Judge Graham, Sq1, president, explains his belief in how predictive modelling can be used to anticipate customer buying patterns to construct more efficient media campaigns, and it proves that knowledge is power.

Predictive modelling or predictive analysis relies on data models of existing qualitative and quantitative consumer data. These models are used to anticipate future customer buying patterns.

Predictive modelling can help you identify and target opportunities for your prospects/customers and implement marketing, sales programs, and ad campaigns in a more efficient way to improve ROI and campaign goals. Using predictive modelling allows the opportunity to pinpoint which components of your campaigns customers are likely to respond to positively and eliminate or restructure those that they do not seem to value.

This effective tactic allows you to select targets that are predisposed to act in a certain way, including:

• Prospects who are most likely to buy/use a new product/service.
• Prospects that convert or have online behaviours similar to ones of your current consumers.
• Identify emerging audiences within your data and find new audiences that behave like the emerging trend.

The role predictive models can play

Predictive models can be used to evaluate how likely a consumer or prospect is to purchase a product or how likely they are to churn.  You’ll also gain a better understanding of current and future customer behaviors.  And that’s an advantage competitors using traditional data tools may not be able to achieve.

With the volumes of data produced in online and offline transactions and engagement with customers, the big question has been “what can I do with all of it?” The answer is to develop insights that help guide decision-making.

To make this process more scientific and less about educated guessing, you need to apply the scientific method of creating models that predict customer behaviour.

This is achieved with tools such as CRM databases, Data management platforms, web analytics platforms and algorithmic delivery systems such as demand-side platforms that provide detailed information about your current and prospective customers, such as demographics, past purchases and behaviours.  This invaluable information makes it possible to better target your customers for particular products/services.

Traditionally, the key behaviour tracked was response levels.  Data modelling can be even more effective by selecting customers based on their contribution to your bottom line based on measuring the ROI for past campaigns and projecting ROI for future initiatives. The key is determining key customer behaviours or customer characteristics.

Predictive modelling applies advanced analytics that can be used to enhance every aspect of your marketing strategy.  It helps you leverage your data and test strategies to optimise your marketing programs. 

Combining database marketing, data mining and advanced analytics can let you identify customers/prospects most likely to buy your products or use your services but you can also:

• Target customers for loyalty programs
• Effectively attract new customers
• Better target though audience and messaging segmentation
• Create content and programs that resonate with site visitors
• Track/analyse marketing program results so you can measure ROI
• Introduce new products or offerings with higher success rates
• Optimise marketing programs

Data mining is a key component

Predictive modelling uses large volumes of information in its analysis, therefore data mining is a key component of the predictive modelling process.  It’s used to identify potential trends.  Every transaction, event, customer contact, online conversion, and Web site visit provides information about your customers and operations – and that includes past behaviour, which is an indicator of future behaviour. 

Your email archives, CRM, sales support, and call centre data are full of these useful insights. Your goal should be to transform this raw data into useful information that can drive your organisation and marketing efforts success.

The major failure of most data mining implementations is that they are only tactically focused on analysis.  High-ROI predictive analytics requires an approach that’s strategically focused on decision optimisation or deploying the results of advanced analysis to achieve business objectives. 

The process begins with an understanding of your business goals and available data, and ends with the deployment of data mining results into business operations to optimise critical decisions and deliver measurable ROI.

With predictive modelling your future looks brighter

Regardless of your objective, predictive modelling can help you transform volumes of customer data into valuable insights that can differentiate your company from your competitors to your customers.  It’s a game changer and the closest thing to a guarantee you’ll find to optimise your marketing program efficacy.  By peering inside the motivations and behaviours of your customers and using past history to predict future actions, you can put the odds of success dramatically in your favour.