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People  Design  Business

Pivoting the planned strategic direction of a flagship product to a user centric solution that meets business goals.  


As UXR lead, I worked with Design, Data Science, Product Manager, Engineering, and Marketing science.


Small business advertising team at a top social media company.

problem space

The ask was to evaluate design mocks for a new direction for an ad product: a subscription model. This direction was aimed at solving the problem of advertisers pausing the ad before it could be optimized. 


A mix of concept testing, generative research, triangulating UXR with experiment data, creating frameworks, design principles and the Design Thinking process.



Ideate and prioritize

Test and iterate

design thinking 2.png



framing the problem

BUSINESS PROBLEM: The Ad product (henceforth referred to as 'Loop' for confidentiality reasons) helped businesses create ads automatically and was meant to run continuously. However, it had low adoption and advertisers paused it within a few days.


GAP IN PROPOSED SOLUTION: While the subscription model would solve for that by running the Ad for at least 30 days, there were several foundational gaps in our knowledge: Why does Loop have low adoption? Why do users pause their Ad? What need or people problem would a subscription solve for?


BALANCING STAKEHOLDER ASKS: There was a push to evaluate the mocks rather than addressing the knowledge gaps first. I explored a middle path, scoping the study to include only those foundational questions that would impact our perspective on subscription. I also engaged key stakeholders throughout the process to ensure buy-in.

expanding the research scope

In addition to evaluative testing, the study was designed to understand reasons to pause the Loop Ad; mental models around subscription and around advertising, especially those relating to the value proposition of the solution: time saving!

findings raise some hard questions

While there were several gaps in comprehension and expected value from the solution, those could have been addressed through iterations to the concept and design. The core issue lay in the misalignment of mental models around subscription and advertising.





Is for products or services where the needs or type of needs are fairly consistent each subscription cycle and you know what to expect.

Business needs, and hence advertising needs change, sometimes at short notice, and small businesses (with limited marketing $$) need the agility to test, learn and pivot.


Appropriate for things that don't require a lot of conscious decision making, and the decisions are not very high stake

While the subscription was meant to save time by "setting it and forgetting it", for small businesses, every dollar counts, and they wanted more control over their investments.


The outcome or benefit from subscriptions is guaranteed, reliable and consistent. They're known in advance and accepted for the price paid.

Ad outcomes vary and are dependent on several factors, not all of which can be controlled or determined in advance. There were large gaps in user expectations and outcomes that were technically feasible for the team. 


You commit to a specific amount each subscription cycle. 

For most small businesses, ad spending behavior is based on 'what can be spared', rather than parking an amount each month.





cross functional triangulation 
Why triangulate: The proposed direction was a big bet, with very high resource needs and interest from leadership. It was essential to examine it critically and holistically to build confidence, especially since research learnings suggested we move away from our plans. 

UX Research showed the low perceived value of the proposed direction: it did not meet a need, solve a pain point or align with their mindset towards advertising and their business. 

UX Research 

Marketing science

Marketing science suggested that the proposed ad strategy within subscription would not result in performance gains. 


Engineering experiments highlighted duration post which an ad would start performing poorly, and the number of variables that would prevent guaranteeing specific results. 

Data Science

Data science analysis indicated low to no increase in adoption of the product with the new direction


The evolution journey for Loop was but half done. The direction not to pursue was clear, but we still had to find the direction to pursue. We did that through a multi-disciplinary human centered design process that ensured the solution solves key people problems, meets business goals and can be executed within timelines.


I led the efforts to get leadership buy-in for pivoting away from subscription. This included collaborating across data functions (mentioned above) to get the needed information, synthesizing it to build a cohesive narrative, creating compelling deliverables in partnership with Designers, Product and Engineering manager to communicate our perspective in reviews. 

Identifying and implementing the new direction



Ideate and prioritize

Prototype and milestone

Test and iterate

Developing foundational understanding of: 

  • Top advertiser needs and challenges

  • Reasons for low adoption of Loop

  • Perceptions towards automation in advertising

  • Desired role of the organization in advertising efforts, elicited through letter writing technique. 

  • Synthesizing observations into problems.

  • Prioritizing the problems to solve

  • Creating a framework on trust in automation

  • Defining design principles for the solution.

  • Designing ideation workshops to brainstorm on prioritized problems.

  • Synthesizing and Prioritizing ideas based on:

    • Desirability (how does the solution help users)

    • Viability (impact on business metrics)

    • Feasibility (engineering effort required).

  • Creating a product requirement document (PRD):

    • Detailing the problem, design principles and framework from Define stage.

    • Developing mocks highlighting key features of the solution. 

    • Alignment on MVP, V1 and north star and success criteria

  • Iterations based on evaluative research and Beta testing.

  • Iterations based on experiments

  • Lead: UX Research

  • Product management

  • Product and content design

  • Engineering

  • Data science

  • Co led by UX Research, Product and Content Design 

  • UX Research

  • Product and content design

  • Engineering

  • Data science

  • PRD led by PM, with contributions from all team leads. 

  • UX Research

  • Product and content design

  • Engineering

  • Product marketing

highlights of principles reflected in the final solution




While small businesses have limited advertising knowledge, they are the experts of their business and want to be involved in decision making, especially when they're spending money. The solution, while automation focused, incorporated their input and enhanced outcomes through the company's technical strengths, rather than taking complete control. The solution enabled the user to focus on parts of the process they wanted to control and allowing automation to help with parts they found difficult.



Trust in an automation solution is not a given, and it is essential to bring the users along by sharing the rationale behind the system's recommendations. While users believed that the system may have data that they don't, it may not have the necessary context to provide quality vs quantity of outcomes. 


Automation and advertising products typically use jargon heavy language that can be hard to understand for small businesses. The language used in our solution was inspired by the terms used by our users. 


Often, small businesses don't have the time or skills to build a strategy of multiple Ads that work together. Our automation solution would not just help improve a particular Ad, but help the business run more strategic Ads in the longer term.


Successfully pivoted the strategy for the team's flagship product, driving alignment across functions and leadership levels to create a product that would be beneficial to the users, as well as directly impact the team's success metrics. 

Prevented investing a large number of resources into building a solution unlikely to be adopted due to poor perceived value and misalignment with mental models. 

Ensured the final implementation reflected research learnings by partnering with design and engineering through iterations, launch and post launch. 


Immersed the team in a foundational understanding of the people they were creating products for, impacting their approach on several other products and projects as well.  

The framework on building trust in automation products influenced the broader efforts on automation across the company. Authored 1 of the 2 conceptual summaries on the automation knowledge hub, helping onboarding of those new to the topic.

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