Investor Connect Podcast Hall T Martin

Business Model Examples for Data Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing. There are several business models used for monetizing data. Here are some of the more commonly used ones: Brokerage -- this model matches data providers for data users for a fee. There are thousands of data sets available and many of them are not easily found. Bundling -- this model combines several data sets together to provide a new data set. By combining multiple data sets from different sources, new value can be created. Data as an asset -- this model sells data that is valuable by itself. An example of this is a customer list of a specific product.  Companies selling similar products purchase it to promote their product. Subscription -- this model provides a continuous stream of data. An example of this is a weather data feed which is required by those providing weather apps. Aggregation -- this model provides a list of sources for a product or service. Sites providing home contractor services require a list of contractors as one example. This is often used for advertising and promotion.  Pay-per-use -- this model provides data as needed and charges for each unit. An example is email generation which generates an email for each contact or verification of an email. Consider these business models for your data set.   Thank you for joining us for the Startup Funding Espresso where we help startups and investors connect for funding. Let’s go startup something today. _______________________________________________________ For more episodes from Investor Connect, please visit the site at:   Check out our other podcasts here:   For Investors check out:   For Startups check out:   For eGuides check out:   For upcoming Events, check out    For Feedback please contact info@tencapital.group    Please , share, and leave a review. Music courtesy of .

Building a Data-Driven Business Model Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing. A data-driven business model can accomplish a number of goals such as increasing brand awareness, understanding the customer better, and improving products and services. Here are the key steps in building a data-driven business model: First, determine the outcome of the business model. Find clarity on the goal of it. Second, what is the deliverable from the effort? Is it a data set that shows how to improve customer satisfaction, increase awareness, or understand customers' perception of the product. Third, what data is needed? The data can come from the customer, partners, or generally available data sources. Fourth, how do you analyze the data? This could be applying prescriptive, descriptive, or predictive types of analysis. Fifth, determine how to monetize the data. This could be selling the data directly, using it to improve internal processes or providing the data to partners or customers to enhance the selling process. Sixth, identify the challenge in capturing and analyzing the data. This could be regulatory requirements, data quality issues, data quantity issues, or data analysis capabilities. Start with your business goals. Consider these steps in implementing a data driven business model in your organization.   Thank you for joining us for the Startup Funding Espresso where we help startups and investors connect for funding. Let’s go startup something today. _______________________________________________________ For more episodes from Investor Connect, please visit the site at:   Check out our other podcasts here:   For Investors check out:   For Startups check out:   For eGuides check out:   For upcoming Events, check out    For Feedback please contact info@tencapital.group    Please , share, and leave a review. Music courtesy of .

In this episode of Investor Connect, Hall T. Martin engages with Marie Wise, Chief Innovation and Partner of WiGL, to explore the company's journey in wireless power and their shift towards saltwater-generated power batteries. Marie discusses the challenges faced in raising over $11 million in four years without launching a product and shares insights into redirecting the company's focus towards consumer markets like camping and disaster relief.   Marie highlights the obstacles encountered due to divergent opinions within the team, where ex-military members prioritize Department of Defense contracts over venture capital funding. This misalignment hindered product development and market entry. The discussion underscores the importance of focusing on consumer markets and engaging anchor clients to drive product development.   The conversation underscores Investor Connect's role in assisting startups like WiGL in refining market strategies, identifying anchor clients, and preparing for investor engagement. Hall outlines Investor Connect's approach to fundraising campaigns and the importance of aligning valuation with market realities, ultimately aiming to facilitate tangible progress and product launches within defined timelines.   Visit the company , or their Instagram - or LinkedIn     ___________________________________________________________________ For more episodes from Investor Connect, please visit the site at:    Check out our other podcasts here:   For Investors check out:   For Startups check out:   For eGuides check out:   For upcoming Events, check out    For Feedback please contact info@tencapital.group    Please , share, and leave a review. Music courtesy of .

Modern Data Stack Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing. The modern data stack is the term for the tools used by tech companies to analyze and integrate data. It’s cloud-based which alleviates many of the challenges in analyzing data with legacy systems. Here are the components of the modern data stack: Data sources -- this includes databases, company products that produce a stream of data, and event streams which log each action a user takes. Data warehouse -- these are the tools used to store the voluminous amounts of data that come from data analysis work. This includes data lakes and other large-scale formats for storing the data. Data analytics -- this includes the ability to query into the data sets and apply analytics to the data. Data transformation -- this moves the data into a format that end users can use for their own queries and analysis.  Data monitoring -- this captures metrics about the data such as how often the data is being used and for what applications. Data governance -- this monitors the use of the data to comply with government regulations. Data applications -- the set of applications which use the data output from the system for applications such as business intelligence.  In setting up a data analytics program at your company consider the modern data stack and its components.   Thank you for joining us for the Startup Funding Espresso where we help startups and investors connect for funding. Let’s go startup something today. _______________________________________________________ For more episodes from Investor Connect, please visit the site at:   Check out our other podcasts here:   For Investors check out:   For Startups check out:   For eGuides check out:   For upcoming Events, check out    For Feedback please contact info@tencapital.group    Please , share, and leave a review. Music courtesy of .

Internal Monetization of Data Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing. In addition to selling your data to other companies, startups can monetize their own data internally. This method uses data to lower costs and increase the revenue of your own startup. Here’s a list of ways to monetize your data internally: Reduce costs -- use it to save time and money in your startup. This could be reducing inventory for items that rarely sell or it could be increasing the utilization of equipment. Increase revenue through new products -- use data to make the product more valuable. This could be taking a nutraceutical and adding software to capture the condition of the patient and then using the software to determine which product the user should take. One can create premium products using data sets. Enter new markets -- your data may reveal new market segments and customer types that were previously overlooked. This could be taking your medical device into other use cases. Increase revenue to current customer targets -- use data to help sell more products to known market segments. This could be identifying the characteristics of the customer and where to find them. Improve efficiency -- use data to reduce the cost of building and delivering the product. This could be identifying redundant steps that don’t add value. Consider these steps in monetizing your data for internal use.    Thank you for joining us for the Startup Funding Espresso where we help startups and investors connect for funding. Let’s go startup something today. _______________________________________________________ For more episodes from Investor Connect, please visit the site at:   Check out our other podcasts here:   For Investors check out:   For Startups check out:   For eGuides check out:   For upcoming Events, check out    For Feedback please contact info@tencapital.group    Please , share, and leave a review. Music courtesy of .

Data Monetization Requirements Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing. Monetizing data requires several key components. Here’s a list of components needed to monetize your startups data: Ability to acquire the data -- the more you can source your own data versus relying on others the more valuable your data will be. Ability to store the data acquired -- you’ll need a data platform to hold the data for analysis and processing. Modeling and testing -- you’ll need to model the data captured and analyze it with databases and algorithms for the result sought.   Customer requirements -- you’ll need to know the customer’s requirements in order to build data sets of value to them. Compliance and regulatory -- you’ll need to know the current laws around the use of data based on regulatory requirements. People who understand data and how to interpret it -- you’ll need a team that understands data and how to analyze, interpret, and present the results. Consider these elements in building out your data analytics program.   Thank you for joining us for the Startup Funding Espresso where we help startups and investors connect for funding. Let’s go startup something today. _______________________________________________________ For more episodes from Investor Connect, please visit the site at:   Check out our other podcasts here:   For Investors check out:   For Startups check out:   For eGuides check out:   For upcoming Events, check out    For Feedback please contact info@tencapital.group    Please , share, and leave a review. Music courtesy of .