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What is Sentiment Analysis?

Leave a Comment / Blog, Digital Shelf Analytics / Expert Opinion

Sentiment analysis answers one vital question – “What is the consumer saying? A study suggests that 57% of consumers buy a product after reading reviews across e-commerce sentiment portals. According to our research positive reviews and ratings uplift sales by at least 18-20%. Alternatively, bad reviews can cause a substantial loss. Consumers share their sentiments about the product/ service through these reviews and ratings. The Sentiment is essentially a consumer’s trust/confidence in a brand that influences their buying behavior. It is easy when a brand has a countable number of reviews as it is easy to read and assimilate the findings. Unfortunately, popular brands have reviews and ratings running into thousands, which is difficult to process. That is exactly where Sentiment Analysis comes into play. Cataloging emotions through customer reviews, a.k.a., sentiment analysis, plays a vital role in this field. Essentially, it is helping brands to gather scattered consumer opinions into positive, negative, and neutral categories. It uses an AI to monitor the textual content of reviews and categorize consumer opinions. Sentiment analysis is a technique through which brands can work on their product offering, understand customer expectations, fine-tune customer service, increase sales, and fine-tune their marketing strategy. In today’s competitive world, delivering an exceptional customer experience accredited with sentiments has become important. A Gartner report states that “customer experience is a primary objective of 89% of companies. Sentiment analysis helps in narrowing down on issues faced by the brand, which helps brand owners make decisions targeted and faster. So, it is the best tool to monitor reputation, performance, and customer experience. 3 Benefits of Sentiment Analysis ● Word Cloud – Sentiment Score & Analysis Reviewing sentiments through the word cloud helps brands understand the areas of improvement and the most appreciated features. It helps the brand identify the exact consumer emotions based on themes like quality, health, delivery, etc. The outcome of an analysis like this helps brands make specific decisions to improve their ratings and reviews under a defined theme. Generic reviews or analyses are inconclusive. Keeping track of Sentiments month-on-month helps the brand owners measure and map their performance vis-à-vis competition. The Sentiment score helps in understanding how favorable or unfavorable a brand is in comparison to the median average in its category. Hence, the score can be used across e-commerce platforms to understand a Brand’s overall customer acceptability. Categorizing reviews by positive, neutral, and negative, helps a brand owner get a real-time understanding of customer responsiveness towards the brand. The monthly aggregated results are a brand’s guide to navigating the e-commerce landscape. ● Valuable Intelligence to Drive Market Strategies Sentiment analysis data helps a brand evaluate its standing against its competition. It helps understand consumer expectations and improve customer engagement. Additionally, it also helps in identifying problems and taking corrective actions in a shorter turnaround time. The valuable intelligence drawn over some time by analyzing the sentiments helps the brand custodian formulate targeted marketing strategies to entice its customer base. This is the foundation for creating a hyper-personalized experience for their customers. ● Future Planning & Overall Competitive Edge Sentiment analysis helps understand the consumer pulse. It is one of the fastest ways of understanding consumer needs and expectations. As a result, it paves the way for new product development and gives them an overall idea about customer reactions. The sentiments can also be used to improve the customer journey in real-time. Faster response time builds a loyal customer base. Gauging and comparing the brands and their competitor’s performance via sentiment analysis gives one an edge over the competition. This can be used as a real-time feedback mechanism to change consumer perception towards being more positive. Takeaway mScanIt powered by mFilterIt has cutting-edge technology that consolidates reviews and ratings across the platform on a single dashboard for easy access and action. It summarizes all positive, negative, and neutral sentiments of the brand along with its competition and equips the brand custodian to decide in real-time. eCom Competitive Analytics helps in deep diving into sentiments/opinions and drawing actionable insights to improve overall performance in the eCommerce landscape. Therefore, it simplifies the complexity of the customer journey by aiding decision-makers with measurable data at various stages of the purchase funnel. 70% of shoppers admit that the price of the product influences their buying decisions across categories. Another study says a prospective buyer will visit 3 or more sites to be assured of the pricing competencies before making the buying decision. Trigger-happy customers are also price-sensitive customers. If 60% of the decision-making process is purely a price-play, brands should take this into cognizance and start treading this landscape meticulously. Determining the right product price is vital for e-commerce brands, as consumers’ buying behavior alters significantly through it. Most consumers compare similar products on the same or different e-commerce stores before making the final buying decision. Their decisions are influenced by promotions, offers, referrals, preferred retailers, etc. Some consumers might even try to reach out to a brick-and-mortar store and compare the price with online stores for buying products at the lowest price. Pricing intelligence is a technique retailers use to stay ahead of the competition. It helps brands monitor and analyze factors like consumer behavior, price trends, and response to price changes. These factors enable brands to develop pricing strategies, boost sales, grow market share and consumer base, and increase revenue. Because of the dynamic nature of e-commerce as a business, pricing intelligence becomes one of the vital tools to combat competition and increase revenues. Research reveals that Amazon changes product prices almost every 3rd minute on average to better sales. 4 Important KPIs for Developing Pricing Intelligence ● Gauging Average Price Vs. Competition The overall average price determines the estimated SKU price of a brand compared to its competition. In other words, it helps to understand the “price market share” of a brand versus its competitors. Additionally, brands get an idea of the top “price performers” based on the total sold units. The Average Selling Price (ASP) is a

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Pricing Intelligence in eCommerce

Leave a Comment / Blog, Digital Shelf Analytics / Expert Opinion

70% of shoppers admit that the price of the product influences their buying decisions across categories. Another study says a prospective buyer will visit 3 or more sites to be assured of the pricing competencies before making the buying decision. Trigger-happy customers are also price-sensitive customers. If 60% of the decision-making process is purely a price-play, brands should take this into cognizance and start treading this landscape meticulously. Determining the right product price is vital for e-commerce brands, as consumers’ buying behavior alters significantly through it. Most consumers compare similar products on the same or different e-commerce stores before making the final buying decision. Their decisions are influenced by promotions, offers, referrals, preferred retailers, etc. Some consumers might even try to reach out to a brick-and-mortar store and compare the price with online stores for buying products at the lowest price. Pricing intelligence is a technique retailers use to stay ahead of the competition. It helps brands monitor and analyze factors like consumer behavior, price trends, and response to price changes. These factors enable brands to develop pricing strategies, boost sales, grow market share and consumer base, and increase revenue. Because of the dynamic nature of e-commerce as a business, pricing intelligence becomes one of the vital tools to combat competition and increase revenues. Research reveals that Amazon changes product prices almost every 3rd minute on average to better sales. 4 Important KPIs for Developing Pricing Intelligence ● Gauging Average Price Vs. Competition The overall average price determines the estimated SKU price of a brand compared to its competition. In other words, it helps to understand the “price market share” of a brand versus its competitors. Additionally, brands get an idea of the top “price performers” based on the total sold units. The Average Selling Price (ASP) is a good place for brands to start their price benchmarking and take a clear stand on their pricing strategies. Pricing higher than the category ASP can classify a brand as premium and pricing lower can make it a mass category. These decisions impact the marketing and advertising strategies and decide the brand’s future. mFilterIt’s e-commerce intelligence solution determines ASPs across pin codes, locations, platforms, sub-categories, SKU-wise, sub-brand-wise, and variants. Using this information, brands can increase/decrease the production of a variant, alter investments in the marketing budget, or increase/decrease distribution in a specific region. ● Average Price Per SKU Unit The average price per SKU is the price per unit (gm/ ml etc.) for a product vs. its competitors in the same category. A brand needs to look closely at this data and correlate it to its performance. The average price per SKU unit helps to understand the market dynamics. It gives a brand a fair idea about how their different SKUs perform on the pricing aspect and helps them understand the customer’s point of view and how price-sensitive the market is. Accordingly, they can decide the type of pricing that works in the market and understand pricing trends. ● Average Price – Platform Wise The estimated price of a brand’s product on different eCommerce stores is called platform-wise average price. Reviewing the ASP across eCommerce stores helps brands find the best platform for boosting their marketing investments and acquiring higher revenue. Moreover, brands can keep a check on competitor prices accordingly and monitor unauthorized reseller prices. mFilterIt’s eCommerce Competitive Analytics helps analyze the ASPs across platforms and allows a brand to maintain its competitive stand in the overall market. ● Average Price by Variant Price by variant allows the brand to monitor the price of a particular product variant across different platforms and understand performance. They can compare their variant’s pricing vs. the competition’s variant and monitor their performance. mFilterIt eCommerce Competitive Analytics also divides it into brands, sub-brands, sub-categories, and variants. The prices can offer an in-depth review of the highest and lowest-performing products across platforms. Moreover, brands can compare the average price of similar variants and draw insights into their pricing strategies. Similarly, brands can acquire a price-based performance overview for their sub-brand categories. Conclusion eCommerce pricing intelligence is no longer an option for online retailers. Pricing delves deep into a consumer’s mind and helps a business understand and survive the crowded landscape. A business’s survival has a lot to depend on the value its customers derive from their brand, the incredible market forces, and the competition that follows it. Pricing as a component is a complex piece of a puzzle. There are no straight answers anywhere. Experience and continuous experimentation are required to deliver the correct consumer value. mScanIt helps brands to dive deeper into the journey by providing results based on real-time data. Brands using the mScanIt pricing tool are no longer required to conduct secondary research for determining pricing trends. Moreover, brands get a single-page price-based overview that helps optimize product pricing across platforms, categories, sub-categories, and variants.

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Ad Fraud in Performance Vs. Brand Campaigns

Leave a Comment / Ad Traffic Validation, Blog / mFilterIt Experts

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

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The Rise of Fraud in Retargeting Campaigns

Leave a Comment / Ad Traffic Validation, Blog / Expert Opinion

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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Ad Fraud Attacks on Gaming Apps

Leave a Comment / Ad Traffic Validation, Blog / Deepanshu jaral

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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What to Look for in Your Affiliate Campaign?

Leave a Comment / Ad Traffic Validation, Blog / Expert Opinion

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?

Leave a Comment / Blog, Digital Shelf Analytics / Expert Opinion

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

Leave a Comment / Blog, Ad Traffic Validation / Ritu Khanna

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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Ad Fraud Detection: Impact of Fraud in Programmatic Advertising

Leave a Comment / Blog, Programmatic / Expert Opinion

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

Leave a Comment / Blog, Digital Shelf Analytics / Expert Opinion

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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