Hey Passionate Marketers, aren't we constantly being bombarded with loads of information and buzz on AI? And why would we need another article on AI here?
Here is the search volume of the term “AI Marketing” on Google website (Source: Google Trends). This stands as a clear indication of the rise of adopting AI Marketing.
But why this article?
1. This article is crafted with the latest sources.
2. This article declutters the noise of AI buzz and uncovers the grounded realities of AI for B2B marketers. I’m jotting down my journey, starting from experiencing the buzz around AI to leveraging AI as a true business enabler.
Before going straight to the discussion, let’s first find common ground by defining ‘what is buzz’ and “what is reality” in the realm of AI for Marketing and the intricate shades that exist between them.
We’re defining buzz as:
- Buzz encompasses the realm of technical possibilities and envisioned use cases that are poised to become realities in the near future.
- Buzz often revolves around anticipated statistics or projections that might shape the landscape.
Reality = % of adoption
- On the other hand, the sphere of reality and adoption encapsulates features and use cases that are already in active deployment, gaining traction, and proving their mettle in the hands of early adopters.
- It's grounded in validated methods, established frameworks, and concrete statistics that have demonstrated their efficacy.
With these definitions, we can set the stage to delve deeper into the spectrum that stretches between the buzz and the reality of AI-driven Marketing.
Use cases of AI for marketing
This involves analyzing vast amounts of customer data, their patterns, their activities, patterns, purchase history, engaged communication channels, and others. This analysis is used to design personas, find appropriate communication channels, conceptualize touch points, create messaging and value propositions, and customer journey maps.
Finding out the accounts (organizations) that are in the buying cycle based on intent signals.
- Once you identify the intent accounts, get more insights such as their technography, business challenges, growth opportunities, recent news, persona growth, revenue growth, change in management, cultural fit, etc.
- Reverse engineering your top 20% customers to design a perfect ICP genome.
- Find people or accounts talking about your product category or services (Social Listening).
- Finding the key stakeholders’ demographics and psychographics.
This needs a significant amount of clean data and sophisticated AI algorithms to ensure the translation of data into meaningful strategies that truly resonate with customers of all segments and in all buying stages.
Ensure your various data sources are integrated with your CRM or CDP. Automating the streams of data can remove the friction in unifying the data and providing a single source of truth on a customer or prospect.
Develop triggers (activities or behavior patterns that bucket the customer into particular segment) and scoring (evaluating the priority of every factor) to fool-proof your AI-driven segmentation.
While AI is a powerful tool, human interpretation remains crucial. Marketers with domain expertise must validate AI-generated insights, adding contextual understanding and creativity to the data-driven strategies. AI algorithms need hand-holding at the initial stages, without which they can lead to disaster.
Conversion rate optimization (CRO)
AI continuously learns from customer interactions, adapting messages in real time to align with evolving preferences and behaviors. Based on these learnings, it can optimize a web page’s messaging, form fields, headlines, images, CTAs, or case studies.
- Creating diverse content in various formats, including images, text, videos, and forms, can result in a more relevant and personalized web experience.
- Do A/B, split, or multi-variate testing of multiple web page elements.
- Use Heatmaps, scrollmaps, and recordings to understand behavioral patterns of every visitor segment.
- Build AI-driven customer segmentations.
To implement CRO tools, we need to be crystal clear and ready with the detailed personas, messaging, value propositions, and use cases developed for all industries and for every stage of buying personas.
Developing data-driven triggers through iterations of multi-variate testing needs a long-term clarity and plan.
Conduct A/B testing, split testing, and multi-variate testing to validate the effectiveness of customized experience. Use historical data to simulate and evaluate AI-driven messaging performance.
To maintain content diversity, develop content templates and modular components that can be dynamically assembled for various segments (i.e. content guidelines). Then, utilize AI-powered content generation tools to create diverse content at scale.
Most importantly, adhere to data protection regulations and obtain explicit consent from customers for data usage. Anonymize and aggregate data to protect individual privacy while enabling effective segmentation.
Writing & designing
According to The State of AI in the Online Marketing Industry 85.1% of AI users are using it for blog content creation, and according to the AI Marketing Benchmark Report 2023, 44.4% of the marketers using AI have used AI for content production.
- Composing emails, articles, headlines, ad copy, scripts, messages, or crafting use cases.
- Designing videos, images, audio, presentations, ebooks, and ads.
Every brand has its own tone and sense of representation. Using AI, this originality can’t be brought about unless the data models are highly trained with a brand’s own data. With the current AI and ML algorithms, it’s challenging to embed human emotions and understand cultural nuances and context.
AI might not be able to help you on topics that you want to trailblaze, or fresh perspectives.
Brands with distinct color palettes and visual guidance can find it difficult to use AI to design images or videos.
Without awareness of the sources of AI-generated content, it could easily create biased or inappropriate content.
Marketers can use AI-generated suggestions as drafts as a starting point and then use their own creativity and context orientation to enhance and tailor the content. Marketers can also use AI for data-driven tasks, allowing them more time to focus on crafting emotionally engaging narratives that resonate with their audience.
AI can be trained with a diverse range of past content to better capture a brand's voice. Marketers can fine-tune AI models by providing specific guidelines and training data that align with their brand's tone and style.
Having known the risks associated with AI, auditing regularly the AI-generated content can correct biases.
Prospecting & researching:
The marketing world has been leveraging AI full throttle on prospecting key stakeholders and researching industries, technologies, competitors, and customer trends. AI has been successfully delivering results in account intelligence, competitive intelligence, industry trends, contact data enrichment, and finding information on key stakeholders.
According to a survey conducted by Forrester in 2023 (Future of AI Marketing Report), close to 70% of marketers say they’re wasting time manually pulling data.
- Market & competitive intelligence - analyzing all your competitor reviews, analyzing the annual and quarterly reports of organizations, and identifying the keywords, customer pain points, preferences, emerging trends, and market sentiments.
- Stakeholder Research: AI can help you find out the hierarchy of the organization, the stakeholders involved in the buying process, their psychographics, their preferred communication channels, their sources of learning, thought leadership sources, etc.
High adoption for many years and increasing concern for data privacy has depleted the accuracy of the data. With compliance and regulatory laws enforced in most countries, data fetching or outreach (the very purpose of data capturing) has to go through strict scrutiny and abide by the laws.
Many AI-based Intent tools do not show the source of intent, leaving the users a heavy burden of evaluating them with their first-hand data and experiences, sometimes leading to finding out the problem when it’s already too late.
The recommendations of the research can be biased or inappropriate because of the lack of contextual, industrial, and technological understanding.
Banking on multiple AI tools could mitigate the inaccuracy of the data. Diversify your data sources to ensure a comprehensive view of trends and insights. To remove outdated or incorrect information, regularly update and cleanse databases.
Ensure compliance with data protection regulations (e.g., GDPR) while collecting and using customer data by obtaining explicit consent from individuals before using their data for outreach.
Rather than taking AI for granted, combine AI insights (out of research) with human expertise for well-rounded decisions. Training AI models to consider contextual factors in their analysis could help you in leveraging AI in the long term.
Before buying an AI-based intent tool, seek clarity from vendors on how they collect and validate intent signals. Check if your ideal customer buying signals are available and can be well integrated with your CRM or CDP.
Many marketers have already started their marketing automation journey by leveraging the capabilities of AI.
- Automate activities and processes through workflows, automated follow-up emails, AI-driven chatbots, lead scoring, A/B testing, segmenting, programmatic advertising, deanonymization of web visitors, and attribution models.
- Streamline operations, optimize campaigns, deliver highly personalized experiences, and achieve better results across the entire B2B buyer journey by integrating AI into marketing automation processes.
Scoring leads or accounts needs a fine-tuned formula that embeds your ICP deep into the algorithm.
Personalization be powerful, but errors can be equally disastrous. One of the major root causes for the errors is inaccuracy and incompleteness in data. Creating relevant and engaging content for various segments can be time-consuming.
Many B2B marketers adopt marketing automation without a clear strategy. Setting up and configuring marketing automation tools can be complex, requiring technical expertise. Having the right team and choosing the right tools with the integration capabilities changes the game of execution.
Improper segmentation and targeting can lead to irrelevant messages and decreased engagement. Not to forget the overcrowded AI Market that places you in the noisy market while choosing the right tools for your organization.
Regularly monitoring the accounts or leads scored to ensure the error margin is zeroed down over a period of time. Implement data cleansing processes, integrate different data sources, and regularly update and maintain your database.
Develop a content strategy that includes a mix of evergreen and dynamic content. Utilize AI-powered tools to automate content creation and personalize messages based on user behavior and preferences.
Choose tools with the features, data-capturing abilities, and integration capabilities that ensure a smooth transition of data and activities.
To avoid improper customer segmentations, analyze customer data to create accurate buyer personas and segments. Use AI to identify patterns and behaviors for more precise targeting.
A B2B marketer can leverage AI for personalization in various ways to create tailored and engaging experiences for their target audience on email, web, collaterals, presentations, events, etc.
Creating dynamic content on websites by deanonymizing visitors or based on the activities of the user can deliver a more relevant and effective experience. AI-driven website personalization can display different content, offers, and calls to action.
Beyond just the recipient's name, AI can customize email content based on a person's role, industry, preferences, expected pain points, buying stage, and recent interactions with your brand.
Create customized messages and content for specific accounts in Account-Based Marketing (ABM) strategies, enhancing engagement with key prospects.
Adhering to data privacy regulations like GDPR can be complex when using AI to process and personalize customer data.
Integrating AI-powered personalization tools into existing systems and workflows can be technically challenging.
Before deciding on the personalization scope that you want to enable, ensure personalization is aligned with the broader marketing strategies and goals.
Too much personalization can come across as invasive or creepy, leading to a negative user experience.
Adhere to data privacy regulations by Implementing robust data protection measures, obtain proper consent, and work closely with legal teams to ensure compliance.
Ensure the required data is being captured to build not just personalized communication but also more relevant and personalized customer journies. Develop a clear personalization strategy that integrates with your overall marketing plan. Ensure AI-driven personalization complements existing efforts
Strike a balance between personalized content and privacy. Respect user boundaries and provide clear opt-out options.
Most importantly, invest in training and upskilling for your marketing team, or consider collaborating with AI experts or agencies to bridge the skill gap.
Analytics is one of the most under-utilized features of AI. Using AI in building dashboards and reports can help teams gain deeper insights, make informed decisions, and enhance their marketing strategies.
- Automating the process of collecting data from various sources, integrating data, performing complex data analysis, identifying patterns, trends, and correlations, creating dashboards, and identifying anomalies in reports.
- Uncovering actionable insights, making data-driven decisions, predicting pipeline, estimating deal-win probability, reverse engineering the ICP, and anticipating market shifts.
Creating AI-powered dashboards requires integrating data from multiple sources, which can be complex and time-consuming.
AI-driven sentiment analysis may struggle with accurately interpreting context, sarcasm, and nuanced language.
AI-based marketing mix models can be challenging to develop due to the complexity of variables and interactions.
AI-powered tools for online reputation management may not always accurately capture the sentiment or context of online conversations.
AI tools used for these tasks require access to customer data, raising concerns about data privacy and compliance with regulations.
AI algorithms used for these tasks may exhibit bias or lack interpretability, leading to skewed results or difficulty in explaining outcomes.
Resistance to change from team members accustomed to traditional methods can hinder the adoption of AI-powered tools.
Invest in data integration tools and platforms that streamline the process of collecting and organizing data from various sources.
Combine AI analysis with human review to ensure accurate sentiment interpretation, especially in cases of complex or ambiguous language.
Implement robust data protection measures, obtain proper consent, and ensure that AI tools adhere to relevant data privacy regulations.
Regularly audit algorithms for bias and interpretability. Choose AI models that offer transparency and provide explanations for their decisions.
Provide education and training to help the team understand the benefits and potential of AI, and highlight successful use cases.
What are your thoughts on AI Marketing? Have the expectations lived up to the reality? Join the conversation with a global network of CMOs and marketing leaders on the CMO Alliance Community Slack channel. Join now for free.