Background

We completed a successful pilot with an HVAC company and questioned if NXT, a programmatic advertising sales software for linear broadcasters, could meet the needs of marketing agencies.

  • I was UX Researcher on Marketron’s Agency Pilot Program. I was responsible for conducting research interviews and synthesizing data.


    • Data Synthesizing

    • Generative Research

    • Research Reports

    • Affinity Diagramming

    • Creating Insights

    • Reporting to C-Suite Executives

  • We conducted 15 interviews in order to discover agencies’ pain points.

  • Conduct 15 to 30 interviews by the end of Q3 to understand the needs of marketing agencies to determine if Marketron has an offering (tech and/or service) that meets their needs.

Business Question

Is there a market space among marketing agencies for the functionality that NXT currently provides? And, where are the gaps?

User Need Question

What tools are media marketing companies using and where do they experience friction and frustration?

Hypothesis

Agencies are using multiple partners to run and manage their campaigns whether it be programmatically, display, or via social. Marketron believes that there is an opportunity to pilot a product that can meet the pain-points of these users so that agencies are able to serve their clientele with more cohesion in cross-channel, cross-platform reporting and data extrapolation.

Our Findings

  • Reporting and Dashboards

  • Creative Management

  • Service Quality

Research approach

We conducted interviews, tagged, stored, and analyzed them utilizing Dovetail.

For a more detailed look into my research initiative and scalable process, check out my research initiatives page

Strategy for tagging

First, we built out a tagging taxonomy to highlight key topics and pain points from our interviews. Using these tags, we established a system that allowed us to filter data to answer the research question and glean insights for our final report

Summary Reports

I tagged and wrote summaries for each interview, which were distributed to c-suite executives as top-line findings.

From these quick summaries, decision makers were able to quickly digest important information, including the tools agencies used, pain point, and opportunities.

Affinity Diagramming

After I successfully tagged all interviews and wrote summaries, I created an affinity diagram to synthesize the common trends among respondents.

Our system

  • Filter through tags

    First, we filtered the highlights through two tagging groups, “tools” and “opportunities” as those were the two groups that directly coincided with the research question, and included multiple relevant tags.

  • Refining our data

    Filtering resulted in data that helped us get closer to answering our research question. From this point, we separated the insights by commonalities.

  • Buckets

    Then we created “buckets” of similar responses.

After the initial sweep, we further refined our highlights to form insights for our research report.

Our main goal was understand users’ pain points and the opportunies those pain points would afford us when designing Marketron’s NXT product.

Marketron sought to understand the pain points before piloting their NXT product. Our research gave Marketron three main take aways to be developed when designing NXT for agencies.

Next steps are to conduct further usability testing in order to see where improvements can be made. After various rounds of usability testing NXT design will be sent to the dev team for product updates.

What I learned

Conducting Research Interviews

Through generative research I learned how to conduct research interviews among various department heads at advertising agencies. I learned how to avoid asking leading questions and how to follow up with interviewees in more of a dialectical approach as opposed to a strict, outlined interview form.

Affinity Mapping & Insighting

After conducting interviews I learned how to work in Dovetail to create affinity maps. Initially, I pulled out common trends and learned how to refine with specificity from the initial broad-sweeping commonalities among responses. After getting into the particulars, I learned how to iterate insights as buckets grew and shrank and we rethought the commonalities between the data.

In-depth Reporting

From the affinity mapping and insighting I put together a top-line report. I later wrote up an in-depth report in addition to the top-line overview. I learned the importance of presenting the information in a flow that helps a reader walk through the findings and insights in an effortless way so that one could arrive at the research conclusion easily.