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Ad Fraud Attacks on Gaming Apps

Leave a Comment / Ad Traffic Validation, Blog /

Market research suggests that mobile gaming apps will witness a CAGR of 12.3% between 2021 and 2026. The increment is attributed to the boost in WFH conditions, increased smartphone users, and tech adoption over the last couple of years. The penetration of mobile games through social apps like Facebook has also contributed to the incremental rise in the installation of gaming apps. The continents with the highest gaming app adoption include North America, Europe, and the Asia Pacific. A NASSCOM study estimated that the potential of the Indian mobile gaming market would reach 628 million users in 2020. The key competitors in the mobile gaming market include Tencent Holdings Limited, Zynga, Activision Blizzard Inc, and many others. The rise in the adoption of gaming apps has increased mobile ad fraud. Our research reveals that less than 36% of the online traffic includes humans, whereas 64% includes good and bad bots. Mobile ad fraud is a technique used by fraudsters to target mobile advertising (bypassing mobile marketing funnels) for obtaining financial gain. Our research states that fraud in apps can range between 18% and 36% depending upon the type. These include click spamming, SDK spoofing, incent fraud, IP fraud, geo fraud, etc. However, these don’t account for the 45% of retargeting ad fraud in the industry. Fraudsters infiltrative apps to steal paid/organic user credits, trigger in-app events, display false event information, etc. Moreover, ad fraud in mobile apps also hampers brand safety. Elimination of mobile ad fraud is important for avoiding misattribution, losing the advertising budget to fraudsters, and building strategies around real-time analytics. 3 Types of Fraud in Gaming Apps ●     CPA Fraud in App CPA models believe that quality users take action after installing the app. Advertisers use factors like tutorial completed, level reached, and in-app purchases to determine users’ lifetime value (LTV). The CPA model was adopted to diminish the install fraud rates. Unfortunately, fraudsters created SIVT or sophisticated invalid traffic to bypass fraud detection and crush in-app economies. Advertisers also generate revenue through in-app marketplaces and purchases. Freemium businesses also make revenue through in-app advertising. Advertisers make revenue based on cost-per-sale models created after analyzing premium users. Fraudsters use ad frauds to obtain the cost per action through in-app ads as the fixed percentages or rates provide high payouts. CPA campaigns are believed to be robust as they deter ad fraud because the general notion assumes that it is difficult to replicate in-app user behavior as compared to faking an install. Unfortunately, the bad actors are far smarter than that. The fraudsters not only use bots to create in-app events, but they go a step further and steal credentials such as account info, and credit details of real users (both organic and paid) who are likely to be far more active within the app. ●     Attribution Hijacking Publishers commonly work with attribution models for tracking events like installs, purchases, link clicks, etc. The fraudster acquires credit for the first/last click before the event, commonly installed in gaming apps. By doing so, fraudsters obtain revenue from advertisers in exchange for the fraud credits. The method affects organic and inorganic users equally. Install hijacking is commonly practiced by injecting false referrals or delivering false click reports. Users who click on an install app are redirected to the Play Store, and whenever the user installs the app on the Android device, the other apps are alerted through Standard Android Broadcast. Any malware installed through another app installation is triggered and builds a fake click report with install attribution towards the partner, even though it came from a media partner. Attribution hijacking is commonly witnessed in retargeting campaigns. ●     SDK Hacking/AKA Spoofing Another fraud that happens through existing malware in user devices through app installation is SDK hacking. This bot fraud spoof installs by tricking servers and providing monetary gain to cybercriminals. Brands using open-source technology or poor encryption should know that fraudsters use these loopholes for manipulating or reverse-engineering attribution codes. Besides installs, SDK spoofing can even tamper with clicks and other engagement signals. Identifying SDK spoofs requires tracking unused SDKs, watching out for install frauds, and generating a report for fraud exposure. The best methods to avoid AKA spoofing are avoiding open source SDKs, ensuring secure communication between SDKs and servers, detecting behavioral anomalies, and using a solution for bot detection. One of the largest drawbacks of mobile ad fraud is account takeover (ATO). It has led to implications like cyberbullying of children, loss of money, and data privacy breaches. Moreover, ATO attacks on apps can reward a fraudster instead of a professional with tournament entries and awards. ATO attacks also cause brand infringements, tarnish a brand’s reputation, and flush goodies out of inactive accounts. Conclusion The presence of ad fraud in the mobile advertising domain is causing serious grievances for advertisers and marketers. Brands should eliminate ad fraud by building marketing strategies with real analytics, saving dollars on spending, and acquiring real conversions. Combating ad fraud and ensuring brand safety requires a 360-degree solution with specialists available around the clock. Moreover, the ad fraud solution should have AI and ML capabilities to detect SIVT, analyze anomalies, and scan the web for brand infringement. By incorporating such a technology, brands can ensure a safer digital ecosystem, ad servers, and systems for managing consumer data. Get in touch to learn more about the Ad fraud attacks.

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affiliate-campaign

What to Look for in Your Affiliate Campaign?

Leave a Comment / Ad Traffic Validation, Blog /

Affiliate marketing means hiring people to promote a brand/product/service to boost sales/leads/installs. Affiliates use websites, videos, and social media for advertising/marketing the product or service. Affiliate marketing drives performance. Affiliate marketing drives conversion/ sales/ revenue for a brand. It is one of the most effective tools for establishing a brand. Affiliate marketers combine multiple efforts to achieve the brand’s goals throughout the funnel. They help in attracting new demographics and help establish the market share of the brand by promoting the brand, positioning it ideally, and tapping the untapped regions. Unfortunately, advertisers/brands often become victims of affiliate fraud, i.e., generating fraudulent results in exchange for collecting financial payouts. In 2017, Kevin Frisch, Head of Performance Marketing and CRM, revealed that Uber diminished $100 million of $150 million yearly affiliate marketing spends. The paid promotion was for the installation of the rider app. The brand discovered no change in campaign performance after decreasing the advertising budget. Moreover, the brand detected that paid channel conversions were occurring through organic sources. In 2018, an IAB report stated that one-third of companies spend more than ten percent of their marketing budget on affiliate marketing. The study even revealed that 11% spent more than $100k monthly, whereas 28% invested $25k monthly. 4 Impacts of Affiliate Marketing Scams ● Loss of Trust The infiltration of fraud in affiliate marketing removes advertisers’ faith in running campaigns through it. Affiliate fraud also hampers the market reputation of legitimate publishers and drives away potential revenue from them. It impacts the company’s goodwill and trust negatively. The biggest fear of any business is to lose customers and frauds like this get you closer to this harsh reality. ● Lower ROIs One of the biggest drawbacks is the exponential increase in conversion costs. Bot-simulated clicks appear human, to begin with, and therefore advertisers pay for it. It is when they start engaging with it, that they realize the fake interaction and by then they have already bled their ad budgets. Affiliate fraud jeopardizes the potential ROI of a campaign. Brands receive fake leads/clicks/events that are meaningless. Third-party cookie dropping to simulate a click even though a user hasn’t engaged is a gross waste of advertising dollars. A closer look at down-the-funnel shows ridiculous ratios of click-to-visit and visit conversion ratios. Ultimately, a performance campaign that does not perform is not welcome by brands. ● Misguided Marketing Strategies Inaccurate numbers for running analytics are the foundation of the biggest strategic blunders that a brand can commit. Brands determine their ad campaign strategies based on the clicks, leads, and conversions. Unfortunately, affiliate fraud data adds fake data into the analysis. Because of the polluted data, it becomes challenging for marketers to determine the correct direction/approach for their upcoming campaigns which results in a potential loss of revenue for the brand. ● Devalues Marketing Efforts A recent study mentioned that two-thirds of the traffic online is contributed by good and bad bots and the balance is humans. In a situation like this falling prey to ad fraud can be the last nail in the coffin. Not having access to well-deserved data sets can choke the marketing funnel and create complete chaos within the organization. Exposure to polluted data from a sales/marketing standpoint can fundamentally jeopardize the business as a whole. 3 Methods to Identify Affiliate Fraud ● Unexpected Campaign Results On average, an install or click conversion rate through advertising campaigns doesn’t exceed 10%. So, if you witness a sudden spike of more than 40%, it is most likely fraudulent. Marketers and advertisers should analyze the affiliate source, compare the affiliate with other sources, etc. A proactive approach would include vetting the marketing partner before signing an agreement. Usually, an abnormally high CTR, very short user session (50% lower than average), high bounce rates, and visitors without cookie files are the most common parameters to understand fraud ● Unresponsive Leads As a marketer, it is very important to completely understand a user’s journey. This essentially helps to map the various stages leading up to conversion. This is the key to differentiating between a legitimate and an illegal engagement. Sudden spikes in traffic can be a very short-lived joy as the chances of fake leads being generated are considerably high. Machines don’t pay for things so taking up bot leads and hitting the wall is a marketer’s worst nightmare. ● Spike in Consumers Complaints Contacting forged data leads can often spike consumer complaints, as the person never connected with the brand or its advertisements. The consumer feels that their privacy is violated and they never convert. Moreover, brands can even witness unusual chargebacks or refunds. Many fraudsters make fake sales using stolen credit cards. So, the merchant might witness a high return volume or chargeback after paying a commission to the cybercriminal. Brands should investigate the cause of such complaints and unearth the fraudulent activity. 4 Types of Affiliate Fraud ● Cookie Stuffing Fraudsters use cookie stuffing for lead misattribution and to obtain financial gain from advertisers. A cookie is a tracking tool used for analyzing consumer journeys on a website. Cybercriminals drop third-party cookies on the visitor’s web browser without consent. These redirect the visitors to the brand’s website whenever they view the brand’s advertisement on a partner site. By doing so, fraud affiliates acquire attribution for click/view on the ads. Hence, cybercriminals hamper the campaigns of legitimate affiliates. ● URL Hijacking A common ad fraud practice associated with cookie stuffing is URL hijacking. Cybercriminals create URLs similar to the brand’s product/service pages with typos. A user redirects to the original website page after clicking these duplicate URLs. The Search Engine Result Pages (SERPs) also display the fraudulent results with the typo URL. This activity allows fraudsters to obtain credit for a visit to the brand’s website by stuffing cookies through the fraudulent webpage on the visitor’s browser. ● Transaction Fraud Transaction fraud involves using information from a stolen credit card to purchase a brand’s products/services. It causes credit card chargebacks, steals ad revenue, and

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How Capturing Dynamics of Discounting on eCom Platforms help Win the Market?

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Discount, sale, promotion, lightning deal, offer, etc., are some of the few words the ecommerce consumers often encounter, and it certainly catches their attention. Discounts and coupons drive 41% of purchase decisions of online users. Although the primary reasons for giving discounts across ecommerce platforms are boosting sales and acquiring a larger customer base, it is not limited to it. Brands might offer discounts if they plan to launch a new or improved product version, clear stocks, nearby expiry date, selling season, occasion, increased subscription, etc. For example, at times, brands might offer products as add-ons, freebies, or gifts as part of their promotion or discounting strategy. Doing so also makes new and existing users on ecommerce platforms about the brand’s products and their existence. Why Should Brands Analyze Discounts Across eCommerce Marketplaces? “It is imperative for brands to understand their competitor’s discounting strategy.” – Ankush Arora, Vertical Head, mScanIt. Regularly analyzing discounts through competitive intelligence solutions like mScanIt aligns your brand with the ongoing market trends. Knowing the discounting trends of the competition has often proven advantageous for brands as it signifies the maximum and minimum discount percentage competitions might offer for their product listings. Comparing your brand’s discount percentage with the competition during festive seasons or occasions could help redefine the marketing and advertising strategies for acquiring a bigger customer base and revenue. For example, after analyzing discount percentages and deep-diving into the analytics, your brand could discover options for product bundling while keeping the brand reputation intact. Similarly, your brand could promote SKU-based discounts if the analytics reveal similar offerings by the competition, as it states that the consumers are engaging with such promotions. Moreover, captivating discounts regularly reveal if a competitor has launched a variant-based discount and states the exact moment of the price war on the ecommerce platform. Using e-commerce competitive analysis helps brands to detect specific variants with high discounts. Here is an example: In this example, Fire-Boltt was already advertising its 1.69″ model, and Noise began selling a similar screen size model at a higher discount, causing a price war on Amazon. 3 Reasons for Analyzing Discounts Through mScanIt Identifying Discount Trends The advertising analytics of an ecommerce platform could give you a glance at the discount campaigns’ performance if you used an advertisement. However, evaluating performance only by lowering the product listing price can become challenging to monitor regularly and doesn’t define the ongoing promotional trends used by the competition. mScanIt, a.k.a., eCom Competitive Analytics, resolves these issues through discounting analysis, which explores competition practices across ecommerce stores. It even compares the percentage of discounts offered for product listings using filters like category, platform, variant, SKU, etc., which gives a clearer understanding of the campaigns. Monitoring the Competition Optimizing sales through discounts requires carefully understanding the industry and platform-based strategies. Traditional or common discounting methods might not provide the desired output to your brand. During seasonal or occasional sales, knowing the sold units, discount percentages, most sold variants, discount range share, etc., of the competition can help your brand boost sales/revenue. Similarly, knowing the discount percentage of the competition on a daily, weekly, and monthly basis while comparing it with your product listings can offer more insights and help in finding new strategies for growth. Mapping the competition helps to discover the highest/lowest discount across platforms of the competition, find discount margins, and create new revenue/sale/pricing opportunities. Additionally, regularly monitoring the competition discount percentage would help your brand know the most favored or preferred discount type, such as sequential, one-time, subscription, etc. So, even without the sales figure of the competition, you can still acquire a larger share of the market revenue and reach a wider audience net through close monitoring using mScanIt. Location-Wise Mapping Discounts might vary based on zone, city, or pin code, as the demand and supply of products vary across locations. Your brand and competition understand this fact and discount mapping of the competition based on this criteria offers multiple advantages. Primarily, it enables brands to manage their discounting strategies based on regions, channel advertising budgets, manage product prices based on data, etc. For instance, in pin code 122017, Brand A offers a discount of 10%, and Brand B, C, and D give 12, 15, and 17 percent off for similar product listings. Under this instance, Brand A would likely create discounting strategies that match the competition a bit closer. Additionally, knowing the change in percentage of the discount every month would help brands decide the percentage figure while keeping other factors in mind. Alternatively, Brand A could bundle the product to offer a higher discount percentage and clear out of stock. Conclusion Discount analysis helps brands make marketing/advertising decisions, get an edge over the competition, acquire a higher customer base, and make more sales/revenue. Moreover, monitoring the competition is essential to learn about the ongoing trends, which can get incorporated into the business. eCom Competitive Analytics helps brands achieve discount analysis at a brand, SKU, variant, sub-brand, and sub-category level across eCom platforms and using multiple filters. Moreover, the solution offers complete discount analysis on a single dashboard with deep-diving results. For more information on the advantages of mScanIt, connect with us through email or leave us a comment.

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reseller-fraud

Reseller Fraud Is Costing You More Than Just Your Ad Budget – Know Why?

Leave a Comment / Blog, Ad Traffic Validation /

The pandemic brought a massive shift in the behavior of a shopper. When the entire world was inside their homes, eCommerce brands became the talk of the town. Every retailer wants to be seen online to reach their desired target audience. During this time, the trend of resellers also took a high pace. A reseller is a person who buys the product in bulk from a wholesaler or retailer and sells it to their own audience usually at a profit. Reselling became a popular option for people for its seamless process and no upfront investment requirement. However, seeing an industry growing the fraudsters also don’t take a setback. Reseller Fraud in The Rise There have been some cases where the resellers are found to be using the discounts the wholesalers provide for the customers on online purchases. The reseller avails these D2C discounts which an end customer is only eligible to make bulk purchases online, instead of buying from the wholesaler. This eventually ruptures the retail chain of the advertiser. The reseller abuses these discounts by employing bots and buys the product from the retailer in bulk at a lower price. Later they resell them at a higher margin to gain benefits and increase their revenue. Real Case: One of the Leading FMCG Brands The brand launched a program for one of its most demanding products. They planned to offer a discount of 15% off to their users. As the limit was to order only one quantity per order, it was detected that there were multiple orders of the same product in a day We further analyzed and detected: The same users were fraudulently making repeat purchases or purchasing bulk orders using the discounts available for retail consumers. Coupons and vouchers were manipulated and abused using scamming practices. Offers usually only valid for first-time users were used repeatedly by multiple email ids/phone numbers. What Brand Lose Due to Reseller Fraud? Friction in Genuine Users: Due to bulk purchases by resellers, the genuine users see a stock out and lose interest in buying from the brand. And when they see the same product available with a third party selling at an inflated price, it directly impacts the consumer’s loyalty to the brand. The users think that the brand doesn’t care about the consumer’s needs and safety which further impacts the brand’s image. Skewed Analytics: Not just the brand image, but the entire growth of the brand is impacted due to reseller fraud. Due to vigorous reseller activities, the brand doesn’t understand the real demand for the product in the market and how much to invest for scaling. This results in a discrepancy in the supply chain of the retailer. mFilterIt’s Way to Combat Reseller Fraud Resellers commit this fraud as organic users and, in some cases, the orders are placed by traffic coming from paid sources/publishers. As a result, the publisher enjoys the payout on the order placed and the resellers take advantage of high discounts on their purchases and an additional commission from the publisher. At mFilterIt, we use the capabilities of AI, ML, and data science to detect these discrepancies in the order placed. We do a deep check on the traffic sources based on information like: Customer/Delivery addresses Customer mobile number Coupon code used Ordered product information – Popular/Unpopular items Further, with our CRM API, we marked the order statuses as Clean or Fraud in the CRM itself. This eventually gives clarity to the retailer to deliver only clean orders placed by genuine users. Stop The Reseller Abuse Reseller abuse not only costs the marketer its revenue, but it also punishes the loyal consumers. Due to resellers placing bulk orders, the prices inflate, and the consumers have to pay more for the same product. Therefore, taking strict action against the reseller must be a priority for the retailers. To ensure that the consumers and brand image are not impacted due to reseller abuse, the retailers must partner with a holistic ad traffic validation solution. The traffic verification suite must be capable of not only weeding out invalid traffic but also reducing the friction amongst consumers and improving the overall customer experience. Validate and protect your brand from reseller abuse with our advanced solutions. Contact our experts today at [email protected] or +91-981 0310 660.

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programmatic-advertising

Ad Fraud Detection: Impact of Fraud in Programmatic Advertising

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Programmatic advertisements are goldmines for cybercriminal syndicates. Statista report estimated that programmatic ad spending would exceed $147 billion in 2020. It also stated that it accounted for 69.2% of the worldwide display ad spend in 2019. Moreover, the estimated cost per lead through programmatic display ads is nearly $40. Our research reveals that $1 out of $4 of advertising spends goes to fraudsters. Cybercriminals create hoax websites, spoof domains, manipulate IPs & geolocations, etc., to obtain financial gain and drain advertising budgets. Such activities also hamper brand safety drastically. Programmatic ad frauds commonly occur through bot impressions, wherein fraudsters use bots, an invalid traffic source for displaying the desired results. Moreover, fraudulent clicks account for more than 60% of the installs and must be rejected. Instead, the ad network and advertisers account for it as postbacks. Post-bid programmatic ad solutions work on detecting and eliminating bad bots and invalid traffic after receiving the first campaign results. Unfortunately, advertisers drain their advertising spending for their first campaign through this method. Moreover, new bots and IVTs may still hamper the upcoming campaigns. On the other hand, pre-bid programmatic ads require placing bids in 200 milliseconds, and the highest bid wins the placements. Studies suggest that bid requests can implode up to 200,000 per second. You might engage in programmatic direct to receive the desired ad results and placements. However, publishers often charge higher than bid costs for ad placements because they provide premium traffic. So, it depletes the programmatic ad budget faster than expected and may not always prove as a useful solution. Ad fraud in programmatic ad leads to grave implications while advertisers are busy buying or bidding on ad placements. Programmatic Ad Frauds: Grave Implications ● Screws Analytics and Ad Spends Cybercriminals use malware, bots, and other methods to mimic the behavior of humans on ads. The two most common techniques used by them are CPM and CPC frauds. CPM fraud involves boosting false impressions for enhancing advertisement costs. Fraudsters use bots to implode impressions (that lie top of the funnel). Ad slots refresh with recurring webpage reloads. Alternatively, they use data centers for targeting unseen iframes with stuffed ads. Cybercriminals even conduct geolocation scams by disguising data center traffic using residential proxies. Another common practice by fraudsters is device fraud. They impersonate iOS devices for showcasing premium lead inventory to advertisers. On the other hand, CPC fraud involves delivering false clicks on click-based ads. Click spamming and click injection are the common methods fraudsters use for CPC campaigns. The high-performance CTR is a result of malware. Differentiating fraudulent and real clicks becomes challenging because the devices used for delivering them are real. The result is screwed analytics and advertising spending. ● Retargets Bots (Through Ads) Most brands detect general invalid traffic (GIVT) on campaigns; however, they may still suffer from the impact of SIVT. Bots mimic the human-like behavior on websites by browsing, adding products to the cart, and exiting the website. Advertisers consider such bots in their premium lead inventories and add them into the retargeting campaigns through cookie-generated or list-based data. The bots click or view display/video ads until the advertiser finishes their placement inventory. By doing so, fraudsters sell bots as premium inventory to the advertiser for financial gain. Studies have shown that nearly 25% of online traffic is human only. So, most visitors to websites are potentially bots. Therefore, brands currently waste advertising spending by retargeting bots through programmatic ads. Ad fraud on programmatic ads cultivated for retargeting also raises the cost of programmatic direct and pre/post-bid placements charges. Moreover, retargeting campaigns account for 10 to 45% of the digital advertising budget for most brands. Therefore, eliminating programmatic ad fraud for retargeting can potentially save millions of dollars. ● Jeopardizes DSP Inventory The rise in programmatic ads has increased the number of fraudulent publishers. Moreover, Marketer predicted that 83% of the displays ads would be programmatic in 2017. Bad and incompatible inventories become a part of the Demand Side Platforms (DSPs). Businesses are still trying to reach the relevant audiences, even with compromised data. Advertisers use DSP data for programmatic campaigns, and ad frauds account for a major portion of them. In 2017, Chase diminished its programmatic reach from 400,000 to 5,000 websites (99%) to understand business outcomes. The brand experienced no change in results. However, we can identify that the invalid domains resulted from fraudulent activities. In 2016, P&G diminished ad spending by $200 million and had no sales implications. The action was based on brand safety, ad fraud, and digital ad clutter concerns by P&G’s Chief Brand Officer – Marc Pritchard. He realized that similar audiences received the brand ads multiple times and attention on Facebook ads was almost negligible (1.7 seconds). The brand optimized brand safety concerns raised by placements on objectionable content on YouTube. Ad fraud elimination helped the brand to obtain the correct measurement of ad placements. So, advertisers can create premium inventories and optimize their DSP data by detecting and eliminating ad fraud through SIVT and GIVT. Takeaway Ad fraud is plaguing the pre- and post-bid programmatic campaigns. Does this mean that advertisers should stop doing programmatic ads? Definitely not! The fight against GIVT is already ongoing by Google. However, brands need to identify and eliminate sophisticated invalid traffic (SIVT) to avoid depletion of the ad spending and improve their campaign analytics. Our programmatic ad fraud prevention tool mFilterIt, Valid8 effectively prevents fraud across the funnel and programmatic platforms include pre-bid and post-bid validation making your advertising effort more productive and boost ROI on programmatic advertising campaigns. This builds trust and transparency, and efficient ad fraud detection provides optimal outputs is a must. It eases navigate through the entire funnel beginning from the impressions and ending with the conversions. It can also drastically affect DSP inventories and retargeting campaigns to enhance performance.  

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search-engine

Why Should Brands Analyze the Share-of-Voice of Sponsored Listings on Search Engines?

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Paid searches on Google, Bing, Microsoft, etc., often reveal news articles, brand websites, eCommerce platforms, product listings, competitor websites, blogs, videos, etc. Higher visibility on search engines means higher brand awareness and could certainly become a contributing factor in the click-through rate of the product. In fact, 40% of global users discover eCommerce product pages through search engines. Sponsored listings on search engines are basically of two forms, namely, text and listings. Share-of-Voice of sponsored listings measures your brand’s visibility versus the competition, and content mediums show the highest results under search analytics. Measuring the Share-of-Voice is important for other reasons too. For example, paid searches or sponsored listings can show lower visibility of your brand on keywords specifically related to your brand name. Search engines like Google won’t flag your competitors for such results, as bidding on competitor keywords is not prohibited. Unfortunately, it will likely impact your paid search campaigns. Monitoring the Share-of-Voice on search engines also gives insights into many probabilities, which we will discuss ahead. How Can Brands Effectively Measure the SOV of Sponsored Listings on Search Engines? Brand Versus Competitor Share Determining the share of your listings on paid searches versus the competition helps to know the leaders on different keywords. Moreover, the overall SOS of the brand versus the competition predicts the brands with the highest visibility through sponsored listings. Reviewing the share of sponsored listings also enables brands to find the keywords with the highest visibility of their product or brand listings. By analyzing the paid searches and measuring their share with the competition, you can also find the most promising content sources, such as blogs, news articles, your brand/competitor website, eCommerce platforms with the highest visibility of your products, etc. Measuring your brand’s share versus the competition on sponsored listings can also reveal another interesting fact. The objective of bidding on keywords is enhancing brand awareness or visibility, increasing traffic on your brand’s website, and boosting sales/revenue through your URLs. The efforts get drained when a competitor achieves higher SOV on your brand specific keywords. It also increases the bidding cost and finishes the advertising budget faster than your expectation. Monitoring the SOV of sponsored listings on search engines using mScanIt, powered by mFilterIt deciphers content result frequencies, which search engine analytics don’t provide.   eCommerce Marketplace Results Analyzing the listings that redirect to eCommerce marketplaces recurringly on your brand’s paid keywords helps to find the best online shopping store for optimizing your listings. Moreover, it also reveals the listings with the highest share of paid searches. Increasing the share of eCommerce platforms or websites constantly on paid searches improves the probability of click-through rate, add-to-cart actions, and conversions/sales. Using eCommerce Competitive Analytics, a.k.a, mScanIt, your e-marketers can even find the share of eCommerce listings on paid searches across devices. Diving even deeper into the forms of ads, namely, text and image, on search engines would help your online marketers to create new advertising strategies and find new avenues for enhancing the visibility of eCommerce platforms. Imagine analyzing the share-of-voice of paid searches at a website, brand, search engine, platform, duration, and other levels. mScanIt detects these insights, so you get a clear picture of your brand’s awareness and the forms of content increasing their visibility. Pro Tip: “Google Advertisers should be aware of the power of keywords for their brand across different content platforms and also discover the competition view of this. We can help brands in tapping content medium beyond their own website.” Conclusion Analyzing the search engines to find the share of voice of your brand versus the competition is vital for knowing the brand’s awareness, visibility, and forms of content, driving click-through rates towards the content sources. Knowing your brand’s presence against the competition helps to find the impact of your Search Engine Optimization (SEO) and advertising efforts across eCommerce platforms and websites. Schedule a demo with us to learn the other impacts of measuring the share of voice for your brand and the tactics involved in effectively measuring the SOV.

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Why Should Brands Pay Attention to Perfect Page Analysis or Content Benchmarking?

Leave a Comment / Blog, Digital Shelf Analytics /

A Perfect Page Analysis is a complete overview of the brand’s product listing across ecommerce platforms like Amazon, BigBasket, Flipkart, etc. Generally, it evaluates the content aspects such as usage of top-performing category keywords, title, bullet points, product description, content placement, presence of product images/videos, SEO friendliness of title, rating, and review count. The character and word limitations might alter across platforms; however, the approach might remain almost similar. Consumers often look at the product page content before making their final buying decision. In fact, 22% of online shoppers make a transaction because of accurate and informative product descriptions. On the other hand, product positioning measures the average ranking of a product listing based on keywords on eCom platforms. The position of the listing matters because most people tend to choose from the top visible listings or the products listed on the top three pages. According to Statista, 40, 38, and 35 percent of consumers find products through search engines, Amazon, and other marketplaces, respectively. Certainly, higher visibility would boost the consumer’s interest in the product listing. However, most brands hardly pay attention to page analysis and positioning, which diminishes their opportunity of grabbing the potential customer base and thus the revenue/sale. Importance of a Perfect Product Page and Positioning A perfect page analysis curates a higher score if it has a specific number of reviews, SEO content, accurate product description, etc. The question is ‘Why?’ Why are these aspects important for a perfect page on an ecommerce marketplace? Having a high perfect page summary score means the consumers are likely to trust your product listing and brand, which might increase the transaction/conversion rate. Besides, it becomes a great factor in ensuring long-term relationships with your brands. For example, SEO-friendly keywords make the product more appealing to the customers and would likely enhance discoverability, even on search engines like Google, Bing, etc. Improving product page content aspects could, in turn, enhance ranking if it is a part of the eCom platform’s algorithm. Another positioning aspect is this – Imagine you searched for a ‘smart watch’ on Amazon and repeatedly came across Brand A compared to others. You would probably trust this brand on this factor and visit any of its variants. Price, rating, and delivery would also become important before visiting the page, but you might visit it because it repeatedly occurs and see what it has to offer? – It’s a likely scenario. Right? According to Praveen Dhama, Manager, mScanIt, powered by mFilterIt “Lower the search rank (brand to be among the top listings of the search page on an ecommerce marketplace), higher the chance of a product being noticed and bought by the consumer.“ We can assume that search ranks on ecommerce marketplaces can alter based on the best, most helpful, and most popular results for the consumers. It is just like search engines like Google and Bing. But how can we be sure about it? Maintaining perfect product pages would likely enhance consumer experience by including images, videos, and texts relevant to the product. Moreover, the pages include optimum reviews and ratings, and 35% of online shoppers make purchasing decisions based on other customers’ reviews. Given this fact and the importance of accurate information (stated earlier), it is certain that eCom marketplaces would likely offer results matching our criteria, as mentioned above. How is Perfect Page Analysis or Content Benchmarking Done? Perfect page analysis is done by reviewing the contents of a product listing, such as the title, bullet points, product description, A+ content, etc. Content benchmarking reviews the brand’s score versus the competition in all such aspects. Analyzing these aspects helps brands review their market positioning’s possibility of consumer interactions, especially through organic keywords, best/worst performing sections, etc., and take measures to enhance the underscoring aspects. We analyzed the average search rank for SKUs of one of our ecommerce clients and discovered it was between 5-20. However, after post-reviewing these aspects and comparing them with the competitors through mScanIt, the brand discovered that using specific keywords in the desired number of times within the ecommerce product listings could alter their ranking. Our eCom Competitive Analytics team found the best keywords using share-of-shelf metrics, Google keyword planner, and Amazon PI keywords. After using the keywords, the brand SKUs ranked among the top three products under various keyword searches, which increased their product page visits and conversions/sales. Conclusion Brands that want to optimize their search results on eCommerce platforms and search engines must pay attention to page analysis and positioning of their SKUs. Measuring their results versus the competition would give an accurate picture of their score and ranking and help detect improvement areas. eCom Competitive Analytics offers such results to brands and insights that they can use to resolve their page analysis issues. Moreover, the automated solution detects such results in real-time and offers an option for exporting reports. For more information about the advantages of mScanIt for your brand, connect with us through email, or leave us a message in the comment or contact section. To know more, get in touch with our experts today!

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Good Bots vs Bad Bots: How They Impact Your Ad Campaigns

Leave a Comment / Blog, Ad Traffic Validation /

Hey Siri! What’s the weather like today? And there comes a voice updating you about the weather. But do you know how it happens? It’s the bot army working in the background to provide relevant information to the users. A bot is a software application that automatically performs tasks. However, these bots are being used for both good and bad purposes. Unlike the bad bots which are used for malicious activities, the good bots are an integral part that helps the web to run smoothly. Bad bot traffic now accounts for 65% of all internet traffic. And the number is increasing by 6.2 percent from the previous year. The bad bots imitate the behavior of a legitimate user and make it harder to detect and prevent. They are used by fraudsters to commit ad fraud by misusing and attacking websites, APIs, and mobile apps. Some of the malicious activities performed by the bad bots are web scraping, personal and financial data harvesting, digital ad fraud, spam, transaction fraud, etc. This leads to further wastage of the advertiser’s ad-media budget without getting any relevant traffic. In this blog, we have covered the list of good bots and bad bots. Know how you can detect which type of bot is coming and impacting your ad campaigns. Below are some examples of Good Bots 1. Search Engine Bots Also known as the Crawler Bots, these bots run in the background and move across the internet to crawl websites. These bots help in performing repetitive tasks like indexing the websites for SEO purposes and logging user data. These bots help the internet to run smoothly and help to detect web errors, bugs, and performance issues. Some of the common search engine bots are Google bots and Bing bots. 2. Social Network Bots These bots crawl the URLs shared on social media networks and provide relevant recommendations to users. They also fight spam and create a safe online environment for users. Some of the common social network bots are Facebook crawlers and Pinterest crawlers. 3. Aggregator Bots The aggregator bots are used to crawl the RSS or Atom feeds of websites to create an automatically generated feed as per the user’s interests and preferences. For example, A Facebook mobile app feed fetcher retrieves the website information to view in Facebook’s in-app browser. Other aggregator bot examples are the Android framework bot and Google feed fetcher. 4. Marketing Bots These bots are present in SEO and Content marketing software that crawl websites for organic and paid keywords, backlinks, and amount of traffic. Some of the known marketing bots are SEMrush bot and Ahrefs Bot. 5. Site Monitoring Bots Bots like Uptime Bot, WordPress pingbacks, and the PRTG Network Monitor crawl the websites to detect the overall performance and whether it is working. 6. Voice Engine Bots Bots like Alexa’s crawler and the Apple Bot work similarly to the search engine bots. These bots crawl the websites to provide relevant answers to the questions users ask the voice assistant devices. 7. Copyright Bots These bots are used to search for web content that is potentially been copied. Some of the common cases are copying someone else’s work without giving the right attribution, incorrect use of proprietary content, and illegal uploads. These bots are commonly used in the segment of social media where the original content creation is essential. One of the examples of this is YouTube’s Content ID which is assigned to people who own the copyright. 8. Chatbots Chatbots are programs developed with artificial intelligence (AI) that responds both in voice and text. These programs are designed to replicate natural human speech patterns. These chatbots are designed in such a way that they can answer frequently asked questions, provide customer service, and can also direct prospective customers towards the purchase of a product. Some of the famous chatbots are – AccuWeather, Sephora, Fandango, etc. 9. Entertainment Bots Also known by the names of Art Bots, and Video Game Bots, these types of bots are designed to appear aesthetically pleasing. The video game bots are known to function as characters for us to play as opponents or for practicing and developing skills in a game. Some of the bots are also used for deep learning, making transcripts of speeches, and learning how to speak like a character. For example, TriviaBot, IdleRPG, and PokeMeow Good bots are like working bees that automate the process and help in smoothening the functioning of the web. However, if you spot the good bots in your ad campaigns ensure to report them immediately to your publishers. List of Bad Bots 1. Click Bots This is a type of bot which is programmed to click fraudulently on ads which further manipulates the data of the advertiser. The click bots not only impact the data, but also results in wastage of ad spends because not only the traffic is fake, but it is not even a real human. 2. Imposter Bots These bots pretend to be authentic search engine bots to bypass the security measures. The imposter bots impact the website traffic and cause malicious activities like automated DDoS agents. 3. Scraper Bots Unlike copyright bots, scraper bots are used to steal the content, product catalogs, and even prices on a website for repurposing somewhere else. In this case, the user often remains in the dark and unknown about the whereabouts of their content. 4. Spam Bots This is one of the most common types of bots that disrupt user engagement by distributing unwanted content. These bots hamper the engagement by doing spam comments, spammy ads, unnecessary and unusual website redirects, phishing emails, and initiating negative SEO against competitors. 5. Spy Bots Spy bots are used to extract individual or business data. They crawl a website to steal semi-personal information like email addresses and other sources of communication. This further results in the misuse of users’ data for malicious activities. 6. Zombie Bots Unlike the name, the zombie bots don’t eat humans, but they creep into

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Programmatic Advertising: How Can Your Upcoming Holiday Campaigns Reach Relevant Audiences?

Leave a Comment / Blog, Digital Advertising /

Q4 is often considered the year’s holiday season, with Halloween, Thanksgiving, Black Friday, Cyber Monday, Christmas, and New Year occurring in sequential order. It is time to optimize advertising campaigns with relevant context to achieve the yearly targets and engage with the customers. Our research suggests that almost 70% of users would engage with contextually relevant ads, and more than 40% of the digital users have tried new brands showcasing relevant ad content. But, beware and ensure that the engagement is aligned around the company’s interests and involves end-user preferences. Custom Contextual Targeting Through Programmatic Campaigns Can Optimize Ad Results in Q4 1. Safer Brand Environments and Optimal Placements Last year, Google announced eliminating Chrome’s third-party cookies by 2023. Meanwhile, Firefox and Safari had already restricted their usage. Such changes would make behavioral advertising challenging for brands. However, custom contextual ads would safeguard privacy, as they don’t rely on cookie targeting or personally identifiable information (PII) for targeting users. So, brands work in a safer environment, and placement accuracy would enhance after performing content analysis. 2. Influenced by Contextual Concepts Brands can target high-intent customers with programmatic advertising. Contextual ads are influenced by smarter targeting using active buying behavior, seasonal trends, or other contextual concepts. The behavioral targeting is based on user action before reaching the landing page. It could include clicking links, reading a specific article, product page visit, etc. Customizing content based on a group’s milestones and interests would make the ads more receptive and target-oriented. For example, if you want to target the life stages of a toddler, you should focus on toddler development phrases. 3. Diminish Manual Maintenance Interactive Advertising Bureau (IAB) has already enlisted 425+ categories, and mFilterIt solutions add more value to these with additional behavioral sub-categories to reach a brand-specific audience. Advertisers can leverage seasonal categories to optimize online campaigns and target consumers with the proper context. Moreover, timely updating of relevant categories diminishes manual maintenance. In-market categories can also enhance custom contextual targeting. 4. Better Campaign Results Increased purchases or uplift campaigns with seasonal context can drive higher ROI. Custom contextual campaigns can result in a 45% higher CTR, a 39% reduction in cost per action, and a 50% lower cost per acquisition. Programmatic campaign managers should understand the value of context for obtaining successful marketing results. Brands can analyze audience demographics, social listening engagement and leverage creatives to create customer profiles and recognize buying behavior. Custom context targeting would become much more effective through this method and even offer a relevant user journey. Takeaway Programmatic advertising offers tremendous benefits, such as an alternative for cookie targeting, policy change & trend alignment, higher ROI, reaching the relevant audience, etc. The upcoming Christmas and New Year’s shopping festivities would require correct ad placements for maintaining brand safety. Custom contextual targeting can even help advertisers to perform relevant content analysis and maintain a brand-safe environment while optimizing ad placements to achieve higher than expected deliverables. mFilterIt supports programmatic advertising because it helps in reducing ad fraud and recognizes it as the future of digital marketing in the upcoming years.

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How mFilterIt is a CISO’s Secret Santa?

Leave a Comment / Blog, Digital Advertising /

Not only ad-fraud mFilterIt also serves as the first line of defense for cybersecurity. There are several studies that suggest that more than half (50%) of any website’s (including mobile apps) traffic is BOT. These are both good and bad bots including spiders and crawlers. As a CISO or the security head of an organization, a line of defense is formed using sandpits, trap doors, honey pots, etc. This helps to capture the bad bots or at least deflect them. Here the CISO is adopting a reactive strategy to avoid DDOS, the nightmare of any security head. While this is a great strategy, how about letting as minimal as possible bad BOT even touch the periphery of the digital assets of any organization? Exactly, that’s where mFilterIt, the secret Santa of a CISO arrives! The larger the number of BOTs hitting any website or an app, the higher is the risk of the protection covers becoming ineffective eventually letting the bad BOTs into the systems. Ad fraud is one of the most common reasons for BOTs to hit the digital assets of any organization irrespective of its sector or size. Cyberattacks of other sorts usually happen with large organizations where an attacker would get something. So mostly, small and medium organizations have lower risk thresholds compared to very large and reputed organizations. But every organization needs traffic and for that uses organic as well as inorganic techniques. It engages through a network of channels, some open to audit while others walled gardens where one is unaware of what’s exactly happening. Leveraging the ad-fraud detection suites for both app and web, a CISO can develop the first line of defense which won’t let bad BOTs interface with the digital territories of an organization. Hence, it will reduce the burden on the cyberattack defense systems that it has in place. This front line of bad BOT detection creates a two-layer protection system, much akin to two-factor authentication systems which harden the security for any logins. Results have shown how two-factor authentication systems have made credentials more secure and reduced the risks of account hacking. For cybersecurity, the approach right now is very reactive where a CISO builds a protection wall. It only activates when someone tries to hit the wall. However, with the BOT protection solution used for ad fraud, the inward traffic is validated at the source. So, it proactively doesn’t let most of the BOTs even reach this protection wall. This not only increases security but reduces costs significantly. Imagine the reduction in processing on the webserver side! On this Christmas and New Year’s Eve, mFilterIt reaches out to CISOs as their secret Santa making their job much easier by increasing productivity. Let the CISOs also enjoy new year holidays while mFilterIt’s globally recognized suites, which are validating billions of impressions and clicks every year, help organizations work with sources that only bring in real humans for the engagement. Happy Holidays!

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