Descriptive and diagnostic analytics both employ a reactive measure for strategic planning as it provides analysis only after certain events have occurred. Predictive analytics is a level above the other two as it can predict the future trends by analyzing historical relationships between multiple variables. Predictive retail analytics uses complex statistical tools and emerging technologies such as machine learning and data mining to forecast future trends. It allows retailers to predict customer behavior and estimate what kind of products will become popular in the upcoming season so that they can plan and strategize beforehand. For instance, predictive models can determine which customers are unhappy with the brand and are likely to defect. Based on such insights, the retailers can then provide offers and incentives to retain the customers.
Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.
Equipment manufacturers, for example, can find it hard to innovate in hardware alone. Product developers can add predictive capabilities to existing solutions to increase value to the customer. Using predictive analytics for equipment maintenance, or predictive maintenance, can anticipate equipment failures, forecast energy needs, and reduce operating costs. For example, sensors that measure vibrations in automotive parts can signal the need for maintenance before the vehicle fails on the road.
Companies also use predictive analytics to create more accurate forecasts, such as forecasting the demand for electricity on the electrical grid. These forecasts enable resource planning (for example, scheduling of various power plants), to be done more effectively.
With all this data, tools are necessary to extract insights and trends. Machine learning techniques are used to find patterns in data and to build models that predict future outcomes. A variety of machine learning algorithms are available, including linear and nonlinear regression, neural networks, support vector machines, decision trees, and other algorithms.
Why is predictive analytics important?
Organizations are turning to predictive analytics to help solve
difficult problems and uncover new opportunities. Common uses include:
Detecting fraud. Combining multiple analytics methods can improve pattern detection and prevent criminal behavior. As cybersecurity becomes a growing concern, high-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities and advanced persistent threats.
Optimizing marketing campaigns. Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.
Improving operations. Many companies use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.
Reducing risk. Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.
No one has the ability to capture and analyze data from the future.
However, there is a way to predict the future using data from the past.
It’s called predictive analytics, and organizations do it every day.
Has your company, for example, developed a customer lifetime value (CLTV) measure? That’s using predictive analytics to determine how much a customer will buy from the company over time. Do you have a “next best offer” or product recommendation capability? That’s an analytical prediction of the product or service that your customer is most likely to buy next. Have you made a forecast of next quarter’s sales? Used digital marketing models to determine what ad to place on what publisher’s site? All of these are forms of predictive analytics.
Predictive analytics are gaining in popularity, but what do you—a manager, not an analyst—really need to know in order to interpret results and make better decisions? How do your data scientists do what they do? By understanding a few basics, you will feel more comfortable working with and communicating with others in your organization about the results and recommendations from predictive analytics. The quantitative analysis isn’t magic—but it is normally done with a lot of past data, a little statistical wizardry, and some important assumptions. Let’s talk about each of these.
The Data: Lack of good data is the most common barrier to organizations seeking to employ predictive analytics. To make predictions about what customers will buy in the future, for example, you need to have good data on who they are buying (which may require a loyalty program, or at least a lot of analysis of their credit cards), what they have bought in the past, the attributes of those products (attribute-based predictions are often more accurate than the “people who buy this also buy this” type of model), and perhaps some demographic attributes of the customer (age, gender, residential location, socioeconomic status, etc.). If you have multiple channels or customer touchpoints, you need to make sure that they capture data on customer purchases in the same way your previous channels did.
All in all, it’s a fairly tough job to create a single customer data warehouse with unique customer IDs on everyone, and all past purchases customers have made through all channels. If you’ve already done that, you’ve got an incredible asset for predictive customer analytics.
The Statistics: Regression analysis in its various forms is the primary tool that organizations use for predictive analytics. It works like this in general: An analyst hypothesizes that a set of independent variables (say, gender, income, visits to a website) are statistically correlated with the purchase of a product for a sample of customers. The analyst performs a regression analysis to see just how correlated each variable is; this usually requires some iteration to find the right combination of variables and the best model. Let’s say that the analyst succeeds and finds that each variable in the model is important in explaining the product purchase, and together the variables explain a lot of variation in the product’s sales. Using that regression equation, the analyst can then use the regression coefficients—the degree to which each variable affects the purchase behavior—to create a score predicting the likelihood of the purchase.
Voila! You have created a predictive model for other customers who weren’t in the sample. All you have to do is compute their score, and offer the product to them if their score exceeds a certain level. It’s quite likely that the high scoring customers will want to buy the product—assuming the analyst did the statistical work well and that the data were of good quality.
The Assumptions: That brings us to the other key factor in any predictive model—the assumptions that underlie it. Every model has them, and it’s important to know what they are and monitor whether they are still true. The big assumption in predictive analytics is that the future will continue to be like the past. As Charles Duhigg describes in his book The Power of Habit, people establish strong patterns of behavior that they usually keep up over time. Sometimes, however, they change those behaviors, and the models that were used to predict them may no longer be valid.
What makes assumptions invalid? The most common reason is time. If your model was created several years ago, it may no longer accurately predict current behavior. The greater the elapsed time, the more likely customer behavior has changed. Some Netflix predictive models, for example, that were created on early Internet users had to be retired because later Internet users were substantially different. The pioneers were more technically-focused and relatively young; later users were essentially everyone.
Another reason a predictive model’s assumptions may no longer be valid is if the analyst didn’t include a key variable in the model, and that variable has changed substantially over time. The great—and scary—example here is the financial crisis of 2008-9, caused largely by invalid models predicting how likely mortgage customers were to repay their loans. The models didn’t include the possibility that housing prices might stop rising, and even that they might fall. When they did start falling, it turned out that the models became poor predictors of mortgage repayment. In essence, the fact that housing prices would always rise was a hidden assumption in the models.
Since faulty or obsolete assumptions can clearly bring down whole banks and even (nearly!) whole economies, it’s pretty important that they be carefully examined. Managers should always ask analysts what the key assumptions are, and what would have to happen for them to no longer be valid. And both managers and analysts should continually monitor the world to see if key factors involved in assumptions might have changed over time.
With these fundamentals in mind, here are a few good questions to ask your analysts:
Prescriptive analytics can also suggest decision options for how to take advantage of a future opportunity or mitigate a future risk, and illustrate the implications of each decision option. In practice, prescriptive analytics can continually and automatically process new data to improve the accuracy of predictions and provide better decision options.
A process-intensive task, the prescriptive approach analyzes potential decisions, the interactions between decisions, the influences that bear upon these decisions and the bearing all of the above has on an outcome to ultimately prescribe an optimal course of action in real time. Prescriptive analytics is not failproof, however, but is subject to the same distortions that can upend descriptive and predictive analytics, including data limitations and unaccounted-for external forces. The effectiveness of predictive analytics also depends on how well the decision model captures the impact of the decisions being analyzed.
Advancements in the speed of computing and the development of complex mathematical algorithms applied to the data sets have made prescriptive analysis possible. Specific techniques used in prescriptive analytics include optimization, simulation, game theory and decision-analysis methods.
A company called Ayata holds the trademark for the (capitalized) term Prescriptive Analytics. Ayata is the Sanskrit word for future.
Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. Predictive analytics does not tell you what will happen in the future. It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessment.
Predictive analytics is the practical result of Big Data and business intelligence
(BI). What do you do when your business collects staggering volumes of
new data? Today's business applications are raking in mountains of new
customer, market, social listening,
and real-time app, cloud, or product performance data. Predictive
analytics is one way to leverage all of that information, gain tangible
new insights, and stay ahead of the competition.
Organizations use predictive analytics in a variety of different ways, from predictive marketing and data mining to applying machine learning (ML) and artificial intelligence (AI) algorithms to optimize business processes and uncover new statistical patterns. It's basically computers learning from past behavior about how to do certain business processes better and deliver new insights into how your organization really functions. But before we get into all of the fascinating ways businesses and technology companies are employing predictive analytics to save time, save money, and gain an edge over the rest of the market, it's important to talk about exactly what predictive analytics is and what it's not.
What Is Predictive Analytics?
Predictive analytics isn't a black-and-white concept or a discrete feature of modern database managers. It's a bunch of data analysis technologies and statistical techniques rolled up under one banner. The core technique is regression analysis, which predicts the related values of multiple, correlated variables based on proving or disproving a particular assumption. Predictive analytics is about recognizing patterns in data to project probability, according to Allison Snow, Senior Analyst of B2B Marketing at Forrester.
"It's key to recognize that analytics is about probabilities, not absolutes," explained Snow, who covers the predictive marketing space. "Unlike traditional analytics, when applying predictive analytics, one doesn't know in advance what data is important. Predictive analytics determine what data is predictive of the outcome you wish to predict."
Think about a sales representative looking at a lead profile in a customer relationship management (CRM) platform such as Salesforce.com$75.00 at salesforce.com. Let's say the assumption is, the lead will buy your product. Other assumptions are that the variables are product cost, the lead's role within a business, and the company's current profitability ratio. Now plop those variables into a regression equation and voila! You've got a predictive model from which to extrapolate an effective strategy for pitching and selling a product to the right leads.
Aside from regression analysis (the intricacies and subsets of which you can read more about in this Harvard Business Review primer), predictive analytics is also using progressively more data mining and ML. Data mining is exactly what it sounds like: you examine large data sets to discover patterns and uncover new information. ML techniques are, with greater regularity, becoming the sifting pans and pickaxes for finding the gold data nuggets. ML innovations such as neural networks and deep learning algorithms can process these unstructured data sets faster than a traditional data scientist or researcher, and with greater and greater accuracy as the algorithms learn and improve. It's the same way IBM Watson works, and open-source toolkits such as Google's TensorFlow and Microsoft's CNTK offer ML functionality along the same lines.
The big change feeding into the
predictive analytics boom is not just the advancement of ML and AI, but
that it's not just data scientists using these techniques anymore. BI
and data visualization tools, along with open-source organizations like
the Apache Software Foundation,
are making Big Data analysis tools more accessible, more efficient, and
easier to use than ever before. ML and data analysis tools are now
self-service and in the hands of everyday business users—from our
salesperson analyzing lead data or the executive trying to decipher
market trends in the boardroom to the customer service rep researching
common customer pain points and the social media marketing manager
gauging follower demographics and social trends to reach the right
targeted audience with a campaign. These use cases are just the tip of
the iceberg in exploring all of the ways predictive analytics is
changing business, many more of which we'll get into below.
That said, predictive analytics is not like a crystal ball or Biff Tannen's sports almanac from Back to the Future 2. The algorithms and models can't tell your business beyond the shadow of a doubt that its next product will be a billion-dollar winner or that the market is about to tank. Data is still a means to make an educated guess; we're simply a lot better educated than we used to be.
Breaking Down Predictive, Prescriptive, and Descriptive AnalyticsIn another Forrester report entitled 'Predictive Analytics Can Infuse Your Applications With An 'Unfair Advantage,'" Principal Analyst Mike Gualtieri points out that "the word 'analytics' in 'predictive analytics' is a bit of a misnomer. Predictive analytics is not a branch of traditional analytics such as reporting or statistical analysis. It is about finding predictive models that firms can use to predict future business outcomes and/or customer behavior."
In short, Snow explained that the term "predictive" inherently denotes likelihood over certainty, breaking down the analytics tooling landscape and how it factors into prescriptive analytics.
"Descriptive analytics, while not particularly 'advanced,' simply capture things that happened," said Snow. "Descriptive or historical analytics is the foundation on which an algorithm might be developed. These are simple metrics but often too voluminous to manage without an analytics tool.
"Generally speaking, dashboards and reporting are the most common use for predictive analytics within organizations today. These tools often lack the link to business decisions, process optimization, customer experience, or any other action. In other words, models produce insights but not explicit instructions on what to do with them. Prescriptive analytics is where insight meets action. They answer the question, 'I now know the probability of an outcome [and] what can be done to influence it in the direction that's positive for me,' whether that be preventing customer churn or making a sale more likely."
Predictive Analytics Is Everywhere
As the BI landscape evolves, predictive analytics is finding its way into more and more business use cases. Tools such as our Editors' Choices Tableau Desktop$42.00 at Tableau Software and Microsoft Power BIFree at Microsoft sport intuitive design and usability, and large collections of data connectors and visualizations to make sense of the massive volumes of data businesses import from sources such as Amazon Elastic MapReduce (EMR), Google BigQuery, and Hadoop distributions from players such as Cloudera, Hortonworks, and MapR. These self-service tools don't necessarily have the most advanced predictive analytics features yet, but they make the Big Data a lot smaller and easier to analyze and understand.
Snow said there is a broad series of use cases for predictive analytics in business today, from detecting point-of-sale (POS) fraud, automatically adjusting digital content based on user context to drive conversions, or initiating proactive customer service for at-risk revenue sources. In B2B marketing, Snow said enterprises and SMBs use predictive marketing for the same reasons they use any strategy, tactic, or technology: to win, retain, and serve customers better than those that don't.
Drilling down deeper, Snow identified three categories of B2B marketing use cases she said dominate early predictive success and lay the foundation for more complex use of predictive marketing analytics.
1. Predictive Scoring: Prioritizing known prospects, leads, and accounts based on their likelihood to take action.
"The most common entry point for B2B marketers into predictive marketing, predictive scoring adds a scientific, mathematical dimension to conventional prioritization that relies on speculation, experimentation, and iteration to derive criteria and weightings," said Snow. "This use case help sales and marketers identify productive accounts faster, spend less time on accounts less likely to convert, and initiate targeted cross-sell or upsell campaigns."
2. Identification Models: Identifying and acquiring prospects with attributes similar to existing customers.
"In this use case, accounts that exhibited desired behavior (made a purchase, renewed a contract, or purchased additional products and services) serve as the basis of an identification model," said Snow. "This use case help sales and marketers find valuable prospects earlier in the sales cycle, uncover new marketers, prioritize existing accounts for expansion, and power account-based marketing (ABM) initiatives by bringing to the surface accounts that can reasonably be expected to be more receptive to sales and marketing messages."
3. Automated Segmentation: Segment leads for personalized messaging.
"B2B marketers have traditionally been able to segment only by generic attributes, like industry, and did so with such manual effort that personalization applied only to highly prioritized campaigns," said Snow. "Now, attributes used to feed predictive algorithms can now be appended to account records to support both intricate and automated segmentation. This use case help sales and marketers drive outbound communications with relevant messages, enable substantial conversations between sales and prospects, and inform content strategy more intelligently."
BI tools and open-source frameworks such as Hadoop are democratizing data as a whole but, aside from B2B marketing, predictive analytics is also being baked into more and more cloud-based software platforms across a host of industries. Take online dating company eHarmony's Elevated Careers website and the handful of other vendors in the "predictive analytics for hiring" space. These platforms are still very much in their early days, but the idea of using data to predict which job seekers are the best fit for specifics jobs and companies has the potential to reinvent how human resources (HR) managers recruit talent.
Money: Predictive analytics can help adopters find ways to save and earn money. Retailers often use predictive models to forecast inventory requirements, manage shipping schedules and configure store layouts to maximize sales. Airlines frequently use predictive analytics to set ticket prices reflecting past travel trends. Hotels, restaurants and other hospitality industry players can use the technology to forecast the number of guests on any given night in order to maximize occupancy and revenue.
Fame: No business ever succeeded by fading into obscurity. By optimizing marketing campaigns with predictive analytics, organizations can generate new customer responses or purchases, as well as promote cross-sell opportunities. Predictive models can help businesses attract, retain and nurture their most valued customers.
Power: Predictive analytics can be used to detect and halt various types of criminal behavior before any serious damage is inflected. By using predictive analytics to study user behaviors and actions, an organization can detect activities that are out of the ordinary, ranging from credit card fraud to corporate spying to cyberattacks.
See also, "How predictive analytics can help prevent network failures."
For a deeper look, see "How to get started with predictive analytics."
Here are a few examples of how organizations are making use of predictive analytics:
Virtually all predictive analytics adopters use tools provided by one or more external developers. Many such tools are tailored to meet the needs of specific enterprises and departments. Major predictive analytics software and service providers include:
Decision trees, one of the most popular techniques, rely on a schematic, tree-shaped diagram that's used to determine a course of action or to show a statistical probability. The branching method can also show every possible outcome of a particular decision and how one choice may lead to the next.
Regression techniques are often used in banking, investing and other finance-oriented models. Regression helps users forecast asset values and comprehend the relationships between variables, such as commodities and stock prices.
On the cutting edge of predictive analytics techniques are neural networks — algorithms designed to identify underlying relationships within a data set by mimicking the way a human mind functions.
Clustering algorithms, for example, are well suited for customer segmentation, community detection and other social-related tasks. To improve customer retention, or to develop a recommendation system, classification algorithms are typically used. A regression algorithm is typically selected to create a credit scoring system or to predict the outcome of many time-driven events.
ove (listed above) has “angled” itself around understanding and improving revenue and engagement from a company’s existing customer base.
Similar to other machine learning domains (such as healthcare, finance, etc…), startups will become more sophisticated in articulating value propositions. Some of these companies will also build more and more robust and powerful technologies to actually deliver on those value propositions (which often is not the case in a nascent field where many founders themselves are just figuring out how the technology can be successfully applied in industry).
Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.
Rise of Big Data
Predictive analytics is often discussed in the context of big data, Engineering data, for example, comes from sensors, instruments, and connected systems out in the world. Business system data at a company might include transaction data, sales results, customer complaints, and marketing information. Increasingly, businesses make data-driven decisions based on this valuable trove of information.Increasing Competition
With increased competition, businesses seek an edge in bringing products and services to crowded markets. Data-driven predictive models can help companies solve long-standing problems in new ways.Equipment manufacturers, for example, can find it hard to innovate in hardware alone. Product developers can add predictive capabilities to existing solutions to increase value to the customer. Using predictive analytics for equipment maintenance, or predictive maintenance, can anticipate equipment failures, forecast energy needs, and reduce operating costs. For example, sensors that measure vibrations in automotive parts can signal the need for maintenance before the vehicle fails on the road.
Companies also use predictive analytics to create more accurate forecasts, such as forecasting the demand for electricity on the electrical grid. These forecasts enable resource planning (for example, scheduling of various power plants), to be done more effectively.
Cutting-Edge Technologies for Big Data and Machine Learning
To extract value from big data, businesses apply algorithms to large data sets using tools such as Hadoop and Spark. The data sources might consist of transactional databases, equipment log files, images, video, audio, sensor, or other types of data. Innovation often comes from combining data from several sources.With all this data, tools are necessary to extract insights and trends. Machine learning techniques are used to find patterns in data and to build models that predict future outcomes. A variety of machine learning algorithms are available, including linear and nonlinear regression, neural networks, support vector machines, decision trees, and other algorithms.
Predictive Analytics Examples
Predictive analytics helps teams in industries as diverse as finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing.- Automotive – Breaking new ground with autonomous vehicles
Companies developing driver assistance technology and new autonomous vehicles use predictive analytics to analyze sensor data from connected vehicles and to build driver assistance algorithms. - Aerospace – Monitoring aircraft engine health
To improve aircraft up-time and reduce maintenance costs, an engine manufacturer created a real-time analytics application to predict subsystem performance for oil, fuel, liftoff, mechanical health, and controls. - Energy Production – Forecasting electricity price and demand
Sophisticated forecasting apps use models that monitor plant availability, historical trends, seasonality, and weather. - Financial Services – Developing credit risk models
Financial institutions use machine learning techniques and quantitative tools to predict credit risk. - Industrial Automation and Machinery – Predicting machine failures
A plastic and thin film producer saves 50,000 Euros monthly using a health monitoring and predictive maintenance application that reduces downtime and minimizes waste. - Medical Devices – Using pattern-detection algorithms to spot asthma and COPD
An asthma management device records and analyzes patients' breathing sounds and provides instant feedback via a smart phone app to help patients manage asthma and COPD.
Predictive analytics is the process of using data analytics
to make predictions based on data. This process uses data along with
analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events.
The term “predictive analytics” describes the application of a statistical or machine learning technique to create a quantitative prediction about the future. Frequently, supervised machine learning techniques are used to predict a future value (How long can this machine run before requiring maintenance?) or to estimate a probability (How likely is this customer to default on a loan?).
Predictive analytics starts with a business goal: to use data to reduce waste, save time, or cut costs. The process harnesses heterogeneous, often massive, data sets into models that can generate clear, actionable outcomes to support achieving that goal, such as less material waste, less stocked inventory, and manufactured product that meets specifications.
The term “predictive analytics” describes the application of a statistical or machine learning technique to create a quantitative prediction about the future. Frequently, supervised machine learning techniques are used to predict a future value (How long can this machine run before requiring maintenance?) or to estimate a probability (How likely is this customer to default on a loan?).
Predictive analytics starts with a business goal: to use data to reduce waste, save time, or cut costs. The process harnesses heterogeneous, often massive, data sets into models that can generate clear, actionable outcomes to support achieving that goal, such as less material waste, less stocked inventory, and manufactured product that meets specifications.
Predictive Analytics Workflow
We are all familiar with predictive models for weather forecasting. A vital industry application of predictive models relates to energy load forecasting to predict energy demand. In this case, energy producers, grid operators, and traders need accurate forecasts of energy load to make decisions for managing loads in the electric grid. Vast amounts of data are available, and using predictive analytics, grid operators can turn this information into actionable insights.Predictive Analytics History & Current Advances
Though predictive analytics has been around for decades, it's a technology whose time has come. More and more organizations are turning to predictive analytics to increase their bottom line and competitive advantage. Why now?- Growing volumes and types of data, and more interest in using data to produce valuable insights.
- Faster, cheaper computers.
- Easier-to-use software.
- Tougher economic conditions and a need for competitive differentiation.
Why is predictive analytics important?
Organizations are turning to predictive analytics to help solve
difficult problems and uncover new opportunities. Common uses include:Detecting fraud. Combining multiple analytics methods can improve pattern detection and prevent criminal behavior. As cybersecurity becomes a growing concern, high-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities and advanced persistent threats.
Optimizing marketing campaigns. Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.
Improving operations. Many companies use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.
Reducing risk. Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.
Predictive Analytics in Today's World
With predictive
analytics, you can go beyond learning what happened and why to
discovering insights about the future. Learn how predictive analytics
shapes the world we live in.
Has your company, for example, developed a customer lifetime value (CLTV) measure? That’s using predictive analytics to determine how much a customer will buy from the company over time. Do you have a “next best offer” or product recommendation capability? That’s an analytical prediction of the product or service that your customer is most likely to buy next. Have you made a forecast of next quarter’s sales? Used digital marketing models to determine what ad to place on what publisher’s site? All of these are forms of predictive analytics.
Predictive analytics are gaining in popularity, but what do you—a manager, not an analyst—really need to know in order to interpret results and make better decisions? How do your data scientists do what they do? By understanding a few basics, you will feel more comfortable working with and communicating with others in your organization about the results and recommendations from predictive analytics. The quantitative analysis isn’t magic—but it is normally done with a lot of past data, a little statistical wizardry, and some important assumptions. Let’s talk about each of these.
The Data: Lack of good data is the most common barrier to organizations seeking to employ predictive analytics. To make predictions about what customers will buy in the future, for example, you need to have good data on who they are buying (which may require a loyalty program, or at least a lot of analysis of their credit cards), what they have bought in the past, the attributes of those products (attribute-based predictions are often more accurate than the “people who buy this also buy this” type of model), and perhaps some demographic attributes of the customer (age, gender, residential location, socioeconomic status, etc.). If you have multiple channels or customer touchpoints, you need to make sure that they capture data on customer purchases in the same way your previous channels did.
All in all, it’s a fairly tough job to create a single customer data warehouse with unique customer IDs on everyone, and all past purchases customers have made through all channels. If you’ve already done that, you’ve got an incredible asset for predictive customer analytics.
The Statistics: Regression analysis in its various forms is the primary tool that organizations use for predictive analytics. It works like this in general: An analyst hypothesizes that a set of independent variables (say, gender, income, visits to a website) are statistically correlated with the purchase of a product for a sample of customers. The analyst performs a regression analysis to see just how correlated each variable is; this usually requires some iteration to find the right combination of variables and the best model. Let’s say that the analyst succeeds and finds that each variable in the model is important in explaining the product purchase, and together the variables explain a lot of variation in the product’s sales. Using that regression equation, the analyst can then use the regression coefficients—the degree to which each variable affects the purchase behavior—to create a score predicting the likelihood of the purchase.
Voila! You have created a predictive model for other customers who weren’t in the sample. All you have to do is compute their score, and offer the product to them if their score exceeds a certain level. It’s quite likely that the high scoring customers will want to buy the product—assuming the analyst did the statistical work well and that the data were of good quality.
The Assumptions: That brings us to the other key factor in any predictive model—the assumptions that underlie it. Every model has them, and it’s important to know what they are and monitor whether they are still true. The big assumption in predictive analytics is that the future will continue to be like the past. As Charles Duhigg describes in his book The Power of Habit, people establish strong patterns of behavior that they usually keep up over time. Sometimes, however, they change those behaviors, and the models that were used to predict them may no longer be valid.
What makes assumptions invalid? The most common reason is time. If your model was created several years ago, it may no longer accurately predict current behavior. The greater the elapsed time, the more likely customer behavior has changed. Some Netflix predictive models, for example, that were created on early Internet users had to be retired because later Internet users were substantially different. The pioneers were more technically-focused and relatively young; later users were essentially everyone.
Another reason a predictive model’s assumptions may no longer be valid is if the analyst didn’t include a key variable in the model, and that variable has changed substantially over time. The great—and scary—example here is the financial crisis of 2008-9, caused largely by invalid models predicting how likely mortgage customers were to repay their loans. The models didn’t include the possibility that housing prices might stop rising, and even that they might fall. When they did start falling, it turned out that the models became poor predictors of mortgage repayment. In essence, the fact that housing prices would always rise was a hidden assumption in the models.
Since faulty or obsolete assumptions can clearly bring down whole banks and even (nearly!) whole economies, it’s pretty important that they be carefully examined. Managers should always ask analysts what the key assumptions are, and what would have to happen for them to no longer be valid. And both managers and analysts should continually monitor the world to see if key factors involved in assumptions might have changed over time.
With these fundamentals in mind, here are a few good questions to ask your analysts:
- Can you tell me something about the source of data you used in your analysis?
- Are you sure the sample data are representative of the population?
- Are there any outliers in your data distribution? How did they affect the results?
- What assumptions are behind your analysis?
- Are there any conditions that would make your assumptions invalid?
Prescriptive analytics can also suggest decision options for how to take advantage of a future opportunity or mitigate a future risk, and illustrate the implications of each decision option. In practice, prescriptive analytics can continually and automatically process new data to improve the accuracy of predictions and provide better decision options.
A process-intensive task, the prescriptive approach analyzes potential decisions, the interactions between decisions, the influences that bear upon these decisions and the bearing all of the above has on an outcome to ultimately prescribe an optimal course of action in real time. Prescriptive analytics is not failproof, however, but is subject to the same distortions that can upend descriptive and predictive analytics, including data limitations and unaccounted-for external forces. The effectiveness of predictive analytics also depends on how well the decision model captures the impact of the decisions being analyzed.
Advancements in the speed of computing and the development of complex mathematical algorithms applied to the data sets have made prescriptive analysis possible. Specific techniques used in prescriptive analytics include optimization, simulation, game theory and decision-analysis methods.
A company called Ayata holds the trademark for the (capitalized) term Prescriptive Analytics. Ayata is the Sanskrit word for future.
Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. Predictive analytics does not tell you what will happen in the future. It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessment.
Predictive Models and Analysis Applied to Business
Predictive models and analysis are typically used to forecast future probabilities. Applied to business, predictive models are used to analyze current data and historical facts in order to better understand customers, products and partners and to identify potential risks and opportunities for a company. It uses a number of techniques, including data mining, statistical modeling and machine learning to help analysts make future business forecasts.
Predictive analytics for marketing would
have been adopted years ago – if only the compute power were more
ubiquitous, the data were more accessible, and the software were easier
to use. Now “predictive analytics” itself is almost a buzzword, after
nearly 30 years of backward-looking marketing tracking.
Today, well over 30 years after the
founding of Lotus Software, even medium-sized businesses are often still
operating their marketing “scoreboards” in Google Sheets or One Drive…
“throw it in a spreadsheet” still works.
But businesses with an eye on the future
want to know more than just what happened in the past. “Scoreboards”
(most analytics tools and tracking) don’t tell you what the score will
be. Some of our recent “AI for marketing”
articles have gained readership because more and more executives are
searching for ways to look forward with their numbers, not just back. SAS defines the term well:
Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.
In this article, we’ll aim to highlight
some of the most promising marketing applications of predictive
analytics, and clarify the role of machine learning and AI in the advent
of predictive analytics tools.
The executive summary-like article will aim to cover the following:
- Current applications of predictive analytics for marketing and advertising
- The role of data and machine learning
- Existing market research on predictive analytics
- Prominent vendors and service providers in predicitive analytics
- Related interviews and articles
Five Current Predictive Analytics Applications for Marketing
Though a full list (and sub-lists) might
extrapolate 20 or more individual use cases, we’ve highlighted 5
current predictive analytics applications that marketers should be
familiar with today:
1 – Predictive Modeling for Customer Behavior
Predicting customer behavior and
preferences is the hallmark of companies like Amazon and eBay (see our
eBay machine learning interview here), but the technology is becoming
increasingly accessible and relevant for smaller companies as well.
Creating a complete catalog of
predictive models would be an extensive and cumbersome process, but
there are a number of relatively simple model types that apply well in
the marketing domain. Silicon Valley-based predictive marketing company AgilOne identifies three primary classes of predictive models:
- Cluster models (segments) – Used for customer segmentation; algorithms segment target groups based on numerous variables, everything from demographics to average order total. Common cluster models include behavioral clustering, product based clustering (also called category based clustering), and brand-based clustering.
- Propensity models (predictions) – Used for giving “true” predictions about customer behavior. Common models include predictive lifetime value; likelihood of engagement; propensity to unsubscribe; propensity to convert; propensity to buy; and propensity to churn.
- Collaborative filtering (recommendations) – Used for recommending products, services, and advertisements to customers based on a variety of variables, including past buying behavior. Common models (like those used by Amazon and Netflix) include up-sell, cross-sell, and next-sell recommendations.
Outcomes for predictive modeling are,
like so many predictive analytics approaches, highly dependent on
proprietary data, but there are several common ways that this
information can be transformed into results, as outlined in the next
four applications.
One concrete example from Tableau’s case study file: Arby’s tracked and found an increase in sales at renovated store locations, resulting in 5 times more store remodels in the following year.
2 – Qualify and Prioritize Leads
A recent study published by Forrester
identified three categories of B2B marketing use cases that reflect
early predictive success and lay the foundation for more complex use of
predictive marketing analytics:
- Predictive Scoring: Prioritizing known prospects, leads, and accounts based on their likelihood to take action.
- Identification Models: Identifying and acquiring prospects with attributes similar to existing customers.
- Automated Segmentation: Segmenting leads for personalized messaging.
All of the above concern qualifying and
prioritizing leads, and doing this groundwork prepares teams to apply
the strategies that follow. Sales staff who can prioritize leads most
likely to buy (or specific next steps most likely to move the sale
forward) will be in a better position to close more often.
It should be noted that while predictive
marketing capabilities will become more and more accessible to startups
and small businesses, these techniques require a high sales volume in
order to build and train a predictive model adequately. While even small
companies can drive billions of impressions or millions of clicks and
low-ticket eCommerce product sales, data about face-to-face sales is
harder to accumulate with smaller or newer companies. This potentially
puts larger companies in favor of successfully yielding a return on
investment from techniques that involve sales data.
3 – Bringing Right Product / Services to Market
Data visualization is
a valuable tool that not only appeals to the eye, but can be used to
inform, inspire and guide actions based on customer behavior (and other
business information).
For example, a brick-and-mortar
marketing team might use all the information available on customers to
make data-based decisions about which products and services are best to
bring to market. By using data visualization to shows which types of
customers live in a store’s neighborhood, teams can hone in on important
guiding questions: Do they buy more hard goods or soft? Is there an
age-range density that shows what should be stocked? Does the
desired product make-up change as you move towards or away from
competitor locations?
This type of information can also be linked to overarching supply chain management strategies.
For readers interested in this topic specifically, we’ve written previously on two specific use cases of machine learning and data visualization in a business context.
4 – Targeting the Right Customers at Right Time with Right Content
Targeting the right customers at the
right moment with the best offer links back to customer segmentation.
This may be the most common marketing application for predictive
analytics because its one of the “simplest” and most direct ways to
optimize a marketing offer and see a quick turnaround on better ROI.
According to a study by the Aberdeen Group, predictive analytics users are twice as likely to identify high-value customers
and market the right offer. Your data set matters, and best practices
dictate using historical data on behavior of existing customers to
segment and target, and using that same data to create personalized
messages.
A range of predictive analytic models
can be used in this application, including affinity analysis, response
modeling, and churn analysis, all of which can, for example, tell
you whether it’s a good idea to combine digital and print subscriptions
or keep them separate, or help you determine content that should be
charged a subscription fee versus content that should be given a
one-time sales price or other structure.
Many vendors, like Salesforce,
are offering a marketing cloud platform, through which marketing teams
can build audience profiles by combining data from multiple avenues,
from CRM to offline data. Feeding the system appropriate data and
tracking behavior over time builds a behavioral model that allows teams
to make data-based decisions in real-time over the long term.
5 – Driving Marketing Strategies Based on Predictive Analytics Insights
In addition to those outlined above, other drilled-down uses for predictive analytics in marketing include:
- Accessing internal structured data
- Accessing social media data
- Applying behavior scoring to customer data
Predictive analytics insights yield an
effective tool to cope with “channel proliferation and changing buyer
behavior”; all of the applications above could be used to determine
whether a marketing campaign through social media will have a greater
impact, or whether one through mobile is more appropriate for the target
audience.
Text analysis and sentiment analysis
applied to social media data is another example of capturing insights
that can be used to help drive marketing campaigns and future product
creation.
Organizations use predictive analytics in a variety of different ways, from predictive marketing and data mining to applying machine learning (ML) and artificial intelligence (AI) algorithms to optimize business processes and uncover new statistical patterns. It's basically computers learning from past behavior about how to do certain business processes better and deliver new insights into how your organization really functions. But before we get into all of the fascinating ways businesses and technology companies are employing predictive analytics to save time, save money, and gain an edge over the rest of the market, it's important to talk about exactly what predictive analytics is and what it's not.
What Is Predictive Analytics?
Predictive analytics isn't a black-and-white concept or a discrete feature of modern database managers. It's a bunch of data analysis technologies and statistical techniques rolled up under one banner. The core technique is regression analysis, which predicts the related values of multiple, correlated variables based on proving or disproving a particular assumption. Predictive analytics is about recognizing patterns in data to project probability, according to Allison Snow, Senior Analyst of B2B Marketing at Forrester.
"It's key to recognize that analytics is about probabilities, not absolutes," explained Snow, who covers the predictive marketing space. "Unlike traditional analytics, when applying predictive analytics, one doesn't know in advance what data is important. Predictive analytics determine what data is predictive of the outcome you wish to predict."
Think about a sales representative looking at a lead profile in a customer relationship management (CRM) platform such as Salesforce.com$75.00 at salesforce.com. Let's say the assumption is, the lead will buy your product. Other assumptions are that the variables are product cost, the lead's role within a business, and the company's current profitability ratio. Now plop those variables into a regression equation and voila! You've got a predictive model from which to extrapolate an effective strategy for pitching and selling a product to the right leads.
Aside from regression analysis (the intricacies and subsets of which you can read more about in this Harvard Business Review primer), predictive analytics is also using progressively more data mining and ML. Data mining is exactly what it sounds like: you examine large data sets to discover patterns and uncover new information. ML techniques are, with greater regularity, becoming the sifting pans and pickaxes for finding the gold data nuggets. ML innovations such as neural networks and deep learning algorithms can process these unstructured data sets faster than a traditional data scientist or researcher, and with greater and greater accuracy as the algorithms learn and improve. It's the same way IBM Watson works, and open-source toolkits such as Google's TensorFlow and Microsoft's CNTK offer ML functionality along the same lines.
That said, predictive analytics is not like a crystal ball or Biff Tannen's sports almanac from Back to the Future 2. The algorithms and models can't tell your business beyond the shadow of a doubt that its next product will be a billion-dollar winner or that the market is about to tank. Data is still a means to make an educated guess; we're simply a lot better educated than we used to be.
Breaking Down Predictive, Prescriptive, and Descriptive AnalyticsIn another Forrester report entitled 'Predictive Analytics Can Infuse Your Applications With An 'Unfair Advantage,'" Principal Analyst Mike Gualtieri points out that "the word 'analytics' in 'predictive analytics' is a bit of a misnomer. Predictive analytics is not a branch of traditional analytics such as reporting or statistical analysis. It is about finding predictive models that firms can use to predict future business outcomes and/or customer behavior."
In short, Snow explained that the term "predictive" inherently denotes likelihood over certainty, breaking down the analytics tooling landscape and how it factors into prescriptive analytics.
"Descriptive analytics, while not particularly 'advanced,' simply capture things that happened," said Snow. "Descriptive or historical analytics is the foundation on which an algorithm might be developed. These are simple metrics but often too voluminous to manage without an analytics tool.
"Generally speaking, dashboards and reporting are the most common use for predictive analytics within organizations today. These tools often lack the link to business decisions, process optimization, customer experience, or any other action. In other words, models produce insights but not explicit instructions on what to do with them. Prescriptive analytics is where insight meets action. They answer the question, 'I now know the probability of an outcome [and] what can be done to influence it in the direction that's positive for me,' whether that be preventing customer churn or making a sale more likely."
Predictive Analytics Is Everywhere
As the BI landscape evolves, predictive analytics is finding its way into more and more business use cases. Tools such as our Editors' Choices Tableau Desktop$42.00 at Tableau Software and Microsoft Power BIFree at Microsoft sport intuitive design and usability, and large collections of data connectors and visualizations to make sense of the massive volumes of data businesses import from sources such as Amazon Elastic MapReduce (EMR), Google BigQuery, and Hadoop distributions from players such as Cloudera, Hortonworks, and MapR. These self-service tools don't necessarily have the most advanced predictive analytics features yet, but they make the Big Data a lot smaller and easier to analyze and understand.
Snow said there is a broad series of use cases for predictive analytics in business today, from detecting point-of-sale (POS) fraud, automatically adjusting digital content based on user context to drive conversions, or initiating proactive customer service for at-risk revenue sources. In B2B marketing, Snow said enterprises and SMBs use predictive marketing for the same reasons they use any strategy, tactic, or technology: to win, retain, and serve customers better than those that don't.
Drilling down deeper, Snow identified three categories of B2B marketing use cases she said dominate early predictive success and lay the foundation for more complex use of predictive marketing analytics.
1. Predictive Scoring: Prioritizing known prospects, leads, and accounts based on their likelihood to take action.
"The most common entry point for B2B marketers into predictive marketing, predictive scoring adds a scientific, mathematical dimension to conventional prioritization that relies on speculation, experimentation, and iteration to derive criteria and weightings," said Snow. "This use case help sales and marketers identify productive accounts faster, spend less time on accounts less likely to convert, and initiate targeted cross-sell or upsell campaigns."
2. Identification Models: Identifying and acquiring prospects with attributes similar to existing customers.
"In this use case, accounts that exhibited desired behavior (made a purchase, renewed a contract, or purchased additional products and services) serve as the basis of an identification model," said Snow. "This use case help sales and marketers find valuable prospects earlier in the sales cycle, uncover new marketers, prioritize existing accounts for expansion, and power account-based marketing (ABM) initiatives by bringing to the surface accounts that can reasonably be expected to be more receptive to sales and marketing messages."
3. Automated Segmentation: Segment leads for personalized messaging.
"B2B marketers have traditionally been able to segment only by generic attributes, like industry, and did so with such manual effort that personalization applied only to highly prioritized campaigns," said Snow. "Now, attributes used to feed predictive algorithms can now be appended to account records to support both intricate and automated segmentation. This use case help sales and marketers drive outbound communications with relevant messages, enable substantial conversations between sales and prospects, and inform content strategy more intelligently."
BI tools and open-source frameworks such as Hadoop are democratizing data as a whole but, aside from B2B marketing, predictive analytics is also being baked into more and more cloud-based software platforms across a host of industries. Take online dating company eHarmony's Elevated Careers website and the handful of other vendors in the "predictive analytics for hiring" space. These platforms are still very much in their early days, but the idea of using data to predict which job seekers are the best fit for specifics jobs and companies has the potential to reinvent how human resources (HR) managers recruit talent.
Why Now? The Role of Data and Machine Learning
While initial applications of predictive
analytics can be said to be nearly as old as the field of machine
learning itself, predictive analytics as a field (and certainly as a
focus of venture capitalists) follows the strong interest in ML-based
technologies following Dr. Geoffrey Hinton‘s
famous victory in the 2012 ImageNet image recognition contest. Hinton
and his research team were quickly hired from University of Toronto into
Google, and the race to apply advanced statistical modeling to
handling data (machine learning) began it’s hyperdrive push for
popularity in the business world.
Similarly, because nearly any business
that exists today operates so many of it’s functions in digital space
(finance, marketing, sales, customer relationships, vendor data, hiring,
etc…), data is now aggregate-able and accessible in ways that it never
way before. Now even a small 2-person eCommerce operation with only
$800,000 in annual revenues has more marketing data to manipulate and
explore (organic search traffic, time-on-site, impressions, various PPC
ad channels, customer lifetime value as tracked within a CRM, etc…) than
a business many times it’s size just ten years ago. Millennials
entering marketing professionals know no other world than the world of
digital, quantifiable metrics and data. This information allows
companies of all sizes to train models and leverage predictive
analytics.
Machine learning would be even more of a
rare “dark art” in business if it wasn’t for an entire ecosystem of
vendors who are making predictive analytics easier to understand and
apply in business, even without an advanced computer science degree.
Existing Market Research on Predictive Analytics for Marketing
Like machine learning, predictive
analytics is getting a lot of attention. While predictive analytics has
been kicking around in the world of business and marketing for longer
than ML, it only appears to have been this year (2016) that teams using
some form of marketing analytics platform are starting to outpace those
who still choose to go without. Forrester published a study a few years
ago outlining two big challenges that modern marketers’ face:
- Creating more personalized and relevant messages for increasingly selective buyers
- Creating customized marketing campaigns that engage a wider range of key decision-makers and influencers earlier in the buying process
The numbers are in, and predictive
analytics appears to have the potential to double marketing success
measures in customer engagement and targeted sales across B2B and B2C
industries. Forrester’s study yielded three key findings:
- Predictive marketing analytics use correlates with better business results and metrics
- Predictive marketing analytics helps marketers play a leading role in their companies
- Predictive marketers use advanced strategies to deliver greater impact across the customer life cycle
Vendors and Service Providers
We’ve also included some vendors in the
predictive analytics space (the numbers are climbing and this is by no
means an inclusive list) to give readers an idea of related platforms
and services being offered:
In a space as competitive (and potentially “hype-ey”) as predictive
analytics, we’re likely to see vendors in the future niching down their
offerings to specific marketing domains, such as eCommerce, enterprise
sales, or some other such unique focus area. For example, vendor company
OptimWhat are the benefits of predictive analytics?
Over the centuries, peering into the future has always had three fundamental goals: money, fame and power. Predictive analytics doesn't really change the reasons why people want to know what's going to happen next week, next month or next year, it just makes looking into the future more accurate and reliable than previous tools.Money: Predictive analytics can help adopters find ways to save and earn money. Retailers often use predictive models to forecast inventory requirements, manage shipping schedules and configure store layouts to maximize sales. Airlines frequently use predictive analytics to set ticket prices reflecting past travel trends. Hotels, restaurants and other hospitality industry players can use the technology to forecast the number of guests on any given night in order to maximize occupancy and revenue.
Fame: No business ever succeeded by fading into obscurity. By optimizing marketing campaigns with predictive analytics, organizations can generate new customer responses or purchases, as well as promote cross-sell opportunities. Predictive models can help businesses attract, retain and nurture their most valued customers.
Power: Predictive analytics can be used to detect and halt various types of criminal behavior before any serious damage is inflected. By using predictive analytics to study user behaviors and actions, an organization can detect activities that are out of the ordinary, ranging from credit card fraud to corporate spying to cyberattacks.
See also, "How predictive analytics can help prevent network failures."
How does predictive analytics differ from traditional analytics?
The difference between conventional analytics and predictive analytics is simple and straightforward. Whereas traditional analytics generally focuses on insights impacting the here and now, predictive analytics aims to allow users to gaze into the near- and long-term future to pinpoint likely trends and upcoming behaviors.How should an organization begin with predictive analytics?
While getting started in predictive analytics isn't exactly a snap, it's a task that virtually any business can handle as long as one remains committed to the approach and is willing to invest the time and funds necessary to get the project moving. Beginning with a limited-scale pilot project in a critical business area is an excellent way to cap start-up costs while minimizing the time before financial rewards begin rolling in. Once a model is put into action, it generally requires little upkeep as it continues to grind out actionable insights for many years.For a deeper look, see "How to get started with predictive analytics."
Predictive analytics examples
Organizations today use predictive analytics in a virtually endless number of ways. The technology helps adopters in fields as diverse as finance, healthcare, retailing, hospitality, pharmaceuticals, automotive, aerospace and manufacturing.Here are a few examples of how organizations are making use of predictive analytics:
- Aerospace: Predict the impact of specific maintenance operations on aircraft reliability, fuel use, availability and uptime.
- Automotive: Incorporate records of component sturdiness and failure into upcoming vehicle manufacturing plans. Study driver behavior to develop better driver assistance technologies and, eventually, autonomous vehicles.
- Energy: Forecast long-term price and demand ratios. Determine the impact of weather events, equipment failure, regulations and other variables on service costs.
- Financial services: Develop credit risk models. Forecast financial market trends. Predict the impact of new policies, laws and regulations on businesses and markets.
- Manufacturing: Predict the location and rate of machine failures. Optimize raw material deliveries based on projected future demands.
- Law enforcement: Use crime trend data to define neighborhoods that may need additional protection at certain times of the year.
-
Retail: Follow an online customer in real-time to
determine whether providing additional product information or incentives
will increase the likelihood of a completed transaction.
Predictive analytics tools
Predictive analytics tools give users deep, real-time insights into an almost endless array of business activities. Tools can be used to predict various types of behavior and patterns, such as how to allocate resources at particular times, when to replenish stock or the best moment to launch a marketing campaign, basing predictions on an analysis of data collected over a period of time.Virtually all predictive analytics adopters use tools provided by one or more external developers. Many such tools are tailored to meet the needs of specific enterprises and departments. Major predictive analytics software and service providers include:
- Acxiom
- IBM
- Information Builders
- Microsoft
- SAP
- SAS Institute
- Tableau Software
- Teradata
- TIBCO Software
Predictive analytics models
Models are the foundation of predictive analytics — the templates that allow users to turn past and current data into actionable insights, creating positive long-term results. Some typical types of predictive models include:- Customer Lifetime Value Model: Pinpoint customers who are most likely to invest more in products and services.
- Customer Segmentation Model: Group customers based on similar characteristics and purchasing behaviors
- Predictive Maintenance Model: Forecast the chances of essential equipment breaking down.
- Quality Assurance Model: Spot and prevent defects to avoid disappointments and extra costs when providing products or services to customers.
Predictive modeling techniques
Model users have access to an almost endless range of predictive modeling techniques. Many methods are unique to specific products and services, but a core of generic techniques, such as decision trees, regression — and even neural networks — are now widely supported across a wide range of predictive analytics platforms.Decision trees, one of the most popular techniques, rely on a schematic, tree-shaped diagram that's used to determine a course of action or to show a statistical probability. The branching method can also show every possible outcome of a particular decision and how one choice may lead to the next.
Regression techniques are often used in banking, investing and other finance-oriented models. Regression helps users forecast asset values and comprehend the relationships between variables, such as commodities and stock prices.
On the cutting edge of predictive analytics techniques are neural networks — algorithms designed to identify underlying relationships within a data set by mimicking the way a human mind functions.
Predictive analytics algorithms
Predictive analytics adopters have easy access to a wide range of statistical, data-mining and machine-learning algorithms designed for use in predictive analysis models. Algorithms are generally designed to solve a specific business problem or series of problems, enhance an existing algorithm or supply some type of unique capability.Clustering algorithms, for example, are well suited for customer segmentation, community detection and other social-related tasks. To improve customer retention, or to develop a recommendation system, classification algorithms are typically used. A regression algorithm is typically selected to create a credit scoring system or to predict the outcome of many time-driven events.
ove (listed above) has “angled” itself around understanding and improving revenue and engagement from a company’s existing customer base.
Similar to other machine learning domains (such as healthcare, finance, etc…), startups will become more sophisticated in articulating value propositions. Some of these companies will also build more and more robust and powerful technologies to actually deliver on those value propositions (which often is not the case in a nascent field where many founders themselves are just figuring out how the technology can be successfully applied in industry).
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