Innovative Steps Startups Are Taking to Reduce App Costs and Detect Fake Accounts
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As apps gain traction through word of mouth or features on websites, blogs and app stores, some of these apps face unique issues. These issues can range from sudden influxes of activity that stress server capacity to fake users that aim to swamp the app with content not relevant for the app’s actual goals. Because of these issues, app maintenance costs can quickly balloon. In fact, social media sites like Instagram and Facebook spend a significant amount of money on account removal already —estimates put worldwide startup spending on fake account removal around $1.3 billion per year.
Whether startups suddenly need to hire new employees to manually audit new users and the content they post or new automated systems have to be developed to reduce the cost of these fake users, startups must quickly come up with clever ways to reduce costs and simultaneously detect new accounts that could be bloating costs. This problem isn’t going away anytime soon, especially as we spend more time on the Internet — which is exactly why we’re taking a look at some of the cleanest, cleverest and most streamlined methods startups have used to reduce the costs of fake accounts on their services.
Clear removal guidelinesIt’s often the case that small companies will have fuzzy guidelines on the content and accounts that should be removed and what should be allowed to stay. But this creates communication bottlenecks where employees must communicate with their managers about each account that doesn’t exactly match those unclear guidelines, wasting time and valuable resources before decisions are made. Surprisingly, one low-tech way that startups have managed to reduce the number of fake accounts is by revamping those removal guidelines and creating clearly delineated removal checklists for the employees who do review inbound accounts.
One company that benefited immensely from clearer removal guidelines is Facebook. From October 2018 to March 2019, Facebook doubled its fake account removals to over 3 billion primarily by updating removal guidelines for employees. While Facebook hasn’t released the numbers on how this has affected server load and cost reductions, storing that many fewer data points likely saves a large sum.
Creating human verificationWhen malicious users want to create hundreds of fake accounts, there’s no way they’ll do it manually. Instead, they’ll try to figure out some way to automate the account creation process, like auto-generating fake names, emails and everything else that goes into a standard sign-up process. And while startups generally want to make the user acquisition process as simple as possible, some entrepreneurs are figuring out that adding in some extra verification steps reduces fake user load by quite a bit.
The average cost for acquiring a new app user for a startup sits at $3.52 — but with an estimated 7 percent of new users as fake, this can create a dilemma for startups. One startup taking an interesting approach to this problem is the dating app Hily (Hey, I Like You). By using real-time image verification — making users snap a photo when they sign up, then comparing it to their existing social media photos — this app verifies the identity of new users to ensure that they are actual people. This significantly reduces the instances of false account creation, since verification steps that require human input are too time-consuming for most malicious actors to bother with.
Startup collaborationLean startups function based on spending as little time developing big projects as possible. This is at odds with the development process necessary to combat fake accounts because machine learning algorithms and manual review of accounts takes too much time. That’s why some app and software startups are approaching the problem by partnering with other startups that specialize in bot and fake account detection.
For example, startups like Digital Shadows and Distil Networks specialize in detecting bot traffic and shutting down malicious campaigns before they even get started through complicated algorithms. That’s an enticing collaboration for an already established startup experiencing bot problems. By partnering with a startup like the two mentioned above, both the established startup and the tech companies can benefit. The bot detection startups would get much-needed publicity and anchor partners, and the established startups would get better protection from bot problems without having to develop their own solutions.
Startups need to brace themselves for fake accounts flooding their systems as a worst-case scenario. The costs associated with these attacks can be quite steep — but so can the development costs associated with protection. However, by applying some lean principles, startups and the entrepreneurs guiding them forward can come up with inventive solutions. The steps listed here, along with new innovative thinking, can help save money and protect app integrity moving forward.