Can AI Help with My Marketing?

Right, let’s cut to the chase. You’re here because you’ve heard the buzz about AI transforming marketing, and you’re wondering if it’s all hype or if there’s genuine substance behind the claims. Well, I’ll tell you straight up – AI isn’t just changing marketing; it’s in essence reshaping how we connect with customers, create content, and measure success. But here’s the kicker: it’s not about replacing your marketing team with robots. It’s about amplifying what humans do best while letting machines handle the heavy lifting.

You know what? I’ve spent the last few years watching businesses struggle with the same marketing challenges: limited budgets, overwhelming data, and the constant pressure to produce more content faster. Then AI tools started emerging, and suddenly, small teams were competing with enterprise-level operations. That’s not science fiction – it’s happening right now, and if you’re not at least exploring these possibilities, you’re leaving money on the table.

Understanding AI Marketing Capabilities

Let me paint you a picture. Remember when you had to manually segment your email lists, spending hours categorising customers based on their purchase history? Or when creating personalised content meant writing dozens of variations by hand? Those days are rapidly becoming ancient history. AI marketing capabilities have evolved from simple automation to sophisticated systems that understand context, predict behaviour, and generate creative content that actually resonates with humans.

The beauty of modern AI marketing lies in its accessibility. You don’t need a PhD in computer science or a Silicon Valley budget to harness these tools. According to IBM’s research on AI in marketing, businesses using AI-powered marketing tools see average revenue increases of 15% within the first year of implementation. That’s not pocket change – that’s radical growth.

But here’s where it gets interesting. AI isn’t a monolithic technology – it’s an umbrella term covering multiple sophisticated systems, each with unique applications in marketing. Think of it like a Swiss Army knife for marketers, where each tool serves a specific purpose, but together they create something far more powerful than the sum of their parts.

Machine Learning Applications

Machine learning – sounds intimidating, doesn’t it? But strip away the jargon, and it’s basically pattern recognition on steroids. Your brain naturally recognises patterns (like knowing your regular customers’ preferences), but machine learning does this at scale, processing millions of data points in seconds.

I’ll give you a real example. A local e-commerce shop I worked with implemented machine learning for product recommendations. Nothing fancy – just a basic algorithm that tracked what customers viewed and purchased together. Within three months, their average order value jumped 23%. The system noticed patterns humans missed, like customers who bought organic dog food also frequently purchasing eco-friendly cleaning products. Who would’ve guessed?

The applications extend far beyond recommendations. Machine learning algorithms now power dynamic pricing strategies, adjusting prices in real-time based on demand, competition, and customer behaviour. They’re predicting customer churn before it happens, identifying which customers are likely to leave and triggering retention campaigns automatically. According to Business News Daily’s analysis of CRM marketing benefits, companies using ML-powered CRM systems see customer retention rates improve by up to 27%.

My experience with machine learning taught me something necessary: it’s not about the complexity of the algorithm; it’s about the quality of your data. Feed garbage in, get garbage out. But feed it clean, consistent customer data? That’s when the magic happens.

Natural Language Processing Tools

Honestly, NLP might be the most underrated AI technology in marketing today. We’re talking about systems that understand not just what people say, but what they mean – including context, sentiment, and intent. It’s like having a mind reader on your marketing team.

Consider chatbots. Five years ago, they were glorified FAQ pages that frustrated more customers than they helped. Today? NLP-powered chatbots handle complex queries, understand emotional context, and seamlessly hand off to human agents when needed. They’re not just answering questions; they’re qualifying leads, booking appointments, and even upselling products.

But NLP goes way beyond chatbots. Social listening tools now use NLP to monitor brand mentions across the web, analysing sentiment in real-time. Imagine knowing instantly when customer sentiment shifts negative, allowing you to address issues before they become PR disasters. One fashion retailer I know prevented a major backlash by detecting negative sentiment around a product launch within hours, allowing them to adjust their messaging before the situation escalated.

Voice search optimisation represents another frontier. With NLP understanding conversational queries, marketers must adapt their content strategy. People don’t type the way they speak – they might type “best pizza NYC” but ask their voice assistant “Where can I get really good pizza near me tonight?” NLP helps bridge this gap, ensuring your content appears for both types of searches.

Predictive Analytics Systems

Let me tell you a secret: predictive analytics isn’t about crystal balls or fortune telling. It’s about probability and patterns. These systems analyse historical data to forecast future outcomes with remarkable accuracy. Think weather forecasting, but for customer behaviour.

The power lies in anticipation. Instead of reacting to customer actions, you’re predicting them. A B2B software company I consulted for used predictive analytics to identify which trial users were most likely to convert to paid plans. By focusing their sales efforts on high-probability prospects, they increased conversion rates by 41% during reducing sales costs.

Lead scoring becomes infinitely more sophisticated with predictive analytics. Traditional lead scoring might assign points based on actions (downloaded whitepaper = 10 points, attended webinar = 20 points). Predictive systems consider hundreds of variables simultaneously, including behavioural patterns, firmographic data, and even external factors like industry trends.

Here’s where it gets properly clever. Predictive analytics can forecast customer lifetime value (CLV) from the moment someone first interacts with your brand. Knowing a customer’s potential value allows you to adjust acquisition spending because of this. Why spend £50 acquiring a customer worth £30 when you could focus on those worth £500?

The U.S. Small Business Administration’s marketing guide emphasises the importance of understanding your customer journey. Predictive analytics takes this concept and supercharges it, mapping not just where customers have been, but where they’re likely to go next.

Computer Vision Technologies

Now, this is where things get properly futuristic. Computer vision allows machines to “see” and interpret visual content – analysing images and videos to extract meaningful insights. For marketers, this opens doors we didn’t even know existed.

Visual search is revolutionising e-commerce. Customers snap a photo of something they like, and AI finds similar products in your catalogue. Pinterest Lens processes over 600 million searches monthly. That’s 600 million opportunities for discovery-based shopping that didn’t exist a decade ago.

But the applications run deeper. Computer vision analyses user-generated content at scale, identifying when and how customers use your products in real life. A sports equipment brand discovered through computer vision analysis that customers were using their yoga mats for outdoor workouts far more than indoor sessions. This insight completely transformed their marketing imagery and messaging.

Social media monitoring takes on new dimensions with computer vision. Beyond tracking text mentions, you can identify when your products appear in images and videos, even without tags or captions. Imagine knowing every time someone posts a photo featuring your product, regardless of whether they mention your brand.

The technology even extends to physical retail. In-store cameras (with appropriate privacy measures) track customer movement patterns, identifying which displays attract attention and which get ignored. It’s like having heat maps for your physical space, optimising layout based on actual behaviour rather than assumptions.

Content Generation and Optimization

Right, let’s address the elephant in the room. Yes, AI can write content. No, it’s not replacing human creativity anytime soon. What it’s doing is handling the grunt work, freeing humans to focus on strategy and genuine creativity. Think of AI as your tireless assistant who never gets writer’s block and can produce variations faster than you can say “A/B testing.”

The content crisis is real. Park University’s analysis of marketing strategies for 2025 highlights how content demands have skyrocketed during budgets haven’t kept pace. Enter AI content tools – not as replacements for human writers, but as force multipliers that make small teams mighty.

Automated Copywriting Tools

I’ll be blunt: if you’re still writing every product description manually, you’re wasting precious time. Modern AI copywriting tools can generate hundreds of unique, SEO-optimised product descriptions in minutes. But here’s the key bit – they’re not just spinning synonyms. They’re creating genuinely unique content that considers context, tone, and audience.

Take email subject lines. Testing different variations used to mean brainstorming sessions and educated guesses. Now? AI generates dozens of options based on what’s worked before, considering factors like length, emotional triggers, and personalisation elements. One e-commerce client saw open rates jump 31% after implementing AI-generated subject lines. The system noticed their audience responded better to curiosity gaps than direct benefits – something that took humans months to figure out.

Social media captions represent another sweet spot. AI tools analyse top-performing posts in your niche, identifying patterns in structure, hashtag usage, and emotional tone. They then generate captions that match these winning formulas at the same time as maintaining your brand voice. It’s like having a social media expert who’s studied every successful post in your industry.

But let’s keep it real. AI-generated copy often needs human editing. The tools excel at structure and covering key points, but nuance, humour, and emotional depth still require human touch. Think first draft, not final copy. My approach? Let AI handle the heavy lifting, then spend my time polishing and adding personality.

SEO Content Enhancement

SEO used to be about keyword stuffing and link schemes. Thank goodness those days are behind us. Modern SEO demands quality content that genuinely serves user intent – and AI tools have become indispensable for achieving this at scale.

Content optimisation tools now use AI to analyse top-ranking pages for any keyword, identifying not just keyword density but semantic relationships, content structure, and user engagement signals. They’ll tell you your article about “email marketing” should also cover authentication protocols, list hygiene, and deliverability – topics you might’ve missed but Google expects.

Here’s something that blew my mind: AI can now predict content performance before publication. By analysing historical data and current trends, these tools forecast how well a piece will rank and what traffic it might generate. It’s like having a crystal ball for content strategy.

The real game-changer is semantic SEO understanding. AI tools identify related concepts and entities that strengthen topical authority. Writing about Italian restaurants? The AI suggests including specific dish names, regional variations, and cooking techniques – building semantic relevance that search engines love.

Internal linking, often overlooked, gets the AI treatment too. Tools now analyse your entire content library, suggesting relevant internal links that boost page authority and improve user experience. One publishing client increased organic traffic 47% just by implementing AI-suggested internal linking strategies.

Dynamic Email Personalization

Remember when email personalisation meant inserting someone’s first name? Those days feel quaint now. Modern AI-driven email personalisation creates genuinely unique experiences for each recipient, considering behaviour, preferences, timing, and context.

Send time optimisation illustrates this perfectly. Instead of blasting emails at 9 AM because some study said it’s optimal, AI analyses when each individual recipient typically engages with emails. John might check emails during his morning commute at 7:23 AM, when Sarah reads them during lunch at 12:47 PM. The system learns and adapts, sending emails when they’re most likely to be opened.

Content personalisation goes beyond product recommendations. AI now adjusts email copy, images, and calls-to-action based on recipient profiles. A fitness brand might show yoga content to one segment and weightlifting to another, all within the same campaign. The subject line, hero image, and even colour scheme adapt dynamically.

My experience with dynamic personalisation revealed something unexpected: sometimes less is more. An over-personalised email can feel creepy. The sweet spot? Subtle personalisation that feels helpful, not invasive. AI helps find this balance by testing variations and measuring engagement.

The SBA’s guide on market research and competitive analysis stresses knowing your customer inside out. AI email personalisation takes this principle and automates it, learning from every interaction to refine future communications.

Customer Experience Enhancement

Let’s talk about something that keeps marketers up at night: customer experience. You’ve probably heard the stats – 86% of buyers will pay more for better customer experience, and 73% say experience is a key factor in purchasing decisions. But here’s the rub: delivering consistent, personalised experiences at scale seemed impossible. Until AI changed the game.

The shift isn’t just technological; it’s philosophical. We’ve moved from treating customers as segments to recognising them as individuals with unique preferences, behaviours, and needs. AI makes this individualisation versatile, creating what I call “mass personalisation” – custom experiences for millions, delivered in milliseconds.

Chatbots That Actually Help

Alright, I know what you’re thinking. “Not another chatbot pitch!” Bear with me. Modern AI chatbots are nothing like those frustrating menu-based systems that made you want to throw your computer out the window. Today’s conversational AI understands context, remembers previous interactions, and knows when to escalate to humans.

I recently helped a SaaS company implement an AI chatbot, and the results were staggering. Support ticket volume dropped 67%, but customer satisfaction scores actually increased. How? The bot handled routine queries instantly – password resets, billing questions, feature explanations – freeing human agents to tackle complex issues that genuinely needed their skill.

The secret sauce is continuous learning. These chatbots improve with every conversation, learning new phrases, understanding industry jargon, and recognising emotional cues. One particularly impressive feature: sentiment detection that identifies frustrated customers and immediately offers human assistance.

Recommendation Engines That Get You

Netflix didn’t become a streaming giant by accident. Their recommendation engine, powered by sophisticated AI, keeps viewers glued to their screens by serving up exactly what they want to watch next. But here’s the thing – this technology isn’t exclusive to tech giants anymore.

Modern recommendation engines go beyond “customers who bought X also bought Y.” They consider browsing patterns, dwell time, cart abandonment, seasonal trends, and even external factors like weather or local events. A clothing retailer might recommend raincoats when storms are forecast in your area, or party dresses when local event calendars show upcoming gatherings.

The sophistication is mind-boggling. These systems now understand complementary products (suggesting wine glasses with wine), substitute products (offering alternatives when items are out of stock), and even aspirational purchases (recognising when customers are browsing above their usual price range and adjusting recommendations so).

Predictive Customer Service

What if you could solve customer problems before they even realise they have them? That’s predictive customer service, and it’s revolutionising how businesses handle support. By analysing patterns in customer behaviour, AI identifies potential issues and proactively addresses them.

Here’s a real example: a software company noticed through AI analysis that users who didn’t complete certain onboarding steps within 48 hours had an 80% higher churn rate. The system now automatically triggers personalised tutorial emails for these users, reducing churn by 35%.

Predictive service extends to product issues too. AI can identify when a customer’s usage patterns suggest they’re experiencing problems, triggering preventive support outreach. Imagine receiving a helpful email saying, “We noticed you might be having trouble with feature X. Here’s a quick guide that might help” before you even contacted support.

Data Analysis and Insights

Data without insights is just expensive storage. That’s where AI transforms marketing from guesswork into science. We’re drowning in data – website analytics, social media metrics, CRM records, sales figures – but making sense of it all? That’s where most businesses struggle.

AI doesn’t just process data faster; it finds connections humans would never spot. It’s like having a team of data scientists working 24/7, constantly analysing, learning, and uncovering insights that drive real business results.

Real-Time Campaign Optimization

Gone are the days of running a campaign for weeks before analysing results. AI enables real-time optimisation, adjusting campaigns on the fly based on performance data. Think of it as evolution at hyperspeed – the weak elements die off during successful ones thrive and multiply.

I watched a retail client’s Black Friday campaign transform before my eyes. The AI system tested dozens of ad variations simultaneously, quickly identifying that mobile users responded better to video content while desktop users preferred static images. It automatically shifted budget allocation, resulting in a 52% improvement in ROI compared to the previous year’s manually managed campaign.

The optimisation goes precise. AI adjusts bidding strategies every few minutes, considers time-of-day performance, and even factors in competitive activity. When a competitor launches a sale, your AI can automatically adjust messaging and bidding to maintain visibility.

Attribution Modelling Magic

Attribution modelling – the marketing world’s biggest headache. Which touchpoint deserves credit for the conversion? First touch? Last touch? Everything in between? AI brings clarity to this chaos through sophisticated multi-touch attribution models that consider the entire customer journey.

Machine learning algorithms analyse thousands of customer paths, identifying which combinations of touchpoints most effectively drive conversions. Maybe it discovers that customers who see a Facebook ad, then read a blog post, then receive an email convert at 3x the rate of other paths. That’s workable intelligence.

The models adapt constantly. As customer behaviour changes, the attribution model evolves, ensuring your marketing spend always goes to the most effective channels. No more arguing about whether SEO or paid search deserves more budget – the data tells the story.

Audience Segmentation Revolution

Traditional segmentation was simple: age, location, maybe purchase history. AI segmentation? It’s like comparing a sketch to a high-resolution photograph. AI identifies micro-segments based on hundreds of variables, creating groups you never knew existed.

One fascinating discovery from a fashion retailer: AI identified a segment of customers who only purchased during lunch hours on weekdays, suggesting office workers shopping during breaks. This insight led to targeted lunchtime flash sales, increasing midday revenue by 28%.

Dynamic segmentation takes this further. Segments aren’t fixed; they evolve as customer behaviour changes. Someone might move from “price-conscious browser” to “premium buyer” based on recent activity, triggering different marketing strategies automatically.

Marketing Automation Evolution

Automation used to mean simple if-then workflows. If someone downloads an ebook, then send them an email. Basic stuff. But AI has transformed marketing automation into something resembling a living, breathing system that adapts and learns.

According to marketing best practice guidelines from the Health and Fitness Association, successful marketing requires consistent, personalised communication across multiple channels. AI makes this achievable for businesses of any size.

Workflow Intelligence

Static workflows are dead. AI-powered workflows adapt based on individual responses, creating unique paths for each customer. Instead of everyone receiving the same five-email sequence, each person gets a customised journey based on their engagement patterns.

The system might notice that technical buyers prefer detailed whitepapers when executives want brief summaries. It automatically adjusts content delivery thus. Some prospects might receive daily touchpoints when others get weekly communications, all optimised for maximum engagement without overwhelming anyone.

Cross-Channel Orchestration

Customers don’t think in channels; they think in experiences. AI orchestrates continuous experiences across email, social media, web, mobile, and even offline touchpoints. It’s like conducting a symphony where every instrument plays in perfect harmony.

Imagine a customer browsing products on mobile, abandoning their cart, then seeing a targeted ad on Facebook, receiving a personalised email, and finally purchasing in-store with a push notification discount. AI coordinates this entire journey, ensuring consistent messaging and optimal timing across every touchpoint.

Lead Nurturing on Autopilot

Lead nurturing requires patience, persistence, and personalisation – exactly what AI excels at. Modern systems score leads continuously, adjusting nurture tactics based on engagement levels and buying signals.

The AI might recognise that a lead has gone cold and automatically trigger a re-engagement campaign. Or it might identify sudden increased activity and alert sales teams to strike as the iron’s hot. It’s like having a dedicated account manager for every single lead.

Practical Implementation Strategies

Right, enough theory. Let’s talk about actually implementing AI in your marketing. Because here’s the truth: the technology is only as good as your implementation strategy. I’ve seen brilliant AI tools fail miserably because businesses jumped in without proper planning.

The key is starting small and scaling smart. You don’t need to revolutionise everything overnight. Pick one area where you’re struggling – maybe it’s email personalisation or social media management – and start there. Success breeds confidence, and confidence breeds bigger successes.

Choosing the Right Tools

The AI marketing tool domain is vast and, frankly, overwhelming. New solutions launch weekly, each promising to revolutionise your marketing. How do you separate wheat from chaff? Start with your specific problems, not the shiniest solutions.

List your biggest marketing challenges. Time-consuming content creation? Poor email engagement? Inefficient ad spending? Then research tools specifically addressing these issues. Don’t get distracted by features you won’t use. A simple tool that solves one problem well beats a complex platform you’ll never fully utilise.

Consider integration capabilities. The best AI tool in the world is useless if it doesn’t play nicely with your existing tech stack. Look for solutions with stable APIs and pre-built integrations with your CRM, email platform, and analytics tools. For businesses looking to expand their online presence, listing in quality directories like jasminedirectory.com can complement your AI marketing efforts by improving visibility and credibility.

Building Your AI Marketing Stack

Your AI marketing stack should evolve organically, not appear overnight. Think of it as building a house – you need a solid foundation before adding fancy features. Start with data infrastructure. Clean, organised data is necessary for AI success.

Next, add analytical tools that help you understand current performance. Then layer in automation tools that handle repetitive tasks. Finally, implement predictive and generative tools that push boundaries and drive innovation.

Here’s my recommended implementation sequence:

First, get your data house in order with a strong CRM and analytics platform. Second, implement AI-powered email marketing for quick wins. Third, add chatbot or conversational AI for customer service. Fourth, integrate content generation tools for scale. Fifth, deploy predictive analytics for planned insights. Finally, implement full marketing automation with AI orchestration.

Measuring ROI and Success

How do you know if AI is actually working? Measurement is necessary, but it’s not always straightforward. Traditional metrics might not capture AI’s full impact. You need both quantitative and qualitative measures.

Track obvious metrics like conversion rates, engagement rates, and ROI. But also monitor performance gains – time saved, tasks automated, decisions improved. If your team spends 50% less time on routine tasks and can focus on strategy, that’s valuable even if revenue hasn’t immediately spiked.

Set realistic expectations. AI isn’t magic; it’s technology that requires optimisation. Most tools need 30-90 days of learning before showing substantial results. Don’t pull the plug too early. Document baseline metrics before implementation, then track improvements monthly.

Common Challenges and Solutions

Let’s address the elephant in the room: implementing AI marketing isn’t always smooth sailing. I’ve seen enough implementations to know what typically goes wrong and, more importantly, how to fix it. Being prepared for these challenges is half the battle.

Data Quality Issues

Garbage in, garbage out – it’s the oldest rule in computing, and it’s especially true for AI. Poor data quality sabotages even the best AI tools. Incomplete customer records, duplicate entries, outdated information – these issues compound when AI tries to find patterns.

The solution starts with a data audit. Identify gaps, inconsistencies, and quality issues. Then implement data governance policies ensuring clean data going forward. It’s tedious work, but consider it an investment. Clean data improves every aspect of marketing, not just AI performance.

Don’t aim for perfection; aim for improvement. Even 80% data quality is better than 60%. Start with important fields like email addresses and purchase history, then gradually improve other areas. Regular data hygiene should become routine, like brushing your teeth.

Integration Nightmares

You’ve bought the perfect AI tool, but it won’t talk to your CRM. Or it technically integrates but loses important data in translation. Integration challenges kill more AI initiatives than any other factor.

Before purchasing any AI tool, map out your integration requirements. What systems need to connect? What data needs to flow where? Get technical teams involved early. What looks simple in a sales demo might be complex in reality.

Consider middleware solutions like Zapier or custom APIs if direct integration isn’t possible. Yes, it adds complexity, but it’s better than manual data transfer or, worse, siloed systems that don’t communicate. And always, always test integrations thoroughly before going live.

Team Resistance and Training

Here’s an uncomfortable truth: your biggest challenge might not be technical; it might be human. Team members fear AI will replace them or make their skills obsolete. This resistance can sabotage even well-planned implementations.

Address fears head-on. Explain that AI handles boring stuff so humans can do interesting work. Provide comprehensive training, not just on how to use tools but on how AI enhances their roles. Celebrate early wins publicly, showing how AI makes everyone’s job easier.

Create AI champions within your team – enthusiasts who can support and encourage others. Start with volunteers eager to learn, then let their success inspire others. Remember, change management is just as important as technology management.

Future Directions

So, where’s all this heading? The AI marketing sector evolves so rapidly that today’s cutting-edge becomes tomorrow’s table stakes. But certain trends are clear, and understanding them helps you prepare for what’s coming.

Hyper-personalisation will become the norm, not the exception. We’re moving toward marketing experiences so tailored they’ll feel like personal conversations. AI will understand not just what customers want but why they want it, considering psychological profiles, life stages, and even mood states.

Voice and conversational interfaces will dominate. As natural language processing improves, typing will feel archaic. Marketing will adapt to conversational commerce, where AI assistants handle entire purchase journeys through natural dialogue. Imagine describing your perfect vacation to an AI that then books everything – flights, hotels, activities – based on your conversation.

Predictive capabilities will extend beyond marketing into product development and business strategy. AI will identify market gaps before competitors, suggest new product features based on customer behaviour, and even predict economic shifts affecting your industry. According to The Nonprofit Marketing Guide, even resource-constrained organisations are finding inventive ways to apply AI for maximum impact.

Ethical AI will become a competitive advantage. As consumers become more aware of AI’s role in marketing, they’ll gravitate toward brands using it responsibly. Transparency about data usage, AI decision-making, and privacy protection will differentiate forward-thinking brands.

Integration will become trouble-free. Future AI tools won’t be separate platforms but invisible layers enhancing every marketing tool. Your email platform, CRM, and website will all have AI baked in, working together without complex integration projects.

Real-time everything will be standard. Campaign adjustments, content generation, and customer responses will happen instantly. The delay between insight and action will approach zero. Marketing will become truly responsive, adapting moment by moment to changing conditions.

Augmented creativity will flourish. AI won’t replace human creativity but grow it. Imagine brainstorming with an AI that understands your brand, audience, and objectives, suggesting ideas you’d never consider. It’s like having a creative partner with infinite knowledge and no ego.

The democratisation of AI marketing continues accelerating. Tools once exclusive to enterprises are becoming accessible to small businesses. Soon, a solo entrepreneur will have marketing capabilities that required entire departments just years ago.

But here’s the key point: success won’t come from having the most advanced AI. It’ll come from using AI thoughtfully, strategically, and ethically. The businesses that thrive will be those that maintain human connection when leveraging machine performance.

As we stand on the brink of this AI-powered marketing revolution, one thing is crystal clear: the question isn’t whether AI can help with your marketing – it’s how quickly you can harness its power while maintaining the human touch that builds genuine connections. The tools are here, they’re accessible, and they’re transforming marketing from guesswork into science. The only question remaining is: are you ready to embrace this transformation?

The future of marketing isn’t about choosing between human creativity and artificial intelligence. It’s about combining them in ways that create extraordinary customer experiences, drive unprecedented growth, and build lasting relationships. And that future? It’s not coming – it’s already here.