Framework for Analysing Success of MarTech Initiatives Part 1 – Setting Up The Context

Marketing Technology, or MarTech, is exploding as a technology category. While several constituents of MarTech, e.g., Web Content Management (WCM or CMS), Digital Asset Management (DAM) and CRM have been there for long time, several new technologies like Customer Data Platforms (CDPs) and Journey Orchestration Engines (JOEs) are emerging. In fact, there are thousands of products and technologies that get clubbed under MarTech.

There are several perceived benefits of implementing these technologies. But I often find customers lamenting that their executives do not have a good idea of how best to benefit from these technologies. Several MarTech projects fail to fully realize the benefits.

My theory is that a key reason for this is that many executives treat these technologies as just another set of tools and not something that enables large scale transformation. I’d argue that while there is a lot of potential, the benefits are not being realized because of a lack of any structured mechanism or framework to analyze impact of “MarTech”.

In a series of posts, I will attempt to build a framework that can be used to analyze potential success of MarTech initiative. I will also create a couple of surveys to validate the framework using real data. If it works out well, I will make the tools, framework and data available for use to everyone. (The idea is based on my PhD work)

There is a bit of statistics involved. This will help to base the framework on sound analysis of data. But don’t worry, I’ll explain all of the analysis and conclusion using simple language.

So let’s get started with some basics.

Constructs of Framework

There are four constructs  in this framework:

  1. MarTech Usage
  2. Challenges
  3. MarTech Readiness
  4. Outcome

MarTech Usage

MarTech Usage  refers to usage of Marketing technology within an organization’s marketing value chain. It consists of activities, with different areas of an organization’s functions, for which MarTech can be used. There are multiple ways to measure MarTech Usage and I’ll explain that in a future post. For example, we can measure it via how many different types of technologies are used (CDP, JOE, WCM etc) or what is the extent of spread of use cases?

MarTech Readiness

I define “MarTech Readiness” as: “Extent to which an organization is prepared with respect to factors related to not just technology but also with respect to organizational and business imperatives to be able to implement MarTech initiatives successfully”.

There are several factors that describe how “Ready” an Organization is to be able to successfully implement MarTech initiative. I will come up with these factors in a future post.

The key point is: The more ready an organization is, the better are the chances of success.


While Readiness factors enable an organization to better use MarTech, Challenges are exactly opposite of that and can hinder adoption and usage of Marketing Technologies. There are several challenges such as lack of well-defined business case, or lack of executive buy-in. Again, I’ll propose some challenges in another post.


This is the final construct.  There can be several outcomes such as increased competitive advantage, higher conversions, increased loyalty etc.

Hypothesized Model

For the more statsy people, here’s my hypothesized model.

Fig 1: Model to analyze MarTech initiatives

This model shows how the different constructs are related. In this model, “Challenges” and “Readiness” are observed exogenous constructs, whereas “MarTech Usage” (Usage) and “Outcome” are observed endogenous constructs. e1 and e2 are unobserved exogenous variables corresponding to error variance for each of the observed exogenous constructs.

The endogenous variables “Challenges” and “Readiness” are correlated. In turn, they are linked to all the other remaining constructs. Finally, ” MarTech Usage ” is  linked to “Outcome”.

Don’t worry if you don’t understand the statistics lingo. What this diagram essentially proposes is:

Fig 2: Model Hypothesis

Okay, I think that is enough for a background.

In future posts, I will:

  1. Expand this basic model by treating these variables as latent variables with their own observed variables. For example, Outcome will be a latent variable, and observed variables might be “increase in conversions”, “gain competitive advantage”, “increase loyalty” or other benefits that can accrue through MarTech initiatives.
  2. Conduct a survey to collect some data and based on that survey, I’ll carry out some statistical analysis (mainly path analysis, regression, Factor Analysis and SEM).
  3. The idea of doing all this is to base the framework on sound analysis. But don’t worry, I’ll explain all of the analysis and conclusion using simple language.

Thanks for reading. I am still evolving the framework and will welcome any feedback.

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