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The demand for business intelligence is increasing at a rapid pace across all industries in today’s tough economic climate. As senior executives look to optimize existing business processes that can lead to bottom-line and top-line benefits, one option is to tap into predictive analytics, a type of data mining that can be used to make reliable predictions of future events based on analysis of historical data.
The science of predictive analytics, for various reasons, has not been leveraged optimally at most enterprises. Some common problems with the implementation of predictive analytics include:
Before addressing these issues, let’s begin with a definition. Predictive analytics comprises a variety of techniques from statistical analysis and data mining that analyze current and historical facts to make predictions about future events. In business, predictive models can capture relationships among many factors associated with a particular set of conditions, and can discover and exploit hidden patterns in historical data.
Basically, these models ensure that the actions taken today will directly achieve the organization’s goals tomorrow. Predictive analytics can help companies optimize existing processes, better understand customer behavior, identify unexpected opportunities, and anticipate problems before they happen.
Vladimir Stojanovski, an engagement manager/solutions architect at IBM, offers a metaphor to describe the relationship between predictive analytics and business intelligence (BI): “If BI is a look in the rearview mirror,” he wrote in his CRM & BI Realms blog, “predictive analytics is the view out the windshield.” While BI is reactive, and looks backward to gauge performance, predictive analytics seeks to use data in real time and helps to make decisions that affect future performance.
Predictive analytics allow companies to move beyond “How are we doing?” to “What does our future look like?”
There are many predictive models that can be applied across industries and domains, based on applicability. Some of the major models include:
In general, these steps should be followed to develop predictive models.
Consider a company that wants to introduce a new product to the market. Though the product is new, it is in a product family that already exists. The following analysis, based on performance of the product family during the last few years, could be carried out before the product is released to the market. This could not only help to create target customers but also to increase revenues based on target marketing.
Objective: Before introducing the product to the market, the organization wants to find out what kind of marketing campaign will give maximum returns, and what the projected revenue from new product will be. The following steps can be helpful:
Step 1: Ascertain the overall purchase value trend of the product family.
Step 2: Evaluate the type marketing campaigns (festival discount, bundling, trade shows, promotions, etc.) that would be most effective for that product family.
Step 3: Decide on the channel (direct contact, newspaper, Internet, etc.) which would be most efficient (based on cost and responses) and allocate the cost to different channels based on purchase value.
Step 4: Using the responses and revenues from the last few years, extrapolate the expected revenues for the current campaign. This can be done through time series modeling.
Step 5: Identify the segments and make clusters based on customers’ demographic factors, product types and channels to prepare the target list of customers.
Step 6: Identify any other products that can cross sell with this new product.
To begin a predictive analytics program, the following few steps are recommended: