’t know they want your product)
- Your company is well suited for advertising on Facebook (you have lots of highly visual creative, for example)
Advertising on Google (PPC)
Google is another powerhouse in the digital marketing advertising industry. Over 5 billion searches are conducted each day on Google, making it the largest repository of intent in the world.
The fundamental value behind advertising on Google is that it allows your brand the opportunity to show up where people are searching. This means that the traffic that comes to your brand has high-intent.
This high-intent traffic is usually further along in the funnel (MOFU to BOFU) since you know that they are searching for your product, your product category, or another keyword in which you target.
The Four Products in Google’s Advertising Suite: Text, Display, Shopping, and Video
Google Search Ads
These ads show up above organic search results and are designated by a small “AD” symbol next to the text itself. These show up in search results that include your target keywords. These ads are great for capturing search intent and when done right can be quick and highly profitable initiatives.
Google Display Ads
These ads are image ads that show up on websites within Google’s advertising network (and partner networks as well). These ads on average have the lowest CPM’s, and also the lowest CTRs of all of the products within Google’s suite.
The most effective way we’ve seen for using display ads for small to medium-sized brands is through display remarketing. This way, we can show display ads only to a BOFU audience for a very low cost.
Google Shopping Ads
If you are in the business of e-commerce, Google Shopping may be one of the most effective channels for your entire channel stack. These shopping ads show up not only in the shopping tab of Google but also in-line with normal search results and show a thumbnail of your product along with some supporting information. These ads are controlled by Google Merchant Center and are linked to your product catalog.
Google Video Ads
These ads show up in any of three places:
- In Youtube Search Results, next to the search results
- In Youtube Videos, as a skippable or non-skippable in-stream video ad
- On Video Partners in the Google Partner Network
The highlight of these three options is Youtube Video ads. This is where Google places your video as an ad before a Youtube video or somewhere inside a Youtube video during an ad break. These video ads work best when your product is something that has a high degree of education needed to drive a purchase. The most successful video ads we see are those with highly engaging educational content and captivate their audience enough so that they don’t skip, continue watching the ad, and eventually click through.
In comparing skippable vs non-skippable ads, we have seen better results using skippable ads for conversion, and non-skippable ads for brand awareness.
Google may be a good channel to use if:
- There is a lot of search intent for your product or service
- The average-cost-per-click for the keywords you intend to target is not cost-prohibitive
- You can clearly explain your product in text form (search ads) or through long-form video (video ads)
- Cheap brand awareness or remarketing is part of your strategy (display)
Advertising on LinkedIn
Functionally is very similar to most other social media advertising platforms, but has a much wider range of targeting functionality geared towards B2B advertisers. Expect higher CPM’s, but higher quality B2B leads than most other platforms.
Advertising on Snapchat
Snapchat is also quite similar to most other social media platforms but has a much lower CPM than other platforms. This makes Snapchat great for brand awareness campaigns, as you can usually garner a large number of impressions for a very low cost.
Other Advertising Platforms
There are a lot of other platforms that offer advertising capability, including Twitter, Pinterest, Quora, Reddit, and many more. The decision on whether to use these platforms comes down to two main considerations:
- Does my target audience spend time here?
- Do the advertising formats available convey my message well?
If the answer to both of those is yes, then it may be a strong channel to explore adding to your advertising portfolio.
Ad Budgeting: Ad Learning and Advertising Algorithms
If your advertising budget is restrictive, then it does not make sense to split this small budget across many platforms. Digital advertising today is highly driven by machine learning and the algorithms that advertisers use to show your ads to the right people. When your budget is small, your ads are shown to a small number of people, increasing the duration of time needed to collect the data to properly test an ad.
A large part of digital advertising today is playing to these algorithms. This means that there is a “learning phase” for ad campaigns, where performance can be significantly lower than campaigns that have exited the learning phase. During the learning phase, these campaigns are being shown to a wide variety of people, and through engagement metrics, the advertiser begins to understand what types of people are more likely to engage with the ad.
What this means for you is that testing an advertisement requires pushing spend into that campaign, and ad learning takes both time and money. If your ad budget is low, it makes sense to test fewer ads at the same time, so that your budget is not split too thin across campaigns or advertising platforms.
For brands with a large advertising budget, we can place a much smaller emphasis on this, since we usually will have enough budget to push ads past the learning phase rather quickly, and can do so at scale.
Designing your Marketing Process
Hopefully, this gave you a good understanding of some of the different strategies you can use to communicate and promote your brand to the world.
Marketing Operations Design is highly custom and depends heavily on the stage of your brand, its goals, and it’s strengths and weaknesses. Highly visual brands should maximize visual channels, whilst brands with extremely compelling missions or stories should maximize earned and organic channels.
Brands that are struggling with paid advertising should dive deep into diagnosing the problem: is it poor creative, poor targeting, or simply a bad channel split?
I sincerely hope this article was helpful!
We've got plenty more content to help you quickly get your marketing machine up to speed so you can fuel new growth. 👇
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
- Marketing Data Visualization To Fully Leverage Your Sources of Truth
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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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