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Data Science for Marketers  -  Tomorrow's Knowledge Today!
Empowering Marketers through Data Science
Tomorrow's Knowledge Today!

Marketing Models - How Important are They?

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 a number of marketing models and the benefits gained from incorporating them into your marketing intelligence function. To begin, let's start with 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, which are very difficult to answer in today's integrated marketing environment. According to CMO.com 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 do not? They are utilizing data modelling to improve marketing intelligence.

The following models enable marketing to answer difficult questions. When leveraged 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 product or service. Utilizing a predictive churn model enables you to focus retention efforts at the critical time, before customers defect.

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 these models enable marketing to size market opportunity, predict propensity to buy, tailor targeted content and more.

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 across your customer base. The results enable you to target specific customers with specific offers and project the potential impact to your bottom line.

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 and compliments regression and attribution models extremely well.

The benefits associated with data modeling extend far beyond the output of model(s) themselves. The very process of developing and interconnecting models will ask questions you did not know needed answers, and help you build a better business.


Quick Tip

Do not spend resources on what you think is happening, rather spend resources to learn what is really happening.

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Fast Fact

Only 8% of companies say they can determine ROI from their social media spending.

- CMO by Adobe

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