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Published on Saturday, September 16, 2017

Marketing Models - Do You Need Them?

What is modeling & does it play a role in Marketing? Modeling is the process of creating data models which analyze data to produce the intel needed to answer difficult questions.

Creating effective marketing data models is broken into three steps: the conceptual model, logical model and physical model - but this is a topic for a separate article.

In this article we will focus on the benefits of utilizing a number of "must-have" marketing models and the benefits gained from incorporating them into your marketing intelligence function?

To begin let's start at the end. What are the top three questions that all business leaders ask Marketers?
  1. What is the return on our investment?
  2. What should (or shouldn't) we be doing?
  3. How much should we be spending?
These are simple questions that are very difficult to answer in today's integrated marketing environment. According to 50% of marketers find it difficult to attribute activity to revenue and only 23% are successfully tracking program ROI.

What do the 23% that are successfully tracking ROI know that others don't? They are utilizing data modelling to improve marketing intelligence. The following models enable marketing to answer leadership's questions. When linked together they provide potent marketing intelligence and a distinct competitive advantage.

Media Mix Model
A media mix model (see separate Linkedin article) gives Marketers insight into which channels and media types are contributing the most (and least) to the performance of key touch-point conversions in the customer decision journey.

Attribution Model
This model determines how credit for sales and conversions is assigned to touch-points throughout the customer decision journey. For example, greater credit may be assigned to activities focused on touch-points that immediately precede sales or other key conversion points.

Regression Model
The regression model analyzes dependent and independent variables with a focus on their relationship to one another. Some may refer to this as a touch-point model. For example, 5% of marketing impressions engage while 25% of engaged leads can be identified.

Churn Model
Churn modeling defines the steps and stages of customer churn. this model identifies which customers are likely to stop buying your service or product. Utilizing a predictive churn model enables you to focus retention efforts.

Customer/Buyer Persona Model
Customer and buyer persona models use demographic and psychographic data to represent your ideal customer/buyer. Personas include such information as behavior patterns, motivations, and goals. Use of such models enable marketing to size market opportunity, predict propensity to buy and tailor targeted content.

Cross/Up-sell Model
Designed to uncover opportunity within an existing customer base this model compares the activity of the ideal customer model to those that you have not fully penetrated. Churn modeling can identify who, what and project the the impact of gaining wallet share.

Budget Allocation Model
Modeling marketing budget allocations simplify the budget planning process by aligning your marketing spend with strategic business plans. Simply put, this model states your marketing plan in financial terms.

While this isn't a comprehensive list of marketing models the benefits associated with data modeling extend far beyond the output of the model(s) themselves. The very process of developing models will ask questions you didn't know needed answers and make your business better.

Driving growth in a competitive marketing is about doing more with less, while translating value to the business. Why wait? Contact Us to gain Tomorrow's Knowledge Today!
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Author: Bill King

Categories: Marketing Data Science



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