Quels sont les moyens les plus efficaces de commercialiser des produits de données ?

One thing to market any product, in general, is to have a solid distribution online around the particular niche. So, in our case, if we want to market data products, make sure that you have a well-established online presence wherein you help the community members by sharing the knowledge you have regarding data. Through this, you will gain the community's trust and as soon as you launch a product, they will be happy to use your product, I believe.

A Value Proposition is essential for any data product, as it helps to attract and retain our target audience. Spotify and Grammarly are excellent examples of Value propositions for data products. We should not confuse the customer profile and the value map and we should focus on the most important customer segment. Also, we should use simple and understandable language that resonates with the audience and also measure the impact of our value proposition on our key metrics such as customer satisfaction, revenue, and market share. Some AI tools like Frase, Beam AI, and Bizway can help us analyze our value proposition.

Uma das maneiras mais úteis e eficazes de definir o valor de um produto de dados é concentrar todos os seus esforços em compreender, descrever e apresentar claramente o problema que ele resolve. Talvez mais do que focar na solução, a ideia aqui é focar no problema, a fim de saber exatamente se é o mesmo, se ele dá "match", com o problema do cliente.

In my opinion, the key to marketing data products effectively is to precisely identify your target market. Figure out who will benefit most from your data, whether it's startups, multinational companies, or niche sectors. This focused approach makes your marketing strategies more relevant and impactful. I can be wrong but that's what i think.

𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲𝘀𝗲 𝗮𝘀𝘁𝘂𝘁𝗲 𝘀𝘂𝗴𝗴𝗲𝘀𝘁𝗶𝗼𝗻𝘀: 1️⃣ Know your audience inside and out. 2️⃣ Highlight benefits in the actual world. 3️⃣ Make things simple with striking images. 4️⃣ Expand your digital presence. 5️⃣ entice with complimentary trials. 6️⃣ Highlight steadfast security. 7️⃣ Motivate with success stories. 8️⃣ Encourage productive teamwork.

Explaining clearly how your product helps customers is very important. Look at who your users are and what problems they face. How does your data solution give them better results? What exact issues does it solve? Summarize this into a short but compelling message that focuses on the customer, not just product features. For example, "Helps retailers optimize inventory and reduce waste through analytics." Keep it simple but hard-hitting. This drives your overall messaging and positioning.

The problems targeted by data products could be very broad, but the inferences are required to be definitive. Hence, clear articulation of product inputs, outputs, operating environment is very important. It enables the prospect to effectively assess the expected impact on their businesses/processes. More clear they are with the story, more chances of them converting, or saying straight ‘No’. Both the scenarios are good for us, as it saves time.

I think leverage compelling storytelling to showcase real-world impact and benefits of your data products. Additionally, engage with your audience through interactive content and demonstrations to create a more personalized and memorable experience.

In our experience trying out several BI tools or any other Data Product, the following are the key aspects that may have to be taken care of to impact the audience's decision to collaborate further: 1. Bridge the Gap Between Technical and Business Language 2. Create Comprehensive Product Documentation 3. Emphasize Integration Capabilities 4. Focus on Scalability and Flexibility 5. Offer Pilot Programs 6. Collaborate with Business Units 7. Educational Workshops for Decision Makers 8. Position Yourself as a Strategic Partner 9. Quantify Business Impact 10. Engage in Industry-Specific Forums 11. Build a Product Roadmap

Drive the marketing of data products by spotlighting the distinctive insights they offer, utilizing content and social to inform and connect with your audience, making free trials available to experience the benefits, showcasing real customer victories, enhancing search visibility, and simplifying the purchasing process.

Identifying a data product's target audience with ML involves defining objectives, collecting relevant data, selecting features, preprocessing data, choosing and training an ML model, evaluating performance, and iteratively optimizing. Deploy the model for real-time identification and regularly monitor and update it to adapt to changes. The iterative nature of ML ensures continuous refinement for accurate and relevant target audience identification in marketing data products.

As a marketing data professional, contribute to identifying the target market by conducting thorough data analysis. Utilize demographic and psychographic data to understand customer characteristics, preferences, and behavior. Analyze past customer interactions, purchase patterns, and engagement metrics to identify trends and commonalities. Leverage data segmentation techniques to categorize customers based on relevant criteria. Develop detailed customer personas that encapsulate key attributes of your target audience. Provide actionable data-driven recommendations to the marketing team, enabling them to tailor strategies and messages effectively to resonate with the identified target market.

The press office of my company often received requests from banks and insurances for detailed real estate data. These requests were collected and analyzed. Business Development then checked whether the revenue to be generated justified the effort involved in preparing the data and developing and maintaining a product for sale. This data product had to be created automatically and offer various options: - one-time purchase of the data - monthly purchase of the data (subscription model) - real-time access to the data The customer could choose to purchase - basic data - extended data - complete data This should then result in an attractive pricing model for the customer.

The measurable results can be more specific and relatable, if they are targeted to people who experience the specific problem. Hence, knowing who could be in trouble, due to the problem, that the data product is solving will help us to know our target sample. Identifying the audience will help us in determining data collection tools, and timeline of the research.

A mon avis, simplement identifier un marché cible ne suffit pas. Il faudra confronter l'outil à des cas concrets pour améliorer son apprentissage. Les premiers clients, en tants qu'early adopter et moyennant un rabais, pourront servir de cas d'utilisations tout à fait pertinents.

One important segmentation that in my experience dictates the development of an AI product is whether it's aimed at businesses (B2B) or consumers (B2C). In case of the former it's going to highly important for the results to be explainable, moreover you probably want to explore the market and see what features competitors are offering. Most enterprises do extensive RfP processes before they pick a solution. While in the latter case, explainability might not be as important as consumers are mostly focused on the best outcome only. In addition market research is important as well but more from the angle of performance (clicks, conversion, etc) and UX, i.e. does my product provide better outcomes and experience for consumers?

Use data to segment your customers and create customized offers that appeal to their interests, needs, and behaviors. For example, Amazon uses data to recommend products that customers are likely to buy based on their browsing and purchase history

The most effective marketing for data products is centered around identifying the target market. This involves a detailed analysis of current customers, identifying unique features and advantages of the product, observing similar successes in the target market, examining competitors and similar products, utilizing business data for better analysis and decision-making, determining multi-faceted marketing strategies, offering personalized messaging, and conducting continuous testing and improvement. These steps ensure that the marketing strategy is executed flexibly and focused on the needs and characteristics of the target market for optimal results.

Conduct thorough market research to understand the characteristics, preferences, and behaviors of your ideal customers. Segment your target market based on relevant criteria, such as industry, company size, geographic location, or specific challenges they face.

This somehow overlaps with value proposition where the target customer group needs to be identified along with their specific preferences.

Every channel is unique. Evaluate reach, engagement metrics, conversion rates and costs for websites, social media, email, content formats etc. See where your buyers currently spend time. Consider multi-channel attribution so no one interaction seems more important. Doing thorough channel audits is hard initially but gives big payoffs later through optimized targeting and higher ROI.

Selecting the right marketing channel for data products is crucial. Begin by understanding your target audience and their online behavior. Utilize platforms like LinkedIn for B2B products, where professionals seek data solutions. Leverage content marketing through blogs and whitepapers to showcase your expertise. Harness the power of search engine optimization (SEO) to enhance visibility. Social media, particularly Twitter and YouTube, can amplify your product's reach. Experiment with email campaigns for direct engagement. Analyze performance metrics continuously to refine your strategy. A data-driven approach ensures optimal channel selection for maximum impact in promoting your machine learning-powered data products.

While still early in my entrepreneur career, I’ve so far found the unpaid forms of marketing (such as starting a YouTube channel and posting in groups) to be more effective than paid marketing, but I imagine this could look different for different types of data products.

Os canais de divulgação mais eficazes para você divulgar o seu produto de dados são as mídias sociais, como LinkedIn e Github, e comunidades e grupos especializados. Se você está começando agora, a melhor ideia é se aproximar de pessoas experientes para vender sua solução junto com elas. O mais importante é saber como identificar quem tem bons contatos com as empresas e aí fazer o seu marketing.

One way to market your product is by estabilishing a partnership with another company. This way you will have more exposure from the start (look at OpenAI + Microsoft) and the potential to use an existing market. Another way to enable your product to enable embedding into an already existing product. This way you make it easier to adopt your product and can leverage an already successful product (e.g. slack or discord AI plugins). The last way I'd like to highlight is the potential or partnering with angel investors or venture capital. In most cases they can help you leverage their network to market your product.

Choosing the right marketing channels for your data product is essential for reaching your target audience and effectively promoting your offering. Here are some factors to consider when selecting the most appropriate channels: - Your target audience: Identify the demographics, interests, and online behavior of your potential customers. Consider their preferred platforms for consuming content, engaging with brands, and making purchasing decisions. - Product nature: Assess the type of data product you're marketing and the nature of its value proposition. For example, technical data products might resonate better with niche online communities or industry publications.

Choosing effective marketing channels for data products involves considering factors such as target audience, product complexity, and communication goals. Telegram is ideal for direct communication in tech communities, WhatsApp offers global personalized messaging, and traditional print may appeal to certain audiences. The choice depends on audience preferences and the nature of the data product. An omnichannel strategy, combining digital and traditional methods, proves effective by reaching diverse audiences through various touchpoints. Regular assessment and adaptation ensure an optimized and dynamic marketing approach.

Select marketing channels that align with your target audience's preferences and behavior. This could include digital channels like social media, content marketing, email campaigns, and search engine optimization (SEO).

Contemporary industry allows a social media marketing process coupled with hyperautomation where ML algorithms can provide an accurate insight into specific customer preferences.

Develop content that educates your audience about the importance of the data your product provides mostly surrounding the narrative. This will enable your product to built credibility and increase user engagement. Few ways in which data products could be boosted - via blog posts, webinars, or tutorials that demonstrate how to use the product effectively and what all use cases it might fit the best - feedback from actual users would add credibility to your marketing efforts and also tries to increase the user retention

Effective marketing content for data products involves a concise presentation of the product's unique value, tailored to a defined target audience. Use real-world examples to showcase practical applications, employ diverse content formats, and integrate social proof through customer testimonials. Implement a clear call to action, optimize for SEO, and ensure regular updates to stay current and engaging. This streamlined approach ensures that your marketing content succinctly communicates the value proposition and encourages meaningful engagement with your data product.

In the current scenario, organizations could make effective use of Generative AI where Large Language Models (LLMs) could come to the rescue to generate appropriate marketing content.

Marketing content should be brief and eye-catching and addition to being informative. It should be tailored cleverly to provide just enough information to make the target audience understand and create curiosity to make them reach out for business or opportunities.

Desenvolver conteúdo que engaja o público é quase o básico hoje em dia para as companhias. Ao criar algo verdadeiramente relevante, o público geralmente responde positivamente. O caminho ideal é integrar, de maneira natural, o serviço que a companhia oferece dentro desse conteúdo. Essa é a nova forma de fazer propaganda e divulgação, um método democrático e mais eficiente. É fundamental aprimorar a qualidade e a relevância desse conteúdo para garantir que ele ressoe e impacte positivamente o público-alvo.

Customize your messaging to appeal to your target demographic while producing marketing content. Create enlightening blog entries, captivating social media postings, and eye-catching films that showcase the advantages and special qualities of your good or service. To draw in and hold the interest of your audience, consistency and relevancy are essential.

Here you create compelling content. For example, develop high quality content that showcases the value and benefits of your data products.

Crafting compelling marketing content is key to effectively communicate your data product's value. It's about creating material that resonates with your audience, addressing their pain points, and showcasing how your data product can make their lives easier or better. Different types of content allow you to engage with your audience in various ways, catering to different preferences and learning styles.

Think of creating marketing content as building a bridge between your data product and your audience. It should be constructed to be informative, engaging, and directly relevant to their needs. Your content serves as a connection, showcasing the strengths and benefits of your data product while addressing the specific challenges and questions your target market may have. Whether it's through blog posts, videos, infographics, or testimonials, each piece of content is a building block that strengthens the connection between your product and the people you aim to reach.

Track your marketing performance across different channels and adjust your strategy accordingly. What works for one audience might not work for another. Think of it like using a data-driven compass to navigate your marketing journey and make course corrections as needed. Building trust and providing ongoing value is key to retaining customers and growing your data product's success. Think of it like planting the seeds of data trust and nurturing them into a flourishing garden of loyal customers.

Above all, evaluation of progress your progress will help you see where you need to channel resources for improvements. Effective evaluation will help in making further decisions or plans.

In the world of data product marketing, assessing performance is key. Define clear goals like awareness and conversions, then track metrics such as website traffic and social media engagement. Analyze this data to understand what resonates with your audience and what needs improvement. This continuous evaluation allows you to refine and enhance your marketing strategy for better results

A lot have been said on this subject ..what I can just say is that one know the targeted audience and find the most effective way to communicate with them ....

Irrespective of the Marketing strategy, that can change with target samples, assessing the impact is very critical. Any marketing strategy applied is required to have clear KPIs and expectations. Frequent assessment of marketing performance will empower us to quickly identify slow movers, and highest gainers. We will be in a position to know, what you should continue doing, what you should stop, and what else is left to be tried. When the choices are clear, decision making becomes easier.

Measure marketing success with ROI, Conversion Rates, CAC, CLV, Traffic Sources, Social Media Engagement, Email Metrics, Brand Awareness, Lead Generation, and Customer Feedback. Regularly assess these to optimize strategies and ensure effective performance.

Measuring your marketing performance is crucial for understanding the effectiveness of your campaigns and optimizing your strategies. By tracking key metrics, you can gain insights into the reach, engagement, and conversion rates of your marketing efforts. Here are some key metrics to track for marketing performance: -Website traffic: This metric measures the number of visitors to your website from different marketing channels. It indicates the overall visibility of your product and the effectiveness of your website's content. -Lead generation: This metric tracks the number of potential customers who have expressed interest in your data product. It can be measured through forms, surveys, or email signups.

Measuring marketing performance for data products involves the systematic assessment of key performance indicators (KPIs) to gauge the effectiveness of your strategies. Utilize tools such as website analytics, conversion rates, and user engagement metrics to track the impact of your marketing efforts. Monitor the success of specific campaigns, analyze customer acquisition costs, and evaluate the return on investment (ROI) to ensure alignment with business objectives. Regularly review and adapt your marketing strategies based on these performance insights, identifying areas for improvement and optimizing tactics to enhance overall effectiveness in reaching and engaging the target audience for your data products.

Companies could make use of classical ML algorithms to assess their performance against thresholds or even use deep learning algorithms if they have sufficiently large datasets.

Ao medir o desempenho de marketing, é crucial ir além dos números fornecidos pelo analytics. Enquanto métricas quantitativas como tráfego do site e taxas de conversão são importantes, uma abordagem holística considera também aspectos qualitativos, como a percepção da marca e a satisfação do cliente. Empresas focadas apenas em dados analíticos podem perder insights valiosos que vêm de feedback direto do cliente, análises de tendências de mercado e até mesmo mudanças no comportamento do consumidor. Uma tendência de mercado provavelmente vai sair do radar do analytics e pode acabar em prejuizo na certa.

In addition to the fundamental aspects of marketing data products such as: 1) Understanding the solution 2) Analyzing competitors 3) Highlighting unique selling points 4) Identifying scope 5) Evaluating the target market and 6) Defining ad campaigns engaging with the audience through conferences and seminars is indeed a crucial component. This engagement throughout the development phases (Build, Test, Market) ensures that the product allows us to gather valuable insights, understand their perspectives, and align the data product as per their needs and expectations.

This article would be more meaningful and succinct if it contrasted the difference between "product" vs "data product". A lot of the content here is broadly applicable to products. Hence, you first need to understand products before data products

1. Craft stories using data to demonstrate product impact. 2. Offer simulations for hands-on experience with your product. 3. Use data challenges to engage users playfully. 4. Partner with data science influencers for broader reach. 5. Participate in online data science communities as a thought leader. 6. Create informative content that subtly incorporates your product. 7. Host niche webinars showcasing your product’s expertise. 8. Collaborate on projects or studies with academic institutions. 9. Use AR for immersive product demonstrations. 10.Showcase your product’s practical utility in real-world problem-solving.

1. Making your data products related information available within a central repository or a data catalog within the organization. 2. Multiple data awareness sessions within the stakeholders community - this takes time. 3. Enhancing the data products as per the requirements of the stakeholders and finalizing short term and long term roadmaps. 4. Documentation fot pre-built reports / available data visualizations to showcase the possibilities.

Continuous Improvement; Gather feedback from customers and use it to improve your data products continually and Keep your products up-to-date with the latest technologies and industry trends

Remember that the effectiveness of marketing strategies can vary depending on your target audience, industry, and specific data product. It's essential to continually evaluate and adjust your marketing approach based on the insights you gather from your analytics and customer feedback.

Building an AI product is a delicate balance between business and engineering. You can build an amazing performing model but if you don't have a way to bring it to the crowd, your product won't be used. On the other hand if you identify a great business opportunity, you'll still have to deliver on performance and UX. My advice is to always have both expertises in the room before going all in to build the next great AI product.

Products fueled by data and machine learning can be a powerful way to solve users’ needs and stave off competition. Classic examples include Google search and Amazon product recommendations, but the opportunity extends far beyond the tech giants. To tackle the challenge, companies should emphasize cross-functional collaboration, evaluate and prioritize data product opportunities with an eye to the long-term, and start simple.