How a global FMCG leader defends and grows its brand portfolio across social with dig

About the customer

The customer is one of the largest consumer goods companies in the world, with a portfolio of household brands sold across Europe, North America, Asia, and the Middle East, and decades of category leadership in its core segments.

How the customer uses dig

• Detect ingredient and product misinformation across video, visuals, and competitor content before it escalates
• Track the brand and category across social on an ongoing basis, and spot how shoppers are actually using the products, including unexpected use cases the team would otherwise miss
• Map the unbranded-search landscape, before and after each campaign
• Brief influencer partners from real consumer language and category behavior, not from a hunch

Background: A brand portfolio that had to defend itself and grow itself in the same feed

By 2025, the customer's core brands were facing two pressures on social, and both of them lived in the same feed.

On one side, misinformation campaigns about the flagship brand had started showing up across creator videos without using the brand name at all. A creator would post a video about a chemical ingredient, a residue concern, or an environmental claim. The conversation would build over days, and the claims would worsen as they spread, what started as "this ingredient isn't healthy" could escalate into "this ingredient causes cancer" before the brand team had heard about it from anyone.

On the other side, shoppers had moved their category searches from Google to TikTok. When someone typed, for example, "the best coffee machine for home" into TikTok's search bar, the brand that came up first had already won the shopper, before any ad ran.

The team brought dig in to handle both sides from a single platform, so brand, comms, legal, and the social team could work from the same data, in real time.

The challenge: Legacy social listening couldn't see the threats or measure the growth

The team's existing social listening could catch any post that mentioned the brand by name. But by 2025, most misinformation didn't start with the brand name. It started with a chemical, or with a visual, a bottle, a pod, a pack shot with no logo on screen that anyone in the category would recognize as the brand. Competitors were running misinformation off those visuals without ever saying or writing the brand name. The legacy tool missed all of it, and by the time the team would have heard about a problem from somewhere else, the claims had usually escalated from mild concern into severe accusation.

When it came to category search, the team couldn't see where the brand showed up against competitors on the unbranded terms shoppers were actually typing. Whether the next shopper searching a top category term on TikTok saw the brand first or fifth was a black box.

And when it came to partnership campaigns, the team could see how many views each video pulled, but not which specific creators had actually moved the brand up those rankings, or how many comments under each post showed real intent to buy versus pure engagement.

So brand, comms, legal, and the social team were each reading a different version of the same feed. Comms was stuck waiting for the brand name to appear in a caption before it could act, by which point the narrative had already spread. Every new campaign got briefed without any clear data on what had worked the last time.

The solution: One engine for brand reputation and growth

Here's what that looks like, in four parts.

1. Risk monitoring across the category

dig monitors social video across the customer's brand and four competing brands in the category for emerging product and ingredient narratives, misinformation, negative claims, and potential attacks.dig surfaces any health-related or environment-related claim or misinformation post in the category, not only the specific ones the team mapped out in advance. The team has flagged the claims they most want watched, but dig reads across all five brands on the same engine and catches the wider field too, so cross-brand patterns surface where they wouldn't on a single-brand listening tool 

None of this depends on the brand being named. When a story about chemical residue started spreading across creator videos in early 2025, dig caught it from the chemical mentions while the claims were still mild, well before they had a chance to drift into the more severe accusations these campaigns tend to escalate toward. A generic health or environment risk in the category counts too, even when it isn't aimed at the brand at all, and the team can plan their strategy around it straight away rather than waiting for a false claim that points at the brand directly. When a damaging post crosses the team's threshold for views and sentiment, dig alerts the team, and the team's legal can have it removed as misinformation within 24 hours.  

When one health-and-environment influencer posted a string of negative videos about the brand,along with misinformation about the product that never named the brand at all, the posts drew close to a million views between them. In the same window, they also quietly posted positive videos about a competitor. dig caught both threads in one place, with timestamps and links. If legal ever needed to look into whether the attacks were coordinated, the evidence was already pulled together.

2. Social and product intelligence

Beyond the risk angle, dig gives the team a live read on what's actually being said about the brand and the wider category across video and social, the everyday conversations, reactions, and behaviors that shape how shoppers talk about the products.

That same read surfaces how shoppers are actually using the products day to day, including use cases the team had no plan for. When dig surfaced that shoppers were using a grease-cutting cleaner from the category to clean their air fryers, that wasn't a risk, it was a new product application the team could lean into.

3. Mapping the unbranded-search landscape, before and after each campaign

dig ran a dedicated unbranded-searches report for the team, scoring the brand against competing brands on the strategic queries shoppers type into TikTok and Instagram when they're looking to buy in the category, showing where the brand was already winning visibility, where competitors were ahead, and where the brand was missing from the conversation entirely.

That mapping also showed which creators were actually moving discovery, not just engagement. On the brand's last partnership campaign, one partner surfaced 34 times across all monitored strategic searches, more than any other creator. A second ranked first in the feed on five separate occasions, and a third drove 2% of the brand's total Discovery score on their own.

dig runs the same report again after each campaign launches, scoring the brand on the same framework so the team can see whether the new content actually moved share of voice on the terms that mattered, the back half of a closed loop the team can run launch over launch.

4. Stronger influencer briefs, informed by dig

The team used past dig findings to brief its next round of influencer campaigns, sharpening the briefs with what dig had already shown was working in the category, and then using dig to see how those campaigns moved the brand on search.

The results: sharper briefs, top search rankings, and misinformation caught early

The work showed up in three places.

First, the briefs. The next campaign launched off briefs written from dig's findings, so the team briefed influencers against what audiences were already reacting to, not what they assumed would land.

Second, the rankings. The unbranded search report showed the company ranked first on nearly every term the team had set out to win:

• #1 of 20 tracked brands on Discovery searches
• #1 of 20 tracked brands on Usage searches
• #2 of 20 tracked brands on Value searches

Third, brand reputation. Ingredient-led misinformation got caught while the claims were still mild, and takedowns closed within 24 hours of a flag, before a small concern could grow into a serious accusation. Brand, comms, legal, and social now read the same data in real time, and were aligned on what they were seeing. 

Conclusion: From monitoring the feed to understanding the narratives shaping it

At this scale, the hard part was never volume. It was seeing what legacy tools missed.

With dig, the brand can catch misinformation before it escalates, spot new ways shoppers are using the products, map where shoppers are actually searching the category, and brief influencers from what the data already shows.

One engine, one live view of the category, and a team that's no longer reading different versions of the same feed.