Generative AI for Customized Marketing Content

If you're going to employ ChatGPT in your content creation process, you need to train it extensively on your brand tone, value prop, unique differentiators, customer reviews and testimonials, images, and external resources that are relevant to your industry and competitors. 👉 You need lengthy chat threads (or create a custom GPT knowledgebase) for it to have the complete scope of what you're trying to accomplish via content. 👉 You need to consistently refine and challenge its responses in order to receive something original that resonates with who you're writing for - whether it's a short-form blog or a video script. 👉 Throw different angles and perspectives at it. Have it try to connect the dots in unique ways that can't be accomplished via a single prompt. And THAT'S why I get discouraged when I see folks on LinkedIn and Twitter writing "complete guides" or courses comprised of single prompts. Even if these prompts are a paragraph or two long, without training and refinement, you're going to get the same bland content it provides every other marketer with. I promise you. I'm hesitant to share my exact process for GPT-4 knowledge-base building and refinement since I know marketers love to abuse these things, but take this post as a sign that you need to do be more intentional about how you're using generative AI for marketing purposes.

Announcing our latest article on the future of content creation Excited to share our latest dive into the future of content creation: "From RAG to Riches: Using Retrieval-Augmented Generative AI with Internal Knowledge Graphs to Automate the Creation of Content for Marketing and Sales" In an era where digital content is king, finding innovative ways to generate engaging, relevant, and personalized content at scale is more crucial than ever. Our latest article explores how the integration of Retrieval-Augmented Generative AI (RAG) with internal knowledge graphs is redefining content creation strategies for businesses, especially small to medium-sized enterprises (SMEs) striving to stay competitive in the digital marketplace. **Key Takeaways:** - Understanding the Challenge: Traditional content creation methods are no longer sufficient to meet the demands of today's fast-paced digital marketplace. - The Power of Generative AI: How generative AI, when combined with internal knowledge graphs, can significantly enhance the efficiency, relevance, and personalization of content production. - Strategic Advantages:   - Customization and Relevance: Tailoring content to the nuanced preferences of diverse customer segments.   - Speed and Efficiency: Dramatically reducing content production time, enabling businesses to respond quickly to market changes.   - Scalability: Generating a vast amount of content without a corresponding increase in resources or costs. - Implementation Roadmap: A step-by-step guide to integrating RAG into your marketing and sales strategies, from initial assessment to deployment and monitoring. Discover how this innovative approach can transform your content creation process, making it more aligned with your brand's voice and your audience's needs. Whether you're a marketer, content creator, or business owner, understanding the capabilities and applications of RAG could be the key to unlocking unprecedented growth and engagement. Read the full article and learn how to leverage the potential of RAG and internal knowledge graphs for your business's content strategy. Let's navigate the future of content creation together and turn data into compelling narratives that captivate and convert. #ContentCreation #GenerativeAI #DigitalMarketing #Innovation #RAG #KnowledgeGraphs #MarketingStrategy #SalesContent #AI #MachineLearning

Ah, the planning season is upon us. No, not Amazon shopping, but what will 2024 look like? This is the time of year that organizations reflect on the past year, and look to come up with the silver bullet that will help "us win" in the year to come. Now there are so many moving parts, revenue goals, headcount, budget, and overall strategy. But, there are two things on many people's minds right now. Doing more with less, and the bigger elephant in the room, AI. Curious how others are approaching the blend of cool and smart? Let's start the discussion first on AI. I'm leaning into strategy first, then planning. Understanding the capabilities of Generative AI is the first step in my mind. I'm pretty tech-savvy, but this is unchartered territory and I find it scary and exciting at the same time. How it can enhance my and my team's creativity while saving precious hours. But that's just the beginning. We are training ourselves to ask - how can this tool carve out a unique space in the market? How can it amplify the voice of our brand? The answers to these questions form the basis of our strategy, which is more about precision than about being everywhere at once. I feel we are already in need of better focus. Next, and frankly where I geek out the most, pipeline impact. Gen AI can generate a torrent of content, but the right alignment is what I'm after. By training the AI on our specific needs and observing its output, we optimize it for conversions and more personalized dialog. When it comes to speaking to buyers, more and more of those key wallet holders expect personalized information. Why not use AI to curate what your audience is looking for and ensure that it generates leads that complement our human sales and marketing efforts? Lastly, I want to make sure to embrace the fun! After all, I moved from sales to marketing to let my creativity soar! LOL. In 2024, my marketing team is looking to leverage many opportunities offered by Generative AI. It's a game-changer. How is everyone else taking on this topic?

#AICustomerService: Because dealing with humans is so passé.   When it comes to customer engagement, #generativeAI is a game changer for ALL businesses.   Generative AI, powered by LLMs like #ChatGPT, can analyze historical customer data and identify patterns, preferences, and trends. LLM's can tailor your marketing messages, product recommendations, and customer support interactions to meet individual customer needs. They can automate customer engagement processes like chatbots or virtual assistants.   Generative AI can also help process customer reviews that reveal their overall feedback on the brand. This information enables businesses to make #datadriven decisions, develop targeted marketing campaigns, and enhance their products or services based on customer feedback.   Though it may all sound hunky-dory, Generative AI algorithms have limitations. They rely on historical data, which can introduce biases or fail to capture evolving customer preferences. For all we know, LLMs are currently working with data only as current as 18 months ago. It's important to be transparent with customers when you integrate AI into their experiences.   It won’t be one-size-fits-all, for now.

🚀 𝐓𝐨𝐰𝐚𝐫𝐝𝐬 𝐕𝐢𝐭𝐚𝐦𝐢𝐧 𝐑: 𝐁𝐥𝐞𝐧𝐝𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐚𝐠𝐞 𝐚𝐧𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 In a marketing world where #bold meets #AI, my thoughts resonate with  the recent, well crafted #must read by Oguz A. Acar. (Links below) My exploration of “𝘊𝘰𝘶𝘳𝘢𝘨𝘦-𝘉𝘢𝘴𝘦𝘥 𝘔𝘢𝘳𝘬𝘦𝘵𝘪𝘯𝘨” and the insightful "𝘈 𝘗𝘳𝘢𝘤𝘵𝘪𝘤𝘢𝘭 𝘎𝘶𝘪𝘥𝘦 𝘧𝘰𝘳 𝘔𝘢𝘳𝘬𝘦𝘵𝘦𝘳𝘴 𝘞𝘩𝘰 𝘞𝘢𝘯𝘵 𝘵𝘰 𝘜𝘴𝘦 𝘎𝘦𝘯𝘈I" by Professor Acar, collectively chart a new course for organizational #GTM efforts. In my article, I explored the critical competencies required for contemporary #marketers, identifying them as instigators, innovators, integrators, and implementers. Also discussed the importance of adopting intelligent risk-taking, achieving quick results on a large scale, and maintaining a focus on the customer - practices increasingly crucial today. Vitamin R goes beyond mere sales orders, standard MQLs/SQLs, and implementation commitments. It represents a collaborative, cross-functional effort to deliver undeniable value and ensure customer delight. Acar’s excellent work details GenAI's four categories of opportunity in marketing: Customization, Creativity, Connectivity, and Cost of Cognition. Examples are provided for how AI can enable hyper-personalized and scalable marketing, boost human creativity, facilitate deeper consumer connections, and drastically cut costs. However, there are also four major risks with using generative AI in marketing: confabulation, consumer backlash, copyright issues, and cybersecurity threats. In this new era, on the GTM front we are not just siloed as marketers or sales or customer success, but rather we are architects of change, in an era where courage meets the transformative power of AI. As always, your thoughts are most welcome. 𝐀𝐁𝐌 𝐢𝐬 𝐠𝐨𝐨𝐝, 𝐛𝐮𝐭 𝐂𝐁𝐌, 𝐜𝐨𝐮𝐫𝐚𝐠𝐞-𝐛𝐚𝐬𝐞𝐝 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠, 𝐢𝐬 𝐛𝐞𝐭𝐭𝐞𝐫 https://lnkd.in/gJfZ4eN Key company examples used are Dove, Nike, Apple, and Levi's. 𝐀 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐆𝐮𝐢𝐝𝐞 𝐟𝐨𝐫 𝐌𝐚𝐫𝐤𝐞𝐭𝐞𝐫𝐬 𝐰𝐡𝐨 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐮𝐬𝐞 𝐆𝐞𝐧𝐚𝐢 https://lnkd.in/gi2k4epA Key company examples are Carvana, Spotify, Character.ai, Coca-Cola, Virgin Voyages, Unilever, and Boston Consulting Group. #DigitalMarketing #ArtificialIntelligence #generativeAi #MarketingStrategy #CustomerExperience #TechTrends #AIInBusiness #GrowthHacking #CourageBasedMarketing #Innovation #StrategicThinking

Marketing and Sales are two of the areas being most immediately impacted (for the better!) by generative AI. McKinsey & Company put out a recent article on how some of this is starting to play out. A few key points and takeaways that Marketing and Sales leaders should be paying attention to: - Gen AI Impact on Marketing and Sales: AI has already transformed marketing and sales. Generative AI is now pushing this further, with the use of open-source platforms reaching the sales frontlines and increasing investments in Gen AI innovations. - Hyper-Personalization is the Future: Gen AI enables hyper-personalized content based on individual customer behavior, persona, and purchase history. This level of personalization is crucial for both B2B and B2C sectors, allowing for targeted marketing and sales offerings. - Generative AI in the Customer Journey: Gen AI offers specific use cases across the customer journey. It can optimize marketing strategies, automate lead-nurturing campaigns, provide real-time negotiation guidance, and even assist in onboarding and retention. This approach is enhancing campaign effectiveness and customer engagement from the start. - Commercial Leaders See the Potential: Research indicates that 90% of commercial leaders expect to frequently use gen AI solutions in the next two years. Companies that invest in AI are witnessing a revenue uplift of 3 to 15% and a sales ROI uplift of 10 to 20%. - Risks and Challenges: While the potential of AI is undeniable, it comes with risks. Concerns range from IP infringement to data privacy and security. Leaders need to strategize and implement mitigation measures to harness the full potential of AI. https://lnkd.in/gSGdxDma

Data and AI leaders: what GenAI use cases are you piloting/implementing in 2024? Here's my take on the compelling use cases. Notably, some early adopters are tackling multiple use cases at once. Check out this summary and tell us what you are doing. Also check out our recent Eckerson Group webinar with Intel Corporation, "The Next Wave of GenAI: Domain-Specific LLMs." https://lnkd.in/emF95Vaq. To boost productivity and gain competitive advantage, GenAI adopters are not just using platforms such as ChatGPT from OpenAI or tools such as GitHub Copilot. They also are building language models into proprietary applications and workflows that consume their own domain-specific data. Companies implement these domain-specific LMs, which Eckerson Group also calls small language models, in one of three ways. They do this by building an LM from scratch, fine-tuning a pre-trained LM, or enriching prompts. > Build from scratch. Data science teams design a new LM and train it on their own domain-specific use of language as well as their own facts. > Fine-tune. They take a pre-trained LM such as Llama or BLOOM and fine-tune it on their domain-specific language and facts. > Enrich prompts. They inject domain-specific data into user prompts to ensure the LM gets the facts right. By getting domain-specific, companies can reduce risks such as hallucinations or mishandling of intellectual property--provided they govern their inputs correctly! Check out this summary and tell us what you think. Also check out our recent Eckerson Group webinar with Intel Corporation, "The Next Wave of GenAI: Domain-Specific LLMs." https://lnkd.in/emF95Vaq Common use cases focus on customer service, document processing, research, sales, and marketing, as shown in the chart here. Here are early adopters that tackle a few at once. > This summer Priceline announced plans for an external chatbot to help customers book travel, as well as internal GenAI tools to help employees develop software and create marketing content. > Health providers at MEDITECH use GenAI to summarize patient histories, auto-generate clinical documents, and place orders. > Insurance provider Lemonade positions GenAI as a strategic differentiator for the entire business. Its latest letter to shareholders boasts  “we have LLMs trained to answer customer emails, review pet medical records, evaluate satellite and other imagery, read home condition reports, and more.” Wayne Eckerson Jay Piscioneri Sumit P. #generativeai #genai #ai #data Sancha Huang N. Ro Shah Arlen Reyes Tiffany Winman