Mobile Application

Using Spider AF as a Third-Party Tool to Strengthen Against Ad Fraud!

By visualizing CTIT data and fraud scores previously impossible to surface in-house, Zucks Co. boosted fraud detection operations and uncovered unauthorized carriers across their publisher network.

Industry
Mobile Application
Company Url
Region
Spider AF Product
ROAS improvement
14 days Time to first insight
The Challenge

Budget disappearing with nothing to show for it

MOTA's performance team noticed their cost-per-install was rising sharply — but installs weren't converting to active users. Something was eating their budget.

  • Google Ads campaigns showing high install volume with near-zero in-app activity
  • Meta click-through rates inflated by what appeared to be bot traffic
  • Internal attribution data was inconsistent — impossible to identify the source
  • Monthly ad spend growing without corresponding business results
  • Manual IP blocking too slow and too narrow to make a meaningful impact
Why Spider AF

The only platform built specifically for ad fraud detection

MOTA needed more than a generic analytics tool. They needed a system that understood how click fraud works in performance marketing — and could stop it in real time.

01

Real-time invalid traffic detection

Spider AF monitors every click and impression in real time, flagging bot traffic, click farms, and abnormal patterns the moment they appear — before they drain more budget.

02

Direct Google & Meta integration

Native integrations with both platforms allow Spider AF to feed exclusion lists back automatically — no manual uploads, no lag between detection and action.

03

Transparent fraud reporting

Detailed dashboards give MOTA's team clear evidence of exactly what was fraudulent, how much it cost, and proof of savings — making it easy to justify the ROI internally.

The Approach

From blind spots to full visibility in four steps

01

Connect & audit

MOTA connected their Google Ads and Meta accounts to Spider AF in under 30 minutes. Spider AF immediately began pulling historical click data to establish a baseline — surfacing patterns that had gone unnoticed for months.

02

Identify fraud sources

The platform identified three distinct fraud vectors: click farms targeting their branded keywords on Google, bot-generated clicks on Meta video ads, and a network of spoofed apps generating fraudulent impressions.

03

Deploy exclusion rules

Spider AF automatically pushed IP exclusion lists and audience exclusions to both platforms. Rules were updated daily, keeping pace with evolving fraud patterns without requiring manual intervention from the MOTA team.

04

Monitor & optimise

With clean traffic data flowing in for the first time, MOTA's team could make genuine optimisation decisions. Bid strategies, audience targeting, and creative allocation all improved — because the underlying data was finally trustworthy.

The Results

Campaign performance before & after Spider AF

Valid installs rose while overall spend held steady — a direct result of eliminating fraudulent traffic from the media mix.

Monthly cost-per-install trend (JPY)

Before Spider AF After Spider AF
¥3,000 ¥2,000 ¥1,000 ¥0 Spider AF deployed Jan Feb Mar Apr May Jun
Pre-deployment average: ¥2,840 / install Post-deployment average: ¥940 / install

"We knew something was wrong, but we had no way to prove it. Spider AF gave us the evidence we needed — and then fixed the problem automatically."

Takeshi Yamamoto
Head of Performance Marketing, MOTA
The Outcome

Clean data. Real results. Confidence restored.

Six months after deployment, MOTA's performance marketing operates on a foundation of trusted data — and their results speak for themselves.

With invalid traffic eliminated, MOTA reallocated ¥2.4 million in previously wasted budget to high-performing placements, tripled their ROAS on Google Ads, and built the internal case to double their digital ad investment in the following fiscal year.

Frequently Asked

Questions about Spider AF for performance marketing

Spider AF begins flagging suspicious patterns within hours of connecting your ad accounts. Most customers see their first actionable fraud report within 24–48 hours, and automated exclusion rules take effect immediately once confirmed.

Yes. Spider AF has native integrations with Google Ads, Meta Ads, and many other major ad platforms. Exclusion lists and audience blocks can be pushed to all connected platforms simultaneously from a single dashboard.

Yes — and that's the point. Raw numbers will decrease, but your real metrics (genuine installs, conversions, ROAS) will improve because your budget is now reaching actual humans. Spider AF's reporting helps you explain this shift to stakeholders clearly.

Absolutely. Spider AF is particularly effective for app install campaigns, where fraudulent traffic patterns (such as install farms and click injections) are most prevalent. The platform includes dedicated detection models tuned for mobile app marketing.

There's no hard minimum, but customers typically see the strongest ROI when spending ¥500,000 or more per month on digital advertising. Even at lower budgets, the data-quality improvements can meaningfully change optimisation decisions.

Is click fraud eating your ad budget right now?

Most companies don't know how much they're losing until they measure it. Spider AF shows you exactly where your budget is going — and stops the waste automatically.

✓ No credit card required ✓ Setup in 30 min ✓ Cancel anytime

Using Spider AF as a Third-Party Tool to Strengthen Against Ad Fraud!

By visualizing CTIT data and fraud scores previously impossible to surface in-house, Zucks Co. boosted fraud detection operations and uncovered unauthorized carriers across their publisher network.

Early on, Zucks Co. developed and used their own fraud detection but now use Spider AF as a third-party tool. Today we’ll be talking with Mr. Misawa and Mr. Oshima from Zucks Co.

Purpose

・While strengthening our own measures against ad fraud, we felt the need to look at this from a third-party point of view

Problem

・Reducing man-hours in our own development
・Necessity for third-party perspective on surveillance and info
・Visualizing data

Result

・Data that couldn’t be visualized could be seen with Spider AF
・Using a third-party tool helped boost strengthening operations and detecting unauthorized carriers
・Gained the ability to share detected personalized fraud within the team

How are you going to visualize large measured data

Q. Please tell us about when you felt the need for fraud detection before introducing Spider AF

Mr. Misawa: Up until now we’ve used our own fraud detection at Zucks Affiliate. Now when we try to push forward fraud prevention, we have to think about the importance of reducing our own development man-hours and surveillance/info from a third-party’s point of view which is why we thought about introducing Spider AF.

Then we were thinking about the issue of how to visualize large measured data.


Q. What kind of visualization were you expecting?

Mr. Misawa: Specifically we were thinking about CTIT (Click to install time). We wanted to visualize the time between a click to installation but we ran into some difficulties at our admin screen when we tried to figure out how to graph this out when dealing with such large quantities of data.

Boost fraud detection from a third-party perspective!

Mr. Misawa: We use our own Zucks Affiliate fraud detection + Spider AF as a third-party tool to help strengthen operations. With SpiderAF, I can look at data that couldn’t be visualized at our company. Through this I got new information that I hadn’t noticed until now and began using it in our operations.

For each publisher there is a fraud score listed out; since Spider AF is linked with our company’s admin screen its easy to compare. In addition, because we can explain this from an unbiased point of view we can push fraud decisions when we’re explaining this to publishers.

Be able to share detected personalized fraud within your team


Mr. Misawa: There were times when I felt that something was suspicious and thought it was fraud but had a hard time sharing within my team since it was personalized. Now since I can visualize the data, it’s been much easier to share this within my team!

Q. What are some of the most useful and frequently used features on Spider AF?

Mr. Misawa: I often see the batch saying that my device is old. When I start thinking “Weird. It keeps coming up but nothing happens” I try to find out what device it is and check with our company’s admin screen to confirm. There is a mutual supporting relationship not only with our company, but also with Spider AF.

Fig : Device batch
(Each device has an overseas / old batch so you can make a decision at a glance)

Mr. Oshima: I feel that it is very useful because I can tell by instinct when it comes out in the hour, minute and second in the new report graph where i’ll think “Wow that looks like a bot.” It’s not possible to make a decision based on the first shot if there is no figure, so I use it as one of the general criteria for making a decision.

Fig: CTIT
(The time between clicking on an ad to installation of an application. It’s normal that there would be variation depending on the person but it would be abnormal for someone to concentrate on something for say 10 seconds. It shouldn’t be unreasonable to complete a series of tasks in a couple seconds.)
Fig: Clicks per second
(By looking at the number of clicks per second, you can easily identify a Bot cycle at a glance.)


Q. Please tells us about your future business/service prospects working with Spider AF!

Mr. Misawa: In the future, we hope that automatic systematization will get advanced enough that it will be linked to our admin screens and won’t require manual work. We want to continue strengthening cooperation between our company’s fraud measures × Spider AF in order to wipe out fraud and provide a safe platform to use.

Within the industry, even a company like Zucks Affiliate – whose been tackling fraud measures from the beginning – rates this third-party measurement tool (market) very highly.


Thank you very much