How to Use Data for B2B Ad Personalisation

Henry McIntosh
Oct 9
15 min read

Want better results from your B2B ads? Personalisation is the key. By leveraging data, you can create campaigns that resonate with specific industries, roles, or even individual accounts. Here's what you need to know:

Why it matters: Generic ads don't work for complex B2B buying cycles. Personalisation addresses unique challenges, making your campaigns relevant and effective.
Key steps:
1. Collect quality data: Use first-party (CRM, analytics), second-party (partners), and third-party sources (LinkedIn, B2B data providers).
2. Segment your audience: Group prospects by industry, company size, behaviour, or intent.
3. Launch tailored campaigns: Craft messages that match pain points, industries, or decision-maker priorities.
4. Measure and optimise: Track metrics like pipeline velocity, conversion rates, and engagement depth to refine your efforts.
Tools to use: Account-based marketing (ABM), predictive analytics, and real-time data enrichment.

Bottom line: Personalisation builds trust, engages decision-makers, and drives better results. Start by gathering the right data, segmenting effectively, and crafting campaigns that speak directly to your audience's needs.

Personalize your ABM strategies for B2B marketing in 2025

Step 1: Collecting Quality Data for Personalisation

Building a successful personalised B2B campaign starts with having high-quality, detailed data. Without accurate and well-rounded information about your prospects, even the most advanced tools won't deliver the desired results. The key is to create a strong data collection strategy that captures both basic company details and deeper insights into behaviours and needs.

Finding Key Data Sources

First-party data is the backbone of personalisation. Use your CRM, website analytics, and email engagement metrics to gather behavioural insights and track the full customer journey. Email engagement data, for instance, can reveal valuable patterns - open rates, click-through behaviours, and content preferences all provide clues about what resonates with different segments. Sales call notes and meeting recordings are also goldmines of information, capturing qualitative insights like specific challenges, pain points, and decision-making criteria.

Second-party data comes from partnerships and collaborations. This could involve data shared by complementary service providers, industry associations, or strategic partners. For example, if you're targeting financial services firms, collaborating with a regulatory compliance consultancy might uncover which companies are grappling with particular regulatory hurdles.

Third-party data fills in the gaps. Resources like Companies House can provide basic company details, while specialised B2B data providers offer deeper insights, such as recent funding rounds, leadership changes, and technology adoption trends. Tools like LinkedIn Sales Navigator deliver real-time updates on prospect companies, including new hires, expansions, and announcements.

By blending first-, second-, and third-party data, you can create a well-rounded prospect profile. Once you've gathered this data, the next step is ensuring its accuracy and compliance.

Maintaining Data Quality and Compliance

The quality of your data has a direct impact on how well your campaigns perform. Outdated contacts waste resources, and inaccurate company details lead to irrelevant messaging. Regular data cleansing is essential - this means removing duplicates, updating job titles, and verifying company information.

For UK businesses, GDPR compliance is non-negotiable. Under GDPR, you need a lawful basis for processing personal data, such as legitimate interest in B2B marketing. However, you must also provide clear opt-out options and respect individual preferences. Keeping detailed records of data sources and consent is critical.

Data security is equally important. Limit access to sensitive information to authorised personnel only, and conduct regular security audits to identify and address potential vulnerabilities.

To catch errors early, consider validating data at the point of collection. Basic checks, like ensuring email formats are correct or cross-referencing company names with trusted databases, can save time later. Automated validation tools can also flag inconsistencies, such as mismatched job titles and company sizes.

Once your data is clean, secure, and compliant, the focus shifts to centralising and enriching it for deeper insights.

Centralising and Enriching Data

When data is spread across multiple systems, it creates gaps and inconsistencies. A Customer Data Platform (CDP) or unified CRM consolidates this information, providing a single, reliable source of truth. These platforms can merge data from various channels, creating complete profiles for each prospect.

Modern CDPs are particularly adept at matching records, even when there are slight variations. For example, they can identify when the same person appears in your email database, website analytics, and sales records, giving you a holistic view of their engagement history.

Data enrichment takes basic details and turns them into actionable insights. Firmographic data, such as company size, revenue, and industry, helps with segmentation. Technographic data, which shows the software and tools a prospect uses, allows for more precise targeting. Intent data highlights the topics and solutions prospects are actively researching.

Behavioural enrichment adds another layer by tracking how prospects engage with your content over time. This includes website visits, social media interactions, event participation, and content downloads. These patterns provide a clearer picture of where prospects are in their buying journey and how interested they are.

Real-time enrichment ensures your data stays up to date. For instance, when a prospect visits your website, automated systems can instantly update their company information, recent news, and technology usage. This allows you to personalise your approach based on the latest information.

The focus should always be on collecting data that directly supports targeted personalisation. Having accurate, relevant details about a smaller group of prospects will always outperform incomplete information about a larger audience.

Step 2: Analysing and Segmenting Your B2B Audience

Turning reliable data into meaningful insights requires strategic segmentation. By identifying patterns and grouping prospects based on shared traits, you can better understand what drives their buying decisions.

B2B Audience Segmentation Methods

One of the primary methods is firmographic segmentation, which categorises companies based on measurable attributes like industry, size, turnover, or location. For instance, you might group financial service firms with turnovers exceeding £50 million, medium-sized manufacturers with 100–500 employees, or healthcare organisations operating across multiple sites. These segments should reflect actual differences in needs and decision-making processes. After all, a startup with 20 employees won't have the same priorities as a multinational bank.

Another approach is technographic segmentation, which looks at the technologies your prospects use. This can reveal whether they rely on modern cloud-based systems or older, on-premise infrastructure. Such insights allow you to craft messaging that highlights integration capabilities or showcases relevant case studies.

Behavioural segmentation focuses on tracking how prospects engage with your brand, offering clues about their readiness to buy.

Meanwhile, intent-based segmentation examines signals like search activity, content consumption, and competitor interactions. These indicators can help you identify prospects actively researching solutions, even if they haven't reached out to your brand yet.

These methods lay the groundwork for more focused, account-specific strategies.

Using Account-Based Marketing (ABM)

Account-based marketing (ABM) shifts the focus from broad targeting to a select group of high-value accounts. Instead of trying to appeal to everyone, ABM zeroes in on specific accounts, creating tailored campaigns for each. This approach works particularly well for complex B2B sales cycles involving multiple decision-makers and significant investments.

The first step in ABM is account selection, which relies on your ideal customer profiles. By analysing your most successful customers, you can identify common traits - such as company size, growth patterns, or technology preferences - and use them to pinpoint similar prospects.

Next comes account research, which goes beyond basic lead qualification. This involves digging into each target account’s business challenges, recent developments, and competitive landscape. You might review their annual reports, press releases, or even leadership changes to get a clearer picture.

Twenty One Twelve Marketing, for example, specialises in ABM strategies for industries like financial services and technology. Their focus is on reaching senior decision-makers who are often hard to engage through traditional marketing.

Stakeholder mapping is another critical step. This involves identifying everyone involved in the buying process, from IT directors concerned with technical details to CFOs focused on costs and ROI. Tailoring your approach to each stakeholder ensures their specific priorities are addressed.

ABM campaigns often span multiple channels, including email, LinkedIn, direct mail, and targeted ads. The challenge is to keep your messaging consistent while adjusting the tone and format for different audiences. For instance, while IT teams might appreciate a technical whitepaper, C-suite executives may prefer an executive summary.

Finally, account-specific content helps demonstrate your understanding of a prospect’s unique needs. This could include custom case studies, ROI calculators tailored to their industry, or demo scenarios that reflect their specific challenges.

Applying Predictive Analytics and AI

To refine your targeting even further, predictive analytics and AI offer powerful tools. These technologies use historical data to anticipate future behaviours, helping you identify which prospects are likely to convert and when they might be ready to buy.

Lead scoring models assign values to prospects based on their traits and interactions. Traditional models might focus on factors like company size or email engagement, but predictive models can uncover more nuanced patterns. For example, a combination of specific page visits and downloads might signal a higher likelihood of conversion.

Advanced predictive tools can also spot less obvious buying signals. For instance, a prospect who visits your pricing page, downloads technical specs, and reads customer testimonials in quick succession may be close to requesting a demo.

Churn prediction helps you identify customers at risk of leaving by analysing factors like usage patterns, support requests, and contract renewal dates. This allows you to take proactive steps to retain them.

Lifetime value prediction estimates the long-term revenue potential of different segments, helping you prioritise efforts on those with the greatest return potential.

AI-powered segmentation takes things a step further by dynamically updating groups as new data comes in. Unlike static segments that require manual updates, AI models learn from campaign results and customer feedback, making adjustments in real time.

Step 3: Running Personalised B2B Ad Campaigns

Now that you’ve defined your audience segments and gathered valuable insights, it’s time to turn that data into action. Running personalised B2B ad campaigns is about taking what you know about your prospects and crafting messages that resonate with their specific needs and circumstances.

Crafting Messages That Speak to Your Audience

The secret to effective personalisation lies in tailoring your content to address the unique challenges and priorities of each segment. Instead of relying on generic ads, develop variations that align with different pain points, industries, or stages in the buyer’s journey.

For example, dynamic content can adjust automatically to match the viewer’s interests. If you’re targeting financial services, highlight compliance and risk management. For technology firms, focus on scalability and integration. The product might be the same, but the messaging shifts to reflect what matters most to each audience.

Your data should guide not only the content but also the tone of your messaging. For instance, manufacturing prospects might respond to ROI-focused language, while healthcare organisations may prioritise patient outcomes. Similarly, technical decision-makers might appreciate detailed specifications, whereas C-suite executives are more likely to engage with high-level strategic benefits.

Personalised landing pages are another must-have. When a prospect clicks on your ad, they should land on a page that feels like it was designed specifically for them. Include elements like industry-specific case studies, testimonials, or product demos that address their unique needs. This ensures a seamless transition from ad to landing page, deepening the sense of personalisation.

Consistency is key. If your LinkedIn ad speaks to HR directors about employee retention, the landing page should expand on that theme with relevant data, success stories from similar companies, and clear next steps tailored to HR professionals.

Tailored offers are another way to make an impact. Different segments respond to different incentives. For example, startups might value free trials or discounted pricing, while larger enterprises may be more interested in comprehensive onboarding or dedicated account management. Your data will help you determine which offers resonate with each group.

Once your messages are ready, automation tools can help you deliver them effectively across multiple channels.

Scaling Personalisation with Automation

To deliver these tailored experiences at scale, you’ll need automation tools that can handle multiple campaigns across different platforms. Tools like programmatic advertising and LinkedIn Campaign Manager provide powerful options for targeting prospects based on job titles, industries, company size, and even specific interests or skills.

By combining your internal data with these tools, you can create a cohesive experience across channels. For instance, a prospect might first encounter your brand through a LinkedIn sponsored post, then see a related display ad while browsing industry sites, and later receive a personalised email. Each touchpoint should feel connected while respecting the unique characteristics of the platform.

Marketing automation can also react to a prospect’s behaviour in real time. If someone downloads a whitepaper, they can be automatically added to a nurture sequence that includes targeted LinkedIn ads, personalised emails, and invites to industry-specific webinars. This keeps your brand top of mind while providing value at every stage.

Modern automation tools also let you optimise campaigns on the fly. If certain messages perform better with specific segments, budgets can be reallocated to maximise their impact while scaling back on underperforming content.

Timing and Frequency: Finding the Right Balance

Even the best-crafted messages won’t work if they’re delivered at the wrong time or too often. This is where your behavioural and predictive data come into play.

Behavioural triggers can help you time your outreach effectively. For instance, if a prospect visits your pricing page multiple times in a short period, they might be ready for a direct sales conversation. On the other hand, someone still in the research phase may benefit from educational content delivered at a slower pace.

Seasonal trends can also guide your timing. For example, B2B tech purchases often spike in the final quarter as companies allocate leftover budgets, while professional services might see increased demand at the start of a new financial year when initiatives are being launched. Adjusting your campaign intensity to align with these patterns can make a big difference.

Frequency capping is essential to avoid overwhelming prospects. Different audiences have different tolerance levels for marketing messages. Senior executives, who are inundated with pitches, may need fewer, more subtle touchpoints. Meanwhile, technical evaluators actively researching solutions might welcome more frequent and detailed communications.

Monitor engagement metrics like click-through rates and unsubscribe rates to gauge whether you’re striking the right balance. A drop in engagement often signals that your outreach is too frequent or poorly timed.

When targeting multiple stakeholders within the same organisation, coordination is crucial. You don’t want to bombard them with conflicting messages. Instead, space out your touchpoints and ensure your outreach feels unified.

Predictive models can also help refine your timing. Early-stage prospects might engage more with educational content during working hours, while those closer to a purchase decision may prefer pricing details outside traditional business hours when they can take a deeper dive.

Step 4: Measuring and Improving Personalised Campaigns

Success in B2B ad personalisation doesn’t happen overnight - it’s a process that requires constant monitoring and adjustment. The data and insights gathered in earlier steps play a crucial role in shaping how campaigns are measured and refined. By keeping a close eye on performance, you can ensure each campaign becomes sharper and more impactful over time.

Key Performance Indicators (KPIs) for B2B Personalisation

Traditional metrics like click-through rates (CTR) and impressions only scratch the surface when it comes to measuring the success of personalised B2B campaigns. To truly understand their impact, you need to focus on metrics that reveal the quality of engagement and how it affects your sales pipeline.

Pipeline velocity: This metric shows how quickly prospects move through your sales funnel after interacting with personalised ads. If your personalisation strategy is working, you’ll see prospects advancing faster from awareness to consideration compared to generic campaigns.
Account engagement depth: This measures how well your campaign engages multiple stakeholders within a target account. For instance, a well-targeted campaign for the financial services sector should generate interest from both CFOs and IT directors within the same organisation.
SQL conversion rates: This helps you gauge the quality of leads generated by your personalised campaigns. Compare these rates with those from non-personalised campaigns to determine whether the extra effort and resources are paying off.
Cost per acquisition (CPA): Personalised campaigns may cost more upfront, but they often yield better-qualified leads. Tracking CPA helps you assess whether the investment is delivering value.
Attribution accuracy: B2B buyers often interact with multiple pieces of content before making a decision. By tracking the entire customer journey, you can identify which personalised elements are driving conversions.
Engagement quality scores: Combine various metrics - such as whitepaper downloads, webinar attendance, and visits to pricing pages - and weigh them based on their historical correlation with sales. This gives you a clearer picture of how well your campaign is performing.

Testing and Improving Campaign Performance

To refine your campaigns without wasting resources, systematic testing is essential. By testing one variable at a time and using statistically significant sample sizes, you can pinpoint what works and what doesn’t.

Message and creative testing: Experiment with different messages, visuals, and offers to see what resonates with your audience. For example, does your audience respond better to cost-saving messages or efficiency-focused ones? Does industry-specific imagery outperform generic business visuals?
Landing page optimisation: Ensure your landing pages align with the personalised ads that lead to them. For instance, if your ad highlights compliance solutions, test whether regulatory case studies or cost-benefit analyses are more effective in converting visitors.
Offer testing: Different segments may respond to different incentives. Some might prefer educational resources like industry reports, while others may be more motivated by product demos or free consultations.

The results from these tests should directly feed into your analytics, allowing for real-time adjustments that keep your campaigns on track.

Using Real-Time Analytics for Continuous Improvement

The ability to adapt on the fly is critical for maintaining effective campaigns. Real-time analytics provide the tools you need to identify trends and make adjustments while your campaigns are still active.

Performance dashboards: Integrate data from your advertising platforms, CRM systems, and marketing tools into a single dashboard. This consolidated view makes it easier to connect personalised touchpoints with business outcomes.
Automated alerts: Set up notifications for when key metrics, like cost per lead or conversion rates, fall outside expected ranges. These alerts ensure you can address issues promptly.
Behavioural tracking: Monitor how prospects interact with your content. If visitors from a specific campaign are spending more time on particular pages, you can amplify those elements in future campaigns.
Competitive intelligence: Keep an eye on broader market trends to understand whether changes in performance are due to your campaign’s effectiveness or external factors, such as industry news or regulatory updates.

You can also automate budget adjustments based on real-time performance. For example, increase spending on campaigns that exceed performance thresholds and reduce budgets for underperforming ones.

The ultimate goal is to create a feedback loop where each campaign informs the next. By documenting what works and what doesn’t, you build a knowledge base that improves the effectiveness of future personalisation efforts. Each campaign becomes a stepping stone towards better audience understanding and stronger results.

Conclusion: Getting Results with Data-Driven B2B Ad Personalisation

In today’s challenging B2B landscape, personalising ads using data isn’t just a nice-to-have; it’s essential for reaching niche, hard-to-access sectors. By following the four-step approach detailed here - gathering reliable data, segmenting audiences effectively, launching tailored campaigns, and closely tracking performance - you create a strong foundation for navigating these complex markets. This method ensures your efforts lead to measurable, consistent progress.

Personalised campaigns go beyond simply improving click-through rates. They address specific pain points, fostering trust and positioning your brand as a reliable partner rather than just another supplier. This relevance not only builds credibility but also strengthens your organisation’s authority within the industry.

When executed with precision, personalisation makes pipeline growth more predictable. Using account-based marketing principles and predictive analytics, you can better identify and nurture high-value prospects. Over time, the data gathered from these campaigns sharpens your strategies, helping you develop a deeper understanding of your audience.

This approach is particularly effective in industries where buyers demand precision and are often sceptical of generic marketing. These decision-makers value expertise, making tailored messaging a critical tool for cutting through the noise. Investing in robust data systems and analytical tools enables you to craft messages that resonate with senior-level stakeholders, ensuring your efforts are impactful.

Success hinges on maintaining consistency across every touchpoint while staying agile enough to adapt to shifting market dynamics. As buyer behaviours evolve, the organisations that excel will be those that refine their personalisation strategies in real time. The framework provided here serves as a guide to building this capability, helping you achieve measurable outcomes and sustained growth.

For businesses operating in intricate B2B sectors, the ultimate goal is to forge meaningful connections that drive long-term relationships and steady pipeline growth. Personalisation, when done right, is the bridge to achieving this.

FAQs

To keep your data accurate and aligned with GDPR regulations, focus on gathering first-party data using clear and honest methods. This includes using straightforward opt-in forms and providing detailed privacy policies. Always let users know exactly how their data will be used and ensure you get their explicit consent.

Consider using privacy-focused approaches like progressive profiling, which collects small amounts of data over time, rather than all at once. Regularly auditing your data collection and processing methods is also essential. These steps not only help you stay compliant but also improve data quality and strengthen trust with your audience.

By sticking to ethical data practices, you’ll be able to create personalised B2B campaigns that respect privacy while still delivering impactful results.

What are the best ways to segment a B2B audience for more personalised advertising?

To craft more tailored B2B advertising, start by breaking down your audience into segments using firmographic data (such as industry, company size, or revenue), geographic details (like location or region), and behavioural insights (including website activity or purchase history). This approach helps pinpoint specific groups within your target market.

For a deeper level of segmentation, tap into first-party data and utilise AI tools to create real-time, personalised experiences. By combining demographic information with behavioural trends, you can fine-tune your audience groups, ensuring your campaigns connect with the right people. Make sure to focus on data accuracy - this is key to boosting the impact of your efforts and fostering meaningful engagement.

How can predictive analytics and AI improve personalised B2B advertising campaigns?

Predictive analytics and AI are transforming personalised B2B advertising by using data to anticipate customer behaviour. This means businesses can achieve sharper targeting and allocate resources more efficiently, resulting in smarter campaigns, higher conversion rates, and a stronger return on investment (ROI).

These technologies take over repetitive tasks, dig deeper into customer insights, and spot trends that might otherwise go unnoticed. With the power of data-driven predictions, marketers can fine-tune their strategies, anticipate demand, and craft messaging that speaks directly to their audience. The result? Campaigns that connect on a personal level and deliver tangible outcomes.

How to Use Data for B2B Ad Personalisation

Personalize your ABM strategies for B2B marketing in 2025