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Pricing For Profits: Three Simple Rules To Price Your Product

However, you should follow some simple rules when pricing every product to ensure success. This article provides common tips to price your SaaS product properly.




4 Benefits of Software for Your Business

SaaS pricing is the most challenging aspect of launching a business. It’s difficult and time-consuming, and if you make any mistakes, it can quickly spell disaster for your company. That being said, it doesn’t have to be this difficult.

Many startups ask services to develop valuable pricing strategies for their businesses to delight customers and increase revenue. However, you should follow some simple rules when pricing every product to ensure success. This article provides common tips to price your SaaS product properly.

1. What Is Product Pricing?

Product pricing is the process of setting the price for a product, which enables customers to purchase a product and receive value for their money. SaaS companies often find it difficult to put a price on their products because they provide value over time. Everybody should follow three major rules while setting a price on SaaS products listed below.

2. Common SaaS Pricing Models

SaaS pricing models are critical to your business and different from other product costs. They are rather simple and make it easy to understand. These pricing models are divided into four common categories:

Flat Rate Pricing

Flat rate pricing is one of the simplest SaaS pricing models. It usually refers to a one-time payment to use the product and get unlimited access. Usually, this model is used for simple products or services that have clearly defined features, while you are charged per month, per year, or the number of users.

This model is good when you’re targeting small businesses because they want simplicity and prefer a cheaper product with fewer features. Your customers may be less price sensitive in this case because they are saving money by paying less upfront.

Usage-Based Pricing

Revenue is generated from the usage of the product. This model is usually used for products that provide a certain level of service or quantity. The cost can be determined by the amount of time spent, storage space, or the number of people using it.

It is also used to sell add-ons like support packs, extra features, or additional products to those who want more than they currently have. This model is good if your customers are looking for more value and expect more features in a product over a longer period, and they want to pay only if they use it after buying.

Tiered Pricing Model

Tiered pricing charges users based on the number of licenses or products they bought. It’s the most popular SaaS pricing model because most customers expect to pay for what they use, and it gives them a chance to save money by paying less for the product they use less often. With this model, you need to provide some basic features at a higher price, and when customers require more features or extra functionality, you will lower the price.

Per User Pricing Model

Per-user pricing is a SaaS pricing model used when you want to set a specific price for each user. This model is good if your product is targeted to different people in terms of features and requirements.

The amount a customer pays will depend on the number of users, which makes customers think carefully before adding users. It’s usually used when the seller doesn’t want to share their secrets and can’t provide a fixed price that covers all users’ needs because different customers require different features.

3. Value-Based Vs. Needs-Based Software Pricing

Value-based pricing is one of the most common pricing strategies in the software industry. Compared to needs-based pricing, it can give a more accurate idea of what value customers get from your product. Value-based vs needs-based software pricing are different, and it will be clear once you know the difference between them.

4. Three Simple Rules To Price Your Product

The three simple rules to price your SaaS product follow below:

Know Your Customers

It is necessary to understand your customers. You need to know their behavior, needs, and what they expect from the product you offer. You should also check out similar products that already exist in the market to gain more information about the current trends of the pricing models and how customers are willing or not ready to pay for them. They will judge the value for the money, and if it’s low, they won’t purchase your product no matter how great it is.

Know The Market

Knowing the market means you should compare your product with other similar products. You should understand the price of these products and how your customers are willing to pay for them. It is also important to check out new features as well as bugs for those similar products, which can tell you about the future of your business and what platform people are using.

Consider The Cost Structure

It is important to consider the cost structure of your product as well. You have to understand how much time, resources, and money it will take to build your product. Also, you need to know what time a customer will spare for it at first so that you can calculate the pricing model accordingly for the business to succeed.

5. The Bottom Line

SaaS pricing is about understanding customers’ needs. You need to understand what your customers want and what they are willing to pay for and know all the significant rules related to SaaS pricing models. It is also important to know your cost structure and how much time and money it will take to build the product. Each SaaS pricing model has unique pros and cons, but they will serve you best if you follow the three simple rules mentioned above.

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How do collect and train data for speech projects?

Data collection is the process of gathering, analyzing, and, measuring accurate data from diverse systems to use for business process decision-making, speech projects, and research.




How do collect and train data for speech projects

With technology evolution, we are moving towards machine learning systems that can understand what we say. In our daily lives, we all have encountered many virtual assistants like Alexa, Siri, and others. These virtual assistants often help us in tuning the lights of our homes, finding information on the internet, and even starting a video conference. But do you know how it does that?

To produce results, these virtual assistants use natural language processing to understand the user’s intent. Natural Language Processing technology enables virtual assistants to understand user intent and produce outcomes. Basically, these virtual assistants are applications of automatic speech recognition and are also known as speech recognition software. This software uses machine learning and NLP to analyze and convert human speech data into text.

But, attaining maximum efficiency of these software requires the collection of substantial speech and audio datasets. The purpose of collecting these audio datasets is to have enough sample recordings that can be fed into automatic speech recognition (ASR) software.

Furthermore, these datasets can be used against the speakers using unspecified speech recognition models. And to make ASR software work as intended, speech data collection and audio datasets must be conducted for all target demographics, locations, languages, dialects, and accents.

Artificial Intelligence can be as intelligent as the data given to it. Hence, collecting data for feeding the machine learning model is a must to maximize the effect of ASR. Let’s discuss steps in speech data collection for effective automatic speech recognition training.

1. Create a Demographic Matrix

For creating a demographic matrix, the enterprise must consider the following information like language, locations, ages, genders, and accents. Along with these, it is a must to note down a variety of information related to environments like busy streets, waiting rooms, offices, and homes. Enterprises can also consider the devices people are using like mobile phones, headsets, and a desktop.

2. Collect and transcribe speech data

To train the speech recognition model, gather speech samples from real humans and take the help of a human transcriptionist to take notes of long and short utterances by following your key demographic matrix. In this way, human is a vital and essential part of building proper audio datasets and labeled speech and further development of applications.

6 Reasons to Transcribe Audio to Text

3. Build a separate test data

Once the text subscription is completed, it’s time to pair the transcribed test with the corresponding audio data and segment them to include one statement in each. Later on, take the segmented pairs and extract a random 20% of the data to form a set for testing.

4. Train the language model

To maximize the effectiveness of the speech recognition model, you can train the language model by adding general additional text that was not additionally recorded. For example in canceling a subscription, you recorded one statement that ‘I want to cancel my subscription, but you can also add texts like “Can I cancel my subscription” or “I want to unsubscribe”. To make it more effective and catchy you can also add expressions and relevant jargon.

5. Measure and Iterate

The last and important step is to evaluate the output of automatic speech recognition software to benchmark its performance. In the next step take the trained model and measure how well it predicts the test set. In case of any gaps and errors, engage your machine learning model in the loop to yield the desired output. 


From travel, transportation, media, and entertainment, the use of speech recognition software is evident. We all have been using voice assistants like Alexa and Siri to complete some of our routine tasks. To effectively use this speech recognition software requires proper training in the audio datasets and the use of relevant data for the machine learning model.

Proper execution and the right use of data make sure the speech recognition software going to work efficiently and enterprises can scale them for further upgrades and development. As data and speech recognition go hand in hand, make sure you are using data with the right approach.

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