What is Predictive Analytics?
Predictive analytics is business intelligence technology
that produces a predictive score for each customer or other organizational
element. Assigning these predictive scores is the job of a predictive model
which has, in turn, been trained over your data, learning from the experience
of your organization Predictive analytics optimizes marketing campaigns and website
behavior to increase customer responses, conversions and clicks, and to decrease churn.
Each customer's predictive score informs actions to be taken with that customer
— business intelligence just doesn't get more actionable than that.
Predictive analytics is the branch of data mining concerned
with the prediction of future probabilities and trends. The central element of
predictive analytics is the predictor, a variable that can be measured for an individual
or other entity to predict future behavior. For example, an insurance company
is likely to take into account potential driving safety predictors such as age,
gender, and driving record when issuing car insurance policies.
Multiple predictors are combined into a predictive model,
which, when subjected to analysis, can be used to forecast future probabilities
with an acceptable level of reliability. In predictive modeling, data is
collected, a statistical model is formulated, predictions are made and the
model is validated (or revised) as additional data becomes available.
Predictive analytics are applied to many research areas, including meteorology,
security, genetics, economics, and marketing
Predictive analytics are used to determine the probable
future outcome of an event or the likelihood of a situation occurring. It is
the branch of data mining concerned with the prediction of future probabilities
and trends. Predictive analytics is used to automatically analyze large amounts
of data with different variables; it includes clustering, decision trees,
market basket analysis, regression modeling, etc
Applications
• Analytical customer relationship management (CRM)
• Clinical decision support systems
• Collection analytics
• Cross-sell
• Customer retention
• Direct marketing
• Fraud detection
• Portfolio, product or economy level prediction
• Underwriting
Predictive Analytics and Business Intelligence
There seems to be a lot of confusion out there on what
predictive analytics really is, and whether traditional business intelligence
solutions are able to address such needs. Hopefully what I'm about to write
will help clear things up a bit. First off, both BI and predictive analytics
have seen tremendous growth, and both deal with making sense of your data.
However, traditional business intelligence often falls short of being able to
robustly analyze existing data, let alone build predictive and other highly
analytical models. Most business intelligence products do a decent job at
measuring operational metrics, operational monitoring, reporting and querying.
The more modern solutions can also build and maintain scorecards and strategy
maps and understand performance against targets at all levels of the
organization. (Such as not only measuring turnover within HR, but more esoteric
strategic goals of 'becoming an employee centric organization' for which a CEO
may be on the hook.) A good BI solution will bring this data together from a variety
of data sources without necessarily having to invest in a data warehouse. In
other words, BI helps answer the question of "How we are doing."
However, many BI solutions lack the ability to robustly
analyze ("Why are we performing this way") and project in the future
("What should we be doing instead"). OLAP--a technology that has been
around for a very long time, and which provides analysis at the speed of
thought--is still not completely and robustly embraced by all BI vendors.
Second, most BI vendors lack the ability to build models
that can project in the future. The bigger players (basically the ones in the
Gartner Magic Quadrant) typically do to some extent, and can perform the more basic
types of advanced analytics, such as Linear Regression, Least Squares
Regression and Predictive Modeling using Multiplicative Analysis. This is
probably sufficient in most cases. However, for more sophisticated models and
profiling, these vendors typically partner with someone that specializes in
this area, such as SPSS.
To tie this all back to the question of BI vs. Predictive
Analytics, a metaphor I've heard used to describe the difference goes something
like this: if BI is a look in the rearview mirror, predictive analytics is the
view out the windshield.
So if your needs require BI with robust analytics, your best
bet is to look up the BI and Performance Management vendors in Gartner's Magic
Quadrant and understand whether they can help you. In certain (and relatively
rare) cases you will need to resort to supplementing the BI solution with SPSS
or SAS analytics.
The market is witnessing an unprecedented shift in business
intelligence (BI), largely because of technological innovation and increasing
business needs. The latest shift in the BI market is the move from traditional
analytics to predictive analytics. Although predictive analytics belongs to the
BI family, it is emerging as a distinct new software sector. Analytical tools
enable greater transparency, and can find and analyze past and present trends,
as well as the hidden nature of data. However, past and present insight and
trend information are not enough to be competitive in business. Business
organizations need to know more about the future, and in particular, about
future trends, patterns, and customer behavior in order to understand the
market better. To meet this demand, many BI vendors developed predictive
analytics to forecast future trends in customer behavior, buying patterns, and
who is coming into and leaving the market and why.
Traditional analytical tools claim to have a real 360° view
of the enterprise or business, but they analyze only historical data—data about
what has already happened. Traditional analytics help gain insight for what was
right and what went wrong in decision-making. Today's tools merely provide rear
view analysis.
However, one cannot change the past, but one can prepare
better for the future and decision makers want to see the predictable future,
control it, and take actions today to attain tomorrow's goals.
Predictive Analytics and Data Mining The future of data
mining lies in predictive analytics. However, the terms data mining and data
extraction are often confused with each other in the market. Data mining is
more than data extraction It is the extraction of hidden predictive information
from large databases or data warehouses. Data mining, also known as
knowledge-discovery in databases, is the practice of automatically searching
large stores of data for patterns. To do this, data mining uses computational
techniques from statistics and pattern recognition.
On the other hand, data extraction is the process of pulling
data from one data source and loading them into a targeted database; for
example, it pulls data from source or legacy system and loading data into
standard database or data warehouse. Thus the critical difference between the
two is data mining looks for patterns in data.
A predictive analytical model is built by data mining tools
and techniques. Data mining tools extract data by accessing massive databases
and then they process the data with advance algorithms to find hidden patterns
and predictive information. Though there is an obvious connection between
statistics and data mining, because methodologies used in data mining have
originated in fields other than statistics.
Data mining sits at the common borders of several domains,
including data base management, artificial intelligence, machine learning,
pattern recognition, and data visualization. Common data mining techniques include
artificial neural networks, decision trees, genetic algorithms, nearest
neighbor method, and rule induction.
Predictive Analytics-The Future Business Intelligence
The market is witnessing an unprecedented shift in business
intelligence (BI), largely because of technological innovation and increasing
business needs. The latest shift in the BI market is the move from traditional
analytics to predictive analytics. Although predictive analytics belongs to the
BI family, it is emerging as a distinct new software sector.
Analytical tools enable greater transparency, and can find
and analyze past and present trends, as well as the hidden nature of data.
However, past and present insight and trend information are not enough to be competitive
in business. Business organizations need to know more about the future, and in
particular, about future trends, patterns, and customer behavior in order to
understand the market better. To meet this demand, many BI vendors developed
predictive analytics to forecast future trends in customer behavior, buying
patterns, and who is coming into and leaving the market and why.
Traditional analytical tools claim to have a real 360° view
of the enterprise or business, but they analyze only historical data—data about
what has already happened. Traditional analytics help gain insight for what was
right and what went wrong in decision-making. Today's tools merely provide rear
view analysis.
However, one cannot change the past, but one can prepare
better for the future and decision makers want to see the predictable future,
control it, and take actions today to attain tomorrow's goals.
A Microscopic and Telescopic View of Your Data
Predictive analytics employs both a microscopic and
telescopic view of data allowing organizations to see and analyze the minute
details of a business, and to peer into the future. Traditional BI tools cannot
accomplish this functionality. Traditional BI tools work with the assumptions
one creates, and then will find if the statistical patterns match those
assumptions. Predictive analytics go beyond those assumptions to discover
previously unknown data; it then looks for patterns and associations anywhere
and everywhere between seemingly disparate information.
Let's use the example of a credit card company operating a
customer loyalty program to describe the application of predictive analytics.
Credit card companies try to retain their existing customers through loyalty
programs. The challenge is predicting the loss of customer. In an ideal world,
a company can look into the future and take appropriate action before customers
switch to competitor companies. In this case, one can build a predictive model
employing three predictors: frequency of use, personal financial situations,
and lower annual percentage rate (APR) offered by competitors. The combination
of these predictors creates a predictive model, which works to find patterns
and associations.
This predictive model can be applied to customers who are starting
using their cards less frequently. Predictive analytics would classify these
less frequent users differently than the regular users. It would then find the pattern
of card usage for this group and predict a probable outcome. The predictive
model could identify patterns between card usage; changes in one's personal
financial situation; and the lower APR offered by competitors. In this
situation, the predictive analytics model can help the company to identify who
are those unsatisfied customers. As a result, company's can respond in a timely
manner to keep those clients loyal by offering them attractive promotional
services to sway them away from switching to a competitor. Predictive analytics
could also help organizations, such as government agencies, banks, immigration
departments, video clubs etc., achieve their business aims by using internal
and external data.
On-line books and music stores also take advantage of
predictive analytics. Many sites provide additional consumer information based
on the type of book one purchased. These additional details are generated by predictive
analytics to potentially up-sell customers to other related products and
services.
Major Predictive Analytics Vendors
SAS -SAS Enterprise Miner,,SPSS,Insightful-Insightful
Miner,StatSoft Inc.-Statistica, Knowledge
Extractions Engines (KXEN)-KXEN Analytic Framework
,Unica-Affinium Model ,Angoss Software
Corporation-Knowledge STUDIO and Knowledge SEEKER ,Fair
Isaac Corporation - Model Builder
2.1, IBM - DB2 Intelligent Miner for Data.
How companies use real-time data to plan for the future.
In a tough global economy, sloppy decision making and
"going with your gut" can get you punished--swiftly. That's why
leading companies are increasingly turning to a new management discipline
called predictive analytics to compete and thrive. Rather than relying on
intuition when pricing products, maintaining inventory or hiring talent,
managers are using data, analysis and systematic reasoning to improve
efficiency, reduce risk and increase profits.
In simple terms analytics means using quantitative methods
to derive insights from data, and then drawing on those insights to shape
business decisions and, ultimately, improve business performance. Thus predictive
analytics is emerging as a game-changer. Instead of looking backward to analyze
"what happened?" predictive analytics help executives answer
"What's next?" and "What should we do about it?"
Research shows that high-performance businesses have a much
more developed analytical orientation than other organizations. They are five
times more likely than their low-performing competitors to view analytical
capabilities as core to the business. Our research shows that there are big
rewards for organizations that embrace analytics decision making.
Some of the most famous examples of analytics in action come
from the world of professional sports, where "quants" increasingly
make the decisions about what players are really worth. Consider these examples
from the business world:
--Best Buy ( BBY - news - people ) was able to determine
through analysis of member data that 7% of its customers were responsible for
43% of its sales. The company then segmented its customers into several archetypes
and redesigned stores and the in-store experience to reflect the buying habits
of particular customer groups.--Olive Garden uses data to forecast staffing
needs and food preparation requirements down to individual menu items and
ingredients. The restaurant chain has been able to manage its staff much more
efficiently and has cut food waste significantly.
--TheU.K.'s Royal Shakespeare Co. used analytics to look at
its audience members' names, addresses, performances attended and prices paid
for tickets over a period of seven years. The theater company then developed a
marketing program that increased regular attendees by more than 70% and its
membership by 40%. Recent Accenture research highlights the desire of many
other companies to become more analytical. In a 2009 survey of
600U.K.andU.S.blue-chip organizations, two-thirds of all respondents cited
"getting their data in order" as an immediate priority. Longer-term,
the top objective for between two-thirds and three quarters of executives is to
develop the ability to model and predict behaviors to the point where
individual decisions can be made in real time, based on the analysis at hand To
achieve this goal, companies must move fast. Almost 40% of our respondents
believe that their current technological resources significantly hinder the
effective use of enterprise-wide analytics. But there is no questioning the
escalating momentum. Whether it is using analytics to predict customer
behavior, set pricing strategy, optimize ad spending or manage risk, analytics
is moving to the top of the management agenda.
So what are the next steps? In their new book, Analytics at
Work: Smarter Decisions, Better Results, Tom Davenport, Jeanne Harris and
Robert Morison describe how organizations can put analytics to work in their organization.
If an analytical organization could be established simply by executive fiat,
the only remaining challenges would be technical ones.
Predictive Analytics: Beyond the Predictions
We make predictions and act on them all the time. I predict
that if I jump into the path of a moving bus, I will be hurt – so I won't jump.
I'd conclude that my prediction had been in alignment with my goals, but if I had
to, I could only prove it by using the laws of physics or examples of other
people's encounters with moving buses.
If done well, predictive analytics help companies avoid
business situations analogous to being struck by a bus. Business situations,
however, are usually less dramatic and much more nuanced than avoiding a moving
vehicle. And, unlike the bus, a company will often not even know there was a
situation worth avoiding.
Even so, business peril requires us to try to stay ahead of
trouble. Predictive analytics are key to the prevention of loss by fraud, churn
and other bad outcomes. Predictive analytics also help prevent the loss of wasted
time and money spent on activities that do not contribute to business goals.
But there are limits to the usefulness of predictive analytics
as we have applied them to date. One conclusion we have reached is that it is
no longer sufficient to simply try to predict an unimpeded future. We must
hedge our predictions with probabilities and be aware that a variety of
reactions to those probabilities might be in order.
Many predictive models are tuned to report a binomial
result, for example, "likely to churn." In practice, multiple actions
could occur as a result of this discovery, including "do nothing."
Whatever the reaction is (even to an event that has not yet taken place), it
must be in alignment with company goals. The predictive models are important
unto themselves, but I will focus here on how to support the actions we take
when using predictive models, the "next steps" that are often
neglected.
Predictive models
Predictive models analyze past performance to assess how
likely a customer is to exhibit a specific behavior in the future in order to
improve marketing effectiveness. This category also encompasses models that
seek out subtle data patterns to answer questions about customer performance,
such as fraud detection models. Predictive models often perform calculations
during live transactions, for example, to evaluate the risk or opportunity of a
given customer or transaction, in order to guide a decision.
Descriptive models
Descriptive models "describe" relationships in
data in a way that is often used to classify customers or prospects into
groups. Unlike predictive models that focus on predicting a single customer
behavior (such as credit risk), descriptive models identify many different
relationships between customers or products. But the descriptive models do not
rank-order customers by their likelihood of taking a particular action the way predictive
models do. Descriptive models are often used "offline," for example,
to categorize customers by their product preferences and life stage.
Decision models
Decision models describe the relationship between all the
elements of a decision — the known data
(including results of predictive models), the decision and
the forecast results of the decision — in order to predict the results of
decisions involving many variables. These models can be used in optimization, a
data driven approach to improving decision logic that involves maximizing
certain outcomes while minimizing others. Decision models are generally used
offline, to develop decision logic or a set of business rules that will produce
the desired action for every customer or circumstance.
Conclusion
Predictive analytics adds great value to a businesses
decision making capabilities by allowing it to formulate smart policies on the
basis of predictions of future outcomes. A broad range of tools and techniques
are available for this type of analysis and their selection is determined by
the analytical maturity of the firm as well as the specific requirements of the
problem being solved.
