Marketing and advertising

Interspace X-lift: Faster, Cost Efficient Ad Fraud Detection With Spider AF

Interspace replaced hours of manual fraud review with automated detection, cutting both detection time and operational costs across their X-lift native ad network.

Industry
Marketing and advertising
Company Url
Region
Spider AF Product
Less Man Hours Used
Less Man Hours Used
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

Interspace X-lift: Faster, Cost Efficient Ad Fraud Detection With Spider AF

Interspace replaced hours of manual fraud review with automated detection, cutting both detection time and operational costs across their X-lift native ad network.

Q. Please tell us about your company’s background and business.

Interspace works with 2 kinds of businesses – the internet advertising business and the web provider business.

”X-lift (cross lift)” is a recommendation widget type of native ad service. This widget creates a safe ad network for related articles/related ads such as “Read Together” or “Recommend Reading” found at the bottom of articles on news sites and info portal sites.

Q. Please tell us about the problems/issues you were having before you introduced Spider AF

Before we introduced Spider AF, we were identifying fraudulent access manually while looking at each individual login.

Because this kind of process took a considerable amount of man-hours, we considered developing our own automatic system for detecting fraud, but since we didn’t have enough time and resources, we had our concerns on what to do.

However, because of Spider AF’s cooperation, our detection times have been shortened and we are now able to operate at low costs.


Q. What kind of expectation did you have for Spider AF prior to introduction?

Prior to the introduction, our expectations were to have a mechanism in place where you could understand what kind of logic is being used to identify fraud. That, and reducing the number of man-hours.

After introducing Spider AF, I think it was great that all members could now easily understand/grasp what kind of web providers were suspected of fraud and for what reason because there was now an admin screen where we could monitor the numbers.


Q. were your man-hours when you actually adopted Spider AF?

There was a part that we were having some difficulty in the initial stage, but once it was introduced, the rest was done automatically, so there was almost no problem in terms of operation.


Q. When you were introducing Spider AF, did you have any difficulties?

Currently, there are none. Since not that much time has passed, I think that from now on it will be necessary to verify what kind of attributes unauthorized access has that get flagged.

When you actually introduced Spider AF


Q. How many people used it?

There are about 2-3 people monitoring the admin screen. It feels like the members of the sales are also using them when needed.


Q. Did Spider AF meet expectations that you had before? (Are there places that could use some improvement or any services you would like to be added?)

I think it is convenient to be able to narrow things down by a variety of conditions. It’s possible to search for each ID by partial matches, etc., and by the types of fraud access. For example, you can narrow things out by specific details like ‘only access from the data center’ – it’s very convenient.


Q. We would like to ask you about the support you received before and after introducing Spider AF. How was Phybbit’s support (engineers/sales)?

Responses were done over a chat program instead of by email so responses were fast and helpful.

Q. Were there places that you wish there was a little bit more support?

After we begin operating, I’m sure there will probably be something but currently, there isn’t anything in particular.

I can talk to you comfortably, so I think that you guys are quick even on that aspect.

Q. If there was one thing you could tell those thinking of introducing Spider AF/companies who are considering Spider AF, what would it be?

I believe that there needs to be normalization in advertising within the entire industry.

Advertisers and agencies who use an advertising platform must have a solid understanding that “Fraudulent ad access should be firmly excluded” and have them cooperate. Otherwise, I think it won’t lead to a sound platform for the entire advertising industry.

In any form, I would like the industry as a whole, regarding fraud protection measures, to move forward.

Q. Finally, could you tell us about your company’s thoughts on ad fraud and how you will tackle the problem of ad fraud in the future?

On the X-lift (cross lift) service, I would like for fraud ad imps and clicks to be made into 0. If there are any other methods for that, I would like to actively adopt them.