In the case of growth marketing, CRO is the heartbeat of performance. You can run the best ad campaigns in the world, but if your landing page leaks conversions, you’re burning ad spend. Every 1% increase in conversion rate on a landing page can compound into massive ROI across acquisition, lead generation, and revenue.

Let’s explore this further with an example:

Imagine you are bringing 10,000 visitors to your landing page that converts 3% of visitors into leads; that’s 300 leads. If you are able to improve your conversion rate to 4%, that’s an extra 100 leads per month. Over a year, that small 1% lift could mean 1,200 more leads and a significantly lower cost per acquisition (CPA).

Here’s another perspective from Walmart, it found that every one-second improvement in page load time, conversions increased by 2% as reported by Cloudflare.

CRO is an intricate process that involves detailed experimentation, data analysis, and the ideation of roadmaps to improve user engagement, where it requires both creativity and technical prowess. So where do LLMs like ChatGPT come in?

Think of ChatGPT as the assistant or intern you’ve always wanted: smart and fast, but only as good as your guidance. In AI terms, that means your prompts are the training. The better your instructions, the sharper the output.

In this article, we will explore how growth marketers can use ChatGPT to streamline CRO workflows; from insight extraction and hypothesis creation to copywriting, design ideation, and reporting with real prompts and examples you can start using today.

Understanding CRO & the Role of ChatGPT

CRO isn’t a single tactic. It’s a continuous loop of data analysis, hypothesis, experimentation, and learning. Where teams often struggle is speed and scale:

• Analyzing thousands of data points
• Generating new test ideas
• Producing dozens of creative variants

Coincidentally, that’s also where ChatGPT and other LLMs excel; processing complex and large data into actionable insights within minutes.

The Stages of Conversion Rate Optimization

When generating and prioritizing test ideas, not every hypothesis deserves immediate attention. Teams need a structured way to decide which ideas to test first, especially when using ChatGPT to produce dozens of potential experiments.

That’s where prioritization frameworks like ICE and PIE come in. ICE and PIE are simple frameworks that help prioritize CRO test ideas based on their potential impact and effort.

• With ICE (Impact, Confidence, Effort), you score each idea from 1-10 for how much it could improve results, how confident you are in it, and how hard it is to execute. It’s perfect for quick-win, fast-testing cycles.
• PIE (Potential, Importance, Ease) focuses on opportunity size and business value, making it better for structured programs on high-traffic or high-revenue pages.

Higher-scoring ideas move up your testing queue, ensuring time and resources go where they’ll deliver the biggest lift.

Why ChatGPT Elevates CRO

1. Faster Insight Discovery: LLMs analyze unstructured data, surveys, NPS responses, and customer reviews and convert them into clear patterns or friction points.
2. Structured Hypotheses: Instead of relying on just instinct, ChatGPT organizes test ideas using frameworks like ICE (Impact, Confidence, Effort) or PIE (Potential, Importance, Ease).
3. Creative Acceleration: This is the most developed use case of AI, it can generate copy and creative variations instantly for rapid A/B testing.
4. Documentation Efficiency: ChatGPT can draft test briefs, control and variant definitions, and analysis summaries (potentially saving hours per experiment).

ChatGPT’s Limitations With CRO

While ChatGPT can speed up and simplify many parts of CRO, it’s not flawless. Here are some things to keep in mind when using ChatGPT for CRO:

1. Always verify outputs before implementation. Never accept them at face value; instead, test, validate, and human-edit every suggestion or variant it produces. 
2. There will be times when ChatGPT’s responses aren’t useful or miss the mark entirely. That’s normal. The key is to use prompt engineering to iterate and refine your prompts until you get results that align with your objective.

As you learn where it performs best, you’ll start using ChatGPT less as a novelty and more as a reliable partner in experimentation

What Information Brands Should Gather for Smarter CRO Decisions

Effective CRO starts with understanding why users convert (or don’t). The best optimization decisions come from data rooted in real customer behavior and sentiment. Therefore, brands should collect a mix of quantitative and qualitative insights to map the full conversion journey:

• Behavioral Data: Things like click paths, heatmaps, scroll depth, form drop-offs, and time on page. These reveal friction or confusion. 
• Demographic & Psychographic Data: Age, gender, region, pain points, and triggers that influence purchase decisions.
• Source & Device Data: Identify where high-intent traffic originates and which experiences perform best on mobile vs. desktop.
• Feedback & Sentiment: Post-purchase surveys, NPS scores, live chat transcripts, or support tickets showing user hesitations or frustrations.
• Voice of Customer (VoC): Exact phrases customers use to describe their needs could prove invaluable for copy and messaging tests.

ChatGPT in Action: Best Practices to Prompt ChatGPT for CRO

The quality of CRO output from ChatGPT (or any LLM, for that matter) depends entirely on how you prompt it. Think of prompting as writing a creative brief for your smartest intern: the clearer the context, the better the work.

A good prompt should always include context (the who, what, and why), the goal (desired outcome or KPI), and the constraints (brand tone, word limits, persona details, etc.).

Must-Haves in ChatGPT CRO Prompts

• Specify target persona, goal metric, and stage of the funnel.
• Provide real data or examples (existing copy, page description, audience insight).
• Ask for structured outputs (tables, bullet points, PIE and ICE frameworks).
• When possible, upload a screenshot or image of your landing page, allowing the AI to see every element of the page

Don’ts of ChatGPT CRO Prompts

• Avoid vague requests (“make my page better”).
• Don’t share confidential or user data.

Prompting ChatGPT for Each CRO Phase

1. Data Interpretation & Insight Extraction

Prompt:

“Act as a CRO strategist to identify friction points and conversion barriers for [URL].

• Persona: [e.g., Mid-market SaaS marketing manager]
• Funnel stage: [e.g. Landing page → demo request]
• KPI: [e.g. Increase lead conversion rate]
• Data: [e.g. GA4 stats, heatmaps, or VoC snippets]
• Visual: [e.g. Landing page screenshot]”

2. Hypothesis Generation & Test Prioritization

Prompt:

“Act as a CRO Strategist to generate and prioritize hypotheses for the following.

• Persona or Segments: [e.g. DTC skincare shopper]
• Funnel Stage: [e.g. Product page → add-to-cart]
• KPI: [e.g. Increase the add-to-cart rate]
• Task: Generate a CRO hypothesis based on the above information and return a table: Hypothesis | Metric | Segment | ICE | PIE | Effort (S/M/L) | Est. Lift% | Notes
  • Recommend the top 3 to test and justify briefly.”

3. Copywriting & Messaging Optimization

Prompt:

“Act as a Website Content Manager to craft messaging angles (prioritize value, urgency, credibility, emotional appeal, curiosity). For each, provide the following:

• Headline (≤60 chars)
• Subheadline (≤120 chars)
• CTA (≤25 chars)
• Voice-of-Customer-based bullet points
• Task: Use above information to present a table with following information: Angle | Headline | Subheadline | CTA | Bullets | Psychological Trigger
  • Ensure language mirrors real customer phrasing.”

4. Design & User Experience Ideation

Prompt:

“Act as a UI / UX Designer to review the uploaded page screenshot and pinpoint UX issues affecting clarity, trust, or navigation.

Task: Suggest layout or component changes to resolve the issues [as identified in the Data Interpretation & Insight Extraction section].

Describe each with content hierarchy, visual order, and intended behavioral impact.”

5. Experiment Planning & Documentation

Prompt:

“Act as a CRO Experimentation Manager to create an A/B test plan.

Include: Goal & Hypothesis, Primary & Secondary Metrics, Control vs. Variant, Audience, Duration, Success Criteria.

Task: Present in a table (Section | Details | Owner | Due Date | Status) and add:

• Pre-launch checklist (tracking, QA, traffic sufficiency, stopping rules).”

6. Analysis & Reporting

Note: Since LLMs can’t directly access your analytics tools, you will need to manually provide the experiment data. Share key metrics like impressions, conversions, conversion rates, or bounce rates, and then request a summarized analysis or report.

With this type of CRO prompt, there can also be two scenarios and the prompt could change in both the scenarios, while the task section can remain the same:

Prompt:

“Act as a Growth Analyst reviewing the results of a CRO experiment to derive insights and next actions.

Scenario 1: If the test was a before-and-after comparison, review the following data:

• Before the test: [Dates, Baseline metrics before test]
• After the test: [Dates, Metrics after test implementation]
• Additional Context: [Add any additional information that’s relevant for the test, like success metric, objective, audience segment, etc.]

Scenario 2: If the test were an A/B experiment, you can use these inputs:

• Success Metric: [e.g., Increase in conversion rate, clicks, or reduction in bounce rate]
• Variation A Results: [Insert data]
• Variation B Results: [Insert data]
• Additional Context: [Add any additional information that’s relevant for the test, like what you were testing, visuals in case some element got changed, the success metric, audience size, etc.]

Task:

• Compare the provided results and calculate the performance lift.
• Assess whether the outcome is statistically and practically significant.
• Explain what likely influenced the results/
• Write a short summary [less than 200 words] with key takeaways and recommended next steps.”

Recap: How to Use ChatGPT for CRO

• Tailor prompts to your specific use case: No two experiments are identical. The prompts shared above are starting points; refine and adapt them to your funnel, audience, and business goals.
• Provide complete, relevant context: Include device type, traffic source, audience segment, and funnel stage for more accurate and actionable outputs.
• Balance your input: Too little context leads to vague or generic suggestions, while too much irrelevant detail can overwhelm or confuse the model.
• Use visuals whenever possible: Upload screenshots or wireframes to help the model understand layout, hierarchy, and design context.
• Iterate on prompts: Rarely is the first response perfect. Follow up with clarifying or refining prompts to sharpen insights and outputs.
• Always test, validate, and human-edit: LLM-generated ideas before implementation. Treat ChatGPT as a brainstorming and analysis partner (not an execution engine). At least not until you build that confidence
• Document everything: Keep a prompt and result log. Over time, this builds an internal library of high-performing prompt structures you can reuse.
• Be mindful of data sensitivity: Never share personally identifiable information (PII) or confidential analytics into public AI tools.
• Combine AI with human judgment: The most effective CRO strategies come from blending data, your experience, and creativity.

If used thoughtfully, ChatGPT can help you get insights faster, run smarter experiments, and help scale optimization programs with confidence; turning your CRO process into a repeatable, data-driven growth engine.

The post Leveraging ChatGPT for Conversion Rate Optimization appeared first on NoGood™: Growth Marketing Agency.

The seeds of this idea go back to 1930, when Dr. Jeffrey Lant introduced the “Rule of 7” in the film industry. The rule states that people need to see a message at least seven times before taking action. This highlighted the importance of consistent, repeated exposure through multiple channels to build brand awareness, trust, and ultimately drive conversions. For decades, that logic worked because journeys were relatively linear. You placed ads, sent mailers, ran TV spots, and exposure did its job.

Fast forward to today, and the journey looks nothing like a straight line. This rule received a makeover in the form of the “7-11-4 Rule,” most frequently attributed to Google. This method suggests modern buyers need to consume seven hours of content, across eleven touchpoints, in at least four formats before they trust a brand enough to act. These seven hours of content and eleven touchpoints don’t happen in one place. They’re scattered across platforms (Social Media, Organic Search, Answer Engines, etc.) and various content types (Blog articles, video, email, podcast, etc.), often happening out of sequence and over weeks or months.

As brand marketers, optimizing campaigns in silos or judging success based on a single touchpoint isn’t an option. Instead, our job is to design, measure, and refine the entire journey across channels; making sure every interaction builds customers’ affinity towards the brand and eventually leads to a conversion.

In this article, you’ll learn how to understand your customers, map critical paths across touchpoints, identify and fix friction, integrate tools and teams, create seamless cross-channel experiences, and measure what truly matters for you.

Step 1: Build Customer Profiles That Actually Work

Customer profiles are the foundations of any marketing activity; you need to know who your customer is. Every multi-channel strategy starts with clarity on who you’re designing it for. Without strong profiles, you’ll spread effort across channels without knowing what matters most.

Why It Matters: Example

A CFO evaluating enterprise accounting software for a multinational firm and a small business owner looking for an affordable, easy-to-use tool to manage invoices and expenses may both use multiple channels, but the content, timing, and triggers that influence them are quite different.

How to Do It

If you’re building profiles for your own business, here’s a practical approach:

1. Start with your data: Use CRM, website analytics, and email engagement to see patterns, who your customers are, where they start, what they click, and which content works.
2. Layer qualitative insights: Ask customers where they first heard of you, what channels they trust, etc. Sales and support teams often have these insights through their interactions with the customers.
3. Connect personas to channels: For each profile, document preferred touchpoints, content formats they engage with, and triggers that push them forward in the journey.

Human-Centered Design Thinking

1. Interview customers about their struggles.
2. Observe actual behavior across channels via behavior tracking tools like MS Clarity, Hotjar, etc.
3. Use empathy mapping to capture what they think, feel, see, and hear at each channel.

Pro Tip: Don’t overcomplicate your persona mapping. Three to four strong personas tied to data will outperform 12 shallow ones.

Step 2: Map the Multi-Channel Journey

Once you know who your customers are, the next step is tracing the path they potentially take across channels. Customer profiles tell you who your customers are. Journey maps show us how they behave.

Why It Matters

If you don’t map the journey, you’ll default to “channel-first” marketing, optimizing email campaigns, paid ads, or social media in silos. But customers don’t see silos, they experience journeys as either a one complete unified flow or a broken or convoluted journey.

How to Do It

1. Identify milestones: Initial brand awareness and discovery, research and information gathering, purchase and conversion, account setup and onboarding, engagement, and advocacy.
2. List every touchpoint: Start from awareness (ad platforms, organic search, AI search, social, etc.) through purchase (website, sales calls, checkout, emails, and app notifications) to loyalty (support, retention campaigns, and loyalty page sign-ins).
3. Track entry and exit points: Where do customers typically start? Where do they drop off?

Step 3: Diagnose Pain Points & Optimize High-Impact Moments

Zoom in on where customers struggle; remember, not all touchpoints are equal. Some are “moments of truth” that define how customers feel about your brand.

Why It Matters

Friction isn’t just inconvenient; it costs revenue. Abandoned carts, unanswered questions, and repeated forms all push customers toward competitors.

How to Do It

1. Identify friction: Look for gapsl are people asked to repeat information? Do they lose context when switching from mobile to desktop? Are they asking for specific information when sales connect with them?
2. Look for common issues:
   • Confusing navigation
   • Missing or hard-to-find info
   • Complicated checkouts
   • Tedious logins
   • Poor handoffs between channels
3. Prioritize frequent, necessary, or emotional moments:
   • First impressions (ads, homepage)
   • Checkout flow
   • Account login
   • Customer support interactions

Step 4: Integrate Tools & Technology

Profiles and journey maps are useless without the right tech stack to bring them to life.

Why It Matters

Customers expect seamlessness. If your app, website, and call center don’t share context, it could make users feel like they’re starting from scratch every time. Most companies struggle here as data lives in different systems: CRM, marketing automation, website analytics, and support tickets.

If you can’t integrate them, you’ll never deliver seamless multi-channel experiences.

How to Do It

1. Pick your CRM as the hub: All customer interactions (sales, support, marketing) should flow into one source of truth.
2. Layer automation tools: Connect your CRM with marketing automation to trigger contextual outreach. For example, if a lead downloads a whitepaper, follow up with a related webinar invite.
3. Add analytics on top: Use attribution tools to see which channels influence deals or purchases. Do remember, there are channels which will never get any attribution here.
4. Use AI carefully: AI can personalize at scale, but only if fed quality data. Test predictive lead scoring and personalized recommendations.

Integration is about data flow. You should invest in a tool that helps you connect CRM and give marketing, sales, and support the same view, or at least the flexibility to build this view.

Step 5: Create Seamless Transitions Across Channels

Seamless transition between channels is the key to a multichannel customer journey.

Why It Matters

True multi‑channel journeys aren’t about hopping from one channel to the next; they are about carrying context forward. A survey by Gartner shows 93% of customers reported high satisfaction (CSAT) when they experienced seamless transitions between channels, even if one of those channels was physical and the other digital.

In customer service specifically, the same survey showed that customers with seamless transitions across self-service and assisted channels spent 27% less time with agents, while still achieving satisfaction. That adds up to both happier customers and lower costs.

How to Do It

Here’s how to turn handoffs from friction points into fluid experiences:

1. Conduct “mystery shoppers” across channels: Play the customer. Add a product to your cart on mobile, then switch to desktop: does your cart carry over? Start a support chat, then escalate to phone: does the agent have context? If not, identify where context drops and fix it.
2. Unify data and context: Ensure customer actions and profile data flow across systems, web, app, in-store, and chat. A centralized CRM or unified customer profile enables agents and automated systems to personalize seamlessly.
3. Design consistency in messaging and branding: Handoff disruption isn’t just functional, it’s perceptual too. Visual branding, tone, navigation, and value messaging should be consistent. This ensures customers feel they’re dealing with one brand, not different teams.
4. Next best action: Every action should lead naturally to the next. For instance, if a user showed interest in Product 1, the next time the same user visits your website, the hero banner can be replaced with Product 1 offering.
5. Personalize transition: Cart abandonment emails should reflect the exact items left behind, or a sales rep should know which whitepapers a lead downloaded.

Step 6: Measure Success & Optimize

Multi-channel journeys involve so many touchpoints that the performance by channel is almost always blurry.

Why It Matters

Identifying the true value of a channel can be tricky because not all channels are measured by the same metrics. For example, a Reddit thread might generate significant visibility and drive brand discovery, but it won’t show up in your performance dashboards since earned platforms don’t track impressions, clicks, or conversions the way paid channels do.

How to Do It

1. Define clear KPIs for each channel.
   • Acquisition: CAC, Attribution per channel, CPC
   • Engagement: Repeat visits, CTR, Time spent on website
   • Conversion: Demo-to-deal rate, Cart completion
   • Retention: CSAT, NPS
2. Choose the right model. Identify channels that can be optimized for last click, but don’t solely rely on it for optimizing for the brand. Multi-touch attribution could help reflect performance in the case of complex journeys.
3. Run controlled experiments. A/B test messaging across channels, not just within one.
4. Iterate. Adopt a test, learn from it, and scale as you deem fit.
5. Fit in Marketing Mix Modeling. Multi-Touch Attribution could miss the bigger picture, especially for offline or earned media like free PR, Reddit threads, or influencer shoutouts. Marketing Mix Modeling (MMM) could help solve this by analyzing aggregate data (sales, spend, external factors) to estimate the true contribution of each channel.

MMM helps with:

• Combining the impact from a variety of platforms.
• Reallocating budgets based on true ROI.
• Forecast “what-if” scenarios for spend shifts across channels.
• Capture halo effects (e.g., a Free PR story boosting search traffic).

Pitfalls to Avoid & Tips to follow

• Disconnected data silos: Customers forced to repeat information. Customers have to repeat themselves because the systems don’t share information is a very poor experience. Users should be able to continue from where they left off. This could be either in the form of content continuation or journey continuation. 
• Over-automation: Bots and automated flows that fail to escalate to humans when needed can impact user trust and experience. Automation should help simplify and reduce human efforts, but shouldn’t alienate the user.
• Ignoring post-purchase journey: Remember, it could be cheaper to retain a customer than acquire a new one, and loyal customers often become advocates.
• Predictive analytics: Instead of waiting for customers to drop off, data can signal who’s at risk. For example, if a SaaS platform detects declining logins, it can trigger an educational campaign or human outreach before cancellation.

Conclusion: Turning Multi-Channel Journeys Into Growth Engines

Multi-channel is how customers actually buy today. Journeys are fragmented, but brands that connect the dots win. Companies with strong multi-channel engagement see 89% higher customer retention.

To stay competitive, marketers must:

• Design connected journeys instead of isolated campaigns.
• Remove friction and focus on high-impact moments.
• Measure beyond the regular metrics to understand the impact of each channel

When journeys are seamless, customers don’t just convert, they stay, return, and advocate for your brand.

The post The Multi-Channel Customer Journey for Modern Marketers appeared first on NoGood™: Growth Marketing Agency.

For marketing executives in the healthcare industry, the real question is how quickly and effectively you can integrate AI into your marketing strategy.

This blog aims to answer pressing questions that are top of mind for every marketer, such as: what is the current state of AI in healthcare marketing, how to strategically use AI for marketing (plus some examples from the industry), and how to measure the performance and ROI of AI-based healthcare marketing.

Where AI in Healthcare Marketing Stands Today

Let the numbers tell this story:

• Approximately 94% of healthcare companies report using AI or machine learning, yet many are still scratching the surface of AI’s marketing potential.
• 92% of healthcare leaders believe Generative AI improves operational efficiency, while 65% see it as a tool for faster decision-making, as per a Deloitte report.
• In the U.S., the Artificial Intelligence in Healthcare market size was estimated at 13.26 billion USD in 2024, and is projected to grow at a CAGR of 36.76% from 2025 to 2033, as per a report by Grand View Research.

This growth will be driven by digital health infrastructure and investments in AI technology.

As the global healthcare and pharma advertising market nears $44.56 billion in 2025, competition for user attention and trust is intensifying, prompting marketers to allocate the largest share of their budgets to digital channels. A report by Digital Silk shows that 72.2% of industry ad spend is going to digital, and U.S. companies are prioritizing paid digital ads (12.5%), followed by social media (11.5%), and traditional media (9.5%).

So what does this mean for healthcare marketers?

Strategic AI Applications in Healthcare Marketing

1. Content Generation & AI Search Optimization

AI platforms like ChatGPT, Gemini, Perplexity, and GPT-powered search tools are rapidly emerging as alternative search platforms. AI agents are transforming how brands are discovered and chosen, as users increasingly rely on them for personalized recommendations, comparisons, and insights.

Optimization for AI Search

To stay visible in AI platforms, you need to understand how they answer different types of queries about your brand, such as:

• Branded queries (“is [your brand] good for healthcare solutions?”)
• Unbranded queries (“best healthcare marketing platforms”)
• Comparative queries (“[brand A] vs. [brand B]”)

Unlike traditional search engines that prioritize keywords and backlinks, AI agents pull insights from across the web, weighing user-generated content, third-party reviews, social mentions, and contextual relevance to form well-rounded answers.

In other words, your brand’s digital footprint must be credible, and consistently positioned. If people and platforms are talking about you, AI is listening; and learning. Investing in Digital PR and Answer Engine Optimization (AEO) is no longer optional. It’s your new visibility play.

Content Personalization at Scale

AI-powered content creation is reshaping how healthcare marketers engage audiences across a wide range of channels. It enables faster production, deeper personalization, and consistent messaging, making it a game-changer.

• Website & SEO: AI helps generate optimized blog posts, service pages, FAQs, and educational content customized to branded and unbranded healthcare queries, improving discoverability and authority.
• Email Marketing: AI enables personalized email sequences based on user behavior, lifecycle stage, or location, helping healthcare marketers build stronger connections with users, providers, or partners.
• Social Media: From LinkedIn thought leadership to Instagram carousels, AI can help build platform-specific content, repurpose assets, and optimize posts for engagement across audience segments.
• Paid Media & Ads: AI supports the creation of multiple ad variations quickly for a variety of ad platforms like Google Search to Meta Ads, allowing performance teams to test and refine messaging with greater speed and accuracy. It can also help improve the landing page content for higher relevance.
• Video & Scriptwriting: Script drafts for explainer videos, educational content, and webinars can be generated using AI, enabling faster production of complex, regulated messaging. Do note; there is a need for a human review for any regulatory implications.
• Sales Enablement: AI helps create case studies, sales decks, and follow-up emails that are customized to industries, specific decision-maker roles, or sales funnel stages, thereby improving the sales cycles and, in some places, shortening them.

2. Intelligent Chatbots & Virtual Assistants

Let’s face it: users today expect instant answers. That’s exactly where AI chatbots and virtual assistants shine. Digital assistants don’t just respond to FAQs; they capture leads, guide users through journeys, and keep your brand available 24/7. For marketers, this means smarter acquisition and better conversions. Here’s how intelligent chatbots are reshaping user engagement in healthcare marketing:

Instant User Interaction

AI chatbots act as your always-on front desk that welcomes visitors with a friendly, “How can I help you today?” and instantly handles common questions like clinic hours, service details, or doctor availability. These routine queries are resolved without any wait time significantly boosting the user experience and leaving a positive brand impression.

Need to book a flu shot? The chatbot can confirm availability and direct the user to a scheduling link, all in one seamless conversation. No forms or callbacks, just quick, convenient interaction that converts interest into action.

Personalized Guidance & Education

Modern chatbots do more than deliver generic responses. They use AI to understand context and personalize support. From symptom checkers that guide users toward appropriate care, to remembering user conditions (like diabetes), and sharing relevant health tips. The following are some applications:

• Symptom Assessment: Chatbots can conduct preliminary symptom assessments and guide users to the right care pathway (e.g., urgent care, telehealth, or self-care).
• Insurance Verification: AI assistants can check insurance eligibility and explain benefits in easy-to-understand language, reducing friction and drop-offs.
• Treatment Education: Chatbots can deliver procedure-specific content tailored to the user’s interest or condition, helping with improving health literacy.

For healthcare marketers, chatbots support user engagement and retention by naturally guiding users through personalized journeys, ultimately boosting retention rates through timely and relevant interactions.

Lead Nurturing & Identifying User Segments

AI chatbots are effective tools for segmenting users and guiding them towards their end goal. A cosmetic clinic’s bot, for example, might ask, “Are you exploring Procedure A or B?” and segment users based on interest.

High-intent leads can be tagged for follow-up or sent directly to booking, while general queries are handled instantly. Urgent questions get escalated to human staff, ensuring the right attention at the right time, all while collecting insights that inform future campaigns.

Case Study: OSF HealthCare’s “Clare”

OSF HealthCare launched Clare, a 24/7 virtual assistant that helps users check symptoms, find doctors, schedule visits, and access health resources. As part of their “digital front door” strategy, Clare helped generate $1.2 million in additional user revenue by capturing leads that would’ve otherwise slipped away after hours. Clare’s impact shows how intelligent automation can convert missed opportunities into millions in new revenue.

3. Predictive Analytics for User Journey Optimization

In marketing, success often hinges on timing, and that’s exactly where predictive analytics can be a game-changer. Powered by AI and machine learning, predictive analytics allows marketers to stop guessing and start forecasting. By analyzing historical and behavioral data, AI can uncover patterns that help teams anticipate user needs and service demand.

Forecasting Demand With Precision

Predictive models help healthcare marketers identify which services are likely to be in demand, and when. For example, if colorectal screenings typically spike in Q2 based on past data, marketers can launch targeted awareness campaigns weeks in advance, maximizing outreach during high-intent periods.

Proactive User Outreach

Instead of reacting to missed appointments or no-shows, marketers can use AI to predict who’s likely to drop out of care and re-engage them with reminders, follow-up content, or appointment nudges. The same applies to preventive screenings and recurring treatments. With predictive models, marketing becomes proactive, not reactive.

Next-Best Action Marketing

Advanced healthcare CRMs now use AI to recommend the next-best marketing action based on past outcomes. For instance, if a user recently underwent surgery, the system might trigger outreach promoting rehab or post-op care services. These insights help marketers prioritize where to focus (and what to promote) based on what’s likely to convert.

Case Study: Virtua Health

Virtua Health used CRM data and predictive AI to identify a user at high risk for breast cancer. After a few targeted email nudges, she finally booked a check-up, leading to a diagnosis, treatment, and surgery within weeks. That one well-timed campaign didn’t just drive engagement; it may have saved her life. This is predictive analytics at its best: smart, timely, and outcomes-driven.

4. Social Listening & Reputation Management

Reputation isn’t just PR; it’s users’ trust, and in the age of online reviews and social media, that trust can shift with a single bad experience. That’s why smart healthcare marketers are turning to AI-powered social listening and reputation tools to stay ahead of the narrative.

Sentiment Analysis

AI can scan thousands of reviews, comments, and ratings across platforms in seconds. Natural Language Processing (NLP) can break down sentiment (positive, neutral, or negative) and identify recurring issues like “long wait times” or “billing confusion.”

Instead of reacting late, you get actionable insights to fix real problems, fast. Advanced AI Search monitoring tools like Goodie can also help gauge the brand sentiment across LLMs, as LLMs primarily use the user-generated content across the web and summarize information intelligently.

Smarter & Faster Responses

AI tools can even suggest empathetic, on-brand responses to negative reviews or social comments. With human oversight, the team can respond within minutes, showing that you listen, care, and act. That kind of engagement builds real credibility.

Avoid PR Crises

Avoiding a crisis is difficult and not always possible. Is AI the perfect solution? Not yet, but it can help with predictions that can mitigate the impact of a crisis. If there’s a spike in complaints about a certain department or a particular service, your marketing team can take proactive steps before your brand takes a hit.

Turn Positive Sentiment Into Marketing Fuel

Last but not least, why let great feedback sit idle? AI helps identify your biggest advocates and happiest users. Use their stories in testimonials, campaigns, and social proof, backed by data; not guesses.

Measuring the Effectiveness of your AI Marketing efforts:

With the current AI buzz, implementing AI in healthcare marketing is exciting, but the real question every CMO or senior marketer asks is, “How do we know it’s working?”

We have curated some important metrics that matter to every business and should give the marketing team a fair idea of where the performance is headed.

1. Acquisition Cost (CAC)

This tells you how much you’re spending to bring in a new user. CAC is generally calculated as the total cost per user acquired.

Why It Matters: Lowering CAC means your marketing is more efficient.

2. Conversion Rates

This measures how many users take a desired action (like booking an appointment or signing up for a webinar) after seeing your content or ad.

Why It Matters: High conversion rates mean your marketing is persuasive and well-aligned with audience needs.

3. Engagement Metrics

Engagement metrics like Open rates, Clicks, and Time on site indicate how users interact with your content: are they opening emails, clicking links, or spending time on your website?

Why It Matters: Strong engagement signals higher content relevance and audience interest.

4. Customer Lifetime Value (CLV)

This metric estimates how much revenue a user will generate over their entire relationship with your organization.

Why It Matters: Users who stay longer and use more services are more valuable.

5. Operational Efficiency

Measures how much faster and smoother marketing workflows become (e.g., content generation, campaign launches, reporting).

Why It Matters: Less manual work = faster go-to-market, more room for creativity, and cost savings.

6. Staff Productivity

Tracks how much more your team can get done with AI.

Why It Matters: When AI handles repetitive tasks (copywriting, segmentation, A/B testing), your team can focus on strategy, creative thinking, and innovation.

7. User Satisfaction

Experience scores and reviews reflect how users perceive their interaction with your brand, often measured via surveys, online reviews, or Net Promoter Score (NPS).

Why It Matters: AI helps improve experience by enabling faster response times, relevant messaging, and consistent follow-up, leading to better reviews and stronger loyalty.

5 Ways to Harness AI in Healthcare Marketing

AI in healthcare marketing isn’t just about acquiring new users, it’s transforming the entire journey from Awareness > Engagement > Nurturing > Acquisition > Retention. AI helps you deliver timely, personalized experiences at every stage, 24/7.

1. Optimize for AI Search & Digital Discovery: Answer engines and AI-powered platforms are becoming the new front door. Invest in Answer Engine Optimization (AEO) to own the brand narrative across answer engines.
2. Content Creation & Personalization at scale: Create blog posts, ad copy, social assets, and more in less time, with more relevance. Targeting user segments across funnel stages is now possible.
3. Drive Engagement: Intelligent Chatbots can handle FAQs to appointment bookings, they ensure real-time, contextual support that improves user experience and captures high-intent leads around the clock.
4. Use Predictive Analytics to Retain and Nurture: Predictive analytics helps stay proactive, reduce drop-offs, and increase lifetime value. Predictive AI can help you anticipate what users need before they ask, whether it’s a follow-up on a missed screening or a nudge toward a wellness check.
5. Protect Brand Trust: Regularly monitor user-generated content to catch brand-damaging conversations early and respond with empathy, and clarity. 

The future of healthcare marketing will favor those who embrace change fast and adapt faster.

The post The AI Revolution in Healthcare Marketing: Your Complete Guide to Competitive Advantage appeared first on NoGood™: Growth Marketing Agency.

Sure, we occasionally see organic social campaigns go viral, which drives millions of views, comments, and conversions during the peak virality of the content. But these conversions phase out, hence why organic social has always been viewed as a slow, engagement-driven tactic with little direct impact on the bottom line.

In this blog, we want to explore the other side of the story: what if organic social could be a true growth lever? What if those consistent, daily efforts (comments, posts, DMs, and content we’re delivering) drive business impact in ways we have only begun to understand?

It’s perhaps an apt time to stop thinking of organic social as the sidekick and recognize it for what it is becoming: a powerful, compounding engine for discovery, trust, and conversion.

In this blog, we will cover:

• Why organic social has been undervalued in growth marketing
• How to rethink the funnel with organic social at the center
• The evolving digital ecosystem that’s tilting the game in organic’s favor
• A step-by-step strategy to make your organic content move the needle

Why Has Organic Social Been Undervalued for Growth Marketing?

Growth marketers love predictability. We are trained to chase the numbers: CAC, RoAS, LTV, you name it.

The equation is simple: Spend X, Get Y. Repeat if LTV > CAC.

It’s clean. It’s trackable. It’s immediate.

Organic search doesn’t work like that, but has still found a lot of value amongst marketers with how it’s able to drive business success over a period of time. Marketers learned patience as they realized good SEO practices are bound to bring results in the long run.

But organic social is different; it doesn’t plug neatly into this formula. The results compound, attribution is murky, and feedback loops take longer. So it feels inefficient. But inefficiency isn’t the same as ineffectiveness.

Organic social isn’t about instant transactions. It’s about earning attention, building trust, and creating a distribution channel that you don’t have to keep paying for.

And when you zoom out, the digital behavior shift is undeniable; it’s perhaps the time to reframe how we think about organic social.

Rethinking the Funnel: Organic Social Strategy as the Growth Driver

There are some major changes taking place which could redefine how organic social content is used by search platforms (in both traditional and AI search spaces) to show results.

1. Social Content Is Equal to Website Content

For most brands, the most profitable and reliable channel has always been organic search. Based on what we’ve seen, it could contribute to anywhere from 15% to 80% of a company’s total traffic and conversions, depending on how big the brand is and the industry it’s in.

But now, the boundaries between social and search are blurring.

On July 10, 2025, Instagram announced that it will let Google, Bing, and other search engines crawl and index public content from professional accounts. That includes photos, videos, carousels, and Reels that were posted on or after January 1, 2020. If you’re a business or creator account and your profile is public, your content can now show up in search results, just like any other webpage.

Your Instagram profile is no longer just a gallery; it’s a collection of potential landing pages, each one a new doorway for customers to discover you.

2. The Search Revolution

The very definition of what it means to “search” is changing. While the traditional platforms still hold a significant share, the dependency on only a select few search platforms is going away.

Users are adopting newer methods of searches including social search and AI searches. As AI voice assistants become smarter thanks to Gen AI models that are powering them (hello voice search); any way you spin it, the search ecosystem is becoming more fragmented than ever.

If we speak about organic social as a search engine, 46% of Gen Z and 35% of Millennials prefer social media over traditional search engines, and 44% of Gen Z discover new brands on social media daily, as per a report by Forbes.

Why? The reasons are simple, and profoundly human.

• It’s Immediate: They get the latest visual, dynamic answers in seconds.
• It’s Relatable: The content is from real people, not just brands.
• It’s Socially Vetted: The results are backed by social proof (likes, comments, and shares) from their peers, not an algorithm.

Audiences aren’t just searching for restaurants or products; they are researching complex topics, learning new skills, and forming their opinions on brands. If your brand isn’t part of this social conversation, you are virtually invisible to this generation of consumers.

3. The Rise of AI Search, Where Reputation = Ranking

The rise of AI search platforms like ChatGPT, Gemini, and Perplexity is the final piece of the puzzle. This new frontier of search led to a new discipline: Answer Engine Optimization (AEO).

Unlike traditional search engines that heavily rely on technical factors like backlinks, AI Search models are designed to understand context, nuance, and sentiment. They learn from the entire web, including the vast conversations happening on social media.

When a user asks an AI search, “What’s the best running shoe for beginners?” or “Is Brand X a good company?”, the AI doesn’t just scan keywords. It synthesizes product reviews, reads customer comments across review platforms, analyzes the tone of content, and assesses the overall sentiment surrounding your brand.

In this new world, your digital reputation is your ranking. A brand that is consistently praised by its customers on social platforms will be described favorably by AI. A brand with a trail of negative comments and unresolved complaints will see that sentiment reflected in its AI search results. Your organic social presence is now actively training the AI that will define your brand to millions.

How to Support Meta’s New Indexing Framework

1. Use a Public Professional Account (Critical for Indexing)

The July 2025 Instagram indexing update only applies to public Business or Creator accounts (for users aged 18+). Make sure you have the right account in place for your brand to maximize visibility in social searches.

2. Write Clear, Keyword-Rich Captions

Treat your captions like mini blog posts: front-load them with relevant search keywords that describe the post content (but don’t get keyword-stuffy).

This helps Google understand what the post is about. Additionally, treat your Instagram posts, Facebook posts, Reels, carousels, and profile bios as searchable web content (because they are).

3. Leverage Reels for Visibility

Google has a new tab in its search results page for Short videos. Instagram Reels are central to this rollout, and are already generating significant visibility in Google search.

Focus on creating short-form video content that is both engaging for social media users, while still being optimized with keywords for search engines.

4. Use Descriptive Alt Text on Images

Instagram and Facebook allow you to manually enter image alt text. This is critical for both accessibility and search engines. When writing optimized alt text, be sure to:

• Describe what’s in the image accurately
• Include relevant keywords naturally (again, avoid keyword stuffing)

5. Treat Hashtags as Meta Tags

Use 5-10 high-intent, niche, and local hashtags to help the algorithm (and now Google’s index) understand your content’s theme. Think of these as secondary keywords.

6. Geo-Tag Your Posts (Especially for Local or Service Brands)

Adding a location (city, neighborhood, venue) helps your post appear in local Google searches. This aligns with keyword-local intent and improves visibility; especially for businesses targeting local traffic.

Organic Social Strategy as a Growth Lever: A Step-by-Step Guide

Step 1: Define the Role of Organic Social in Your Growth Funnel

Start by asking: what job does organic social do for us right now? If the answer is “get likes” or “improve brand sentiment,” you’re thinking too small.

Organic social can, and should, support the full funnel:

• TOFU (Top of Funnel): Spark attention from your target market with thought leadership, storytelling, and relatable content.
• MOFU (Middle): Educate and nurture interested users with product demos, comparisons, and explainers.
• BOFU (Bottom): Convert high-intent audiences with social proof, case studies, and testimonials.

The “Rule of 7” in marketing suggests that a prospect needs to encounter your brand message at least seven times before taking action. While not a hard rule, it underscores the importance of consistent, repeated exposure across channels.

Organic social excels here. It allows brands to stay present in the feed, in conversation, and in context without spending a dollar per impression.

Step 2: Build Content Pillars

Create content that fits your strategic business intent. The number of pillars can vary, but we recommend that you begin with at least three content pillars.

Identify 3-5 content pillars at the intersection of:

• What your audience cares about
• What your product enables
• What your team can speak to credibly

Something to keep in mind while you’re identifying your pillars is to also assign each of them a place in the funnel. Here’s the type of content that typically falls under each part of the customer funnel:

• TOFU: Educational content, guides, industry trend reports, infographics
• MOFU: Comparisons, case studies, webinars, explainer videos
• BOFU: Comparisons with strong CTAs, testimonials and reviews, pricing information, detailed breakdowns

Each pillar feeds your content calendar and allows structured experimentation across formats and platforms.

Step 3: Turn Anchor Content Into Multi-Platform

Treat organic social channels and their content as a distribution engine; this is the art of repurposing content, a vital component of organic social strategy.

1. Create one anchor asset (e.g., a webinar, podcast, founder interview, or case study).
2. Break it down into:
   • 5–10 video clips (for Reels, Shorts, and LinkedIn)
   • Carousels
   • Quote graphics
   • Email snippets
   • X or Threads-length posts
   • Blog summaries or LinkedIn posts

Step 4: Measure What Matters

Attribution for organic social content is not perfect, but here’s how you can start measuring what matters from a growth perspective:

Look at directional indicators and trendlines; not just isolated metrics.

Step 5: Optimize for AI Search

A study by Goodie AI shows how the traffic from AI searches is far more valuable in terms of conversion as compared to the traditional searches.

After analyzing over 100,000 leads, the study concluded that while AI search traffic is still contributing to only 6% of total sessions, it converted at 2x the rate when compared to traditional search channels (primarily due to context-rich and trust-centric content).

Step 6: Align Paid & SEO to Amplify Organic Social

Just as alignment between organic and paid search initiatives empowers the success of growth strategies, we can also let the magic of unified campaigns work for organic social:

• Use paid media to amplify high-performing organic content (e.g. boosted posts). Use the most engaged with organic social content to further drive conversions through a dedicated paid performance campaign.
• Build retargeting audiences for paid advertising based on video viewership demographic data.
• Use SEO insights to build sought-after social explainer content, or use social insights to inform your next SEO content cluster.

Organic Social Strategy: Final Thoughts

Organic social is a core part of how modern growth happens. It drives brand discovery, builds trust, powers SEO and AI visibility, and influences buying decisions long before a user ever lands on your site. Unlike paid channels, organic doesn’t switch off when the budget runs out. It compounds.

It’s absolutely fair for brands to rely on paid media ads to drive immediate conversions. However, being solely dependent on paid media and not having an organic social strategy in place can be a recipe for disaster in the long term.

The brands who want to win tomorrow are already investing in organic today; not as support, but as a system.

The post Scaling With Organic Social Strategy as a Growth Lever appeared first on NoGood™: Growth Marketing Agency.

To address this gap, our team of experienced marketers conducted the first impartial study assessing the leading LLMs in the market. We recognized the need for a study that was not only conducted for marketers by marketers but also one that focused on the practical applications and challenges faced by marketing professionals in their daily work.

Background

The global AI market has already been valued at $241.8 billion in 2023 as compared to $135 billion in 2022, and it is expected to surge to a staggering $740 billion by 2030, reflecting a CAGR of 17.3%, according to Statista. This surge is being spearheaded by the United States, boasting the world’s strongest AI research capabilities, followed closely by Europe. Enterprises are seeing a steep adoption rate. According to OpenAI, 90% of Fortune 500 brands are actively using their tools and API internally.

Generative AI Adoption in Marketing

The marketing and advertising industry has emerged as a frontrunner in AI adoption, with a remarkable 37% of professionals already utilizing this technology in their daily tasks.

This rapid embrace is not surprising, considering the unique blend of creativity and data analysis inherent to marketing. AI seamlessly integrates with this blend, offering powerful tools for both creative exploration and data-driven decision-making.

In a 2023 study conducted with marketers in the United States, 73% of respondents reported using generative artificial intelligence tools, such as chatbots, as a part of their company’s work. This widespread adoption indicates a growing comfort level and understanding of the potential benefits these tools offer.

Generative AI, particularly Large Language Models (LLMs), has revolutionized the way people work across industries, providing innovative solutions to complex tasks. It has found prominent usage in various job functions, notably in industries such as IT and Technology, Finance, Healthcare, and Marketing.

Marketing Evaluation to Leading LLMs Report

The study involved a panel of 20 seasoned marketers, each with a minimum of 6 years of experience in the field. These experts were tasked with reviewing the output of anonymized leading LLMs across a range of the most common marketing use cases, including copywriting, marketing strategy development, content creation, creative ideation, and campaign planning. To ensure an unbiased evaluation, we carefully crafted prompts that were highly practical, realistic, and consistent across each model. This approach guaranteed that no single model was favored or engineered to outperform others, a common issue in LLM evaluations published by the creators of the models themselves.

The LLMs covered in the study:

• LLama 2 by Meta
• Gemini 1.5 by Google
• Perplexity by Perplexity
• ChatGPT (publicly available GPT 3.5) by OpenAI
• Claude 2.1 by Anthropic
• Claude 3 – Sonnet by Anthropic

By conducting this study, we aim to provide marketers with valuable insights into the strengths and weaknesses of each LLM, empowering them to make informed decisions when incorporating these tools into their marketing strategies and workflows. Our findings shed light on the variability in performance across different aspects, such as creativity, thoroughness, and coherence, highlighting the importance of selecting the right LLM for specific marketing tasks. The data presented in this report demonstrates the significant potential of AI applicability use case in marketing as the industry continues to evolve.

The graphs below offer a broad overview of how various Large Language Models (LLMs) performed across different marketing tasks. For a more in-depth analysis and breakdown of these findings, download the free report by clicking the download button below.

As the use of Generative Artificial Intelligence (Gen AI) continues to grow, a critical question emerges: Which Gen AI tool is best suited for what purpose, and how do the leading solutions compare? This report addresses this question by evaluating the performance of six Large Language Models (LLMs) across various marketing tasks and against diverse evaluation criteria.

The post Marketing Generative AI Report: A Comprehensive Evaluation of Leading LLMs appeared first on NoGood™: Growth Marketing Agency.

Enters contextual marketing, a strategy that is known for its ability to offer relevant content to the right audience at the perfect moment. In this comprehensive blog post, we will dive into the benefits of contextual marketing, common challenges faced by marketers with contextual marketing and how to overcome those and useful tips to execute an effective and successful contextual marketing strategy.

What is Contextual Marketing?

Harvard Business Review published a magazine in 2000, and this is how they explained contextual marketing. The article reads, “Instead of trying to create destinations that people will come to, they need to use the power and reach of the Internet to deliver tailored messages and information to customers at the point of need.“

They need to become what we call contextual marketers.” This definition still holds. In other words, “it’s delivering the right piece of communication using the right medium to the right user at the right time in their purchase journey.” And if you get enough things done right, you have a perfectly executed contextual marketing campaign. Here’s a classic example of contextual ads.

For example, if a user is reading an article about the best credit cards for travel rewards, contextual ads may promote a travel credit card ad. Or, webmail ads could target users actively engaging with email platforms. The relevance works to grab attention when consumer interest peaks to turn their intent into conversions.

Here’s a classic example of contextual advertising:

Benefits of Contextual Marketing

1. Enhanced relevance: Relevant ads and content that are not intrusive and aligned with the interests and needs of the users help in getting higher engagement or better click-through rates than the traditional targeting methods.
2. Building long-term relationships: Contextual marketing fosters genuine connections between a brand and a customer as the communication is based on the customer’s current needs and interests, which eventually leads to lasting loyalty.
3. Cost-effectiveness: Targeting based on context can be more cost-effective than other traditional methods.
4. Better ROI: By focusing on meaningful engagement, contextual ads help in delivering measurable return on investment.

Common Challenges with Contextual Marketing

1. Limited Data 

For years, 3rd party cookies played a crucial role in digital marketing, especially behavioral advertising, like tracking user behavior across websites and enabling highly targeted advertising, but also raised privacy concerns. Deprecation of 3rd party cookies is fundamentally changing the game for marketers.

Mozilla Firefox was the first to stop 3rd party cookies in 2019, followed by Apple’s Safari browser. Now, the industry giant Google Chrome has joined the movement, phasing out third-party cookies by the end of 2024.

To overcome this challenge, ensure you focus on enriching your first-party data through website content, user engagement, and surveys. Leverage user actions and search queries/ search terms on your website to create segments. Also, utilize the customer preferences and purchase history from the CRM system.

2. Scaling Effectively

Utilize automation tools and pre-built contextual segments offered by marketing platforms. Prioritize high-impact channels and audiences for initial campaigns. Utilizing marketing automation tools alongside CRM systems has transitioned from being merely optional to becoming a mandatory practice.

3. Overcoming Ad Fatigue 

To combat ad fatigue, marketers can use dynamic content optimization to rotate advertisements and personalize messages based on user interaction history. Not all platforms offer this functionality; in such cases, you can opt to have a “winner vs contender model”.

For example, you run an A/B test with Creative A vs B for 2 months, then at the end of the testing cycle, you can evaluate the performance based on metrics that are relevant for the experiment. You can retain the winner and add a new contender creative for the next testing cycle. The duration of the test can be defined based on various factors like platform, product, conversion cycle, etc. But the model should work for all types of tests.

4. Measuring Success

Go beyond just clicks and impressions. Focus on metrics like level of engagement, time spent, conversions, and downstream sales to measure the true effectiveness of your contextual campaigns. It has been the most demanded feature by digital marketers and yet one of the most difficult when it comes to execution.

Thankfully, there have been advancements in the way last mile measurement can be tracked. Well, it might not be possible, especially for brands and products where the life cycle of the product is longer. In such cases, it’s best to start with an alternative metric that aligns with the marketing objective and can be measured in the short term while you keep inching closer toward sharper metrics.

Tips for Executing a Successful Contextual Marketing Strategy

1. Structure Campaigns by Buying Stages 

Go beyond generic top-of-funnel targeting with a customer journey viewpoint. Use the AIDA model to reach interested users at multiple touchpoints

2. Embrace Contextual Advertising Platforms

Partner with platforms that offer contextual ad networks, enabling you to display your ads on websites and apps aligned with your target audience’s interests and current context. For contextual ads, Google Demand Gen, Taboola, Outbrain, and Quora could be used. This is not the complete list of ad platforms available out there. You should pick the relevant ones that add value to your brand and business objectives.

3. Expand Beyond Paid Channels 

Contextual marketing strategies can be done effectively without relying solely on paid campaigns. Here are some examples

CRO and SEO: Interlink relevant content on your website. You should guide the user by sharing relevant internal links or contextual popups that are non-intrusive and offer personalized recommendations based on their interest or previous selections on the website. This activity should ideally expose customers to relevant product and service pages based on their purchase funnel.

Content Marketing: Craft content in the form of blog posts, FAQs, product pages, tutorial videos, and testimonials that address current events, industry trends, or common pain points within your target audience. This establishes your expertise and resonates with the customers. With the ever-increasing consumption of video content and social media, it is advisable to create visually appealing infographics or videos that share valuable information in an engaging format. 

Email marketing: Email marketing, when combined with contextual data, can be a powerful tool and can deliver personalized messages that resonate deeply and drive action. It has become a common practice to target behavior segments like welcome emails, cart abandoners, and browsing specific products or product categories. However, in addition to these, marketers can also indulge in email conversions by sharing interactive or personalized content.

Examples of Interactive content include:

• Quizzes: recommendations based on their answers, 
• Dynamic Polls to gauge user preference and tailor future campaigns.

Examples of personalized content:

• Content related to trending topics
• Promotion of events
• Dynamic product recommendations
• Webinars/Product demo invitations.

Influencer partnerships: Collaborate with influencers or brand advocates who align with your brand values and target audience. Partnering with influencers can help you reach new audiences and build credibility and trust with their followers. This could include sponsored posts, product reviews, tutorials, or behind-the-scenes glimpses that resonate with the influencer’s audience and align with your brand messaging.

4. Experiment and Measure

Like any marketing strategy, continuous testing and analysis are crucial. Experiment with different contextual targeting options, platforms, and creative formats, and measure the impact on key metrics like engagement, conversion rates, and ROI. No one nails it perfectly in their first attempt, but constant monitoring and adjustments will help you find the sweet spot for effective and well-received contextual marketing.

5. Strike the Right Balance 

Contextual marketing can be a powerful tool, but when overdone, it can backfire. To achieve scale, it’s a common practice to go overboard by getting in the area of irrelevant ads, intrusive channels, or untimely messages.

Remember the annoyance of the remarketing ads that followed you everywhere or excessive push notifications that eventually made you uninstall an app? Intrusive contextual marketing can create negative experiences. Be mindful of getting the right balance for a winning strategy.

Final Thoughts

With the deprecation of third-party cookies, the need for contextual marketing is at its peak! Contextual marketing offers numerous benefits, such as enhanced relevance, fostering long-term relationships, cost-effectiveness, and better ROI. However, it also presents challenges such as limited data, scaling effectively, overcoming ad fatigue, and measuring success.

To execute a successful contextual marketing strategy, marketers should structure campaigns based on buying stages, embrace contextual advertising platforms, expand beyond paid channels through methods like CRO, SEO, content marketing, email marketing, and influencer partnerships, experiment and measure results, and strike the right balance to avoid intrusive experiences.

The post Unlocking Contextual Marketing: Benefits, Challenges, and Strategies for Success appeared first on NoGood™: Growth Marketing Agency.