Big data on regional weather conditions continues to grow. This historical data offers a wealth of statistical data that can be added to predictive models to help predict the weather and improve business profits.
Companies such as Planalytics have integrated a wealth of historical weather data into automated predictive analytical packages to help companies plan for increased or reduced demand that results from weather conditions. With the company's predictive heat map snapshots, business can see estimates of regional-specific demand for heaters, air conditioners and other types of weather dependent products as the weather changes.
Weather influences many businesses in many different ways besides the sale of products. Bad weather can delay shipments, shut down factories and increase delivery costs. Planalytics addresses the weather issues for all types of industries. The company has weather predictive analytics solutions for retail, insurance and manufacturing companies (just to name a few).
Planaltyics client list tells the story of just how important weather driven business analytics is to companies these days. There client list includes well known companies from almost every industrial sector. Among the company's massive list of clients are Starbucks, Baskin Robins, Payless, Ace Hardware, Bloomberg and DuraFlame.
Planaltyics weather solutions help these companies not only predict demand based on the weather, but also more accurately forecast sales at all their regional operations throughout the world. This means these companies can adequately address inventory stocking needs as well as regional labor needs and specific regional problems so that the effects on profits are mitigated.
Monday, December 31, 2012
Sunday, December 30, 2012
Predictive Analytics and Predictive Modeling Made Simple
Predictive Analytics and Predictive Modeling Made Simple
Predictive analysis allows companies to make decisions based on data, large amounts of data, often known as big data. This data could be keywords in tweets, weather patterns or vast amounts of consumer data. .With customer data integration software a company can completely break down the demographics of their customer base. With real time customer data integration software, changing demographic trends can be analyzed in real time and companies can change their marketing strategies accordingly.
Today though, changing your marketing strategies is also an automated real time process. When a potential customer comes to web site, their consumer data profile is read and the advertising is reconfigured to match the consumers profile.The potential buyer's profile then is matched with the ad database. Probability rankings are generated for each type of ad based on the potential buyer's profile. Then the ad is placed on the potential buyers screen.
It doesn't stop there though. Whether or not the ad resulted in a purchase is made is then recorded. The probability rankings are then recalculated for the ad and statistical data stored in the predictive model. In this way the predictive model is continually improved so that optimal sales revenue is achieved.
Developing a predictive model requires one to have a database of stimulus often ads or words. Ideally, it also requires one to have subjects, the potential buyers, to view or read those ads and words. The more subjects that are exposed to the stimulus the more data the predictive model has to work with. So over time as the number of stimulus grows and the number of subjects increases, the better the predictive model gets.
The factors that go into building a consumer predictive model are many. These can include the consumer's buying history, geographic location, occupation, interests, and time of year, just to name a few.
However, the first step in building a predictive model doesn't necessarily require any advanced analytics, technology or big data. . Simple surveys of the customer base can be conducted and the results used to generate the predictive model seed.
Predictive analysis allows companies to make decisions based on data, large amounts of data, often known as big data. This data could be keywords in tweets, weather patterns or vast amounts of consumer data. .With customer data integration software a company can completely break down the demographics of their customer base. With real time customer data integration software, changing demographic trends can be analyzed in real time and companies can change their marketing strategies accordingly.
Today though, changing your marketing strategies is also an automated real time process. When a potential customer comes to web site, their consumer data profile is read and the advertising is reconfigured to match the consumers profile.The potential buyer's profile then is matched with the ad database. Probability rankings are generated for each type of ad based on the potential buyer's profile. Then the ad is placed on the potential buyers screen.
It doesn't stop there though. Whether or not the ad resulted in a purchase is made is then recorded. The probability rankings are then recalculated for the ad and statistical data stored in the predictive model. In this way the predictive model is continually improved so that optimal sales revenue is achieved.
Developing a predictive model requires one to have a database of stimulus often ads or words. Ideally, it also requires one to have subjects, the potential buyers, to view or read those ads and words. The more subjects that are exposed to the stimulus the more data the predictive model has to work with. So over time as the number of stimulus grows and the number of subjects increases, the better the predictive model gets.
The factors that go into building a consumer predictive model are many. These can include the consumer's buying history, geographic location, occupation, interests, and time of year, just to name a few.
However, the first step in building a predictive model doesn't necessarily require any advanced analytics, technology or big data. . Simple surveys of the customer base can be conducted and the results used to generate the predictive model seed.
Saturday, December 29, 2012
Predictive analytics companies sales continue to rise
The predictive analytics market, considered one of the highest growth markets in the business intelligence sector, continues to gain steam. The latest news from SAS, based in Cary, North Caroline, is that its new SAS data mining software licenses increased 28 percent year-over-year.
This type of sales trend is expected to continue for other companies in the predictive analytics market. One of the primary reasons is that predictive analytics tools offer businesses and governments new ways to lower costs, generate revenue and provide longer term economic stability.
Predictive analytics is not only considered necessary for companies to excel, but also is gaining notoriety as the only way for many companies to remain competitive. Jim Davis, SAS Senior Vice President and Chief Marketing Officer emphasized that his company's analytic products give companies the competitive edge, “Lightning-fast analytic insights provide a powerful competitive advantage. SAS Visual Analytics can evaluate numerous scenarios simultaneously, so banks can spot opportunities or detect emerging issues and respond immediately to market conditions. Retailers can personalize offers on the spot, based on structured and unstructured data from sales and social media, boosting sales.”
Why SAS is out in the lead is an open question. It could be because of its award winning products or its market focus. SAS has a diverse range of analytical tools packaged in an integrated analytics environment for predictive and descriptive modeling, data mining, text analytics, forecasting and a number of other business intelligence taks. It also offers industry specific solutions for customer intelligence and risk management. SAS focuses on the health care, banking, insurance, retail and communications market.
Similar ales increases are expected for the other predictive analytics vendors. The major reason is the growth of big data statistics warehoused in corporations and governments throughout the world. The automated pooling and analysis of this data will give businesses and governments the information they need to develop products and respond to marketing, economic and geopolitical conditions in real time.
This type of sales trend is expected to continue for other companies in the predictive analytics market. One of the primary reasons is that predictive analytics tools offer businesses and governments new ways to lower costs, generate revenue and provide longer term economic stability.
Predictive analytics is not only considered necessary for companies to excel, but also is gaining notoriety as the only way for many companies to remain competitive. Jim Davis, SAS Senior Vice President and Chief Marketing Officer emphasized that his company's analytic products give companies the competitive edge, “Lightning-fast analytic insights provide a powerful competitive advantage. SAS Visual Analytics can evaluate numerous scenarios simultaneously, so banks can spot opportunities or detect emerging issues and respond immediately to market conditions. Retailers can personalize offers on the spot, based on structured and unstructured data from sales and social media, boosting sales.”
Why SAS is out in the lead is an open question. It could be because of its award winning products or its market focus. SAS has a diverse range of analytical tools packaged in an integrated analytics environment for predictive and descriptive modeling, data mining, text analytics, forecasting and a number of other business intelligence taks. It also offers industry specific solutions for customer intelligence and risk management. SAS focuses on the health care, banking, insurance, retail and communications market.
Similar ales increases are expected for the other predictive analytics vendors. The major reason is the growth of big data statistics warehoused in corporations and governments throughout the world. The automated pooling and analysis of this data will give businesses and governments the information they need to develop products and respond to marketing, economic and geopolitical conditions in real time.
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