Marketing analytics help support your company’s marketing strategy development and are your greatest resource for creating predictive models. There are several reasons your marketing mix should utilize predictive modeling since it’s a fundamental structure that forms a picture of the key features of your overall marketing strategy.

It also allows CMOs and CEOs to determine which products and services attract customers and where products and services are greatest in demand. From this data, marketing strategies are more reliably predictable.

How Does Predictive Modeling Enhance Marketing and Sales?

Believe it or not, marketing strategies are often implemented without beta testing. In predictive modeling, strategies are implemented based on solid data mining and quantitative information gathered. This includes:

• Customer buying habits
• Buying frequencies
• Customer locations

Predictive modeling also allows your marketing team to examine existing customer transaction details and add them to your model to highlight issues such as top resale customers, fastest turnaround time from order entry to customer purchases, payment frequency and account activities. Predictive modeling enhances existing marketing efforts by drawing forth the key elements of your business goals and expanding them for greater sales reach.

Key Features of Your Predictive Marketing Model

To make the best use of your predictive model, sales and marketing reports need to be generated and sorted on a regular basis. For example, reports relating to customer orders and total sales should be sorted according to each category of products or services offered. The data gathered from this information form key features of your predictive marketing model. These include:

• Model testing
• Inter-related sales programs
• Model deployment
• Marketing challenges

Model Testing

Once your predictive model has been created, it’s time to implement aggregate data and compare hard statistics of the model with their “predictive” or likely results. CMOs know this must be cost-effective and may choose to test the model only for a specific period of time. The amount of time allotted to model test depends on the business marketing strategies added to the model.

Some CMOs and marketing analysts prefer to test a model as part of their new product development plans or sales campaigns. Others may choose existing products that have been part of the marketing mix and have shown reasonably reliable results.

Inter-related Sales Programs

A healthy marketing mix may have some overlap in terms of the end goal. Inter-related sales programs create a seamless relationship between products and services. With inter-related sales programs, the predictive modeling results can be achieved with minimal effort.

For example, a business that offers electronic equipment sold in kits can create a predictive model of the most likely results of successful sales and resales among a broader pool of users based off of previous kits sold. In this example, the model will predict when, where and to whom kits will be marketed.

Model Deployment

Creating the ideal predictive model should be followed by model deployment. CMOs and CEOs should provide ready access to the predictive model for their marketing staff. Communicating marketing model strategies and displaying data mined from existing marketing and sales is the best method of model deployment.

However, model deployment is similar to discovering new marketing techniques. Model deployment should be carefully monitored so that the model can be upgraded to meet actual and projected sales.

Marketing Challenges

CMOs who plan to implement a predictive model should be aware that any type of model can be affected by marketing challenges and obstacles. The predictive model is, in actuality, hypothetical and therefore subject to potential curves. Try to design flexible predictive modeling that allows for these potential curves in the event of a sudden hiccup.

Inflexible predictive modeling can result in negative impacts on your marketing mix. It may scatter marketing efforts in each segment of your planning to a degree that renders your predictive modeling wholly ineffective.

Conclusion

The functional premise of predictive modeling is similar to studying known marketing facts and having the ability to make useful predictions of marketing and sales outcomes. This type of modeling is applicable to all phases of the marketing process from advertising and promotion to wider regions of building a strong, flourishing market for your products and/or services. If you can see conclusive evidence of the success of your predictive modeling, the benefits include greater business ranking and branding, and an increase in customers.