How to Track Brand Mentions in DeepSeek

Tracking brand mentions in DeepSeek means sampling a designed prompt set on a fixed cadence, scoring each answer for presence, position, sentiment, and accuracy, and separating search-grounded answers from parametric ones. DeepSeek's heavy reliance on model knowledge makes the parametric half unusually important: much of what it says about your brand was decided at training time, and tracking is how you find out what that is before your prospects do.

How do you design the prompt set?

Build 30–60 prompts across four intents, weighted toward how buyers actually phrase things:

Write prompts in the natural language of your market, including non-English variants if your buyers use them — DeepSeek's bilingual strength means Chinese-language answers about your category may diverge completely from English ones, and both can matter.

What cadence and sample size produce a real signal?

Run each prompt at least five times per checkpoint, weekly for the core set and monthly for the long tail. Single runs mislead: sampling variance can flip a borderline brand in or out of an answer, so the metric that matters is mention rate across repeated runs, not any individual response. Where DeepSeek exposes its web-search toggle, run the set both ways. Grounded answers tell you about your current retrievability; ungrounded answers tell you about your standing in the model's weights — two different problems with two different fixes, as the mention-tracking guide lays out.

How should each answer be scored?

Score four dimensions per run. Presence: mentioned or not. Position: first recommendation, listed among several, or a caveat ("some users prefer..."). Sentiment: the framing attached to you — "best for enterprises," "cheaper but limited" — captured as a category, not a vibe. Accuracy: whether stated facts (pricing, features, founding details) are correct, because parametric models confidently repeat stale or wrong facts, and a hallucinated limitation in a sales-adjacent answer is a revenue problem. Log cited sources when grounding is on; those domains are your citation supply chain.

When does automation become necessary?

The arithmetic is unforgiving: 40 prompts × 5 runs × 2 modes is 400 answers per weekly checkpoint, before scoring. Manual tracking survives about two weeks of enthusiasm. Menra's visibility platform runs the sampling continuously across DeepSeek and the other major engines, scores mentions and sentiment automatically, and produces trend reports that show movement per prompt and per competitor. Whatever tooling you use, keep the prompt set frozen for at least a quarter — every edit breaks trend comparability — and version any changes so historical numbers stay interpretable.

What do you do with the findings?

Three outputs feed action. Missing from discovery prompts: a corpus and retrieval problem — pursue corroboration and ranking. Present but framed poorly: a reputation-corpus problem — trace the framing to its likely sources (reviews, old comparisons) and fix upstream. Present but factually wrong: an entity-consistency problem — correct the fact everywhere it appears publicly, since the model learned it somewhere. Tracking without this routing is just watching; with it, each weekly report becomes a prioritized work queue.