The Ultimate Guide for Scaling Personalization Using Generative AI

January 6, 2024
min read
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The case for personalization is clear. According to the Boston Consulting Group, companies with stellar personalization strategies boost growth rates by 6% to 10%.

Marketers agree. Ninety percent say personalization significantly contributes to business profitability. So what has held back progress? For most, it has been the amount of time and money required to scale. The emergence of generative AI, when combined with modern data and automation practices, has changed the calculus dramatically. Now companies can use technology to create and scale impactful personalization strategies at a fraction of the cost.

As avid practitioners of personalization, we are bombarded with questions like what is it, how can it help my business, how does it work, what are the common use cases, and so on. In this Ultimate Guide, we answer these questions and others so you can start personalizing your customer interactions the way you have always wanted.

Questions we will answer:

What is personalization in marketing?
Why is personalization important?
How is personalization changing?
How fast is generative AI improving?
How is AI different from generative AI?
What is the relationship between generative AI and LLMs?
What can you personalize today?
Marketing use cases for personalization?
How to build a personalization strategy?
What tools and processes do you need?
What criteria should you use to select a project

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What is personalization in marketing?

Personalization in marketing refers to the practice of tailoring marketing messages, content, products, and services to individual buyers based on their preferences, behaviors, demographics, and past interactions. The result is a more relevant and engaging experience for each one. Companies reap business benefits like a healthier pipeline, stronger customer relationships, and superior marketing efficiency.

Personalization can be applied across various marketing channels and content types, including email marketing, website content, voice and video, social media, and more. Here are a few ways personalization is commonly used in marketing:

1. Product Recommendations

Websites often use personalization to suggest products to customers based on their browsing history, purchase history, and preferences. This can lead to higher conversion rates by showing customers items they are more likely to be interested in.

2. Email Marketing

Marketers use personalization in email campaigns by addressing recipients by their names, recommending products related to their past purchases, and sending tailored content based on their interests.

3. Dynamic Website Content

Websites can dynamically display content based on a visitor’s behavior and preferences. This might include showing relevant blog posts, offers, or featured products. Personalized landing pages are another example.

4. Social Media

Platforms like LinkedIn, Facebook, and others allow advertisers to create highly targeted ads based on users’ interests, behaviors, and demographics.

5. Location-Based Marketing

Personalizing marketing messages based on the user’s location can be effective, especially for brick-and-mortar businesses. It can involve sending location-specific offers or information.

6. Retargeting

When users leave a website without completing a desired action, like making a purchase, retargeting ads can follow them across the web, reminding them of the products they showed interest in.

7. Content Customization

Content marketing can also involve personalization. For instance, an online news platform might show different articles to different users based on their reading history.

8. App Personalization

Mobile apps can personalize the user experience by tailoring content, recommendations, and notifications to each user’s behavior within the app.

9. Predictive Analytics

Utilizing data analytics and machine learning, marketers can predict a user’s preferences and behaviors to personalize their experiences even before the user explicitly provides that information.

Now that we have a common definition of what personalization in marketing means, we can transition to why and how it has changed dramatically over time. Advances in data, automation, and most recently generative AI, have revolutionized the efficacy and economics of personalization for B2B marketers.

Why is personalization important?

Personalization helps companies create stronger connections with customers using custom messaging, special offers, services, and more. The result is higher sales and greater loyalty. According to Salesforce, 66% of customers expect companies to understand their needs and expectations, and 84% of business buyers are more likely to buy when their goals are understood.

The application of generative AI, data, and automation technologies makes creating and scaling personalization strategies possible like never before.

How is personalization changing?

B2B marketers have incredible power at their fingertips today, but this was not always the case. Early personalization began with direct mail. Some readers may remember those Publishers Clearing House drops! While these were largely generic, marketers would occasionally tailor messages based on industry or company size thanks to the integration of database marketing techniques in the late 1999s.

The early 2000s saw the emergence of digital technology and the growth of customer relationship management (CRM) systems. Businesses could record customer information and segment their audiences more effectively. For example, simple name insertions to targeted content based on prior interactions in email became increasingly standard in email marketing.

Next came big data and analytics. These were transformative tools for B2B marketing. Marketers could use real-time insights to predict buying behavior, assess pain points, and craft campaigns. Account-Based Marketing (ABM) extended B2B personalization to large strategic accounts. Unfortunately, only the largest companies would afford this type of engagement.

Fast forward to today. Artificial intelligence (AI) and machine learning technologies enable marketers to automate personalization at scale. Algorithms can analyze large datasets, determine patterns, and automate personalized content delivery in ways that were previously unimaginable.

"AI is reaching the tipping point where CEOs who are not yet invested become concerned that they are missing something competitively important."

Mark Raskino
Distinguished VP Analyst, Gartner

Generative AI like text, voice, and video is accelerating this revolution at mind-numbing speed. For example, within just two months of its launch, ChatGPT was estimated to have reached 100 million monthly active users.

Just how fast is generative AI improving?

Generative AI has seen remarkable improvements in accuracy and relevance. Consider the evolution from OpenAI’s GPT-2 to GPT-3. GPT-2, trained on 1.5 billion parameters, was impressive in generating human-like text. However, GPT-3, with its whopping 175 billion parameters, displayed an even greater ability to generate highly relevant and coherent content across diverse prompts.

According to some sources, GPT-4 has 1.7 trillion parameters. This makes it 1000 times larger than GPT-2 and nearly 1000 times larger than GPT-3.

Another testament to its progress is the myriad of applications built using GPT-3. For example, GPT-3 has been incorporated into practical applications like drafting emails, coding assistance, tutoring in multiple subjects, and content creation. The rate of improvement will not slow down.

For the avoidance of doubt, it is important to understand the relationship between Generative AI, AI, and Large Language Models (LLMs), respectively.

How is AI different from generative AI?

Think of AI as a big toolbox. Inside this toolbox, you have different tools designed for various jobs, like understanding speech, recognizing faces, or helping cars drive themselves. These tools are all parts of AI because they help machines think and act a bit like humans.

Generative AI is a tool in the AI toolbox. Instead of just understanding or analyzing things, generative AI is like an artist. It can create music, pictures, or even write stories. It looks at lots of examples of something, like thousands of paintings, and then tries to make its own new original painting.

A computer scientist would likely describe AI and generative AI as distinct aspects of the broader field of machine intelligence, with different objectives and functionalities.

For example, artificial Intelligence encompasses a wide range of computational techniques which enable machines to mimic human-like abilities such as learning, reasoning, perception, and decision-making. AI includes a variety of subfields, from neural networks and robotics to natural language processing and expert systems. The overarching goal of AI is to create systems that can perform tasks that traditionally required human intelligence.

Generative AI, on the other hand, is a subset of AI that generates new content or data. It’s driven by algorithms that can produce novel outputs, such as images, texts, or music, given certain inputs. The most notable examples are models like OpenAI’s GPT. These systems are designed to understand patterns and structures from provided data and generate new, original pieces based on that understanding.

What is the relationship between large language models and generative AI?

Generative AI and large language models (LLMs) like OpenAI’s GPT share a close relationship. In essence, LLMs are a subset of generative AI, specifically designed for handling and producing human language.

Here’s a simple breakdown:

Generative AI is about creating new content or data. Whether it’s images, music, videos, or text, the goal is to generate outputs that can often feel “original” or “creative”. The algorithms are designed to understand patterns in the data they’re trained on and then produce something new based on those patterns.

LLMs are a type of generative AI optimized for text. They’re trained on vast amounts of textual data, enabling them to generate coherent and contextually relevant sentences, paragraphs, or even whole articles. When you ask an LLM a question or give it a prompt, it “thinks” based on all the text it has seen during its training and then generates a relevant response.

With the context setting behind us, we can turn our attention to the importance of personalization.

How many variables can you personalize today?

The capability to personalize using generative AI and data and scaling using automation is vast and constantly evolving. Theoretically, there’s no fixed upper limit to the number of variables a B2B marketer can personalize, but the complexity, data quality, and practical considerations will define the feasible limits.

While impressive, the sheer quantity of variables available to personalize is less interesting than the importance of focusing on quality, relevance, and impact. Having said that, there are seven categories of variables that can be personalized in B2B marketing along with several examples of attributes:‍

Category Attribute
Demographic Variables

Company size

Industry sector

Geographic location

Company revenue

Behavioral Variables

Website interactions (pages visited, time spent)

Past purchase behavior and frequency

Email engagement (open rate, click-through rate)

Event attendance (webinars, trade shows)

Content consumption patterns (e.g., types of articles or videos viewed)

Firmographic Variables

Organizational structure

Technographic data (technologies used by the company)

Company growth trends

Key decision-makers and their roles

Psychographic Variables

Pain points and challenges

Company goals and objectives

Company values and culture

Engagement Stage Variables

Stage in the sales funnel (awareness, consideration, decision)

Previous touchpoints with the brand (ad clicks, customer service interactions)

Feedback and satisfaction from past interactions

Temporal Variables

Time of the year (e.g., fiscal year-end)

Specific company events (e.g., product launches, mergers)

Personal Preference Variables

Preferred mode of communication (email, phone, video call)

Past content preferences (e.g., infographics, case studies, whitepapers)

Interaction frequency preference

External Variables

Industry trends and shifts

Economic indicators related to the company’s sector

Competitor actions and positioning

Keep in mind that as the number of variables increases, the data required to make meaningful predictions or personalizations grows exponentially. This is often referred to as the “curse of dimensionality” in data science. It means that even if technology can handle countless variables, it may not always be efficient or yield better results to do so.

Marketing use cases for personalization

A question we often hear from executives involves identifying use cases. After canvasing chief marketing officers and marketing directors, these ten stand out.

1. Personalized content recommendations

AI algorithms analyze customer data, preferences, and behavior to deliver highly personalized content recommendations. By tailoring content to match individual interests, marketers can enhance customer engagement and increase the likelihood of conversions. When customers receive content that resonates with them, they are more likely to take action, resulting in improved lead generation and revenue growth.

2. Predictive lead scoring

AI-powered predictive lead scoring helps marketers identify high-quality leads with a higher probability of converting into customers. By analyzing historical data and customer behavior, AI models assign scores to leads, allowing marketing teams to prioritize their efforts on the most promising opportunities. As a result, sales teams can focus on leads with the highest potential, leading to faster conversions and revenue growth.

3. Automated email marketing campaigns

AI streamlines email marketing campaigns by automating content creation, scheduling, and personalization. Through AI-driven insights, marketers can segment audiences based on preferences and behavior, sending targeted and timely emails. Automated email campaigns result in improved open rates, click-through rates, and conversions, ultimately driving revenue growth. As more data is collected and feedback loops are completed, the AI systems learn and improve, continually refining the targeting, content, and scheduling of emails for even better results over time. This continuous improvement cycle, powered by AI, transforms email marketing from a mass communication tool into a personalized marketing powerhouse.

4. Chatbots for real-time customer support

AI-powered chatbots provide instant and efficient customer support 24/7. By answering queries and resolving issues in real time, chatbots enhance customer satisfaction and loyalty. Satisfied customers are more likely to make repeat purchases and refer others, leading to increased revenue growth.

5. Customer behavior analysis for cross-selling and upselling

AI can analyze customer behavior and purchasing patterns to identify opportunities for cross-selling and upselling. By offering relevant products or services based on customer preferences, marketers can increase average order values and revenue per customer.

6. Social media sentiment analysis

AI-driven sentiment analysis enables marketers to gauge customer opinions and feelings on social media platforms. By understanding customer sentiment, businesses can respond promptly to negative feedback, engage with positive comments, and tailor marketing strategies accordingly. Positive interactions on social media can lead to improved brand perception and increased customer loyalty, ultimately supporting revenue growth.

7. Dynamic pricing optimization

AI-powered dynamic pricing helps businesses optimize product pricing based on various factors, such as demand, competitor pricing, and customer behavior. By adjusting prices in real time, marketers can maximize revenue by offering the right prices to different customer segments. What sets AI-powered dynamic pricing apart is its real-time responsiveness. Prices can be adjusted on the fly, responding to sudden changes in market conditions or customer behavior. This capability allows businesses to take advantage of fleeting opportunities, such as a surge in demand, or mitigate risks, such as a competitive price cut.

8. AI-enhanced SEO and content strategy

AI can analyze search trends and customer behavior to optimize SEO and content strategies. By creating content that aligns with customer interests and search intent, businesses can improve search rankings, drive more organic traffic, and ultimately boost revenue.

9. Automated social media marketing

AI automates social media marketing tasks such as content scheduling, posting, and performance analysis. Marketers can focus on creating engaging content, while AI handles the logistics. Consistent and strategic social media marketing increases brand visibility, attracts new customers, and drives revenue growth.

10. AI-powered customer retention strategies

I analyzes customer data to predict churn risk and identify at-risk customers. By proactively addressing customer concerns and offering personalized incentives, businesses can improve customer retention rates. Retaining existing customers is more cost-effective than acquiring new ones, and loyal customers are more likely to make repeat purchases, supporting long-term revenue growth.

How to build a personalization strategy in the age of generative AI

As generative AI systems like OpenAI’s GPT series become prevalent, businesses are leveraging them to revolutionize the way they interact with their users. One significant potential is in personalized content creation and experience optimization. But how can businesses craft a robust personalization strategy that harnesses the prowess of generative AI?

Here’s a step-by-step guide:

1. Define personalization goals

Begin by understanding what personalization means for your business. Do you want to offer personalized product recommendations, tailor content to individual user preferences, or maybe customize user interfaces? Setting clear objectives is paramount.

2. Gather and analyze data

Generative AI thrives on data. The more you know about your users – be it from their browsing habits, purchase history, or interactions – the more accurate and effective the AI can be. Use advanced analytics tools to understand user behaviors and segment them.

3. Integrate generative AI systems

Instead of just relying on traditional rule-based algorithms, integrate systems like GPT or its successors into your personalization toolkit. These models can generate content on-the-fly, be it marketing copy, product descriptions, or even interactive responses.

4. Implement continuous learning Generative AI models can evolve and adapt. As users interact with the content, collect feedback, and retrain your models. This ensures the AI is always learning and refining its output based on real-world results.

5. Ensure ethical use

With great power comes great responsibility. Generative AI can craft exceptionally personalized content, but it’s crucial to ensure that such personalization doesn’t border on invasion of privacy. Always obtain user consent and ensure data privacy is a top priority.

6. Test and optimize

Just because AI can generate content doesn’t mean it’s always perfect. A/B test AI-generated content versus human-crafted content. Understand where AI shines and where the human touch remains irreplaceable.

7. Educate your audience

In an age where deep fakes and misinformation are concerns, transparency is crucial. Let your users know when and how you’re using generative AI, ensuring they trust the content they’re receiving.

8. Stay updated

The world of AI is rapidly evolving. Stay updated with advancements in generative models, new methodologies, and best practices. This not only enhances your personalization efforts but also ensures that you’re providing the best experience for your users.

What tools and processes do you need to support a personalization strategy?

B2B marketing carries its own unique set of challenges and requirements, which are distinct from B2C. When applying generative AI, data, and automation to craft a B2B personalization strategy, the focus should be placed on understanding industry needs, decision-making hierarchies, and the longer sales cycles typical of B2B transactions.

Below is a list of tools and processes tailored for B2B marketing personalization:

Tools

Category Tools
Data collection and storage tools

CRM systems: Salesforce, HubSpot, Microsoft Dynamics.

Data warehouses: Redshift, Snowflake, Google BigQuery.

Lead scoring and tracking Lead management: Pardot, Marketo, LeadSquared
Data cleaning and processing

ETL tools: Talend, Apache Nifi

Data cleaning libraries: Pandas (Python), Tidyverse (R)

Generative AI platforms OpenAI API or custom-built models using platforms like TensorFlow and PyTorch
B2B personalization engines Account-based marketing (ABM) platforms: Terminus, Demandbase, Engagio
Feedback loop tools

Feedback collection: B2Bmetric, UserReport

Engagement analytics: PathFactory, Looker

A/B testing and optimization Convert, VWO, Optimizely
Data visualization and reporting Tableau, PowerBI
Email marketing and automation HubSpot, Mailchimp, SendinBlue
Privacy and compliance management OneTrust, TrustArc

Process

Category Process
Data collection

Sync CRM and other B2B tools to collect and centralize data on leads, accounts, and opportunities.

Capture insights from events, webinars, and industry reports.

Data cleaning & pre-processing

Remove outdated or irrelevant company data.

Align data from various sources for a unified view of the lead/account.

Segmentation

Segment companies based on size, industry, and purchasing power.

Categorize decision-makers based on their roles.

Model training Train generative AI models for creating personalized content, from email outreach to tailored industry reports.
Account-based marketing Identify key accounts and customize content and campaigns based on their specific needs and challenges.
Feedback collection

Gather feedback on AI-generated content, ensuring it resonates with the B2B audience.

Monitor engagement and lead progression.

A/B testing Test various personalization strategies to understand what resonates best with decision-makers.
Iteration and optimization

Refine generative AI models based on feedback and engagement metrics.

Update personalization strategies to keep them aligned with industry trends.

Email marketing automation

Design and automate personalized email campaigns using insights from generative AI.

Monitor open rates, CTRs, and other engagement metrics.

Compliance and data management

Ensure GDPR, CCPA, and other regulations are adhered to.

Regularly update CRM data and purge outdated information.

What criteria should you use to select a project?

For a B2B marketer, determining where to start requires a strategic approach. Otherwise, using these technologies without a clear purpose can lead to inefficiencies or even counterproductive results. Here are the criteria to consider:

Criteria Definition
Return on investment (ROI) potential

Revenue impact: Will the project directly drive sales or lead conversion?

Cost savings: Can automation reduce significant manual work, thus cutting down operational costs?

Alignment with business goals Ensure the project aligns with the broader organizational objectives, whether that’s market expansion, increased customer retention, or other key milestones.
Data availability and quality Generative AI thrives on quality data. Ensure you have access to clean, relevant, and substantial data before considering AI deployment.
Complexity vs. automation potential High-frequency, repetitive tasks are ideal for automation. Conversely, tasks requiring deep human intuition and relationship-building may not benefit as much from AI.
Scalability Projects that can be scaled easily are prime candidates. If the solution can be applied across different products, regions, or sectors, it’s more valuable.
Customer impact Evaluate how the project will impact the customer experience. Will it lead to faster response times, personalized content, or more efficient service?
Technological compatibility Ensure your existing tech stack can integrate with the AI and automation solutions you’re considering. Compatibility issues can stall or complicate projects.
Ethical and compliance considerations Especially in sectors like healthcare or finance, regulatory constraints can be significant. Ensure any AI or data project is compliant with industry standards and ethics.
Competitive advantage Determine if the project can give you an edge over competitors. If the project positions you as an innovator in your industry, it might be worth the investment.
Feasibility and timelines Assess if the project is technically feasible within the desired timelines. Consider the learning curve and the time required to train AI models effectively.
Stakeholder buy-in Projects that have clear support from leadership and other key stakeholders are more likely to receive the resources and attention they need to succeed.
Long-term value Look beyond the immediate benefits. Does the project have the potential to evolve and provide value in the long run?

By considering these criteria, B2B marketers can prioritize projects that not only bring immediate value but also align with long-term strategic objectives. Always approach project selection with a clear vision and purpose to maximize outcome potential.

Taking the next step

Knowing how to get started is often the greatest challenge. Now that you know what personalization is in the age of generative AI, data, and automation, many companies need a partner to help guide the initiative. Often implementation support is necessary too.

Schedule a free consultation with one of our experts. We will take the time to get to know you, your business, and your vision, then shape an engagement built just for you. Book Now

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