Imagine a world where AI runs everything—making decisions, creating content, optimizing business strategies—without human input. 

While this might sound futuristic, it’s not entirely impossible. AI is advancing rapidly, automating tasks, analyzing vast amounts of data, and enhancing efficiency across industries. But does this mean humans are becoming obsolete? Not quite.

The real conversation isn’t AI vs. humans—it’s AI and humans working together. AI excels at data-driven tasks, automation, and predictive analytics, but it lacks human intuition, creativity, and ethical judgment. Organizations that strike the right balance between AI automation and human oversight will gain a competitive advantage.

This article explores how businesses can prepare for AI success. We’ll discuss where AI should take the lead, where human input remains crucial, and how to structure teams, processes, and data strategies to maximize AI’s potential while ensuring responsible and effective decision-making.

Understanding AI’s Strengths and Limitations

AI is transforming how businesses operate, making processes faster, smarter, and more efficient. But while AI can handle vast amounts of data and automate decision-making, it isn’t a silver bullet. To leverage AI effectively, organizations must understand where it thrives and where human expertise remains irreplaceable.

AI’s Strengths

Data processing at scale. AI can analyze and process massive datasets in real time, far beyond human capability. In marketing and advertising, AI sifts through millions of data points per second to detect patterns and provide actionable insights.

Pattern recognition and predictive analytics. AI can predict future outcomes by analyzing historical trends. Whether forecasting customer behaviour, optimizing ad spend, or detecting fraudulent transactions, AI helps advertisers make data-driven decisions with speed.

Automation of repetitive and data-driven tasks. AI eliminates the need for humans to handle time-consuming, manual tasks such as data entry, performance tracking, and basic customer support.

Optimization in programmatic advertising. In digital advertising, AI is critical in real-time bidding (RTB), audience segmentation, and campaign optimization. AI can analyze engagement metrics, adjust bids, and refine targeting strategies within milliseconds, ensuring maximum ROI.

AI’s Limitations

Lack of contextual understanding and human intuition. AI can process data but often struggles to interpret context accurately. While AI models can generate content or recommend marketing strategies, they lack the emotional intelligence, nuance, and cultural awareness that human decision-makers bring.

Struggles with creativity and decision-making in uncertain scenarios. Generative AI can assist in creative tasks, but it still requires human guidance to produce high-quality, original content. AI-generated ad copy or designs may be efficient, but human creativity ensures that messaging is compelling, brand-aligned, and emotionally resonant.

The “cold start” problem. AI learns by analyzing past data, meaning it struggles in new situations where no precedent exists. For example, if a company launches an entirely new product or enters an untapped market, AI has no prior data to base decisions on—requiring human strategists to fill the gap.

Ethical concerns. AI models can inherit biases from training data, leading to discriminatory outcomes in hiring, lending, and advertising. Additionally, over-relying on AI for decision-making without human oversight can result in misinformation, privacy violations, or poor judgment calls. Responsible AI implementation requires human review and ethical considerations.

The Role of Humans in AI Optimization

AI is only as effective as the humans guiding it. Human oversight, direction, and collaboration are the keys to AI success. Let’s break it down.

1. Strategic Thinking

AI may process data and make recommendations, but humans define what success looks like. Organizations must determine:

• What are the key performance indicators (KPIs) that AI should optimize for?
• How does AI fit into the company’s broader business strategy?
• What are the limitations of AI, and when should human intervention be required?

For example, in programmatic advertising, AI can automatically adjust bids and target audiences in real time, but marketing teams must set the overall strategy—whether that’s increasing brand awareness, improving conversions, or optimizing cost per acquisition. Without clear objectives, AI can operate efficiently but without strategic alignment.

2. Ethical Oversight

AI is only as ethical as the data and guidelines it’s built upon. Without human oversight, AI can reinforce biases, make unethical decisions, or inadvertently violate regulations. Companies must ensure that AI aligns with their ethical standards and complies with global regulations like GDPR and CCPA.

Human responsibilities in AI ethics include:

• Auditing AI decisions to detect and correct biases.
• Ensuring transparency in AI-driven processes (e.g., explainable AI in hiring and lending decisions).
• Avoiding over-reliance on AI in sensitive areas like healthcare, finance, and law, where human judgment is critical.

For instance, AI-powered hiring tools have been known to exhibit biases against certain demographics due to skewed training data. Human oversight is necessary to review AI-generated recommendations and ensure fairness.

3. Creativity and Branding

AI can generate content, but it lacks human creativity, emotional intelligence, and storytelling ability. Brands must balance AI-generated efficiency with human-led creativity to maintain authenticity.

Here’s how AI and humans collaborate in creative tasks:

• AI can generate headlines, product descriptions, and ad copy, but humans refine and add personality.
• AI can analyze which content performs best, but marketers develop brand messaging and storytelling.
• AI can suggest visuals, but designers ensure they align with the brand’s identity.

For example, AI can optimize an e-commerce store’s product recommendations based on user behaviour, but it cannot craft an engaging brand narrative that builds long-term loyalty. AI assists with content creation, but human marketers shape the voice and story.

4. Data Curation

AI is only as good as the data it learns from. If the data is incomplete, biased, or outdated, AI will produce flawed results. Humans play a crucial role in curating, cleaning, and updating data to ensure AI makes accurate and fair decisions.

Key human-driven tasks in data curation include:

• Identifying and removing biased or irrelevant data points.
• Ensuring AI models are trained on diverse and representative datasets.
• Updating AI systems with new data to keep them relevant.

For example, in digital marketing, AI-driven audience segmentation relies on historical data. AI may exclude potential customers if the data is skewed toward a specific demographic, limiting campaign effectiveness. Marketers must regularly review and refine data inputs to ensure AI models remain accurate and inclusive.

Identifying the Best Use Cases for AI in Your Organization

Organizations that strategically implement AI in the right areas can drive efficiency, improve decision-making, and enhance customer experiences. Below are the key areas where AI delivers the most impact.

High-Volume Data Analysis

One of AI’s greatest strengths is its ability to process and analyze large datasets in real time, making it invaluable for industries that rely on fast, data-driven decision-making.

AI optimizes digital ad placements by analyzing user behaviour, market trends, and engagement metrics. It adjusts bids in programmatic advertising within milliseconds to maximize ROI.

Predictive Analytics

AI’s ability to detect patterns in historical data makes it a valuable tool for predictive analytics. Businesses use AI to anticipate trends, mitigate risks, and tailor their strategies accordingly.

For example, AI analyzes purchase history, social media interactions, and web activity to predict what products or services customers will likely buy next and place the right ads at the right time.

Process Automation

AI automates time-consuming, repetitive tasks, allowing teams to focus on strategy, creativity, and campaign optimization.

For instance, AI automates bid adjustments, audience segmentation, and ad placements in real time. It analyzes performance data across multiple platforms to optimize budget allocation, ensuring ads reach the right audience at the right time.

Generative AI in Marketing

AI allows marketers to hyper-personalize content and automate campaigns. While human creativity remains essential, AI enhances marketing efficiency and effectiveness.

AI generates variations of ad copy, images, and videos, optimizing content based on engagement metrics. It analyzes customer preferences and behaviour to deliver personalized recommendations, product suggestions, and tailored messaging. It also segments audiences, predicts the best time to send emails, and generates content based on customer interactions.

Structuring Your Organization To Prepare For AI

Organizations need more than just cutting-edge technology to fully capitalize on AI’s potential—they need a solid framework that integrates AI seamlessly into their workflows. Below are the key steps organizations should take to optimize AI implementation.

Assessing Your Organization’s AI Readiness

Before deploying AI solutions, organizations must assess their readiness by evaluating infrastructure, skills, and processes. An AI maturity assessment helps determine how extensively AI is already used, which processes can benefit from automation, and AI’s impact on decision-making. Identifying AI knowledge gaps within teams is also crucial—businesses should evaluate employee familiarity with AI tools, pinpoint areas needing training, and consider hiring AI specialists or working with AI-savvy agencies if internal expertise is limited.

Equally important is ensuring a strong data infrastructure. High-quality, well-organized data is essential for AI training, and businesses must assess whether their CRM, ERP, and marketing platforms can integrate AI solutions. Investing in data management tools can enhance accessibility and security, ensuring AI operates effectively. 

For example, a retail company using AI for personalized marketing must first establish a structured customer database with purchase history, preferences, and engagement metrics before deploying AI-powered recommendations.

Building AI and Human Collaboration

AI should complement human intelligence, not replace it. A “human-in-the-loop” approach ensures AI-driven decisions remain informed, ethical, and aligned with business goals. Rather than functioning as an autopilot, AI should act as a co-pilot, enhancing human expertise while allowing employees to oversee, interpret, and adjust AI outputs, particularly in high-stakes scenarios.

Successful AI adoption requires upskilling teams and fostering collaboration. Organizations should provide AI literacy training, encourage cross-functional learning, and develop AI ambassadors to guide adoption. Cross-functional teams—combining AI specialists, marketers, and data scientists—help ensure AI is applied effectively. 

For example, in digital marketing, data scientists fine-tune algorithms, marketers align campaigns with brand messaging, and AI specialists optimize automation, creating a balanced approach to AI-powered strategy.

Creating AI Governance Frameworks

As AI plays a larger role in decision-making, organizations must establish governance policies to ensure transparency, ethics, and fairness. Clear AI ethics guidelines should define principles of accountability and compliance with regulations like GDPR and CCPA, particularly in handling sensitive data.

Regular audits help detect biases and inaccuracies, ensuring AI remains fair and effective. AI decisions should be explainable, not “black boxes,” allowing employees and stakeholders to understand how conclusions are reached. 

For example, advertisers using AI for audience targeting must regularly audit their models to prevent biased ad distribution and ensure transparency in how audiences are segmented and reached.

Measuring AI’s Impact on Business Performance

Implementing AI is not just about automation and efficiency—it’s about delivering measurable business value. Measuring AI’s impact provides insights into what’s working, what needs improvement, and how businesses can optimize AI for long-term success.

The table below shows some AI-specific KPIs you can measure.

KPI Category	Measurement Criteria	Marketing/Advertising Example
Accuracy and efficiency	Reduction in manual effort, faster data processing, and fewer errors.	AI-powered chatbots measured by resolution accuracy and reduced response time.
Customer engagement and satisfaction	AI-driven personalization, customer satisfaction (CSAT, NPS), retention and conversions.	AI recommendation engines in e-commerce measured by click-through rates and average order value.
Revenue and ROI	Increased revenue, cost savings, and higher marketing ROI.	AI-driven programmatic advertising evaluated by cost-per-acquisition (CPA) and ad spend efficiency.
Continuous testing and improvement	A/B testing AI-generated vs. human-created content, pricing strategies, email campaigns.	Testing AI-generated ad creatives against human-designed ads to measure engagement.
Benchmarking against human-led methods	Comparing AI-driven ad targeting, creative optimization, and campaign management to human-led approaches.	Comparing AI-driven audience segmentation with manual audience targeting to assess performance.

The Future of AI and Human Collaboration

AI is no longer just a tool for data analysis—it’s increasingly making decisions in marketing and operations. Future AI systems will autonomously optimize ad campaigns, manage supply chains, and detect financial fraud with minimal human intervention. However, businesses must ensure AI remains transparent, ethical, and aligned with company goals.

The next wave of AI innovation includes:

• AI agents – Self-learning systems that take action without human intervention, such as autonomous chatbots handling complex customer inquiries.
• Multimodal AI – AI models combining text, images, video, and audio for more natural interactions, such as AI assistants analyzing voice and facial cues.
• Advanced generative AI – More sophisticated AI-generated content, from hyper-personalized ads to lifelike AI video avatars.

AI is a powerful tool, but its success depends on human oversight. Organizations that strike the right balance—leveraging AI for efficiency while maintaining human control over strategy, ethics, and creativity—will gain a competitive edge. 

The key is not just adopting AI but integrating it thoughtfully, upskilling employees, and ensuring AI-driven decisions align with business goals and values. Organizations that proactively embrace this approach will be best positioned to innovate, grow, and lead in the AI-driven future.

In B2B, decision-making often involves a diverse group of stakeholders, each with unique perspectives and priorities. 

According to Gartner, a typical B2B buying group involves six to ten decision makers, each with five to six pieces of information to help them make a decision.

Clearly, targeting a single decision maker is not a viable strategy. This article explores how B2B marketers can effectively engage multiple stakeholders and optimize their campaigns in real time.

Understanding the B2B Decision-Making Process

Recently, a CRM company spoke to our large stakeholder group. This group included representatives from various organizations within the company. 

For instance, I represented the sales organization, while others represented marketing, operations, and finance. I realized that each stakeholder viewed the solution differently based on their unique needs and challenges.

In B2B marketing, we often make the mistake of targeting only the finance person, thinking they hold all the decision-making power. 

However, we need to engage everyone. We need to ask ourselves: 

• Which teams and roles are a part of the buying committee for my offering?
• Who are the influencers and their titles within these organizations?
• What is important to each of the leaders across these organizations?
• How do we need to speak to each of these decision makers based on what’s important to them and the value we can provide?

Different organizational roles perceive value and challenges in unique ways based on their specific responsibilities and objectives. This diversity in perspectives makes it essential for B2B marketers to tailor their messaging to each stakeholder.

Identifying Key B2B Decision Makers

For instance, let’s consider the scenario where a company is evaluating a new CRM system.

The sales leader is focused on how the CRM can drive sales and improve the bottom line. They want to know if the system can help identify new leads, streamline the sales process, and provide robust analytics to forecast revenue more accurately. Their main challenge is ensuring the CRM contributes to revenue growth and offers a good return on investment.

The marketing leader is concerned with how the CRM can improve campaign effectiveness and lead nurturing. They look for features like better segmentation, personalized marketing efforts, and detailed performance tracking. Their challenge is to ensure the CRM integrates well with other marketing tools and provides actionable insights to boost marketing ROI.

The operations leader values how the CRM can improve efficiency and productivity. They want streamlined workflows, reduced redundancies, and improved collaboration across departments. Their challenge is implementing a system that minimizes disruption and maximizes operational efficiency.

The finance leader examines the CRM from a cost perspective. They assess the upfront and ongoing costs, potential cost savings, and overall financial impact. Their challenge is to ensure the CRM fits within the budget and delivers measurable financial benefits.

Here’s how a CRM company might tailor their pitch to each stakeholder:

To the sales leader	“Our CRM system boosts your sales team’s efficiency by 30%, leading to a potential 20% increase in revenue.  “With advanced lead scoring and automated follow-ups, your team can focus on closing deals rather than administrative tasks.”
To the marketing leader	“With our CRM’s robust segmentation and personalization tools, you can create highly-targeted campaigns that drive engagement and conversions.  “Our integrated analytics dashboard provides real-time insights into campaign performance, allowing you to adjust strategies on the fly.”
To the operations leader	“Our CRM streamlines your workflows by integrating seamlessly with your existing tools and platforms. It reduces manual data entry by 40%, freeing up your team to focus on strategic initiatives.  “Plus, our user-friendly interface ensures quick adoption across departments.”
To the finance leader	“Our CRM offers a competitive pricing model with clear ROI. You can expect a significant reduction in customer acquisition costs by improving sales and marketing efficiency.  “Additionally, our real-time reporting helps you make data-driven financial decisions.”

A Programmatic Solution for Reaching B2B Stakeholders

The challenge for B2B marketers is figuring out how to effectively communicate with various decision makers within an organization, each with their own priorities. Instead of relying solely on the finance decision maker, you need to engage everyone in meaningful ways. 

StackAdapt has developed a solution for this within the programmatic ecosystem, leveraging ABM data and firmographic information to target decision makers across different organizational departments. Whether it’s the sales leader, marketing leader, or any other key player, the platform allows you to reach them effectively. 

A programmatic platform like StackAdapt provides real-time measurement and reporting capabilities. You can target individuals with tailored creative content and measure the impact directly within the platform. This allows you to track the effectiveness of the lead conversion journey, identifying which types of content resonate most with different leaders. For instance, if you notice that sales leaders engage more with certain types of content, you can allocate more resources to those channels immediately.

Some DSPs wait for end-of-campaign reports, which can prove to be inefficient. StackAdapt provides real-time insights and adjustments, and features like dynamic creative optimization allow for more personalized experiences as you can build and scale creative content based on individual user data. 

This means you can deliver personalized messages to sales, finance, and marketing leaders, optimizing the content in real-time to maximize impact.

How Marketers Can Influence the B2B Decision Making Process

Here are three steps you can take to get started.

1. Understand the Influencers 

Start by gaining a deep understanding of the various stakeholders within an organization who influence the decision-making process. 

Identify these individuals, whether they are in marketing, sales, operations, or finance. Consider their roles and levels, from directors to SVPs. Understand what drives them and what signals are important to each group.

2. Tailor Your Messaging 

Once you’ve identified the key stakeholders, think about their specific incentives and motivations. What motivates a sales leader might be different from what motivates an operations leader or a marketing leader. 

Extract the unique value propositions for each group and craft impactful creative content tailored to their needs. Use dynamic creative optimization to test and refine your campaigns, ensuring that your messages resonate with each stakeholder.

3. Measure the Impact of Your Messaging

It’s important to measure how your messaging impacts each stakeholder group. Analyze which types of content engage different decision makers and drive conversions. 

This insight helps you understand what works best for each group so you can adjust your strategies and allocate resources more effectively.

4. Leverage Real-Time Optimization 

Use measurement and reporting capabilities to monitor and optimize your campaigns. With StackAdapt, you can do this on the fly. This allows you to see which channels and messages are driving the most impact and make adjustments in real-time. 

You don’t need to wait until the end of the campaign to see results; you can optimize as you go, ensuring that you reach the right decision makers and drive conversions effectively.

When you understand who the decision makers are, tailor your messaging to their specific needs. This way, you can create highly-effective B2B marketing campaigns that engage multiple stakeholders and drive success.

Ready to revolutionize your B2B marketing strategy? Request a demo of StackAdapt today and see how our solutions can help you reach and resonate with every key player in your target organizations. These capabilities make StackAdapt a unique and powerful tool in the programmatic ecosystem, offering solutions that few other DSPs can match.

B2B marketing is boring—at least that’s the sentiment doing the rounds in social media.

So B2B marketers are trying different gimmicks to make their ads fun, even weird—from Adobe’s Click, Baby, Click! ad to Zendesk’s “I like it when he gives me the business” commercial.

Those ads are clever. But do they convert? Hard to know. 

What we know is that 77% of B2B buyers won’t purchase without personalized content. That means that your best bet is on customized experiences rather than humour. 

As we enter a cookieless world, though, marketers’ ability to personalize their B2B campaigns is taking a hit.

Third-party cookies have traditionally enabled advertisers to track users’ browsing activities across different sites to target ads effectively. However, with their usage declining, you need to lean heavily on first-party data.

What is B2B First-Party Data?

First-party data is information collected directly from your sources (e.g. your CRM) about your customers and users. This includes interactions on your website, customer feedback, email subscriptions, and any direct engagements. 

Using first-party data has several advantages over third-party data:

1. Improved data quality and relevance: Since first-party data comes from direct interactions with your brand, it’s generally more accurate and relevant. This results in better insights and more effective targeting.
2. Enhanced customer relationships: Using first-party data means you are engaging directly with those who have shown interest in your brand or have already interacted with you. This direct engagement helps build stronger, more personal relationships with potential and existing customers.
3. Increased privacy compliance: You ensure compliance with data privacy regulations like GDPR and CCPA as this data is collected with explicit consent from your users.

There are some drawbacks, however, that you need to consider. For instance, you will need to enhance your data collection methods to compensate for the loss of third-party data. Consider employing more interactive content, better CRM tools, or compelling offers to encourage users to share their information willingly.

You might also need sophisticated data processing and analytics capabilities to get actionable insights from the data you collect.

The Role of B2B First-Party Data in Personalization

First-party data plays a pivotal role in crafting personalized experiences. It enables you to tailor your marketing efforts based on your customer’s specific needs and behaviours, providing them with a more relevant and engaging experience. 

This personalization not only improves customer satisfaction but also boosts the effectiveness of your marketing campaigns.

As the advertising landscape evolves away from third-party cookies, first-party data isn’t just a backup plan—it’s the foundation of effective, compliant, and customer-centric marketing strategies in B2B.

Best Practices for Using First-Party Data in B2B Marketing

A report by Forrester and Acoustic reveals a significant gap between the importance marketers place on first-party data and their actual ability to collect it effectively. 

• While 84% of marketers recognize the importance of channel engagement data from emails, mobile phones, or websites, only 68% successfully collect this data. 
• Similar gaps exist in the collection of web and mobile data (81% find it important, but only 63% collect it) and real-time experience data (75% consider it critical, yet merely 47% gather it). 

The primary challenge in collecting first-party data lies in the infrastructure required—for the data collection itself and for governance and compliance.

Your marketing strategy should focus on effectively collecting, managing, and leveraging first-party data to drive sales, personalize experiences, and enhance your marketing efforts. 

Here are some best practices and strategies to help you in this process:

Collecting First-Party Data

A common tactic in B2B marketing for collecting data is to host events and webinars that require registration to obtain contact information and insights from the attendees. 

If you are doing programmatic advertising, encourage more interactions with your ads by offering valuable content, interactive tools, or surveys that prompt users to share their information.

When companies lack the infrastructure to collect data, they can partner with data-rich platforms to extend their reach. 

With StackAdapt, for example, you can onboard and activate your first-party data from your CRM, email marketing system, or conference and webinar registrations, and use it to target audiences that have shown interest in your business. Then, you can extend this first-party reach and impact by using your CRM data as a seed to build out lookalike audiences. Lookalike audiences use information about your first-party users to find audiences that look just like your ideal buyer.

Managing First-Party Data

Always obtain explicit consent when collecting data to comply with data protection laws and keep your data security measures stringent and up-to-date.

Use a robust CRM system to organize and manage your data effectively. Ensure your data is segmented and easily accessible for targeted marketing campaigns. Remember to keep your data fresh and accurate by regularly updating it and allowing customers to update their preferences. This ensures you are working with the most current information.

Leveraging First-Party Data

Use the insights gained from your data to tailor marketing messages and offers to meet the specific needs and interests of customers. Personalization can significantly increase engagement and conversion rates.

Break your audience into smaller segments based on their behaviour or demographics. Tailored messages to these segments can be more effective than broad campaigns.

Finally, analyze your data to predict future buying behaviours and preferences. This can guide your product development and marketing strategies, allowing you to meet customer needs proactively.

Enhancing Marketing Efforts

Implement account-based marketing (ABM). Use first-party data to identify key accounts and create personalized marketing campaigns focused on the specific needs and decision-making processes of those accounts.

Data insights help fine-tune your content strategy, ensuring it resonates well with your target audience. Develop content that addresses the specific challenges and needs of your segments. 

Finally, optimize customer journeys—track and analyze how customers interact with your brand across different touchpoints. Use this data to optimize these interactions and improve customer satisfaction and loyalty.

These strategies will help you position your brand to capitalize on the depth and quality of insights that first-party data offers. But for B2B, the most valuable strategy is using first-party data to personalize your ABM campaigns.

Account-Based Marketing With Personalization

Using first-party data in your ABM efforts allows for more targeted and personalized experiences that resonate deeply with your key accounts. 

Here’s how to leverage first-party data to refine your ABM strategies and integrate personalized content and offers.

1. Deepen account insights: First-party data gives you a comprehensive view of how specific accounts interact with your brand, from website visits to email engagements. These insights allow you to understand the pain points, interests, and needs of each account more deeply, enabling highly-targeted campaign strategies.
2. Refine targeting accuracy: Use the detailed information from your first-party data to create precise customer profiles for your key accounts. This level of detail increases the accuracy of your targeting, making your ABM efforts more relevant and impactful.
3. Track engagement in real time: With real-time data on how accounts interact with your current campaigns, you can quickly adjust your strategies to optimize performance and engagement levels.

For instance, StackAdapt has a B2B targeting and measurement tool designed to reach verified B2B professionals. This tool allows for precise tracking of account-level engagement, helping you generate qualified leads more effectively. With its accurate and transparent attribution capabilities, you can create seamless end-to-end marketing strategies that integrate smoothly with sales and revenue operations.

Strategies for Personalized Content and Offers in ABM

Use the insights gathered from your first-party data to craft messages that speak directly to the specific context and business challenges of each account. Tailored messaging feels more relevant to the recipient, increasing the chances of engagement and conversion.

Even within a single account, there can be varying needs and preferences depending on the department or job role. Segment your contact lists by these criteria and customize your communications accordingly to address the unique needs of each subgroup.

Implement technology that adapts the content displayed on your ads, your website or in your emails based on the known preferences and past behaviours of account members. This could mean showing different content to different users from the same account when they visit your site.

You can also create personalized offers or incentives based on the purchase history and interaction data of an account. For example, if an account has shown interest in a particular service or product, offer a trial or a time-sensitive discount specifically tailored to that interest.

One way to do this is through dynamic creative optimization (DCO), which allows you to automatically create personalized ads based on data about the viewer at the moment of ad serving. This technology uses real-time data to change the creative elements of an ad to match the viewer’s preferences, behaviours, and context. 

Overall, ensure that the personalized experience is consistent across all channels, from email to ads to your website. This consistency helps reinforce your message and brand, making each interaction feel more connected and thoughtful.

Measuring Success: The Impact of Personalization on B2B Sales

In B2B marketing, tracking the effectiveness of personalization strategies involves focusing on several key performance indicators (KPIs). 

These metrics help you measure how well your personalized content and campaigns are resonating with your target audience and driving business objectives:

Conversion Rate: Track how many leads or customers are converted after receiving personalized messages compared to a generic approach. A higher conversion rate indicates successful personalization.

Engagement Rate: Measure the level of interaction with your personalized content across various platforms, like email opens, clicks, website visits, and social media engagement. Increased engagement rates suggest that the content is relevant and appealing to your audience.

Lead Quality and Sales Metrics: Assess the quality of leads generated through personalized campaigns. Look at the number of sales-qualified leads (SQLs) and the impact on sales metrics, such as deal size or sales cycle length, to determine if personalization is attracting better-fit customers.

Customer Retention Rates: Track retention rates to see if personalized approaches are improving customer loyalty and reducing churn.

Account Penetration: For account-based marketing, measure how deeply you are engaging with targeted accounts. Check if personalized content leads to broader engagement within key accounts, including increased contacts or cross-departmental interactions.

ROI and ROAS of personalized campaigns: Ultimately, evaluate the return on investment (ROI) and return on ad spend (ROAS) from personalized marketing efforts by comparing the revenue generated to the cost of the campaigns.

Keeping track of these metrics is hard if you don’t have the right tools and workflows in place. That’s why with StackAdapt, you can visualize account engagement and report on the metrics that matter most for B2B. You can track progress across the buyer’s journey, quantify your marketing dollars to prove ROAS, and understand who your interested and engaged accounts are so you can prioritize them with your sales team.

Interested in learning more? Request a demo today to see how StackAdapt can help you leverage your first-party data to grow your business.

In the world of B2B advertising, marketers, agencies, and consultants alike are under constant pressure to connect the dots like never before, squeezing every ounce of value from their marketing budgets. 

But here’s the catch—there are too many variables in the B2B equation. The B2B buyer’s journey isn’t a straight line, but a winding path with twists and turns. Decision making is a team sport, with multiple stakeholders pulling the strings. 

Navigating this landscape requires a deep understanding of the entire journey, from initial awareness to final decision, and the ability to tie every touchpoint back to tangible return on investment (ROI). 

It’s not enough to host another webinar and hope for the best. Marketers need to understand the multifaceted strategies that drive engagement and ultimately, sales.

As we address 3 key challenges of B2B advertising, remember: it’s about connecting every dot, influencing every decision maker, and aligning every piece of the marketing and sales machinery to overcome the complexities of today’s buyer’s journey.

1. Reaching the Right Decision Makers

This is a common scenario: a sales representative meets the VP of Sales of a prospective client, who, despite having the final say, doesn’t pull the trigger without getting a nod from a whole committee spanning various departments. 

The challenge for this rep, then, is to identify and engage the correct individuals across these departments. 

But decision-making processes vary significantly from one company to another, depending on factors, such as company size, industry, and corporate culture. Decision makers and influencers can range from technical experts in IT to financial gatekeepers in accounting, not to mention the strategic overseers in executive leadership. 

Engaging with this diverse group requires a nuanced approach, one that recognizes and addresses the specific needs and pain points of each stakeholder.

How do you navigate this complexity? I suggest two solutions: B2B data graphs and account-based marketing.

Advanced Targeting Strategies Using B2B Data Graphs 

Companies can gain deep insights into the professional relationships and networks that exist within their target accounts leveraging B2B data graphs. 

These data graphs go beyond basic demographic information, providing a rich tapestry of connections, job roles, and influence levels. 

Utilizing programmatic advertising, businesses can then target their messaging with precision, ensuring that it reaches the decision makers and influencers across the relevant departments. 

This approach increases the efficiency of advertising spend and enhances the likelihood of your message resonating with the right people.

Account-Based Marketing (ABM) for Personalized Engagement

ABM enables companies to craft highly personalized marketing and sales efforts tailored to the specific context and needs of a select group of high-value accounts. 

This personalization extends across various touchpoints, from customized email campaigns to personalized web content, ensuring that each interaction addresses the unique challenges and opportunities of the target account. 

ABM’s strength lies in its ability to foster meaningful engagements, building a solid foundation for long-term relationships with key stakeholders.

These solutions pierce the veil of complexity surrounding the identification and engagement of the right decision makers, paving the way for more successful B2B transactions.

A great example is Centerline, who partnered with StackAdapt to open up new avenues of advertising and leveraged multiple B2B targeting tactics to accurately reach their client’s B2B IT decision-maker audience. Centerline implemented different B2B targeting tactics, including ABM lists, Page Context AI, 3rd-party segments and Intersection Segments to hone in on their target B2B user. They were able to significantly increase their video viewership and engagement while decreasing average CPA costs.

2. Bridging the Gap Between Sales and Marketing

The second key challenge in B2B advertising is the perennial misalignment between sales and marketing teams. 

Despite both teams playing for the same side, their paths to victory often seem divergent, leading to frustration and missed opportunities. 

Many times the cause is diverging goals and a notable lack of transparent communication. 

Marketing teams might be crafting targeted ads, and engaging the right audience, yet these efforts don’t always translate into the sales outcomes that the sales team is vigorously pursuing. This discord creates a murky gap between marketing’s perception of success and the reality faced by sales on the frontline.

This gap isn’t merely about differing objectives; it’s also about the challenge of converting advertising initiatives into tangible sales results. The intricate dance of leading a prospect from awareness to a decision often gets lost in translation, with crucial insights and opportunities slipping through the cracks. 

The solution lies not in pointing fingers, but in building bridges—both metaphorically and practically. Here are two solutions:

Develop Unified Goals and KPIs 

One of the first steps towards alignment is establishing a common set of goals and key performance indicators (KPIs) that resonate with both sales and marketing. 

This shared scoreboard fosters a sense of unity and collaboration, guiding both teams toward mutual success. It’s about transitioning from a mindset of “your objectives” and “my targets” to “our goals.”

Let’s say, for example, that your company is launching an advertising campaign, where sales and marketing have a unified goal: increase customer engagement by 25%, leading to a significant increase in both sales qualified leads (SQLs) by 15% and closed deals by 10% in the second quarter. 

This objective is supported by a set of aligned KPIs that serve as a roadmap for both teams, including increasing lead generation through digital advertising, improving lead quality and engagement rates, reducing sales follow-up times, boosting customer satisfaction scores, and achieving revenue growth from new customers.

The campaign strategy involves targeted advertising efforts focused on addressing specific industry pain points and producing content that resonates with the intended audience, coupled with a rapid sales response strategy to engage leads promptly. 

To ensure these efforts are in harmony, sales and marketing hold weekly meetings to review progress, exchange feedback, and adjust tactics as necessary, all while monitoring a shared dashboard that tracks real-time advancements toward their KPIs.

Implement ABM Reporting and Customer Journey Mapping 

To illuminate the once opaque areas between sales and marketing, adopting ABM reporting and customer journey mapping is invaluable.

ABM reporting allows for a granular analysis of how marketing efforts are impacting key accounts, offering insights that both sales and marketing can act upon. 

Similarly, customer journey mapping provides a detailed visualization of the customer’s path, highlighting touchpoints where sales and marketing efforts can be synchronized for maximum impact.

Examples of Tools and Strategies for Enhanced Alignment and Transparency 

Tools such as CRM systems, marketing automation platforms, and ABM software are critical in facilitating this alignment. 

For instance, integrating CRM and marketing automation platforms ensures that both sales and marketing have access to real-time data on customer engagements and behaviours. When conducting programmatic advertising, StackAdapt can quickly synchronize with large sets of CRM data and produce lookalike audiences based on multiple identifiers.

Furthermore, leveraging tools like LinkedIn Sales Navigator for social selling, or StackAdapt for ABM, can enhance targeted efforts, making it easier for sales and marketing to jointly identify, understand, and engage high-value prospects. Strategies, such as regular cross-functional meetings, shared dashboards, and joint strategy sessions help improve transparency and can encourage ongoing dialogue and collaboration.

It’s important to have the right reporting and measurement to create transparency and alignment for paid media from brand to demand. For example, teams need access to account-level media metrics as well as aggregated audience metrics across programmatic buys.

StackAdapt’s brand reporting offers this superior level of access, with built-in brand-lift and incrementality measurement.

These solutions allow companies to effectively narrow the gap between sales and marketing, ensuring that both teams are aligned, informed, and equipped to collectively drive towards shared business objectives. Having a variety of reports and tracking, from ABM to brand lift, brings alignment and transparency to sales and marketing.

3. Addressing Increased Buyer Journey Complexity

Lastly, the buyer’s journey has become a complex web, thanks to shifting digital landscapes like the fading away of cookies and device IDs. 

This complexity is compounded by the need for identity resolution amidst this signal loss, making it harder to connect the dots across a buyer’s path that now involves, believe it or not, up to 87 touchpoints before becoming a sales qualified lead. 

Buyers are on the lookout for content that resonates with them and only reach out once they’re good and ready.

B2B companies can address the increased complexity of the buyer’s journey through a comprehensive strategy that includes a full-funnel approach, advanced technological solutions, and contextual targeting. 

Embracing a Full-Funnel Marketing Approach

Recognizing and engaging with potential buyers at each stage of their journey is crucial. A full-funnel strategy ensures that marketing efforts are not just focused on generating leads, but also on nurturing these prospects through the entirety of the buying process. 

This approach requires a blend of targeted content, strategic outreach, and continuous engagement, tailored to meet the buyers where they are and guide them towards making a decision.

Investment in Technologies for Enhanced Tracking and Analytics 

As the digital landscape evolves, so too must the tools we use to navigate it. Investing in technologies that offer robust tracking and analytics capabilities becomes paramount. 

These tools should be capable of piecing together disparate data points into a coherent picture of the buyer’s journey, even in the absence of traditional identifiers like cookies. Solutions like customer data platforms (CDPs) and advanced analytics software can help bridge this gap, providing deeper insights into customer behaviour and preferences.

Using the Power of Contextual Targeting

Content remains king in B2B marketing, but it doesn’t always have to be content you create.

With contextual targeting, you can reach decision makers as they are consuming relevant content across the internet. 

Contextual advertising targets ads based on the page in which the ad appears. In other words, ads are shown to users based on the content they are consuming at that moment. 

You can identify keywords and topics based on the buyer’s journey of your targeted decision makers and use the contextual targeting feature in StackAdapt to reach them.

The Future of B2B Advertising

The future of B2B advertising is undeniably geared towards continuous adaptation and innovation. 

Marketers must remain agile, ready to pivot strategies in response to new data, trends, and technologies. The emphasis on understanding and engaging with the full buyer’s journey, coupled with the necessity for alignment between marketing and sales, highlights a shift towards a more integrated, customer-centric approach. 

This evolution requires not just the right tools and technologies, but also a mindset that values collaboration, transparency, and the pursuit of shared goals.

At StackAdapt, we are innovating to offer you the tools to reach decision makers through the different phases of the buyer journey, while giving you access to data and insights that will keep marketing and sales on the same page. Interested in finding out more? Request a demo today.

One of the main challenges in marketing analytics is making the leap from raw numerical data to insights that resonate on a human level—especially for non-technical marketers.

This is where Natural Language Processing (NLP) and generative AI come to the rescue. These technologies act as bridges, transforming hard data into actionable, human-understandable insights. 

Large Language Models, like GPT-4 or Claude, are trained on vast amounts of internet data, from social media posts and news articles to mathematical equations, absorbing a bit of everything.

These models can capture and interpret complex data sets. One key application of NLP and generative AI is leveraging this internet-scale knowledge to glean specific insights from your marketing data.

Here are three benefits of NLP for insight generation:

1. Enhanced Decision-Making through Accessible Insights

NLP technologies transform complex, voluminous datasets into clear, actionable insights that marketers can easily interpret and use. 

This accessibility means marketers don’t need deep, technical expertise to understand consumer trends, sentiment analysis, or market shifts. NLP converts data into straightforward narratives or summaries, allowing marketing teams to make informed decisions swiftly, and ensuring strategies respond to current market dynamics. 

For example, analyzing customer feedback across multiple channels can reveal insights into product improvements, leading to better-aligned offerings with customer expectations.

2. Increased Efficiency in Data Analysis Processes

Manual data analysis can be incredibly time consuming, especially when dealing with unstructured data from varied sources, such as social media, customer reviews, and forums. 

NLP automates the extraction of relevant information, significantly reducing the time needed to gather and analyze data. This increased efficiency frees up marketing teams to focus on strategy and implementation rather than getting bogged down by data processing. 

Moreover, NLP can continuously monitor and analyze data streams in real-time, ensuring marketers have the latest insights at their fingertips to quickly adapt to market changes or capitalize on emerging opportunities.

3. Improved Accuracy of Customer Insights and Trend Predictions

NLP’s ability to process and analyze large datasets with an understanding of language nuances greatly enhances the accuracy of customer insights and trend predictions. NLP can also help navigate regional differences so marketers can make more informed decisions.

Traditional analysis methods might miss subtle sentiments expressed in customer feedback or emerging trends hidden in the vast amounts of data. NLP, however, can detect these subtleties, providing a more accurate and comprehensive view of customer sentiments, preferences, and behaviours. 

This precision helps marketers tailor their approach more effectively, leading to better customer engagement, targeted marketing campaigns, and ultimately, improved return on investment (ROI) on marketing efforts. 

For instance, sentiment analysis can uncover not just how many people are talking about a product, but how they feel about it, enabling more nuanced adjustments to product development and marketing strategies.

Incorporating NLP into marketing analytics not only streamlines data analysis, but also enriches the insights derived, leading to more informed decision making, efficiency in operations, and precision in targeting and engaging customers.

Implementing NLP in Marketing Data Analysis

What are the steps for integrating NLP into marketing analytics?

Large-scale models like GPT-3 or GPT-4 represent the pinnacle of NLP capabilities, trained on extensive datasets to understand and generate human-like text. However, developing such models in-house is expensive, with costs for computing resources reaching tens of thousands of dollars.

For most businesses, directly training these behemoths isn’t feasible. 

Instead, they can access them through platforms like OpenAI via API endpoints. This approach democratizes advanced NLP capabilities, allowing even small teams to harness the model’s insights without the need to invest in prohibitive training costs. 

However, a notable limitation is that while these models are versed in a broad range of internet knowledge, they’re not inherently tuned to understand proprietary data, such as internal reports or customer feedback specific to a company.

To bridge this gap, two main strategies emerge: fine-tuning and utilizing middleware for personalized data integration. 

Fine-tuning is a cost-effective method where a pre-existing model is further trained (or fine-tuned) on a specific dataset, such as a company’s customer interaction logs. This process imbues the model with a layer of customization, making it more adept at understanding and generating insights relevant to the business’s unique context.

Another innovative approach involves using middleware solutions that act as interpreters between your proprietary data and the pre-trained models. 

Although these models may not directly “learn” from your data, they can apply their “general intelligence” to analyze your specific datasets, identifying relevant patterns or insights.

For instance, an internal chatbot could sift through company documents, guided by the model’s broad knowledge base, to pinpoint the most pertinent information without direct training on those documents.

These methods show how businesses can leverage cutting-edge NLP technologies to enhance their marketing efforts, from personalized customer insights to streamlined content strategies, without bearing the full cost of developing large-scale models from scratch.

Practical Applications of NLP in Marketing

Let’s look at some applications of NLP in marketing, which are integral to refining brand strategy, personalizing customer experiences, and optimizing content for better engagement and visibility.

1. Sentiment Analysis for Brand Perception

Sentiment analysis leverages NLP to comb through vast amounts of data from social media, reviews, forums, and more, to gauge public sentiment about a brand or product. 

This analysis isn’t just about counting positive versus negative mentions; it’s about understanding the nuances of how people feel and why. 

NLP identifies patterns in customer feedback, so you can pinpoint areas for improvement, recognize what you’re doing right, and adapt your strategies as needed.

2. Customer Segmentation and Personalization

NLP transforms customer segmentation by analyzing behaviour, preferences, and feedback to create highly detailed customer profiles. 

Traditional segmentation might rely on broad demographic data, but NLP allows for a more nuanced approach. It can interpret unstructured data (like customer reviews or social media interactions) to identify specific interests, needs, and even sentiment, enabling businesses to segment their market with a high degree of precision. 

This refined segmentation supports targeted marketing efforts that resonate on a personal level, enhancing customer engagement and loyalty. 

For example, NLP can help identify a segment of customers who are passionate about eco-friendly products, allowing a company to tailor its messaging and offers to this group.

3. Content Optimization and SEO

NLP aids in the optimization of online content by analyzing current trends, competitor content, and search engine algorithms to recommend keywords and topics that are likely to attract and engage the target audience. 

This goes beyond simple keyword stuffing, integrating semantic analysis to understand the context and intent behind search queries. NLP tools can suggest improvements to make content more relevant to user queries, potentially boosting SEO rankings and visibility. 

Additionally, by analyzing successful content within your niche, NLP can guide the creation of new content that’s not only optimized for search engines, but also highly engaging for readers, ensuring that your content strategy remains dynamic and effective in capturing audience interest.

NLP in Programmatic Advertising

Imagine being able to dive into your marketing platform and casually ask, “Which campaign had the highest conversion rate last week?” or “What’s been the trend over the past two weeks?” 

Traditionally, answering these questions would involve a skilled data analyst crafting complex queries, and engineers navigating databases to extract the needed insights. However, with the advent of NLP and generative AI, this process has become as simple as having a conversation with a chatbot.

This chatbot processes your data and translates it back into easily understandable insights, delivered straight to you in conversation. It’s a game-changer for companies looking to leverage their marketing data without the need for extensive engineering resources.

When it comes to utilizing tools like ChatGPT for marketing insights, the beauty lies in its simplicity and flexibility. ChatGPT, while known for its short-term memory, can still hold onto the information you provide within a single session. This means you can feed it a document for summarization, or request it to craft an email copy with a specific tone, all without the need for in-depth customization or expensive fine-tuning. It’s like explaining a concept to a colleague who can immediately apply that knowledge to generate actionable outputs.

This approachable, conversation-based interaction with data not only democratizes access to analytics, but also opens up a world of possibilities for generating content that resonates with your audience.

NLP is revolutionizing programmatic advertising by making campaigns more intelligent, targeted, and efficient. 

Here are specific examples of how NLP facilitates this transformation across different facets of programmatic advertising:

Targeted Advertising

A fitness apparel brand uses NLP to analyze social media conversations and blog posts related to fitness trends, identifying potential customers who are discussing running, yoga, or gym workouts. 

Understanding the context and sentiment of these discussions can help brands dynamically serve personalized ad content that resonates with each user’s specific fitness interests, such as showcasing their latest running shoes to someone who frequently posts about morning runs.

Ad Optimization

An e-commerce company implements NLP algorithms to continuously analyze the performance data of their ad campaigns across various platforms. The system assesses which ad copy and imagery combinations yield the highest engagement and conversion rates among different demographic segments. 

Based on these insights, the algorithm automatically adjusts future ads in real-time, optimizing for factors like time of day, user location, and previously interacted content, thereby increasing ROI through higher click-through and conversion rates.

Sentiment Analysis

A beverage company uses NLP-powered sentiment analysis to monitor and analyze online conversations about its brand and products across social media, forums, and review sites. 

The brand can gauge public sentiment trends, and identify shifts in consumer perception, such as increased positivity towards a new product launch or negative reactions to a recent advertisement. 

This allows the company to fine-tune its messaging and creative assets in upcoming ad campaigns to address consumer concerns, capitalize on positive sentiment, and avoid areas of negative association.

Contextual Targeting

A travel agency employs NLP to analyze the content of web pages and articles that users are reading in real time. 

For someone reading a blog post about “Best Winter Getaways,” the system identifies keywords and themes related to winter travel and destinations. The travel agency’s ads for ski resort packages or winter travel deals are then contextually placed alongside the article, targeting users at the moment their interest is piqued. This method, known as contextual targeting, ensures that ads are relevant to the content being consumed, increasing the likelihood of engagement and action.

These examples underscore how NLP is essential in enhancing the precision, relevance, and effectiveness of programmatic advertising. And all these tools are available within the StackAdapt platform.

With StackAdapt, you can leverage deep insights into user behaviour, sentiment, and context. Advertisers can craft campaigns that can reach the right audience at the right time, and can  resonate on a personal level, driving superior engagement and results.

Overcoming Challenges with NLP in Marketing

Navigating the world of NLP in marketing presents a unique set of challenges, chief among them being the art of prompt engineering. 

This skill has emerged as a critical component due to the opaque nature of these AI models, which operate as a “black box”—their internal workings and the data they’ve been trained on are not transparent to users. 

Crafting the right prompt is crucial; a slight change in wording can lead to vastly different outcomes, and missing a key piece of information might not yield the desired result. Marketers must learn to specify their requests, including the tone and format, to leverage these models effectively.

Another notable challenge is the phenomenon of “hallucination,” where models generate convincing, but inaccurate or fictional information. This underscores the importance of human oversight in the process, ensuring outputs align with factual accuracy and are contextually appropriate.

The handling of proprietary data introduces further complexities. Utilizing models like OpenAI’s involves sharing information with their systems, which might not be suitable for all types of data, especially sensitive or confidential information. 

While it’s possible to train private models to address these concerns, this approach requires significant resources, both in terms of engineering expertise and financial investment. For companies without these resources, exploring services that offer private model training might be a viable alternative. These services can manage the technical heavy lifting, allowing businesses to maintain control over their data without the need for in-house expertise.

Deciding on the best approach involves assessing your specific needs and use cases. For general inquiries or applications where data sensitivity is low, leveraging existing public models may suffice. 

However, for projects involving sensitive customer data, more secure, customized solutions are necessary, even if this means investing in specialized services or in-house capabilities to ensure data privacy and security.

These challenges highlight the delicate balance between harnessing the power of NLP in marketing and navigating the technical, ethical, and operational hurdles that come with it. As the field evolves, so too will the strategies for overcoming these obstacles, paving the way for more sophisticated and secure applications of NLP in the marketing domain.

Embracing the Future of Marketing

NLP is reshaping marketing, offering insights and efficiency that grant a competitive edge. This leap in data analytics and customer understanding requires that marketing leaders integrate AI and NLP into their strategies to stay ahead.

As a marketer, you should actively explore NLP tools, focusing on areas ripe for enhancement like customer interaction or content strategy. 

• Identify specific areas within your marketing strategy that could benefit most from automation and enhanced insights, such as content optimization, ad targeting, or audience segmentation. 
• Seek out reputable platforms and services that offer accessible ways to implement NLP, whether through API integrations, customizable chatbots, or sentiment analysis tools. 
• Invest in training and resources to understand the fundamentals of prompt engineering and data security will also pay dividends. 

Take proactive steps to incorporate NLP into your marketing toolkit, so you can unlock new levels of efficiency and engagement that propel your brand forward.

Finally, take advantage of the NLP tools for programmatic advertising available with StackAdapt. Request a demo today to find out more.

Ads today are weirding people out—especially when they get super specific. 

People feel like advertisers are reading their minds or listening to their conversations. “How did they know I was into leather pants?” (that kind of thing). 

They feel uncomfortable about their data being used to manipulate them into buying things; it feels like an invasion of their privacy.

But, here’s the deal from the inside of the ad world: we’re not trying to creep anyone out. It’s just that sometimes, ads can feel a bit too close for comfort, if they’re not hitting the mark in a human way.

For example, you see an ad for pink running shoes. It feels out of the blue. You never even mentioned it out loud. But instead of just throwing shoes at you, imagine the ad actually gets why you like those bright pink shoes—maybe because they make you feel safer running at night. That’s when the ad stops being just an ad. It becomes a connection. It’s not just about the brand knowing your secret shoe love; it’s about them showing they get you.

The trick is in how marketers talk to people, making sure they’re not just barging into people’s heads with their ads, so they feel heard, not followed. 

Sure, sometimes it feels like ads have a direct line to our thoughts, but I’ve been there too. I’ve clicked on ads for stuff I didn’t even know I needed, and it’s like, “Wow, this is perfect for me.”

So, it’s all about striking that balance—making sure when, as a marketer, you reach out, it feels like you’re handing them something they’ll love, not just something you think they’ll like. It’s about marketers making ads feel less like a cold call and more like a friend dropping by with exactly what they need, even before they know they need it.

What Is Human-Centric Marketing? 

Human-centric marketing is putting the real, live human at the heart of every marketing decision. Instead of slicing and dicing people into groups based on stats (segments and demographics), it’s about getting the full picture—their feelings, dreams, and what gets them excited.

There are three reasons why this is important.

1. Human-centric marketing helps brands build trust. When you treat customers as individuals and show that you understand their unique challenges and desires,  you’re not just another brand to them. You’re someone they can trust and believe in.
2. Human-centric marketing enhances the customer experience. This approach allows you to tailor your offerings and interactions to meet customer expectations, so every interaction they have with your brand feels special and memorable. It’s what sets you apart from the crowd.
3. Human-centric marketing drives customer loyalty. When customers feel valued and understood, it leads to increased loyalty and repeat business.

The data backs this up. Brands that speak to their customers see up to 63% more clicks and up to 1,400 more conversions.

Tailored experience is the name of the game, if you want to win big.

One great example is Spotify, which shows record growth, with its personalized playlists and curated recommendations. Spotify leverages user data and algorithms to make targeted music suggestions. That’s human-centric marketing in action—keeping you hooked and feeling like Spotify simply gets you.

How to Implement Human-Centric Marketing Strategies

Let’s look at two human-centric marketing strategies you can implement to understand the human behind the data.

1. Customer Journey Mapping

Customer journey mapping is like drawing a map of every step your customer takes, from discovering your (or the) brand to making a purchase and beyond. 

• Start with research. Talk to your customers, conduct surveys, and use analytics to understand how they find and interact with your brand.
• Identify key touchpoints. Map out every interaction point, from social media engagement and website visits to customer service calls and post-purchase follow-ups.
• Spot the pain points. Look for places where customers get stuck, frustrated, or just lose interest. These are your opportunities to improve.
• Create solutions. Design interventions for these pain points. Maybe it’s clearer information on your website, a more intuitive checkout process, or personalized follow-up emails.

When you understand and address the specific needs and desires at each stage of the customer journey, you make the entire experience smoother and more enjoyable. 

For instance, when you’re mindful that you’re engaging with real people, not just potential sales, even your calls to action (CTAs) change. 

The goal, of course, is to encourage a purchase, but the way you communicate should never feel like a blunt invitation to simply spend money. 

Instead, the art lies in crafting messages that resonate on a personal level, subtly aligning with the individual’s lifestyle or interests without explicitly asking for a transaction. It’s about presenting options in a manner that leads the person to conclude, “This is for me,” thereby naturally progressing toward a purchase.

People are constantly bombarded with CTAs, like “Buy now” or “Sign up today,” so the need for a human touch in messaging is more important than ever. Such direct CTAs, while clear and straightforward, miss the mark on making a human connection. 

Our approach should weave through the customer’s journey with empathy and understanding, gently guiding rather than pushing toward the end goal. The messaging should evolve from initial soft engagement, building a relationship and trust, to a point where the final step—the purchase—feels like a natural decision for the customer, not a command from the brand.

This perspective emphasizes the importance of the entire journey, from awareness to consideration, leading to a conversion that feels personal and meaningful.

2. Data-Driven Personalization

This strategy uses customer data to tailor marketing efforts to the individual level. Instead of one-size-fits-all messages, you’re creating personalized interactions based on what you know about each customer.

• Collect data ethically. Use every interaction to learn about your customers—what they browse, purchase, and engage with. Ensure you’re doing this ethically and transparently.
• Analyze for insights. Dive into this data to identify patterns, preferences, and behaviours. Tools and platforms can help segment your audience based on their interests, past purchases, and even predictive behaviour.
• Personalize touchpoints. Use these insights to customize your communications. This could be product recommendations that feel handpicked, email content that speaks directly to their interests, or offers that hit at just the right moment.

Personalization shows your customers that you’re paying attention to what matters to them. It makes your marketing feel more relevant and engaging, increasing the likelihood of conversion and fostering a deeper connection with your brand.

Both customer journey mapping and data-driven personalization help in understanding and catering to the human element in marketing. These strategies show you’re not just selling a product; you’re providing a tailored experience that resonates on a personal level. 

The Role of Martech and Adtech in Human-Centric Marketing

Martech and adtech tools provide a lot of data to paint a vivid picture of customer behaviours and preferences. Here are some ways to leverage these tech tools for a more human-centric marketing.

A/B Testing

You can’t just guess what your audience will connect with. Instead, use martech and adtech tools to run some A/B tests on your messages to see how they land. 

The first try might not work, but it’s a starting point for understanding what resonates. Then, it’s all about refining and tweaking, bit by bit, to nail that perfect message. For example, when you change the word from “grow” to “climb” all of a sudden you’re speaking to the right people. 

It’s a process of evolving your approach based on feedback. 

And that’s where martech and adtech shine—they make adjusting on the fly a breeze, offering insights so you can make informed tweaks. 

Didn’t get the reaction you hoped for? Was it the headline, the target audience, or where the ad was placed? Pinpointing what works, and what doesn’t, becomes easier with detailed data.

Tools like StackAdapt let you test and analyze the entire customer journey, so you can know where the drop-off is happening—whether it’s targeting, messaging, or conversions. 

Contextual Targeting

Tech also steps in to help you find your audience from the get-go. If you’re working with basic tools that only segment your audience by broad categories like gender, you’re missing out on the nuance. The real magic happens when you dive into what people are interested in. 

This is where contextual targeting comes in. It’s not about who your audience is, but what they’re into—the articles they read, and the topics they’re drawn to. When you align your message with the right context and content, you’re more likely to strike a chord.

Insights

Facing a blank slate can be daunting, but with tech, you can get forecasts, placement suggestions, and even targeted insights right out of the gate. It’s all about testing the waters, refining, and going again. 

Marketers have an incredible array of martech and adtech tools at their disposal. These tools can decipher vast amounts of data to reveal insights into customer behaviours, preferences, and journeys. 

But the true power of these tools lies not in the data they collect, but in how we use this information to foster genuine connections–leveraging technology to map customer journeys more accurately, personalize interactions, and refine messaging to create marketing experiences that resonate deeply with our audience.

What Matters is the Human Touch 

As a marketer, you have a great opportunity in front of you. You can either use the data and tech tools at your disposal to stalk and creep people out or connect with them at a human level. 

Technology alone is not the answer. It’s the human touch in interpreting and applying these insights that transform good marketing into great. Your goal should be to use these tools to amplify your understanding of the people you serve, crafting strategies and messages that feel personal, relevant, and deeply human.

The transition to a more human-centric approach in marketing isn’t just a trend; it’s a powerful strategy for building trust, loyalty, and meaningful relationships with your consumers.

But adopting human-centric marketing is more than just a strategic decision—it’s a commitment to understanding and respecting the individuals behind every transaction.

Request a demo to learn how to use data to humanize your advertising campaigns with StackAdapt.