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Why Researchers Should Care about Marketing Technology

New technology vendors pop up every day offering CMO’s marketing automation tools that promise ‘smart data’ and improved analytics. For researchers, these emerging technologies can provide new opportunities to provide services and expertise that augments this data.

Editor’s Note: It is not new news at this point that marketing technology is changing marketing; this trend has been rapidly accelerating for several years now. From the perspective of market researchers and insights professionals, this trend has meant that the “language” of marketing is changing too. For researchers to stay relevant, they need to be able to speak that same language, in order to understand the new needs of their marketing clients and work through the opportunities and challenges these technology changes represent. Lisa Horwich discusses some of these opportunities and challenges, and what they mean for insights professionals going forward.  

The ‘Rise of the Machines’ and How We Got Here

When I graduated from business school back in the late 90’s I never dreamed I would become a total tech geek…in fact, I really thought I was going to be a high-powered consultant (think McKinsey, Bain, BCG). Instead, somehow, I found myself implementing large-scale computer systems (fears of Y2K!) and then became a product manager for a small software company. My journey to tech geekdom had begun without me even knowing it.

Fast forward to today, after spending much of my time working on quantitative and qualitative research for large tech companies, I can honestly say that I really love learning and studying technology.

With this in mind, about a year ago a prediction from Gartner (the big technology industry research firm) caught my eye – their analyst Laura McLellan predicted that by 2017 CMO’s will spend more on technology than CIO’s. She was almost correct – it happened in 2016, a year ahead of schedule.

Think about it…marketing departments are now spending more on information technology than the department that is responsible for a company’s technology infrastructure. Crazy, I know!

This has led to a proliferation of companies clamoring for a piece of this MarTech pie. From 2011 when 150 companies offered MarTech solutions, we are now in 2019 looking at over 7000 companies competing in this space.

What are all these solutions aimed at? Or more importantly, what has changed with CMO’s to prompt this massive investment in technology? It really boils down to three main factors:

  1. Most CMO’s now share P&L responsibility – instead of just being a ‘cost center’, Marketing is looked at as a fundamental part of revenue generation.
  2. Marketing funds and designs the entire cross-functional Customer Experience (CX). If you think of CX holistically from generating awareness until post-sales feedback, it makes sense that marketing is in charge.
  3. Finally – and arguably most importantly – with the soaring costs involved in attracting, maintaining and growing the customer base, marketing now has to justify the ROI of their activities.

CMO’s are turning to data-driven solutions that help them deeply understand every phase of the customer journey – tracking and quantifying the ROI of all marketing activities along this journey. They are also investing heavily in solutions that personalize the customer’s experience with the hope of converting these interactions into greater sales opportunities.

Technology Solutions and Their Uses

As researchers, we need to know what types of technologies our clients are spending significant portions of their overall budgets (~30%) on so we can recognize where our roles as human insight professionals fit in. We don’t have to be experts in tech, just conversant so when we walk in the door, and our clients say they are using a new “Artificial Intelligence email optimization tool”, we understand what that is and can talk about how our services complement and augment this tool.

To that end, I’ve put together a few charts and tables outlining some of the fundamental building blocks of these solutions. Most MarTech offerings are powered by technologies such as Artificial IntelligenceMachine LearningBusiness Intelligence, and Real-Time Analytics. I find it useful to see the interaction of these technologies with a chart like this:

And to understand how these technologies are defined and common uses, this table is a quick reference:

Common Uses
Real-Time Analytics
Unified customer data platforms predictive analytics, and contextual customer journey interactions. Understand customer journey stages; Increase customer loyalty & retention; increase customer lifetime value; Quantify marketing ROI
Business Intelligence
Applications, infrastructure, and tools that enable access to and analysis of information. Improve and optimize decisions and performance; Identify trends; Improve efficiency; Executive dashboards
Artificial Intelligence
Any intelligent system that notably augments human decisions or independently comes up with conclusions that appear to be well considered. Optimize marketing/email campaigns; Upsell options; Reduce churn; Revive dormant customers; Customer retention
Machine Learning
Any system that learns from past data to make judgments about previously unseen new data. Optimize ad campaigns and other metrics; Predict churn

Opportunities for Researchers

Many of the technologies outlined above inherently have limitations – which I like to think of as “Opportunities” for researchers. Most of the limitations center around the data – quality(how good is your data) and quantity (do you have enough of the right type of data). In addition, the other major limitation is having enough marketing content – a major bottleneck in the quest for personalized customer engagement.

Decisions are made solely on data – past and present Use the data as a launching point for deeper analysis
Existing data is not predictive enough for decision-making Create and maintain communities focused on pinpointing predictive behavior
Need exponentially more messaging content for personalization Assist in narrowing target messaging by identifying key characteristics valued by customers
Insufficient data to train the machine/AI Provide personas and other descriptive metrics to help ‘train’ algorithms
Lack of ‘industry specific’ attributes Create detailed feature lists to describe the unique features inherent to that industry


While the above ideas are great tactical opportunities, strategically, our most important job as researchers to remind our clients how, in a world of automation, humanizing the experience of individual customers is key to authenticity.

Lisa Horwich is a member of Qualitative Research Consultants Association (QRCA). This piece was originally published on QRCA’s Qual Power Blog on April 30, 2019.

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Lisa Horwich

Lisa Horwich

Founder & Research Principal , Pallas Research Associates