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Bots and marketing: The good, bad, and ugly

Author: Laura Ballam


Do you ever feel like your marketing budget is being siphoned away by sneaky bots? You may be right! Bots serve a purpose and can be used for good or bad, but either way, detecting them is essential for maintaining the integrity and security of online environments.  Bots are almost everywhere, and you don’t want to block all of them (imagine never showing up in search results!). Good bots can used for content scraping, search engine crawlers, and checking voucher codes, but some marketing-related bots can be bad — used to overwhelm entertainment sites with bulk ticket purchases, blast hundreds of fake reviews, execute form spam, and commit pay-per-click (PPC) fraud.

Malicious bots wreak havoc on your marketing campaigns, cheating you out of money and opportunity through their deceptive tactics. It’s important for marketers to understand how these bad bots impact marketing and analytics so you can combat them effectively.

The impact of bots in marketing

Bots can cost marketing departments a lot of money through various deceptive tactics, taking advantage of the digital advertising ecosystem and the way marketing campaigns are run. Here are some all-too-common ways malicious bots jeopardize your marketing efforts:

1. Ad/click fraud: The most common “bad” bots in marketing engage in ad fraud by simulating ad views and clicks. They create fake impressions and click on ads, appearing as genuine user engagement. Click fraud bots repeatedly click on PPC ads, exhausting your marketing budget quickly without generating conversions. Some bots are deployed strategically by competitors to purposefully drain the marketing budget of rival companies and reduce the impact of their campaigns. Ad fraud bots trick marketers into believing their ads are performing well when there’s no genuine interest from potential customers. They also cause:

    • Distorted metrics that lead to poor decision-making based on false data
    • Higher investment in ad campaigns that aren’t reaching real audiences
    • Wasted advertising budget
    • Increased CPC because the algorithms see the search terms as popular, and raise prices based on perceived demand

By identifying these malicious ad bots and making decisions based on accurate metrics, you can dramatically increase the ROI of your PPC campaigns.

2. Fake lead generation: Bots fill out lead generation forms with fake information, giving the impression of increased interest and potential customer engagement. However, these leads are worthless — wasting your marketing department's time and resources on leads that won’t convert.

3. Spoofing analytics: Bots can mimic human behavior on websites, leading to inaccurate analytics data. This skews decision-making based on false data, causing you to implement ineffective strategies and waste money on campaigns that appear to perform well but lack real engagement. It’s estimated that 30—47% of all web traffic is bots, so it’s easy to see the impact on your analytics!

4. Fake social media engagement: Bots can create fake social media profiles and generate likes, shares, and comments on posts. This deceptive engagement gives the impression of popularity and reach, leading marketers to invest more in social media strategies that aren’t reaching a genuine audience.

5. Misleading attribution: Bots interfere with attribution models by creating fake touchpoints in the customer journey. This causes marketers to attribute conversions or actions to the wrong channels, and potentially invest in less effective marketing channels and strategies, or even the wrong ones altogether. You need true data to ensure accurate marketing attribution.

Why is bot detection so challenging?

Bot detection refers to the process of identifying and distinguishing between human users and automated computer programs (bots) on digital platforms, websites, or applications. While the definition is simple, the execution of it isn’t.

Bot developers use evolving tactics, continuously refining their techniques to make bots mimic human behavior more effectively. This includes using sophisticated algorithms, machine learning, and AI, which makes it harder to distinguish bots from genuine users. Unfortunately, bots can also adapt their behavior in real time based on environmental factors or responses from the system. This dynamic behavior makes it harder to create static rules for detection. And since not all bots are harmful — many are beneficial, like search engine crawlers — it’s even more of a challenge to defend against some (the bad ones) while allowing the beneficial ones. Distinguishing between good and bad bots is complex because their intentions aren’t always immediately obvious.

Another concern is avoiding false positives (flagging legitimate users as bots) and negatives (failing to detect actual bots). Striking the right balance between accurately detecting bots and avoiding false positives and false negatives is a constant challenge. There are many techniques bots employ to enhance their appearance as “real” and avoid detection. Here are some common ones:

Advanced impersonation: Malicious bots can employ tactics like browser automation, device emulation, IP rotation, and user-agent spoofing to impersonate human-like actions and characteristics, making it difficult to spot them.

Distributed infrastructure: Bot operators often use a distributed infrastructure, involving multiple IP addresses and devices, to avoid detection. This decentralized approach makes it more challenging to identify and block them.

Botnets: Botnets, which are networks of compromised computers controlled by a single entity, further complicate detection efforts. The actions of individual bots within a botnet can vary widely, making them challenging to track and identify.

Anonymization: Bot operators can use various anonymization techniques to hide their identities and location, such as using proxy servers and virtual private networks (VPNs), making it more difficult to trace them.

How to combat bot marketing scams and overcome bot detection challenges

To combat bot scams, marketing departments must leverage advanced bot detection. Anti-bot measures such as CAPTCHAs can help, but since bot technology is ever-evolving, organizations need a more advanced bot detection solution that includes monitoring traffic patterns, biometrics, and user behavior. As with any marketing effort, remember the outcome will only be as good as the input (i.e., data). Most marketing departments are working with bad data, incomplete data, and latent data that prevents them from accurately identifying bots. As a marketer, you need complete data – not just what’s been tagged and coded for a pre-defined use case. If you’re still using web tagging as an input, it’s holding you back. The bots you’re fighting are quick to adapt, so your countermeasures must be even more agile.

Working with reputable advertising platforms and partners can also help reduce the risk of falling victim to bot scams in marketing. Regular monitoring, data analysis, and staying updated on the latest bot tactics are crucial to protecting marketing budgets and ensuring the effectiveness of marketing campaigns.

Behavioral analytics and biometrics are another great way to detect bot activity since it’s harder to “act like a human” over longer periods of time. Some advanced bots can seem legitimate, programmed to browse pages and make a few clicks before heading to their intended target. But this is typically surface-level and a good behavioral analysis platform will be able to differentiate bot activity from a real user’s behavior. For example, a human won’t complete the same steps in the same order with precise timing every time they visit a site — but a bot will. An advanced bot detection solution also keeps a repository of servers and locations that are known to send bots, and provides contextualized data to enhance bot detection. Finally, a robust data platform with bot detection enables you to trigger and activate defined behaviors and experiences when a bot is detected.

When it comes to bots in marketing, the best way to overcome bot detection challenges is to use a combination of methods, such as analyzing user behavior patterns, checking for anomalies, monitoring IP addresses, and leveraging machine learning models and AI to differentiate humans from bots more efficiently. Continuous improvement and updates to bot detection systems are also crucial to stay ahead of ever-evolving bot technologies and tactics. There is no “set it and forget it” solution to bot detection — so make sure you’re choosing an advanced solution that can adapt.


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