Article | May 31, 2021
According to Google trends, predictive data analytics has gained a significant amount of popularity over the last few years. Many businesses have implemented predictive analytics applications to increase their business reach, gain new customers, forecast sales, and more.
Predictive Analytics is a type of data analytics technology that makes predictions with the help of data sets, statistical modeling, and machine learning. Predictive analytics uses historical data. This historical data is fed into a mathematical model that recognizes patterns and trends that are then applied to current data to forecast trends, practices, and behaviors from milliseconds to days and even years.
Based on the parameters supplied to them, organizations find patterns within that data to detect risks, opportunities, forecast conditions, and events that would occur at a particular time. At its heart, the use of predictive analytics answers a simple question, “What would happen based on my current data and what can be done to change the outcome.”
In the current times, businesses have multiple products offerings at their disposal to choose from vendors of big data predictive analytics in different industries. They can help these businesses leverage historical data discovering complex data correlation, recognizing patterns, and forecasting.
Organizations are turning to predictive analytics to increase their bottom line and gain advantages against their competition. Some of those reasons are listed below:
• With the growing amount and types of data, there is more interest in utilizing it to produce valuable insights
• Better computers
• An abundance of easy to use software
• Need of competitive differentiation due to tougher
As more and more easy-to-use software have been introduced, businesses no longer need statisticians and mathematicians for predictive analytics and forecasting.
Benefits of Predictive Analytics
Competitive edge over other businesses
The most common reason why multiple companies picked up predictive analytics was to gain an advantage over their competitors. Customer trends and buying patterns keep changing from time to time. The ones who can identify it first will go ahead in the game. Embracing predictive analytics is how you will stay ahead of your competition. Predictive analytics will aid in qualified lead generation and give you an insight into the present and potential customers.
Businesses opt for predictive analytics to predict customer behavior, preferences, and responses. Using this information, they attract their target audience and entice them into becoming loyal customers. Predictive analytics gives valuable information about your customers such as which of them are likely to lapse, how to retain them, whether you should market directly at them, etc. The more you know about them, the stronger your marketing will become. Your business will become the leader in predicting your customer’s exact needs.
Retaining existing customers is almost five times more difficult than acquiring new ones. The most successful company is the one that invests money in retaining those customers as much as acquiring new ones.
Predictive analytics helps in directing marketing strategies towards your existing customers and get them to return frequently. The analytics tool will make sure your marketing strategy caters to the diverse requirements of your customers.
Earlier marketing strategies revolved around the ‘one size fits all’ approach, but gone are those days. If you want to retain and acquire new customers, you have to create personalized marketing campaigns to attract customers.
Predictive analytics and data management help you to get new information about customer expectations, previous purchases, buying behaviors, and patterns. Using this data, you can create these personalized marketing strategies that will help keep up the engagement and acquire new customers.
Application of Predictive Analytics
Customer targeting divides the customer base into different demographic groups according to age, gender, interests, buying, and spending habits. It helps companies to create tailored marketing communications specifically to the customers who are likely to buy their products. Traditional techniques do not even come close to identifying potential customers as well as predictive analytics does.
The major constituents that create these customer groups are:
• Socio-demographic factors: age, gender, education, and marital status
• Engagement factors: recent interaction, frequency, spending habits, etc.
• Past campaign response: contact response, type, day, month, etc.
The customer-specific targeting for the company is highly advantageous. They can:
• Better communicate with the customers
• Save money on marketing
• Increase profits
Customer churn prevention
Customer churn prevention creates major hurdles in a company’s growth. Although it has been proven that retaining customers is cheaper than gaining new ones, it can become a problem. Detecting a client’s dissatisfaction is not an easy task as they can abruptly stop using your services without any warning.
Here, churn prevention comes into the picture. Churn prevention aims to predict who will end their relationship with the company, when, and why. The existing data sets can help develop predictive models so companies can be proactive to prevent the fallout.
Factors that can influence the churn are as follows:
• Customer variables
• Service use
• Competitor variables
Using these variables, companies can then take necessary steps to avoid the churn by offering customers personalized services or products.
Risk assessment and management processes in many companies are antiquated. Even though customer information is abundantly available for evaluation, it is still antiquated.
With advanced analytics, this data can be quickly and accurately analyzed while maintaining customer privacy and boundaries. Risk assessment thus allows companies to analyze problems with any business. Predictive analytics can approximate with certainty which operations are profitable and which are not.
Risk assessment analyzes the following data types:
• Socio-demographic factors
• Product details
• Customer behavior
• Risk metrics
Evaluating the previous history, seasonality, and market-affecting events make revenue predicting vital for a company’s planning and result in a company’s demand for a product or a service. This can be applied to short-term, medium-term, and long-term forecasting.
Predictive models help in anticipating a customer’s reaction to the factors that affect sales.
Following factors can be used in sales forecasting:
• Calendar data
• Weather data
• Company data
• Social data
• Demand data
Sales forecasting allows revenue prediction and optimal resource allocation.
Healthcare organizations have begun to use predictive analytics as this technology is helping them save money. They are using predictive analytics in several different ways. With the help of this technology, based on past trends they can now allocate facility resources, optimize staff schedules, identify patients at risk, adding intelligence to pharmaceutical and supply acquisition management.
Using predictive analytics in the health domain has also helped in preventing cases and risks of developing health complications like diabetes, asthma, and other life-threatening problems. The application of predictive analytics in health care can lead to making better clinical decisions for patients.
Predictive analytics is being used across different industries and is good way to advance your company’s growth and forecast future events to act accordingly. It has gained support from many different organizations at a global scale and will continue to grow rapidly.
Frequently Asked Questions
What is predictive analytics?
Predictive analytics uses historical data to predict future events. The historical data is used to build mathematical model that captures essential trends. That predictive model is based on current data that predicts what will happen next or suggest steps to take for optimal outcomes.
How to do predictive analytics?
• Define business objectives
• Collect relevant data available from resources
• Improve on collected data by data cleaning methods
• Choose a model or build your own to test data
• Evaluate and validate the predictive model to ensure
How does predictive analytics work for business?
Predictive analytics helps businesses attract, retain, and grow their profitable customers. It also helps them in improving their operations.
What tools are used for predictive analytics?
Some tools used for predictive analytics are:
• SAS Advanced Analytics
• Oracle DataScience
• IBM SPSS Statistics
• SAP Predictive Analytics
• Q Research
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"text": "Predictive analytics uses historical data to predict future events. The historical data is used to build a mathematical model that captures essential trends. That predictive model is based on current data that predicts what will happen next or suggest steps to take for optimal outcomes."
"name": "How to do predictive analytics?",
"text": "Define business objectives
Collect relevant data available from resources
Improve on collected data by data cleaning methods
Choose a model or build your own to test data
Evaluate and validate the predictive model to ensure "
"name": "How does predictive analytics work for business?",
"text": "Predictive analytics helps businesses attract, retain, and grow their profitable customers. It also helps them in improving their operations."
"name": "What tools are used for predictive analytics?",
"text": "Some tools used for predictive analytics are:
SAS Advanced Analytics
IBM SPSS Statistics
SAP Predictive Analytics
Article | June 10, 2021
We discursive creatures are construed within a meaningful, bounded communicative environment, namely context(s) and not in a vacuum.
Context(s) co-occur in different scenarios, that is, in mundane talk as well as in academic discourse where the goal of natural language communication is mutual intelligibility, hence the negotiation of meaning. Discursive research focuses on the context-sensitive use of the linguistic code and its social practice in particular settings, such as medical talk, courtroom interactions, financial/economic and political discourse which may restrict its validity when ascribing to a theoretical framework and its propositions regarding its application. This is also reflected in the case of artificial intelligence approaches to context(s) such as the development of context-sensitive parsers, context-sensitive translation machines and context-sensitive information systems where the validity of an argument and its propositions is at stake.
Context is at the heart of pragmatics or even better said context is the anchor of any pragmatic theory: sociopragmatics, discourse analysis and ethnomethodological conversation analysis. Academic disciplines, such as linguistics, philosophy, anthropology, psychology and literary theory have also studied various aspects of the context phenomena. Yet, the concept of context has remained fuzzy or is generally undefined. It seems that the denotation of the word [context] has become murkier as its uses have been extended in many directions.
Context or/ and contexts? Now in order to be “felicitous” integrated into the pragmatic construct, the definition of context needs some delimitations. Depending on the frame of research, context is delimitated to the global surroundings of the phenomenon to be investigated, for instance if its surrounding is of extra-linguistic nature it is called the socio-cultural context, if it comprises features of a speech situation, it is called the linguistic context and if it refers to the cognitive material, that is a mental representation, it is called the cognitive context. Context is a transcendental notion which plays a key role in interpretation.
Language is no longer considered as decontextualized sentences. Instead language is seen as embedded in larger activities, through which they become meaningful. In a dynamic outlook on communication, the acts of speaking (which generates a form discourse, for instance, conversational discourse, lecture or speech) and interpreting build contexts and at the same time constrain the building of such contexts. In Heritage’s terminology, “the production of talk is doubly contextual” (Heritage 1984: 242). An utterance relies upon the existing context for its production and interpretation, and it is, in its own right, an event that shapes a new context for the action that will follow. A linguistic context can be decontextualized at a local level, and it can be recontextualized at a global level. There is intra-discursive recontextualization anchored to local decontextualization, and there is interdiscursive recontextualization anchored to global recontextualization. “A given context not only 'legislates' the interpretation of indexical elements; indexical elements can also mold the background of the context” (Ochs, 1990). In the case of recontextualization, in a particular scenario, it is valid to ask what do you mean or how do you mean. Making a reference to context and a reference to meaning helps to clarify when there is a controversy about the communicative status and at the same time provides a frame for the recontextualization.
A linguistic context is intrinsically linked to a social context and a subcategory of the latter, the socio-cultural context. The social context can be considered as unmarked, hence a default context, whereas a socio-cultural context can be conceived as a marked type of context in which specific variables are interpreted in a particular mode. Culture provides us, the participants, with a filter mechanism which allows us to interpret a social context in accordance with particular socio-cultural context constraints and requirements. Besides, socially constitutive qualities of context are unavoidable since each interaction updates the existing context and prepares new ground for subsequent interaction.
Now, how these aforementioned conceptualizations and views are reflected in NLP? Most of the research work has focused in the linguistic context, that is, in the word level surroundings and the lexical meaning. An approach to producing sense embeddings for the lexical meanings within a lexical knowledge base which lie in a space that is comparable to that of contextualized word vectors.
Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information. The task of associating a word in context with the most suitable meaning from a predefined sense inventory is better known as Word Sense Disambiguation (Navigli, 2009). Linguistically speaking, “context encompasses the total linguistic and non-linguistic background of a text” (Crystal, 1991). Notice that the nature of context(s) is clearly crucial when reconstructing the meaning of a text. Therefore, “meaning-in-context should be regarded as a probabilistic weighting, of the list of potential meanings available to the user of the language.” The so-called disambiguating role of context should be taken with a pinch of salt.
The main reason for language models such as BERT (Devlin et al., 2019), RoBERTA (Liu et al., 2019) and SBERT (Reimers, 2019) proved to be beneficial in most NLP task is that contextualized embeddings of words encode the semantics defined by their input context. In the same vein, a novel method for contextualized sense representations has recently been employed: SensEmBERT (Scarlini et al., 2020) which computes sense representations that can be applied directly to disambiguation.
Still, there is a long way to go regarding context(s) research. The linguistic context is just one of the necessary conditions for sentence embeddedness in “a” context. For interpretation to take place, well-formed sentences and well-formed constructions, that is, linguistic strings which must be grammatical but may be constrained by cognitive sentence-processability and pragmatic relevance, particular linguistic-context and social-context configurations, which make their production and interpretation meaningful, will be needed.
Article | April 7, 2020
The Korea Advanced Institute of Science and Technology (KAIST) announced on April 6 that professor Yoo Hoi-jun and his research team have succeeded in developing a generative adversarial networks processing unit (GANPU) as an AI chip processing GAN with low power and high efficiency. The AI chip is capable of quickly processing arithmetic operations required for image synthesis and restoration on a mobile basis. The single chip is capable of realizing image recognition, inference, learning and determination with low power and high efficiency and is expected to contribute to the use of AI in mobile devices for more purposes.
Article | July 22, 2021
The software-as-a-service industry is rapidly growing with an estimate to reach $219.5 billion by 2027. SaaS marketing strategies is highly different from other industries; thus, tracking the right metrics for marketing is necessary. SaaS kpis or metrics measure an enterprise’s performance, growth, and momentum. These saas marketing metrics are have been designed to evaluate the health of a business by tracking sales, marketing, and customer success. Direct access to data will help you develop your business and show whether there is any room for development.
SaaS KPIs: What Are They and Why Do They Matter?
Marketing metrics for SaaS indicate growth in different ways. SaaS KPIs, just like regular KPIs, helps business to evaluate their business models and strategies. These key metrics for SaaS companies give a deep insight into which sectors perform well and require reassessment. To optimize any company’s exposure, SaaS metrics for marketing are highly essential. They measure the performance of sales, marketing, and customer retention. SaaS companies believe in the entire life cycle of the customer, while traditional web-based companies focus on immediate sales. The overall goal of SaaS companies is to build long-lasting customer relationships since most revenue is generated through their recurring payments.
SaaS marketing technology are SaaS marketers’ greatest asset if they take the time and effort to understand and implement them. There are essential and unimportant metrics. Knowing which metrics to pay attention to is a challenge. Once you get these metrics right, they will help you to detect your company’s strengths and weaknesses and help you understand whether they are working or not.
There are more than fifteen metrics one can track but make you lose sight of what matters. In this article, we have identified the critical metrics every SaaS should track:
This metric measures the number of visitors your website or page sees in a specific time period. If someone visits your website four to five times in that given time period, it will be counted as one unique visitor. Recording this metric is crucial as it shows you what type of visitors your site receives and from what channels they arrive. When the number of unique visitors is high, it indicates to the SaaS marketers that their content resonates with the target customers. It is vital to note, however, which channels these unique visitors reach your website. These channels can be:
SaaS marketers should, at this point, identify which channels are working and double down on those. Once you know these channels, you can allocate budgets and optimize these channels for better performance.
Google Analytics is the best free tool to track unique visitors. The tool enables you to refine by dates and compare time periods and generate a report.
Leads is a broad term that can be broken down into two sub-categories: Sales Qualified Leads (SQL) and Marketing Qualified Leads (MQL). Defining SQL and MQL is important as they can be different for every business. So, let us break down the definitions for the two:
MQLs are those leads that have moved past the visitor phase in the customer lifecycle. They have taken steps to move ahead and become qualified to become potential customers. They have engaged with your website multiple times. For example, they have visited your website to check out prices, case studies or have downloaded your whitepapers more than two times.
SQLs actively engage with your site and are more qualified than MQLs. This lead is what you have deemed as the ideal sales candidate. They are way past the initial search stage, evaluating vendors, and are ready for a direct sales pitch. The most crucial distinction between the two is that your sales team has deemed them sales-worthy.
After distinguishing between the two leads, you need to take the next appropriate steps. The best way to measure these leads is through closed-loop automation tools like HubSpot, Marketo, or Pardot. These automation tools will help you set up the criteria that automatically set up an individual as lead based on your website's SQL and MQL actions. Next, track the website traffic to ensure these unique visitors turn into potential leads.
The churn rate, in short, refers to the number of customers lost in a given time frame. It is the number of revenue SaaS customers who cancel their recurring revenue services. Since SaaS is a subscription-based service, losing customers directly correlates to losing money. The churn rate also indicates that your customers aren’t getting what they want from your service.
Like most of your saas KPIs, you will be reporting on the churn rate every month. To calculate the churn rate, take the total number of customers you lost in the month you’re reporting on. Next, divide that by the number of customers you had at the beginning of the reporting month. Then, multiply that number by 100 to get the percentage.
A churn is natural for any business. However, a high churn rate is an indicator that your business is in trouble. Therefore, it is an essential metric to track for your SaaS company.
Customer Lifetime Value
Customer lifetime value (CLV) measures how valuable a customer is to your business. It is the average amount of money your customers pay during their involvement with your SaaS company. You measure not only their value based on purchases but also the overall relationship. Keeping an existing client is more important than acquiring a new one which makes this metric important.
Measuring CLV is a bit complicated than measuring other metrics. First, calculate the average customer lifetime by taking the number one divided by the customer churn rate. As an example, let’s say your monthly churn rate is 1%. Your average customer lifetime would be 1/0.01 = 100 months.
Then take the average customer lifetime and multiply it by the average revenue per account (ARPA) over a given time period. If your company, for example, brought in $100,000 in revenue last month off of 100 customers, that would be $1,000 in revenue per account.
Finally, this brings us to CLV. You’ll now need to multiply customer lifetime (100 months) by your ARPA ($1,000). That brings us to 100 x $1,000, or $100,000 CLV.
CLV is crucial as it indicates whether or not there is a proper strategy in place for business growth. It also shows investors the value of your company.
Customer Acquisition Cost
Customer acquisition cost (CAC) tells you how much you should spend on acquiring a new customer. The two main factors that determine the CAC are:
Lead generation costs
Cost of converting that lead into a client
The CAC predicts the resources needed to acquire new customers. It is vital to understand this metric if you want to grow your customer base and make a profit. To calculate your CAC for any given period, divide your marketing and sales spend over that time period by the number of customers gained during the same time. It might cost more to acquire a new customer, but what if that customer ends up spending more than most? That’s where the CLV to CAC ratio comes into play.
CLV: CAC Ratio
CLV: CAC ratio go hand in hand. Comparing the two will help you understand the impact of your business. The CLV: CAC ratio shows the lifetime value of your customers and the amount you spend to gain new ones in a single metric. The ultimate goal of your company should be to have a high CLV: CAC ratio. According to SaaS analytics, a healthy business should have a CLV three times greater than its CAC. Just divide your calculated CLV by CAC to get the ratio. Some top-performing companies even have a ratio of 5:1.
SaaS companies use this number to measure the health of marketing programs to invest in campaigns that work well or divert the resources to those campaigns that work well.
Always remember to set healthy marketing KPIs. Reporting on these numbers is never enough. Ensure that everything you do in marketing ties up to all the goals you have set for your company. Goal-driven SaaS marketing strategies always pay off and empower you and your company to be successful.
Frequently Asked Questions
What are the 5 most important metrics for SaaS companies?
The five most important metrics for SaaS companies are Unique Visitors, Churn, Customer Lifetime Value, Customer Acquisition Cost, and Lead to Customer Conversion Rate.
Why should we measure SaaS marketing metrics?
Measuring marketing metrics are critically important because they help brands determine whether campaigns are successful, and provide insights to adjust future campaigns accordingly. They help marketers understand how their campaigns are driving towards their business goals, and inform decisions for optimizing their campaigns and marketing channels.
How to measure the success of your SaaS marketing?
The success of SaaS marketing can be measured by identifying the metrics that help them succeed. Some examples of those metrics are: Unique Visitors, Churn, Customer Lifetime Value, Customer Acquisition Cost, and Lead to Customer Conversion Rate.
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"text": "The five most important metrics for SaaS companies are Unique Visitors, Churn, Customer Lifetime Value, Customer Acquisition Cost, and Lead to Customer Conversion Rate."
"name": "Why should we measure SaaS marketing metrics?",
"text": "Measuring marketing metrics are critically important because they help brands determine whether campaigns are successful, and provide insights to adjust future campaigns accordingly. They help marketers understand how their campaigns are driving towards their business goals, and inform decisions for optimizing their campaigns and marketing channels."
"name": "How to measure the success of your SaaS marketing?",
"text": "The success of SaaS marketing can be measured by identifying the metrics that help them succeed. Some examples of those metrics are: Unique Visitors, Churn, Customer Lifetime Value, Customer Acquisition Cost, and Lead to Customer Conversion Rate."