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Ad Fraud in Performance Vs. Brand Campaigns

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Digital Marketing spending is growing at a 9% CAGR globally, and digital media today has become a non-negotiable medium to reach out to consumers/audiences. For a marketer, the 2 obvious choices are to run either performance or brand campaigns to reach, act, convert, and engage with their target audience. While Performance campaigns are directly associated with results achieved, Brand campaigns’ focus is to ensure visibility and recall. Brand campaigns rely largely on viewability (impressions), while performance campaigns focus on down-the-funnel metrics. Performance marketing focuses on CPI, CPV, cost per sale, conversion rate, etc. on the other hand, the share of voice through mentions, sentiments, tags, etc., measures brand campaigns. Marketers and advertisers spend a large portion (>50%) of their digital advertising budget on these two campaigns. Our research suggests that it takes 6 to 8 impressions for someone to build a recall value of your brand. The regular reappearance of the brand ensures higher recognition of the solutions it offers. Viewability, according to IAB is, 50% of the ad’s pixels are visible in the browser window for a continuous 1 second. For larger ads (those greater than 242,000 pixels), 30% of the ad’s pixels are visible in the browser window. The same applies to video ads but for a minimum of two seconds. Ad viewability is the topmost layer of an ad metric. Fraudsters use fake impressions, bot impressions, ad stacking, and pixel stuffing for impression fraud. Meanwhile, performance campaigns work down the funnel and measure clicks, visits, events, and conversions. Clicks are important because they define the website traffic from online advertising. Visits account for the number of people who viewed the URL associated with the ad. Similarly, events could include installs, add-to carts, registrations, signups, conversions, etc. A close look at click-to-visit ratios and a visit-to-conversion ratio will give you the efficacy of your performance campaign. Cybercriminals impact these through click fraud, lead generation fraud, CPA fraud, influencer marketing fraud, cookie stuffing, click farms, and domain spoofing. The impact of ad fraud also influences programmatic, affiliate, and retargeting campaigns. The result of ad fraud is higher ad budgets, lower ROIs, diminished brand safety, fraudulent analytics, and infiltration of cybercriminals in customer data systems and ad servers. Ad Fraud in Brand Campaigns Impressions are the measure of brand recognition through online ad campaigns. Most digital brand advertisements are based on cost-per-mille (CPM), a.k.a., cost per thousand impressions. Total impressions determine the campaign cost in a CPM advert. The impressions also determine the reach of the advertising channel and total ad viewers in a specific channel. Ad fraud in impression-based campaigns happens when a fraudster opens a fake website, joins an ad exchange, loads ads on a fake website uses bots for page loading & impressions, and sells the impression inventory to the ad exchange. The common methods of impression fraud include the following: Pixel Stuffing: Loading a 1×1 pixel ad on a page counts as an ad served but is not visible to the human eye. Ad Stacking: Piling one ad on top of the other and keeping the original ad at the top. The impression counts for all ads, even when the top ad blocks ads below it. Fake Websites: Using bad bots to generate impressions on fake websites created solely to sell inventory that does not have human visitations. Bot Inventory on Genuine Websites: Fraudsters use bots to fulfill the “most required inventory” needs of the advertisers for acquiring credit and financial gain. Auto Impressions: Running in-app ads (even on inactive apps) on mobile devices to auto-generate impressions. Determining ad fraud in impression-based campaigns is challenging because the analytics reveal more data than performance-oriented ads. You only have the option of comparing CTR with impressions. High impressions mean an advertisement has significant exposure. Typically, campaigns with high impressions experience a high click-through rate (CTR). Under the unlikely circumstance that you have low CTR and high impressions, the ad is possibly incurring fraudulent activity in the background. Businesses thinking that programmatic or retargeting can resolve issues about brand campaigns should know that it’s not true. Fraudsters have spoofed domains, penetrated customer data systems, and used bots to act as a target for remarketing lists. So, ad fraud is prevalent in brand campaigns. Furthermore, brands should optimize programmatic campaigns by incorporating inclusion lists consisting of URLs where the ads should be placed. This fear of programmatic ads landing on sites built for ad fraud has become a common affair. Fake websites distort the analytics of brand campaigns. The unexplainable ad impressions can only account for invalid traffic as only 36% of the online traffic is human. Moreover, sometimes programmatic campaigns declare results higher than the population of a location. So, ensuring that ads are delivered to humans is a serious concern. Ad Fraud in Performance Campaigns All marketers and advertisers rely on analytics for creating brand strategies. Infiltration of ad fraud into the data falsifies the results, gives false hopes, increases the marketing budget, and doesn’t reach a large proportion of the human audience. Popular researchers quote that ad fraud would exceed $50 billion by 2023. Moreover, nearly 40% of advertisers think that ad fraud is a significant downside of programmatic ads. For example, fake clicks display that the campaign achieved higher performance than expected, but in reality, engagements with bots will not bring home any business. Ad fraud is still happening even after optimizing the campaign with geolocation, remarketing lists, and pre-bid programmatic placements. Fraudsters use the following methods to target performance campaigns of brands: Click Spamming: Executing clicks on behalf of real users without their consent in the background and claiming credit for obtaining financial gain from advertisers. Click Injection: Using malware in apps to stay alert about “install broadcasts” and obtaining the last-click attribution through click firing before the new app installation. SDK Spoofing: Tricking advertisers to believe that their ad will appear on premium apps, whereas it appears on fraudulent apps through SDK spoofing. Lead Generation Fraud: Filling lead forms using real or fake user information with the assistance of bots. Eliminating Ad Fraud in

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The Rise of Fraud in Retargeting Campaigns

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Brands enhance awareness, conversions, and ROIs through retargeting campaigns. Our research suggests that engagement rates go up by 2X through retargeting ads. Pixel, dynamic, and list-based retargeting are three of the most common methods used by brands to reach their potential buyers (through retargeting campaigns). Companies invest a large portion of their advertising budgets in such ads. Our research suggests that most brands invest 30-40% of their ad budget on approaching prospects through retargeting campaigns. The retargeting campaigns have been an attractive proposition for fraudsters to commit ad fraud. Retargeting fraud happens through bots, click injection, autoloading, install fraud, automatic redirects, inappropriate ads, and crypto miners. Furthermore, optimizing reach through programmatic retargeting ads increases fraud percentages because of the increase in programmatic ad frauds. The Most Commonly Used Methods in Retargeting Fraud ● Fake Impressions and Cookie Bombing Remarketing campaigns often involve view-through conversions. Fraudsters bombard them with fake cookies and impressions. Our research suggests that almost 40-50% of the time, the display ads are not visible to the users. Cybercriminals engage in pixel stacking or hidden iframes to record views on the remarketing cookie. The fraudster takes attribution for purchases on a legitimate website and receives payment for the same. Advertisers often spend more on websites delivering higher conversions on their ads. Fraudsters engage in cookie bombing to maximize conversions by delivering cookies to unique users. In reality, the fraudulent ad impressions are not visible to the users on these websites. These are generated by ads commonly stacked above each other or resized to 1×1 pixels. Fraudsters target unique users for poaching conversion attributions through the cookies—the view-through conversions of fraudulent advertisers skyrocket. Most ad platforms state that view-through conversions are nearly 9-10x higher than click-through conversions. ● Auto-generated Fake Clicks Users who visit a website add products/services to the cart and leave without purchasing are often considered prospects. Brands add their data to the remarketing list using cookies, pixels, lists, etc., and retarget through ads. Now, the user visits another website wherein a fraudster serves invisible ads and clicks automatically to the original publisher’s website. So, the click displays customer visits to the website. Now, the remarketing cookie of the customer displays that the customer viewed an ad, clicked on it, and visited the website. The credit goes to the ad whenever the customer purchases on the website. This practice is referred to as attribution hijacking. Under such an instance, advertisers think that their remarketing platform is performing better than expected and tend to increase their budget. However, in reality, a fraudulent click gets the attribution and even receives the payment for it. ● Hijacking with Remarketing Pixels Prospects visiting another website and not making any clicks may still record a view on the remarketing cookie. Whenever the customer revisits your website, the pixel script detects an ad view and loads an invisible iFrame. The iFrame generates an automatic click and visits after loading the original ad. The remarketing cookie of the legit customer now has a view, click, and visit from the ad. Moreover, the analytics support that the customer came through ad clicks instead of an organic source. The credit for the conversion goes to the ad whenever the customer makes a purchase. Furthermore, the remarketing platform of the analytics would display jacked-up visitors and conversions. However, the reality is that a fraudster hijacked the remarketing pixels and displayed the organic visitors as customers. Takeaway Retargeting campaigns help brands boost awareness, incur higher ROIs, and generate more sales. However, retargeting fraud causes a serious loss of advertiser revenue and should not be taken lightly. Fake impressions, cookie bombing, auto-generated fake clicks, and hijacked remarketing pixels obstruct real analytics, market reach, and conversions. Eliminating retargeting fraud can enrich these data points. However, sophisticated invalid traffic is not commonly detected easily. Ad fraud solution providers can even help to optimize ad spending on walled gardens (Google and Facebook). Driving impactful results through such a solution requires data trust and transparency between service providers and brands. Get in touch to learn more about the rise of fraud in retargeting campaigns.

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What to Look for in Your Affiliate Campaign?

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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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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 advertising 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 ad fraud in programmatic ads 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. Data trust and transparency between advertisers and ad fraud solution providers for optimal outputs is a must. In addition, the solution should navigate through the entire funnel beginning from the impressions and ending with the conversions. The ad fraud pre-requisites can also drastically affect DSP inventories and retargeting campaigns.

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

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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?

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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?

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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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Is Cyber Security Now Synonymous to Brand Safety?

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Lessons from Facebook Malware threats on verified account A recent incident of cybercriminals sending out fake copyright complaint notifications to verified Facebook users has pointed out an extremely neglected, ignored, and underrated aspect of the digital ecosystem- Can a cybersecurity issue become a brand safety issue and vice-versa? In the quoted incident, these criminals are targeting verified accounts of Facebook users (politicians, celebrities, and government officials) and sending notifications in the name of the Facebook security team. The notification states that the (targeted) user’s page is non-compliant with the terms & conditions of Facebook as other users have reported the page. In order to comply with the Facebook terms and services, the owner has to re-verify their account. The notification contains a malicious link that can potentially (not just) harm the device, hijack it, steal personal information such as bank login details, web browsing history, initiate surveillance, and many more issues which are beyond imagination. Let us try to break down each and every aspect in finer detail to scan the impending disaster. To begin with, we’ll first understand the Brand Safety and infringement element. With cybercriminals sending out notifications on the pretext of Facebook’s Security Team, the user may as well trust these notifications since scammers have veiled their identity behind a big brand (jeopardizing Facebook’s Brand Safety). Little do the users and brands realize that the trust and goodwill the brand uphold and maintains with its users will soon be shattered once it is learned that the entire scheme is a fraud. The criminals have intentionally infringed the brand’s name, logo, and reputation to carry out this fraud scheme. Now, these cybercriminals are specifically targeting verified users on Facebook which becomes a great threat to their (targeted user’s) brand safety. Hypothetically, if a government official or a celebrity clicks on the link, what could be the possible scenarios? The malware will hijack the device of the user Steal bank details and commit a financial fraud Unimaginable serious (and potentially dangerous scenarios) with the stolen identity Wipe out their bank account to fund terrorism Breach privacy and initiate surveillance, a national security threat These scenarios are not based on the movie “Eagle Eye” but real threats which await the nation at large. Putting numbers in perspective 760 million Smartphone users Around 630 million active internet users 448 million active social media users These numbers represent a massive playground for fraudsters to infiltrate the digital ecosystem and commit fraud. Phishing, malware, domain spoofing, brand impersonation, SMS fraud, fake web pages, are all some common types of threats which are becoming a cause to worry. The threats today are much larger than just financial loss. India alone witnessed 1.16 million cases of cyber-security issues in 2020 and there’s been no dearth of more cases. Remember Paytm’s KYC fraud? KBC WhatsApp Fraud, fake Flipkart’s big billion sale page issues? Every brand, every consumer needs brand safety solutions, from bigger brands to personal brands, from the president of the nation to a commoner in the state, this wave of brand safety and cyber security issues affects all the people in the ecosystem. Accelerated digital adoption, Covid19, ever-evolving technology, they’re all contributing to the expanding digital threat landscape. Brands are thriving on digital to reach their customers and fraudsters are simply following places where money flows- digital ecosystem. It’s about time where brands, consumers, cyber security experts and economies together understand the broader context of brand safety and address the impending disaster before its too late.

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