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Organic-Stealing

How Bad is Organic Stealing for Your Brand?

Leave a Comment / Ad Traffic Validation, Blog /

Unpaid/Organic traffic signifies that a brand has obtained a market reputation and trust of its consumers. The traffic growth happens over time and is a collaborative result of the product, marketing, promotion, and other teams. Organic traffic is beneficial for brands for many reasons such as visibility, garnering consumer trust, free engagement with qualified prospects, etc. Meanwhile, inorganic traffic boosts reachability/visibility to the target audience, influences brand searches, acquiring leads, and more. On average, 16-20% of conversions happen through organic users. The term ‘organic stealing’ often stirs up emotions like fear, anger, frustration, etc. “By doing organic theft, fraudsters deprive brands of new user acquisition and steal attributions of existing loyal users.” For any given brand, a user base is quantified by the revenue. The purpose of doing advertisements is to increase this very base. But what if your ads are not bringing in over and above the revenues that it is expected to increase the current traffic base? You might be a victim of organic stealing. What essentially happened is that you are paying for customers who are already engaged with your brand, and no new addition has happened. Your ad budgets went inlining the cybercriminal’s pocket. Stealing organic traffic also causes other repercussions for a brand. For example, a fraud affiliate may continuously inject clicks and register attributions with the Mobile Measurement Partner (MMP). Besides money, the brand receives falsified campaign analytics because the user never clicked on the ad. Additionally, misattribution results are used to develop marketing strategies, and an additional budget is used to boost the fraud affiliate’s activities. App optimization efforts also get scrutinized due to organic traffic theft. A team working on app optimization would use the behavioral data to check organic downloads/installs. The team would use the exact data for optimizing features, functionality, and user experience (UX). Unfortunately, the data visible to the analysts is falsified or doesn’t display its full potential. 2 Ways to Combat Organic Stealing ● Continuously Review CTRs on Every Campaign Paid digital ads witness meager conversion rates whenever organic stealing comes into the picture. Your click-through rate (CTR) substantially plummets for every campaign and demands reviewing the analytics. Upon review, you would notice high impressions or views but drastically low clicks or visits. As such, paid ads should increase users because the brand has enhanced visibility. Unfortunately, the reverse impact is happening and draining the advertising budget faster than before. ● Ad Traffic Validation The best method of staying away from fraud affiliates and avoiding organic stealing is by using mFilterIt’s Ad Traffic Validation. The solution works 24×7 and alerts about analytical anomalies in real time. Therefore, brands can eliminate ad fraud and retain their organic traffic. Furthermore, detecting authentic inorganic sources helps brands identify the actual top performers of their affiliate marketing campaigns and build marketing strategies using accurate analytics. Besides this, the solution also validates traffic sources on the URLs shared by the brand. So, brands can eliminate ad fraud and keep their customer data systems/remarketing lists clean. Conclusion Cannibalizing organic traffic drains the marketing budget and straightforwardly showcases that ad fraud is prevalent. Moreover, it dramatically threatens analytics concerning marketing strategies and compromises asset optimization. Therefore, brands need to mandate an Ad Traffic Validation solution. mFilterIt is a pioneer in ad fraud detection and elimination. The company’s solution is safeguarding brands across six continents from advertising fraud. Get in touch to learn more about Organic Stealing.

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Ad-Fraud

Is Having OTP Validation Sufficient to Protect Your Brand Campaigns from Ad Fraud?

Leave a Comment / Blog, Digital Advertising /

Brands run lead-generation campaigns to get people interested in their products/services. They often incorporate form submissions to acquire personal details for callbacks and address lead inquiries. In marketing terminology, a Single form submission is called a ‘punch-in. Brands frequently incorporate OTPs in such forms to validate that a human is submitting them. All industries, including BSFI, Ed-tech, OTT, E-com, Gaming, etc., are acquiring leads through form-filling and OTP validation. But unfortunately, fraudsters have found a method of OTP bypass. Fraudsters collate personal details of real users, such as names, phone numbers, email IDs, etc., from different sources. Fraudsters use bots to fill user information in forms and punch them as leads. Unfortunately, brands don’t realize whether the information was filled in by a BOT or by a genuine user. On the other hand, the advertiser assumes that an actual person filled the lead. As a result, the sales team contacts the lead to convert it into a potential customer. The unconsented call receiver gets irritated because the person was never interested in the brand to begin with. These are commonly referred to as ‘fake leads’. The consequences can be near fatal as ad budgets are drained without generating any ROI and the brand reputation is put at stake. Repercussions of OTP Bypass Cybercriminals use bots for bypassing OTPs and submitting forms. The brands connect with the actual user; however, the lead gets irritated because the submitted details were unconsented. Hence, a real person is no longer interested in the brand and wants to disassociate from it. Another disadvantage of OTP bypass is the loss of marketing budget to fraudsters. Cybercriminals use form-filling or lead-generation bots to submit the wrong user information. Alternatively, fraudsters even submit duplicate user information after altering names, surnames, and emails while keeping the same phone number. The permutation and combination of altering user information could be endless. As a result, brands waste valuable time and money connecting with “fake leads.” Brands lose valuable marketing budgets to fraudsters because they never vetted the traffic sources of their affiliates or used an ad fraud elimination solution like mFilterIt. Alternatively, they increase their marketing budgets to get high campaign performance which most likely leads to increasing the “cost-per-lead”. Fraudsters code bots to click ads and then visit a landing page for submitting forms. As a result, an additional drawback of form-filling bots is an exponential rise in click fraud. Moreover, cyber criminals hide their ongoing CVR, i.e., 100%, by continuously using sophisticated invalid traffic (SIVT) on ads during specific time intervals. The spiked traffic also transforms the click-to-visit and visit-to-conversion ratio into believable percentages. As a result, brands think that increasing the budget for the CPL campaign would help increase revenue. But, by doing so, they lose even higher amounts to fraudsters. Many cybercrime experts advocate two-factor authentication that can safeguard brands against form-filling bots. However, bypassing OTP during a CPL campaign for form submission eliminates this myth. Moreover, this is a severe breach as it is sent to a user’s device for a specific duration. Furthermore, it means that the fraudster has found a method to breach the “user device” and read the messages. Therefore, user safety is hampered and can even cause severe issues like ATO, as the fraudsters have device access and can reset passwords. Last year, OTP and message breaches had already happened in India and were reported in the news. So, brands facing such issues lose reputation and consumer trust, directly impacting revenue or growth opportunities. Moreover, these practices can diminish ad fraud but never eliminate it. Therefore, the need for an ad fraud elimination service provider like mFilterIt has risen drastically across continents. Here is an example of our exemplary work with a single client: Case Study of a BFSI Company One of our leading BFSI clients running regular performance campaigns involving product offerings became a victim of fake leads. The brand used multiple new landing pages/micro-sites based on the occasion’s theme, like festive campaigns, home loan campaigns, etc. Moreover, the client regularly ran diagnostic checks on their website; but they often failed to test the new landing pages.   Fraud affiliates took advantage of the loophole by doing high-volume lead punch-ins through OTP bypass. One of their methods involved filling multiple leads using a single device. These fraudulent activities substantially affected the campaign’s performance resulting in financial losses in terms of marketing spending happening to these ad fraud activities.   Our analysts have found that brands have witnessed 25-30% fraud in lead generation campaigns involving form submissions with OTP validation. The victims (brands) have lost nearly $130,000 or more through this fraud in a month.   mFilterIt helped the brand identify such fake leads and optimized the call center cost by helping the brand detect and eliminate fraud. After analyzing similar instances, we have found that the average monthly punched leads ratio across affiliates ranges from 28 to 42 percent. Moreover, we have witnessed that two types of lead punching are widespread: ● Basic Lead Punching Affiliates punch all leads from the same device and don’t change anything (cookies and properties). The average number of leads received from these devices in a month is ~4k from one device. ● Advanced Lead Punching Affiliates change device properties like cookie and IP but use the same device, and the repetition is as high as filling 400-500 leads/day from the same device. Conclusion Detecting loopholes in CPL campaigns is essential for preventing ad fraud. Moreover, brands can face significant drawbacks due to form-filling bots, which could easily be avoided by mFilterIt’s ad fraud elimination solution. The solution uses data science, AI, and ML for checking analytical anomalies and verifying fraud activities on the targeted landing pages. Moreover, brands receive real-time alerts as the solution works 24×7. Protecting the ad campaigns through mFilterIt’s ad fraud elimination solution can also offer advantages like more real leads, connection with real human traffic, higher revenue, etc., especially on CPL campaigns. Get in touch to learn more about the Ad Fraud.

Is Having OTP Validation Sufficient to Protect Your Brand Campaigns from Ad Fraud? Read More »

top-keywords

Top Keywords: It all begins with Identifying them, Correctly!

Leave a Comment / Blog, Digital Shelf Analytics /

Generally, consumers find new products on eCommerce platforms like Amazon, Flipkart, Shopee, etc., by navigating or searching on the platform itself. Whenever users type an alphabet or word in the search query, it displays head/short-tail, long-tail, and LSI keywords, which most users have used to find their desired product and the brands bid to rank their products. Brands also optimize their product pages by using the top searched keywords in their product page so that the listings display on the top. According to a source, 38% of global customers use Amazon to find their desired products. Top keywords are the ‘most popular searches’ under a category of an eCommerce platform. The popularity is based on the keywords with the highest search volume. Ranking high on such words or phrases ensures a higher share-of-shelf on the top pages as well as higher visibility, brand awareness, click-through rate, add-to-cart, and conversions/sales. Today, brands need to measure the search performance of their product’s top generic, brand, and competition keywords under a category to achieve a higher market share. Searches on eCommerce marketplaces have navigational, commercial, transactional, or informational intent. For example, navigational intent keywords consist of brand names with the product type, enabling consumers to find their desired product faster, e.g., ‘MIVI earbuds.’ Similarly, transactional intent could mean ‘t-shirt under 799’ or ‘shoes under 15000,’ wherein the online shopper is searching for products under a defined price tag and could desire a specific brand too, e.g., ‘Levis t-shirt under 1200’. Likewise, commercial keywords refer to comparative searches, like ‘Batman t-shirt’ – here, the brand is non-specific, but the intent to buy a specific product type is clear. Informational keywords refer to ‘best shoes for football’ or ‘best gaming smartphones.’ Identifying Top Keywords Using AI and ML Keywords can entice, hook, or allure consumers to check out a product on the eCommerce platform. Finding the top keywords of your brand can offer insight into the consumer’s mindset while searching for your product and that of your competitors under a category. Many marketers might suggest that traditional methods of using URLs, suggested keywords, consumer intent keywords, etc., could help to find keywords, but these methods have limitations. Now, brands can easily identify and analyze their performance on top generic, competition, and sponsored keywords and the competition using our eCommerce Competitive Analytics tool, mScanIt. This tool enables the brands to see their share at a deeper level compared to their competition by using the multiple filters given in the tool such as brand, sub-brand, variants, SKU, etc. Moreover, the brand could also use a keyword planning tool to find the keywords with the highest share and optimize the Product Detail Pages (PDPs) accordingly. On the other hand, the highest-grossing competition keywords would help the brand find areas of improvement across platforms or ask whether the product needs to have this keyword. A few common questions that brands can answer using mScanIt capabilities: What is the overall SOS of your brand compared to the competition on the top keywords of the eCommerce platform? Are the ongoing page optimization delivering successful SEO (Search Engine Optimization) and SERP (Search Engine Results Pages) on e-commerce platforms? Can/Should the brand add new keywords to its product page copies? Which keywords are the competition brands more focused upon, and what is its impact on the brand’s SOS on the eCommerce platform? Are any keywords with high competition share more relevant than your existing ones? Is there any other keyword that is more consumer intent-centric than your existing ones? Which keywords are most of your competitors using – long-tail, short-tail, or LSI? Are the competitors spending more on paid or relying on organic searches? Discoverability becomes vital to brands as eCommerce platforms are built for purchasing products. Understanding the relevance of the top keywords by measuring your brand’s Share-of-Shelf becomes crucial for enhancing the product discoverability. Some marketers suggest that monitoring reviews offers insights into the pros and cons of a product, which is certainly true and simultaneously enables one to find long-tail keywords. Unfortunately, identifying keywords relevant only through a single source doesn’t offer perspective. Therefore, brands need to identify the top keywords from every possible source. Using multiple filters, the solution also sights the most promising/critical aspects of a brand and its competitors. (Read here) A single category page on Amazon could consist of up to 50 product listings, which means your brand’s single variant competes with 49 other listings, some of these could be of your brand as well. Analyzing the top keywords on the eCommerce marketplace’s digital shelf helps the brand increase its page positioning/ranking and enhance visibility. Bottom Line Targeting top keywords has become necessary for increasing the brand’s discoverability and thus, sales on e-commerce platforms. Our solution, mScanIt, makes it possible for brands to weigh the relevancy of their relevant keywords on eCommerce marketplaces, especially when paid searches get involved in the business. Today, brands need to focus on the top keywords for measuring their SEO and SERP, making the PDPs more consumer-centric, assessing the keywords the competitors are focused upon, and more. Get in touch to learn more about the advantages of eCommerce Competitive Analytics for your business.

Top Keywords: It all begins with Identifying them, Correctly! Read More »

price-monitoring

Why is Price Monitoring Important for eCom Brands?

Leave a Comment / Blog, Digital Shelf Analytics /

Price is one of the most crucial factors contributing to the final buying decision on e-commerce platforms. According to research, 60% of customers would shop for a product online based on the best price or value. Seeing that price is a leading reason for most customers and has the potential of driving customers towards or away from your brand, monitoring price is no longer a brand’s choice. Price monitoring is the practice of continuously keeping an eye on the price of your products as well as similar listings of your competitors. However, the analytics of e-commerce marketplaces like Amazon, BigBasket, BlinkIt (formerly Grofers), etc., don’t offer this choice to the brands. What could happen if you don’t monitor competitors’ prices? You would miss out on chances of increasing sales, develop imperfect pricing strategies, never know the difference between your and competitors’ prices of similar listings in real-time, etc. On the other hand, a price monitoring solution can give you an overview of the market scenario, deep-dive into buyer personas, explore new target audiences, etc. The scope of price monitoring extends beyond these reasons. During a conversation, our mScanIt Vertical Head, Ankush Arora, stated “Tracking online prices helps to know who started the price war. and, What was the price? – which are definite factors in creating pricing trends, sighting MAP violations, changing discounting strategies and analysis, and more.“ 5 Major Advantages of Price Monitoring Enhances the Pricing Strategy Margin is an important part of deciding the product prices. It is essentially the amount achieved after deducting the purchase cost from the retail cost. However, most brands have the lowest price they can offer, even though they might lose a significant proportion of the profits. eCom brands often lower the price to acquire a higher customer base, build loyalty, sell out multiple products, avoid expiration, etc. Therefore, the brand might not make the highest profits on its single product but has the potential to acquire higher basket purchases. Such a scenario becomes impossible without knowing the ongoing prices of the competitors in real-time. Analyzing the rival’s product prices also gives a perspective of your product’s market positioning and makes you aware that a competitor has begun a price war for a similar listing. When such an opportunity presents itself, offering a price that keeps the market reputation intact but increases the conversions/sales could significantly impact your rivals across search engines and e-commerce platforms. Reviewing prices of competitor listings also offers a chance to know the lowest/highest costs daily, weekly, and monthly across e-commerce platforms and enables your brand to create individual platform-wise price strategies. Recognizes the Pricing Trends Price is often correlated with discounts and sponsored ads on e-commerce platforms. Monitoring competitor prices and comparing them with discounts gives an overview of a trend of distinct periods. For example, Brand A is offering a 10% discount on a smartwatch and is offering the product at Rs 3,500. On the other hand, your brand has a similar listing priced at Rs 3,200. So, money-savers would often opt for Brand A versus yours. Herein lies the opportunity to offer a 10% discount and still offer the product for Rs 3,350, if possible. Similarly, mScanIt or eCom Competitive Analysis can help to compare pricing intelligence with stock availability. By knowing the prices at which the competitors faced stockouts and the duration, you have the opportunity to identify trends and create new strategies. Likewise, pricing analysis compared with share-of-shelf would decipher buyer preferences based on higher search rank and visibility as well as lower prices. Such a trend would help identify the share of sponsored ads of your competitors within the top three page results, which is also effective for channeling sales/conversions. Herein, you can identify the pricing trends based on the top keywords used by the competitors. Helps to Avoid MAP Violations The Minimum Advertised Price (MAP) is the lowest price at which a product can be sold. However, e-commerce sellers often indulge in MAP violations for clearing stocks, building a market presence, attracting new customers, etc. Doing so damages the brand’s reputation in the eyes of the consumers and creates a bad price perception. Moreover, legitimate sellers and resellers associated with the brand lose their faith and seek products of their competitors that can keep their reputation and price perception intact while maintaining their share of the sale. Moreover, price perception is one of the leading reasons consumers can lose faith in a brand. Imagine an iPhone 13 available for Rs 30,000, which is less than half its existing market price across e-commerce platforms. If consumers no longer have faith in the brand, the product demand shifts towards the competition, and resellers/sellers would want to get associated with them. Implementing pricing intelligence through automated technology like eCom Competitive Analytics helps to sight instances of MAP violations in real-time and fight back against them by flagging, reporting, or revoking them on e-commerce platforms. Identifies Counterfeit Sellers A pricing insight solution such as mScanIt shows in-depth reports on prices and types of sellers across e-commerce marketplaces like Amazon, BlinkIt, BigBasket, etc. Tracking prices helps your brands to know if a new seller has recently launched your product listing at discounts, causing a MAP violation. Your authorized partners would lose their profits due to unauthorized or counterfeit product sellers. The bigger impact would fall on the brand reputation and create negative publicity on the e-commerce platform towards your brands, losing out even a loyal customer base. eCom Competitive Analysis also offers the chance to set alerts or notifications to identify similar instances and take measures against counterfeit sellers. Revoking fake products is necessary and an everyday task that requires a solution that can deliver real-time reporting, a feature offered by mScanIt. Boosts the Customer Base Recognizing the pricing mistakes and building pricing strategies using real-time competitor & brand data can certainly help to boost the customer base. For example, tracking competitors’ prices can reveal the best prices across e-commerce stores for promotions such as deals, offers, discounts, etc.,

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digital-marketing-budget

Are You Using Your Digital Marketing Budgets for Paying Your Offline Orders?

Leave a Comment / Ad Traffic Validation, Blog /

Yes, this is one of the digital ad frauds that the affiliate networks are doing, and the advertisers end up paying for orders that are not online orders. As a result, offline orders get cannibalized into online orders, and marketing budgets are drained. Apart from the loss of digital marketing budgets, the advertiser pays online offers/discounts for these offline orders. How is this Fraud Happening? In the below graph, a fraud affiliate has used a Bot to send spiked traffic during a specific hour of the day to control the conversion ratio. However, orders are placed during the whole day from two devices only. As a result, the flat line conversion rate is at 26%, in which orders are coming from two devices (offline cannibalizing). However, the spiked traffic conversion rate is 2%, in which orders are all coming from the same two devices. This clarifies that this traffic is purposely spiked in this hour to control the overall conversion ratio. Actual repetitive events are coming from visits. Moreover, these visits are from the same desktop and IP, which indicates that the visits are coming from the exact location, and offline orders are getting punched in. These orders can be cash on deliveries and canceled at any time or a meager value that adds nothing to the bottom line. What Does the Network Achieve? ● Advertisers Make Payouts Affiliates team up with the retailer to earn commission from advertisers by completing the KPIs of a campaign by unethical means, which are other than digital sources. This happens when a retailer places orders using affiliate and referral links. The affiliate receives a monetary benefit from the advertiser for delivering high conversion rates or confirmed orders. Moreover, most affiliates receive a fixed payout from the advertiser for delivering confirmed purchases. But, on the other hand, the “order value” is not often set. Therefore, the affiliate could receive a payout of Rs 20,000 or Rs 200/100 order, whereas the order value could be as low as Rs 50/order or Rs 5,000 for 100 orders. Assuming the affiliate pays a fixed amount to the retailer for placing the order, let’s say Rs 5,000, the fraudulent affiliate still makes Rs 15,000. Moreover, the retailer can place CODs and reject or cancel online orders. So, the advertiser might not even receive money but must make payouts to the fraud affiliate. Therefore, the advertiser loses money for non-online orders. ● Loss of Commission/Offer/Discount on Online Orders The retailer partnered up with the affiliates to get a better-discounted value on their sale. They also can resell the products to their offline customers at the MRP. As a result, the retailer is making money by reselling products and even receives money from the fraud affiliate for placing the orders. mFilterIt & Ad Fraud Solution checks for retailers creating multiple fake profiles on e-commerce apps by detecting their location and device as it remains constant. By detecting such anomalies, brands can avoid cannibalizing their online orders and making commission payouts for offline orders to fraudulent retailers. What Losses Do Advertisers Incur? ● Offline Orders Get Cannibalized The retailer orders for multiple customers by creating various profiles with a single delivery location. Therefore, the retailer cannibalizes online orders offline and makes additional money by reselling at MRP to offline customers after availing of online discounts/offers. On the other hand, the brand suffers a loss as it doesn’t expand its customer base. Moreover, the advertiser fails to explore the scope of areas with the demand, as the existing customer base lies with the retailer. Furthermore, the advertiser only receives a signal that a single location has high demand when the reality is different. ● Digital Marketing Budgets Incur Offline Order Payments Through their digital marketing budget, brands make payouts to affiliates and retailers. As mentioned earlier, the retailer places the order using the fraud affiliate and resources, and the fraud affiliate shares a portion of the earned commission with the retailer. Moreover, the retailer will likely resell these orders at MRP to offline customers and earn more money from the sale. There were no payouts required for these offline orders as the affiliate is incentivizing retailers for placing orders, a.k.a., incentive fraud. Furthermore, the affiliate hides the peaking conversion rate by using bots to deliver high traffic within certain time intervals. For example, an affiliate spikes 1000 visits using bots and displays a 100% conversion rate. As such, CVR can never achieve its full scale. On the other hand, legit affiliates have a 1-1.5% CVR. Therefore, such a high CVR indicates ad fraud and is only done by fraud affiliates to hide their fraud CVR. In reality, the fraud affiliate might be using 100 visits to deliver 100 purchases. Therefore, advertisers are losing the marketing budgets by paying commissions to fraud affiliates and retailers. ● Derails Reachability to Potential New Customers The fraud affiliate uses a retailer to create multiple profiles and place orders at the exact location. As a result, the advertiser fails to build a new customer base. Moreover, the customer base created by the advertiser is also displaying falsified information, as the same retailer creates the profile. Additionally, we can say that fake users also avail of discounts/ offers. Moreover, the advertiser pays a commission to the fraud affiliate for delivering bot traffic and not a single customer. Such partnerships between affiliates and retailers drain the marketing budget to attract legitimate customers from different locations. Furthermore, the advertiser may attract zero customers if the retailer cancels the orders. Takeaway Partnerships between affiliates and retailers are cannibalizing offline orders as online. As a result, brands and advertisers lose their marketing budgets to commission payouts. Also, it leads to warped marketing analytics and polluted business numbers. Putting an end to this is possible by detecting ad fraud, especially incent fraud in marketing campaigns. mFilterIt ad fraud solution detects any sudden spike in online traffic and displays the source credentials to advertisers. As a result, advertisers can recognize the fraud affiliate and attract a new

Are You Using Your Digital Marketing Budgets for Paying Your Offline Orders? Read More »

brand-infringement

Brand Infringement: Impact of Copycat Apps

Leave a Comment / Blog, Brand Safety & Protection /

Originality or uniqueness is one of the contributing factors in forming brand identity. Consumers resonate with brands and foster relationships based on this. Any person stealing a brand, such as an app, puts the reputation at stake and diminishes credibility. A tarnished reputation drives users away from a brand and causes a loss of revenue. Unfortunately, the implications of brand infringement through copycat apps don’t nearly stop at this point. Fraudsters use this as an opportunity for financial and identity theft by selling the user details on the dark web to acquire money and maliciously attacking devices. Brands and users often become victims of cloned apps on third-party stores. Therefore, putting an end to brand infringement through duplicated apps has become highly essential. Brands should keep a constant lookout for any form of infringement to save their consumer community. 5 Negative Impacts of Copycat Apps for Brands ● Cultivates a Culture of Distrust Most copycat apps are hard to tell apart from the original app to a user. Moreover, users become vulnerable to cloned apps as fraudsters package these apps with unbelievable offers and deep discounts on the original app. However, fraudsters end up tarnishing the brand and goodwill. For example, a cloned app could sell free access to OTT platforms post-registration but never provide access. Furthermore, the original app owner is spending millions or billions of dollars annually to deliver a good User Experience (UX); however, fraudsters are not worried about this. Instead, cybercriminals use this opportunity to commit identity/financial theft and implicate the brand in the process. After experiencing false promises/bad UX, the user gets agitated and shares a bad review against the brand. Brands unaware of cloned apps become too late to address consumer issues and, once again, lose customer loyalty. They even experience a high percentage of user drop-offs on their original app. In addition, the troll of bad reviews creates bad publicity and spoils the reputation. ● Brands Lose Edge Over Their Competition Brands work relentlessly to become leaders in their field. Unfortunately, cloned apps destroy their leadership goals by diminishing their value in the eyes of the users. As a result, users quickly switch to the second-best alternative which is a more trustworthy brand. The consumer is spoilt with choices across product/ service categories. One bad experience can snowball into multiple others in a matter of time. One brand mistake is another brand opportunity window in this ruthless world. Damages due to impersonation take longer to recover than anticipated. It is in the interest of the brand to keep its ears and eyes open to keep its digital assets secured at all times in the digital ecosystem. ● Leads to Account Takeover (ATO) Users unwaveringly register on a duplicate app, log in to their accounts using their credentials, and make their accounts susceptible to fraud. The cybercriminal uses the credentials of the new users to log into the original app, change email/phone numbers, and overtake the user account. Moreover, fraudsters obtain access to financial details like saved debit/credit cards linked to payment partners and digital wallets and engage in financial fraud. Cybercriminals end up selling such creds on the deep/dark web for monetary gain. Fake apps are also laced with malware to acquire OTPs and passwords by accessing the device messages. Fraudsters access other apps replace the email id/phone number, and block the original user from their accounts. Fraudsters, once again, steal user identities and credentials loaded on such apps. Therefore, multiple brands face the consequences of such ATOs as consumers raise complaints/claims on forums and boards. ● Inflates Marketing Budget for Revamping Reputation Customer acquisition is one of the most uphill tasks for any brand. Once the copycat apps hamper the brand reputation, restoring it becomes challenging, and twice as much effort is to win back customers if they are lucky. Reacquiring lost customers and building inroads for new consumers can increase ad budgets phenomenally. It is a double-edged sword that the brand needs to combat. Restoring faith/trust is both time and money-consuming. Hence a preventive approach works better. ● Strong-arms App Stores The responsibility of not removing the replicated apps falls on the app stores. For example, in 2020, Google Play Store removed 600 copycat apps that deliberately covered user devices with full-screen ad pop-ups. Unfortunately, third-party app stores don’t monitor cloned apps constantly like Google Play and Apple stores. Therefore, brands blame such stores for not removing the apps that cause brand infringement. Unfortunately, users’ trust in the brand is not restored until the complaints are resolved, or the app store removes the cloned apps. However, recovering from irreparable trust damage becomes challenging for brands even after taking measures to resolve the problem. Solution of Brand Infringement from Copycat Apps mFilterIt & Brand Safety & Infringement solution, a state-of-the-art Open Source Intelligence system that leverages AI & ML driven technology and is backed by a repository created by mFilterIt&3B+ device coverage & blacklist databases to identify brand infringements like cloned apps across the digital landscape. It checks for any instances of replicated apps across the Google Play, Apple, and OTT stores. Moreover, the solution checks for duped apps advertised on fake websites, social media sites, and apps with incent walls. Therefore, brands can maintain their online reputation and credibility and avoid the blowbacks of duplicated apps. Conclusion Safeguarding a brand reputation increases consumer trust and boosts engagement. But unfortunately, fraudsters have found a loophole to attack brands’ apps by copying them and relishing monetary benefits. The brands face direct consequences by not removing the duplicated apps from stores and revamping the brand reputation becomes challenging. Therefore, the need to use a brand safety solution that incorporates measures to fight back against brand infringement from copycat apps has strongly risen. mFilterIt & Brand Hygiene Protection undertakes measures to provide overall safety to the brand by identifying the culprits and taking down infringed assets. Get in touch to learn more about the Brand Infringement.

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sentiment-analysis

What is Sentiment Analysis?

Leave a Comment / Blog, Digital Shelf Analytics /

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

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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

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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

Leave a Comment / Ad Traffic Validation, Blog /

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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