Marketing Attribution Strategy

Cookie Monster > Portfolios > Adobe Analytics, Adobe Launch, B2B, Data Layer, Google 360, Tag Management System, Tealium > Marketing Attribution Strategy
City Streets Night Timelapse symbolizing marketing attribution aka customer journey
Marketing Attribution Model bij TendInc.com
Marketing Attribution Models vs Market Mix Models
  • Client:Virgin Media Business | Telecom | 2017-2019
  • Categories:Business Strategy, Marketing Attribution, Marketing Technology

This page describes a project in which I contributed to a marketing attribution strategy. 

Goal

Creating and implementing a marketing attribution strategy was a next step in the maturity model of this client. The primary goal was to further improve the return on media investments (ROMI).

Approach within marketing attribution

Before you can adopt any marketing attribution model, you have to meet at least two conditions.

  1. Unified user identification (at least pseudo-anonymous)
  2. Harmonized order tracking across multiple tools and platforms

This was defined as the initial step within this project. During this phase we also needed to answers important questions such as:

  • How do we manage existing agreements with indirect sales partners and affiliates?
  • Do we include post-view conversions y/n?
  • Do we include non-paid channels (i.e. SEO, chat) y/n?
  • And if so, how do we include cost for these non-paid channels?

After these prerequisites had been defined, the next step was to collect and analyze data for a number a weeks. And based on these insights, determine which model(s) to use.

Results

Marketing attribution was successfully implemented and quickly generated new insights. As a result we did improve the return on media investment within specific campaigns.

But the most important insight was disappointing, in a way. Although marketing attribution models created new insights to optimize campaigns and indirect sales. It offered a very limited view of the full customer journey. For example factors such as macroeconomics and competitors’ prices did seem to play a much bigger role in the decision making process of a B2B customer.

Therefore media and market mix modeling was suggested as a next step, following this project.

Additional learning
Scalability proved to be a challenge. To gain deeper insights, you need to segment users. However absolute volumes (# of users) within B2B tend to be relatively low. Especially within in advertising and social channels this proved to be a significant hurdle for executing the next phase.

Tools

The primary marketing technology platforms used in this this project are:

If you like to see more projects, please check all my portfolios here.