Enterprise Dashboard

Designing an enterprise B2B dashboard for PairAnything

I designed an enterprise dashboard to automatically surface PairAnything's shopper data and deliver marketing recommendations to customers like Target. Through user testing, I validated what data our customers found most valuable — and used those findings to make a strategic decision about where to focus our product development.

Role

Product Designer

Duration

2 months

Team

CEO, Engineers

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Context

Shopper Data as a B2B enterprise product

PairAnything is a B2B2C SaaS product that enterprise brands like Target use to engage shoppers in-store. Shoppers scan a QR code in the wine aisle and use PairAnything to find food and wine pairing recommendations. In turn, PairAnything delivers unique shopper insights back to enterprise customers so they can market their wines more effectively. This project is how I designed a dashboard to automatically surface those insights and marketing recommendations for our B2B customers.

Target shoppers can scan a QR code in store and search for any food or wine and receive a matching pairing.
‍View the full case study of the shopper experience.

Research

What kind of data do our enterprise customers value?

I interviewed our retail and winery customers and learned that:
‍
Amanda, a wine club manager, looks at Google Analytics but doesn't feel equipped to act on the data.

John, VP at Target's marketing agency, wanted to know which wines shoppers engaged with most, which food and wine pairings were most searched, and how long shoppers spent in the experience.

Jenn, Director of Adult Beverage at Target, measured success by how many shoppers we converted to visit the Target website or purchase a bottle in-store.

“

Google Analytics can be overwhelming… who are the top people? Who have we not seen in a while? I want to directly market to them… I wish there was a model of 'do this, then this.'

- Amanda, wine club manager

Design

How can we help our enterprise customers market and sell more wine?

From these interviews, I identified what data our customers found most valuable, and saw an opportunity for us to provide marketing suggestions that translate unique shopper data into timely, actionable steps that our customers could take to improve their marketing strategy.

I designed a mock-up to pitch the enterprise dashboard to internal leadership and get early feedback from retail and winery customers. The design centered on displaying data our customers cared about, and pairing it with suggested next steps.

Enterprise customers of PairAnything can use the dashboard to evaluate activation performance, and get marketing suggestions based on what foods and wines their shoppers are searching for.

Testing

Validating the concept

I wanted to know whether our customers found the information valuable and whether the suggested next steps would motivate them to act. I asked customers, “Imagine I give you $10 to spend on all the pieces of information on this dashboard, how would you spend it?” This revealed that Andrew, a project manager at Target's marketing agency, valued the engagement data — but the suggested next steps didn't motivate him to take action.

“

Imagine I give you $10 to spend on all the pieces of information on this dashboard, how would you spend it?

- Me, asking customers to test the design

Highlighted in red are the areas Andrew spent the most money on. Engagement metrics ranked highest, while marketing suggestions received minimal investment — just $1 — and geography received none.

Result

Andrew's feedback invalidated my hypothesis — our customers valued the data, but the suggested actions weren't compelling enough to drive behavior. Without validated customer demand, and given the technical complexity of connecting the data sources, we made the call to pause development and prioritize consumer-facing B2C products where we had clearer product-market fit. I continued delivering data insights to customers manually in the meantime, in the form of slide decks.

Learnings

I should have focused on large enterprise customers first, instead of including small winery customers in the inital MVP. With 21 store activations, Target had a much stronger need for automated data insights than our winery customers, who were typically live in one tasting room or website. Designing for two customer types at once made it harder to validate the product clearly.