AI in Marketing: How to Design an AI Marketing Strategy

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AI in Marketing: How to Design Your Marketing Strategy

Artificial intelligence and machine learning are rapidly transforming the field of marketing. Brands that embrace AI technology now will gain a distinct competitive advantage and become the marketing leaders of the future. According to a recent IDC report, over 75% of enterprise executives say the use of AI solutions is pivotal to achieving their strategic priorities. The capabilities of generative AI allow marketers to gain predictive insights into customer behavior, automate repetitive processes, create highly personalized ad and content experiences, and significantly improve marketing campaign performance. With the help of machine learning algorithms and vast amounts of data, AI can optimize the end-to-end customer journey for acquisition, conversion, and loyalty.

However, successfully designing and implementing an AI-driven marketing strategy takes careful planning and preparation. Marketers can’t just plug in an AI tool and expect to create content with immediate results. Thoughtful consideration must be given to assessing your organization’s data infrastructure, evaluating use cases, building the technology stack, and managing change to digital marketing. This outline provides a comprehensive guide to the key steps involved in creating an effective AI marketing strategy. It covers best practices from understanding where AI can make the biggest impact in your organization to developing a roadmap for adoption. With the right strategic approach to integrating artificial intelligence, marketing teams can accelerate performance and create customer experiences that were inconceivable just a short time ago. The future of marketing is undoubtedly AI-powered. Follow this blueprint to start your AI marketing tool for your next strategy in the journey of marketing efforts today.

Understand How AI Can Be Applied to Your Marketing Strategy

Marketing Automation

AI-driven marketing automation tools are becoming essential for executing personalized marketing, email marketing, and complex nurturing journeys at scale. Machine learning algorithms can segment customers, determine optimal send times, recommend relevant content, and automate multi-channel engagement. An example of AI for marketing is HubSpot’s, an AI-enabled automation tool that analyzes customer data to identify sales-ready leads and automatically enroll them in a nurturing workflow. Other key features that reflect the power of AI include predictive lead scoring, intelligent list segmentation, dynamic content blocks, and real-time alerting when high-value prospects engage.

Predictive Analytics

Sophisticated machine learning techniques like regression, decision trees, and neural networks uncover correlations, and patterns in data that humans could never manually detect. Marketers can leverage these predictive models to accurately forecast sales, reduce customer churn, optimize pricing, size markets, model customer lifetime value, and identify cross-sell opportunities. For instance, an example of AI in marketing is through content recommendation engines on Netflix and Amazon rely on AI algorithms to analyze viewing and purchase history and then recommend relevant titles most likely to be enjoyed. AI-enabled predictive analytics provide actionable insights that optimize nearly all areas of marketing processes.

Personalization

AI algorithms allow marketers to serve up hyper-personalized experiences and deliver the right message to the right customer at the right time. Techniques like collaborative filtering analyze vast datasets of past user behaviors and preferences to build customer profiles. These types of AI models dynamically curate unique product and content recommendations tailored to each user. Spotify’s Discover Weekly playlist leverages AI to detect music preferences and suggest new artists that will appeal to each subscriber. Social media platforms use AI to populate custom feeds based on interests. Geotargeting and sentiment analysis further refine personalization.

Content Creation

AI can help marketers revolutionize marketing content creation through automated copywriting and video generation. Natural language generation tools can auto-create blog posts, social captions, product descriptions, ad copy, and more. The AI is fed topics and inputs and leverages language models to write original, human-sounding content. Companies like Synthesia take automated video creation further by using AI to generate interactive video content using avatars. This allows for faster, more scalable content creation.

Ad Targeting/Bidding

AI applied to programmatic advertising improves performance through real-time optimization of bids, budgets, and placements powered by machine learning. Key techniques like reinforcement learning allow the algorithms to continuously learn and adapt to new events and data. AI-driven ad tools like Albert analyze historical ad performance data to identify trends and patterns. Albert then uses machine learning to monitor campaigns and take actions like pausing poorly performing ads to reduce wasted spend.

Assess Your Internal AI Readiness to Use AI in Marketing

Incorporating AI into your marketing requires a careful audit of existing data, technology, skills, and organizational dynamics. Rushing into AI without understanding current capabilities and gaps risks major pitfalls.

Data Infrastructure

Robust data infrastructure forms the foundation for AI success. Key areas to audit include:

Data volume – Machine learning models need vast amounts of training data – hundreds of thousands or millions of rows depending on use case complexity. Assess whether your data corpus is large enough.
Data quality – AI tools amplify any issues with dirty, incomplete, or unstructured data. Rigorously inspect data hygiene and build pipelines to cleanse issues.
Data consolidation – Disparate, siloed data severely limits AI capabilities. Evaluate current progress toward a customer data platform or data lake to unify all sources.
Infrastructure – Server capacity, cloud services, and tools should be adapted to support major upticks in data processing and model-building requirements.
Data governance – Formal oversight of data collection, privacy, security, and ethical use takes on greater importance with AI. Ensure policies and compliance controls are in place.

Analytics Tools and Skills

A team well-versed in statistical modeling and analytics is crucial for maximizing the business impact of AI. Key readiness checks:

Team skills – Audit data scientists and analysts on proficiency with AI approaches like machine learning, neural networks, and NLP. Prioritize training to close skill gaps.
Model building expertise – Expand capabilities to properly develop, test, iterate and validate AI models. Learn best practices like train-test data splitting.
Model governance – Institute model monitoring, explainability, and version control protocols to maintain rigorous model oversight.
Emerging technology – AI-first tools may be needed to augment traditional analytics platforms. Shortlist top vendors to fill gaps.

Cross-Functional Collaboration

AI rarely succeeds in isolation. Input from stakeholders across the organization is vital:

Executive sponsorship – Ensure executive leaders support AI initiatives to set the vision and unlock needed resources.
IT partnership – Deep collaboration with technical teams is crucial for data infrastructure, system integration and scaling AI.
Adoption nurturing – Limit change resistance through training and communication that builds trust and understanding of AI.
Agile approach – Move projects out of IT backlogs by adopting agile, iterative project approaches with rapid prototyping.

Conducting an expansive audit of AI readiness spotlight priorities for investment and transformation to set your marketing team up for artificial intelligence success. Let me know if you need any other details!

Define Marketing Problems AI Tool Can Solve

Identifying high-value use cases is essential for ensuring AI implementations deliver tangible impact. Avoid taking a scattershot approach by carefully prioritizing areas where machine learning can optimize performance.

Prioritizing Solutions

When first implementing AI, focus on 1-2 high-impact use cases rather than taking on all areas at once. Quickly piloting AI for targeted needs builds internal buy-in for expansion.

Criteria for prioritization:

Cost to implement – Both monetary and resources
Expected performance lift
Feasibility based on current data and infrastructure
Alignment to overarching marketing and business goals

Stay focused on driving tangible outcomes vs vanity metrics with AI. Maintain a priority list of use cases and evolve it as new needs and opportunities emerge.

How to Develop the Best AI Marketing Technology Stack

Successfully integrating AI into marketing requires a strategic approach to building your technology stack. Conducting a detailed audit and gap analysis is the critical first step.

Audit Existing Stack

Thoroughly evaluate current marketing technologies through the lens of existing and potential AI capabilities, including:

Customer data platform – Assess the ability to consolidate, cleanse, and manage access to unified customer data at scale to power AI.
Analytics – Review predictive modeling, forecasting, attribution features, and how easily insights can be operationalized.
Automation – Check segmentation, journey building, and personalization capabilities essential for AI-driven campaigns.
Ad tech – Examine programmatic functionality like automated bidding, budget pacing, and campaign optimization using machine learning algorithms.
Digital experience – Audit recommendation engines, chatbots, and other AI features enhancing customer experience.

Identify Gaps

Map technologies to current and desired marketing use cases and workflows. Identify high-impact gaps where introducing AI could provide a major performance lift. For example:

Limited personalization reducing conversion rates on your e-commerce site
Ineffective predictive lead scoring stunting sales funnel velocity
Lack of lifetime value modeling constraining retention programs

Shortlist Solutions

Research leading AI solution vendors that can address identified gaps. Blend best-of-breed point solutions with marketing clouds offering integrated AI capabilities.

Point solutions – Specialized for specific high-value use cases like predictive analytics, media optimization, or NLP.
Marketing clouds – Provide a broad range of AI features natively integrated together on a single platform.

Weigh Build vs. Buy Tradeoffs

For niche or emerging use cases, it may be advantageous to build custom AI solutions in-house leveraging internal data science teams. Factors to consider:

Feasibility of internal development – Available data science skills and resources
Accelerated time to market using vendor platforms
Total cost comparison of building vs. buying
Importance of proprietary algorithms fine-tuned to your needs

Continuous Improvement

Re-evaluate technology needs frequently as new solutions and features enter the market. Add capabilities incrementally to stay on the cutting edge while maintaining integration.

How to Create an AI Marketing Strategy Roadmap

A phased, agile roadmap is essential for scaling AI capabilities thoughtfully across the marketing organization. It provides structure and milestones for expanding use cases in a logical sequence.

Pilot High-Potential Use Cases

Start with 1-2 high-impact pilots focused on the most feasible opportunities with a fast path to demonstrable ROI. For example:

Automated ad bid manager for paid search campaigns
Predictive lead scoring and routing for sales team prioritization

Keep pilots small and focused. Limit to a single campaign or channel initially. Define success metrics and collect benchmark data.

Expand to New Use Cases

Use an iterative approach to build on initial pilots. Prioritize new use cases that leverage existing data infrastructure, models, and platforms. For example:

Expand predictive lead scoring to additional products/segments
Apply bid automation capabilities to programmatic ad channels

Sequence expansion in a logical order matching capability maturity. Win buy-in for further investment by highlighting previous pilot results.

Map Data, Technology, and Skills Needed

Outline key requirements ahead for each expansion phase to ensure smooth rollout including:

New data needed – integrations, unifying customer data, enhancing datasets
Technology upgrades – specialized AI tools, expanded storage/processing
Model building skills – growing data science team capabilities
Vendor selection – pilots, procurement process, managing partnerships

Set Milestones

Roadmap major capability launch milestones along with data readiness and platform integration targets. Share milestones across stakeholders for visibility. For example:

“Customer data platform rollout by Q2 to enable enhanced targeting for holiday campaign in Q3”

By taking an agile, phased approach, brands can scale AI strategically. Adjust the roadmap as capabilities and business needs evolve.

Manage the Organizational Impact of AI

Integrating artificial intelligence brings massive changes to marketing workflows, skill sets, and governance. Savvy change management and communication are essential to drive adoption. Key areas to address include:

Upskill Teams

Provide extensive AI and data literacy training to get marketing teams up to speed. Data scientists need proficiency in machine learning applications. Marketers should understand how to interpret and act on AI-driven insights. Change resistance declines when teams understand AI capabilities.

Conduct skills gap analyses to identify AI and data science training needs across marketing:

Data scientists – Proficiency in machine learning, NLP and model building
Analysts – Statistics, experimental design, interpreting model outputs
Marketers – Understanding AI use cases and applying insights

Develop or source comprehensive training curriculums. Leverage online learning platforms for scale. Incentivize certifications.

Change Management

Prepare teams for significant workflow changes driven by AI automation of repetitive tasks. Define new roles and responsibilities. Listen to concerns and gather input to ease transitions. Celebrate quick wins and milestones to build confidence.

AI automation will fundamentally alter marketing workflows. Prepare teams for upcoming changes:

Define new roles needed to support AI, like data ops engineers
Reassign team members from repetitive tasks to higher-value analysis
Address fears of job loss through transparency and attrition planning
Celebrate quick AI wins to build confidence in new capabilities

Governance

Install appropriate oversight for transparency, bias detection, and ethics in AI model use. Document processes for auditing datasets, and algorithms before productionalization. Continuously monitor outputs. Course correct freely to address issues.

Install guardrails as AI is embedded in processes:

Set model validation protocols to detect biases and ethical issues
Document ongoing model monitoring procedures
Build human override steps and controls into AI-automated workflows
Maintain transparency on AI use cases and data being utilized

Iterative Approach

Take an iterative approach to rolling out new capabilities, collecting feedback, and expanding slowly. Get stakeholder buy-in for change through ongoing communication and demos. Provide hands-on support for adoption.

Take an iterative approach to roll out capabilities, gathering feedback, and expanding carefully. Nurture adoption through:

Hands-on support and mentoring for teams new to leveraging AI
Open communication channels for concerns and change suggestions
Gathering user feedback to improve integration and experience

With extensive support, strong governance, and a spirit of iteration, marketing teams will embrace the competitive advantage of AI.

AI brings disruption but strong change management and governance minimizes resistance. Upskilling marketers to leverage AI while proactively addressing needs breeds long-term success. Adoption expands as teams gain confidence in AI-enhanced performance.

Activate Marketing AI to Compete in the New Era of Intelligent Engagement

The marketing AI revolution is here. Sophisticated artificial intelligence and machine learning technologies are completely transforming how brands attract, engage, and delight customers. The competitive advantage gained by organizations that thoughtfully integrate AI throughout their marketing stack is already separating the leaders from the laggards.

But simply plugging in AI tools without proper strategy often leads to disappointment. As outlined in this guide, truly activating AI requires careful orchestration of people, processes, data, and supporting technology. By first identifying your highest-value AI opportunities, assessing organizational readiness, and creating a phased roadmap, you can iteratively scale AI in a logical way that drives real business lift.

Do not wait any longer to future-proof your marketing capabilities. The brands already realizing incredible performance gains from AI will soon be lightyears ahead. Book a demo with our AI specialists today to get started on building an AI-powered marketing engine tailored to your unique needs. Our team of data scientists and martech experts will partner with you to activate machine learning and artificial intelligence across your technology stack, workflows, and data infrastructure. Let us help you achieve rapid wins that demonstrate AI’s immense potential while building the foundations and skills for sustained advantage. Seize the power of marketing AI now before your competitors do.

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