Data as a Service Business Model

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

Data as a service provides data both internally and externally to improve the functioning of the business.

Here are some key points to consider in setting up a data as a service function for your company:

Look for gaps in your data and find new data to fill the critical holes.

Consider setting up external data storage as the volume of data can overwhelm your internal servers.

After building data for an internal application, consider monetizing it by selling it to external entities.

Focus business applications on increasing revenue.

Look for ways to predict future outcomes not past results.

Look for existing revenue streams to see how to enhance the revenue.

Consider the regulatory issues around personalized data.

Mind the optics of public perception on collecting and using data.

Data as a service applications maintain Saas-like valuations and margins so it’s highly profitable.

Everything is moving online and will be digitized.

Look for that which has not yet moved online to focus your search for a new business application.

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.

_________________________________________________________

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Five Business Models for Linked Data

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

Linked data is structured data that is linked to other data making it more useful for searches and queries.

A wiki site often holds linked data sets.

Here’s a list of five business models around the use of linked data.

Subscription revenue such as monetizing the data directly.

This works well for users who need a continuous feed of new data.

Advertising revenue by placing ads around the data.

This works well for non-subscription applications such as one-time use data.

Authority revenue such as providing validation or compliance on the data.

This works well for users that need to verify the validity of the data.

Affiliate revenue such as including links in the data.

This can be used for ecommerce applications.

Value-add data revenue by combining multiple sources of data to create a new data set.

An example is adding demographics and purchase history on prospective customers.

This gives insight into what product the prospect may be interested in.

You can also combine one or more of these models into one application.

Consider these five monetization models for your linked 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.

_______________________________________________________

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Data-Driven Business Applications

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

Companies are growing their business using data to drive applications.

Here’s a list of driven-driven business applications to consider:

Streaming services use data to predict what customers want to watch.

By using a recommendation engine, they can provide a better quality of service.

This works particularly well when the customer needs discovery services before using the product.

Recruiting departments are using more data analytic tools to identify the right candidates for the job.  

One use case is the use of resume screening which searches for keywords on a candidate's resume and matches the job requirements.

Advertising departments continue to gather data to better understand and target their audience.

This is often used to find the right media outlet for their product.

Financial companies use data to match the right funding tool to the right candidate.

By looking at credit scores and other data sources, the financial company can determine how much to loan and at what price.

Ridesharing services use data to predict how long a ride will take.

By capturing data streams from traffic sources and reviewing historical data for the time of day and day of week, the ridesharing service can better estimate the time of a ride.

Consider how data can improve your product, sales, or service. 

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.

_______________________________________________________

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Data-Driven Business Models

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

A data-driven business uses data to drive business decisions and processes.

A data-driven business model is one in which the business is structured around data and changes price, costs, and responsibilities according to the incoming data.

Many companies already use some data to drive decision making but a data-driven business takes it to the next level and uses it for strategic decisions.

The benefits of a data-driven business model is that it brings greater productivity.

It can decrease costs.

It can speed up decision making. 

It can create better products and services.

It can increase revenue through new sources of income.

To implement a data-driven business start with a strategy about how data can drive the business.

Identify the key data sets needed.

Implement an analysis of the data to make the incoming data useful for decision making.

Install a process for using the data in the actual decision making program.

There are limits to data-driven decision making as the complexity of business often requires more than just a data set to determine the next steps.

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.

_______________________________________________________

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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.

_______________________________________________________

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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.

_______________________________________________________

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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.

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Let’s go startup something today.

_______________________________________________________

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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. 

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Let’s go startup something today.

_______________________________________________________

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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.

_______________________________________________________

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Data Business Models

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

There are several ways to approach data monetization.

Here are four approaches to consider for your data:

Basic content -- this business model provides raw or analyzed data that can be used as is. 

An example is a customer list with contact information.

One can sell this list to other companies who have related products to your company.

Information about products -- this business model makes available information about the product.

An example is an online database of your past purchases indicating what was bought and when.

For companies tracking their use of a product, this can be helpful for budgeting purposes. 

Data search-- this business model matches a company’s need for data with a data source.

For example a company seeking a customer list for their trading product could find it through a “data broker” who can match them to another company with that list.

Data aggregation -- this business model has a company capturing data from several sources into one dataset to sell.   

For example, a company could capture all the products being sold for a specific application and then contrast and compare the products for price and quality.

This saves buyers time in finding the right product.

Consider these business models for your data monetization.

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.

_______________________________________________________

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Data Strategy

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

In monetizing data for your startup, you’ll need a data strategy.

Here are the key components of a data strategy:

Identify the problem to solve -- the problem must be specific enough to indicate the type of data needed to present a solution.

Store the data -- you’ll need to capture raw data, analyze it and create final data sets. 

This requires a data platform for capturing and storing the data that everyone can access and share the results.

Format the data -- you’ll need to format your data into several forms so it can be used for multiple purposes by different groups.

Analyze the data -- you’ll need to analyze the data to create the result the customer wants.

Manage the data -- you’ll need to manage how the data will be used so you maintain compliance with government regulations.

There are several governing bodies including Europe, state-level, and industry level requirements around use of data.

Present the data -- you’ll need a process for turning the data into meaningful results for customers such as data sets and presentations.

In selling the data it’s important to tell a story about what the data says.  

Raw data is more valuable with an analysis of how a customer can use it. 

Consider these elements in building your data strategy.

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.

_______________________________________________________

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Why Monetize Data

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

Data brings another dimension to the startup in product, market size, and revenue.

Here are some reasons why you should monetize your startup’s data:

Give your company an additional competitive advantage over the competition.

Data sets you apart from the others. 

Generate new sources of revenue.

Data is another product you can package and sell.

Improve your operations.

Data can help you increase your efficiency and make your business run more effectively.

Create stronger ties to partners.

Providing data to partners can increase your strategic relationships.

In raising funding data gives your startup an additional advantage over other startups.

It increases your total available market size as it gives you more potential customers.

Investors find data highly attractive for the margins it brings.

Consider these points in pursuing data monetization for your startup.

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.

_________________________________________________________

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How To Monetize Your 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 for monetizing data.

Consider these models for your startup:

Mining your own data -- this business model takes your own startups data and uses it to provide new services and products for the business.

This could be creating a resource list from your research to sell as an additional data product to existing and new customers.

For example, a business could provide a data product that lists other sources to buy key products.  

This becomes a content marketing tool for drawing more potential customers to your website.

Providing data sets for other businesses -- this business model captures a data set such as the current stock market prices and makes it available for other businesses to use in their product.

Another example is tracking the number of people walking down a specific street over time would be useful information to businesses selling in that location. 

It shows the best times for the most traffic.

Providing higher level information for other businesses -- this business model takes analyzed data and provides an answer to questions that other businesses have.

For example this could be an analysis of the characteristics of a customer buying a product and where they currently look for those products.

This answers the question, who should we target to sell more of our current product and where do we find them.

Each type of data is useful.

The more analysis often leads to higher monetization levels.

Consider how to use these in your business. 

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.

_________________________________________________________

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Type of Data Analytics

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

Finding data to monetize comes in steps and stages.

Each step leads to a more detailed understanding of what data can be monetized and how to generate it.

Here’s a list of key steps to consider:

Reactive analytics -- this data captures how customers find products and services.

This could be where customers find the product and how they access it.

Descriptive analytics -- this data captures how customers use products and services.

This could be how long they engage with it, what they do with it, and what they take from it.

Diagnostic analytics -- this is the core data that others find most useful.

For example, this could be characterizing what triggers a customer to buy a product or service.

Proactive analytics -- this is additional data that can be generated using what was learned before.

This could be setting up additional triggers to generate more customer purchases.

You can modify current products or create new ones to facilitate it.

Data evolves over time from raw data to analyzed data to products that make use of the data.

Consider these steps in identifying what data you can monetize in your product.

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.

_________________________________________________________

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Data Monetization Models

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

There are several data monetization models for startups 

Sources of data come from a large network of customers, in depth content, or heavily trafficked websites.

Consider these for your startup.

Data as a service -- takes data generated by the startup and makes it available to other companies. 

For example, weather data captured by one startup can be sold to other companies.

The data is captured by the software and then made available in machine readable form to other companies’ websites. 

An automatic feed continuously generates the data and sends it to other sites.

Direct data transfer -- takes data directly from the startup and sells it to other companies.

This could be a customer list with email addresses.

This works well when you have a list of customers that others want to sell to.

Data augmentation -- taking other data sets and combining with your own to create a new data set that is more valuable.

A startup could take their customer list and augment it with data from other sources to provide a richer set of data to sell to other companies.

For example a startup could take their customer list and add contact and location information and sell it to other companies.

Automating this process makes the data more valuable as it reduces the friction between source and use cases.

Consider these data monetization models for your startup.

More analysis often leads to higher monetization levels.

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.

_________________________________________________________

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What Is a Data Strategy and Why Is It Important

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

A data strategy is a comprehensive plan to use data to drive business decisions.

You can use it to monetize data to achieve your business goals.

Data strategy has four components:

Story -- it tells the goals of your business and how data enables it to succeed.

This includes the vision, goals, and what success looks like.

Processes -- it shows the processes needed to use the data to accomplish the goals.

This includes the key roles and responsibilities and the deliverables to be achieved.

Transformation -- it shows how the business will change to implement the processes.

This includes the data requirements, data architecture, and methodology to be used. 

Culture -- it shows the values the company holds with regards to data and its usage.

This includes the organization structure, training, and practices to be used.

Data strategy is important for aligning the organization to include data into the process.

It creates new products for the company and additional revenue streams.

It requires a fundamental change to the business to incorporate it. 

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.

_________________________________________________________

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UX Design

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

UX design provides the experience the user encounters when using a product.

This is different from the user interface which is simply how the information is rendered to the user.

The UI consists of the typography, color palettes, and navigation.

The UX is the customer journey with the product.

It consists of the experience strategy which meets the needs of the user and the company.

The interaction design is how the user interacts with the product.

This comes from user research which identifies what the user must accomplish and what tools are required.

It rests on an information architecture which structures the information for the work to be done.

Good UX design achieves the following benefits:

Anyone can use the product.

It is simple and intuitive to use.

It provides useful information to the user at the right time.

It can handle user mistakes and recover from errors.

It is easy to follow.

Products with good UX outsell products with poor UX as users have little patience for hard to use products. 

Ensure UX design is part of your product management duties.

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.

_______________________________________________________

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Product Engagement Metrics

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

Product engagement is a key metric to track as it can predict revenue growth and churn rates. 

To track product engagement metrics consider the following:

Build tracking metrics into the product that captures user activities.

Capture key metrics such as number of active daily/weekly/monthly users.

Capture trial to paying customer conversion rates.

Measure new feature adoption to see which features are actually being used and how often.

Capture the retention rate of users to see how long they stay with the product before leaving. 

Identify which features provide stickiness for retaining customers.

Feed the metrics back to the developers and the support team to improve the quality of the product.

Update your marketing with the results of the product engagement metrics.

Consider providing additional training and tools for features that are hard to use.

Track product engagement metrics by customer segment to see the different use cases. 

Product engagement metrics indicate the quality of the product by its user engagement.

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: http://investorconnect.org 

Check out our other podcasts here: https://investorconnect.org/ 
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Product Data

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

Product data refers to the data about a particular product or generated by that product. 

This is separate from version control which tracks each version of the product and the features it contains.

Product data refers to the brand name, product description, schematics, source code, cost to build, price to sell, sales forecasts and results and more.

This typically involves storing the product data in a central location that all stakeholders can access such as developers, marketers, sales, and others.

Product data managers often use product data for A/B testing by capturing the results of each test.

Product managers use the data to review past sales and make future forecasts.

It can also be used to map our product roadmaps.

Product data can also refer to the data the product captures and creates.

Customer usage can be a critical data element for some products as it can be monetized by itself.

Other companies will buy your product data as the customers or their data may be a good fit for their product.

Some companies seek product data that can be used for testing algorithms.

As data becomes more valuable, more companies will seek to monetize their data.

Consider the data running through your product to see how it can be used.

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: http://investorconnect.org 

Check out our other podcasts here: https://investorconnect.org/ 
For Investors check out: https://tencapital.group/investor-landing/ 
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Pre-Seed Product Work

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

In the early stages of the startup before the product is designed and launched, there’s product management work to be done.

Consider these points for the pre seed stage product work:

Focus on the problem the customer has.

Understand it very well. 

Don’t get your heart set on  a specific instantiation of the product.  

Early stage products will morph and change over time.

Set intermediate goals for customer research, MVP designs and customer feedback.

Adopt the fail forward attitude.

Look for fast failures so you can move the project forward more quickly.

Create a short list of customers you can contact and get immediate feedback.

Launch several MVPs and vary the scale and scope of them.

Not everything must be built into a product to get feedback.

Some MVPs can be small experiments such as a web page capturing the number of users landing and converting.

Other MVPs can be actual products the customer can use. 

Set up your team to capture customer input from all sides.

Share customer feedback with everyone on the team on a regular basis.

Product management in the early stages focuses on a core set of customers and problems to be solved.

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: http://investorconnect.org 

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For Investors check out: https://tencapital.group/investor-landing/ 
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Gamification For Your Product

Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing.

Gamification is adding game techniques in a software product to increase user engagement.

The content inspires the user to continue engaging with the product.

Consider adding these gamification techniques to your product:

Set up goals for the user to accomplish.  

This provides a challenge to the user to complete the task.

Assign points for completing the tasks.

Use leaderboards to show the status of the users compared to each other.

Organize users into teams to complete the tasks.

This builds community among the users. 

Embed educational information into the tasks to increase the skill of the user.

This could be tips, quizzes, and other techniques to provide information.

Offer rewards for completing tasks.

This could be virtual such as assigning points, avatars, and badges.

Gaming has proven techniques for fostering engagement.

Use those techniques to foster more engagement with your product. 

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: http://investorconnect.org 

Check out our other podcasts here: https://investorconnect.org/ 
For Investors check out: https://tencapital.group/investor-landing/ 
For Startups check out: https://tencapital.group/company-landing/ 
For eGuides check out: https://tencapital.group/education/ 
For upcoming Events, check out https://tencapital.group/events/  

For Feedback please contact info@tencapital.group   

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Music courtesy of Bensound.