Users had to leave Reacher for market research before returning to launch outreach

Users wanted better ways to understand TikTok Shop market data before deciding who to contact, what products to promote, or which creator opportunities were worth pursuing.

Before Social Intelligence, market research and outreach lived in separate places, the workflow was fragmented:

Competitor research clarified baseline expectations and differentiation

I reviewed competitor and adjacent TikTok Shop data tools to understand how users were already researching products, brands, creators, and videos outside Reacher.

The opportunity was to bring data discovery and creator action into one workflow

The design challenge was deciding how TikTok Shop/API signals should help affiliate managers make better growth decisions inside Reacher.

How might we help affiliate managers move from market research to creator action without leaving Reacher?

I mapped the workflow from market signals to creator opportunity

Instead of treating Social Intelligence as a static analytics page, I mapped it around the decisions affiliate managers need to make.

This helped clarify that Social Intelligence needed to support both exploration — understanding what is happening in the market — and action: turning insights into creator targeting, saved lists, or outreach.

The MVP focused on who is growing, what is selling, and which videos are working

To keep the MVP focused, we organized the experience around three high-value growth questions:

This gave each tab a clear job and made the MVP easier to understand.

1. Connected decision surfaces for brands, products, and videos

I structured Social Intelligence around the three questions affiliate teams ask most often: which brands are growing, what products are selling, and which videos are driving traction.

The tabs, rankings, filters, and save actions were designed as one connected workflow, helping users move from broad market scanning to specific opportunities they could return to or act on later.

2. AI search and filters narrowed broad market data into targeted discovery

Market data can become overwhelming when users do not know exactly what to look for. I designed AI search and filtering patterns that let users express intent more naturally and narrow large data sets by category, content signal, product type, or creator fit.

This moved Social Intelligence from passive browsing toward AI-assisted discovery.

3. Data explanations made the intelligence layer easier to trust

For a data-heavy product, trust is part of the user experience. I designed supporting states that helped explain where numbers came from, why data might be missing, and how often signals were refreshed.

Those details made the intelligence layer feel more transparent, especially for users comparing brands, products, and videos before taking action.

4. V1 shipped the core intelligence surfaces while the action layer became the next step

Social Intelligence was designed as an intelligence-to-action workflow, but V1 needed to stay focused. We prioritized the core discovery surfaces first so users could explore brands, products, and videos with confidence.

The action layer — including sending video insights to Discord and creating automations from video context — was designed alongside the system and sequenced for the next version because it required deeper engineering integration.