Connect with us

Startups

How to Avoid Getting Scammed by Influencers With Fake Followings

Published

on

Opinions expressed by Entrepreneur contributors are their own.

Social media influencers are essential components of information networks since they leverage their reputation within a specific niche to inform their followers while providing trusted information regarding breaking events, emerging trends and new products. According to Insider Intelligence’s Influencer Marketing, this market will exceed $15 billion by 2022.

However, the industry has attracted nefarious actors that pose as legitimate experts. In 2020, according to PRWeek, at least half (55%) of Instagram’s influencers engaged in fraudulent activities. while a new study by HypeAuditor revealed that 45% of the site’s accounts are fake.

The increased uptake of fake influencers stems from the fact that top brands prefer working with influencers to generate a consistent stream of customers to new services. Per data released by Matter Communications, the industry hinges on the undeniable fact that 61% of customers are more likely to trust an influencer’s recommendations compared to just 38% who would rather trust branded social media information.

Couple that with a new survey conducted by California Figs which found that a third of those polled posted online food pics that they didn’t actually consume, (with 19% confessing they’d never intended to eat what they ordered in the first place) and know that all social media platforms are rife with deception. 

Half of all brands can’t distinguish between legit and fake influencers

Brands and social media taste-makers have a quid pro quo arrangement: Influencers drive new customers to a brand in exchange for financial gains. Unfortunately, with the industry poised to replace traditional advertising, numerous social media users seek to tap the opportunities by engaging in shady influencer tactics.

One of the most questionable is buying fake followers where, according to HypeAuditor’s State of Influencer Marketing 2021 report, 55% and 45% of Instagram followers are real users and inactive users or bots, respectively. Therefore, brands that depend on influencer partnerships market their products face an uphill task in identifying inauthentic engagements and fake social media followers.

There has been a phenomenal surge in fake followers across various platforms which influencer profiles purchase in order to dupe unsuspecting brands into believing they have a large following. Key influencer marketing statistics for 2021 published by Micro Biz Mag reveal that the Google search query “buy Instagram followers” averages 21,500 hits every month, followed by “buy Instagram likes”, which averages 8,400 searches.

Consider the following statistics that illustrate why you should worry about fake influencers.

  1. Almost 40% of all social media influencers have inflated followings, according to Invesp.
  2. An average of 55.39% of influencers engaged in fraudulent activities, according to data shared by Statista Research Department.
  3. According to Digiday’s cheat sheet on what you need to know about influencer fraud, at least 50% of influencer engagements are fake.

How can you tell when an influencer cheats?

A new study by Procter & Gamble Co. revealed that one company that was a victim of influencer fraud spent $600,000 on fake influencers, only to later realize that solely fake followers engaged with the influencer’s posts.

While the loss may not destroy established brands, it is a large amount of money that can disrupt the business operations of smaller enterprises. Therefore, you can tell fake influencers from legitimate ones by understanding how they cheat and how you can avoid them.

Related: 3 Ways Direct-to-Consumer Brands Can Leverage Media Coverage

Sudden spikes in followers

A sudden surge in engagement is typically a sure sign that an influencer is involved in fraudulent activities and usually indicates the acquisition of bot accounts. An influencer may have paid fake social media users to engage with their posts by liking, commenting on, and sharing them. Most verified influencers build a huge following consistently over a given period and may experience infrequent deviations.

An increased follower count due to authentic viral content rises gradually even after the initial spike. A fake influencer will most likely record a significant drop in followers since they buy bot accounts instead of building a loyal following.

Therefore, analyzing the follower count and engagement ratio is a good starting point to determine if the account is authentic before partnering with an influencer. 

A poor quality audience

The term social media influencer was coined to describe a user that can manipulate the decisions of a large number of followers towards a particular brand, view, or political inclination. Therefore, the quality of an audience can help determine the authenticity of a particular influencer.

You will find that the audiences of most fake influencers are not varied. The same user profiles deploy similar engagement methods for every post made via the account. This means that the same so-called audience likes, comments on and shares every post without any variation. How common is it that only a certain audience will be the first to comment on and like every post?

Established influencers always attract a varied audience. Posts attract a larger number of users who have different opinions, praises and criticisms. Thus, if a fraudulent influencer generates comments and likes using bot accounts, there may be noticeable patterns that suggest the audience is also fake.

Related: 5 Ways AI Will Change the Digital Marketing Game in 2022

Fake post engagements

You can determine if the engagements are real by carefully analyzing the comments. Irrelevant comments that don’t reflect the purpose of the post are a clear indication that they are false and only there to make it look real and engaging. 

Fake engagements on a fraudulent influencer account may be generic or emoji-only, such that anyone can use them on any post regardless of the brand. Some generic engagements may include comments like “great image”, “awesome product”, etc. 

Suspicious ratios

A fake influencer may buy hundreds of thousands of fake followers but record an unusually low interaction or engagement rate. As such, only a few of the bought followers may be willing to engage with a post. You may find an identical and consistent engagement rate even for those who engage with the posts.  

The fake followers may not be willing to spend time giving an honest opinion, but rather apply the same engagement technique and move on to the next post. As a result, some fake influencers may achieve high rates of engagements but be low-quality.

Nevertheless, understanding an influencer’s engagement ratio is insufficient to determine if the influencer is authentic or fake. You need to have an unmatched understanding of your brand’s industry benchmarks so that you can effectively compare them with engagement ratios.

It is also essential to appreciate that lower engagement rates than expected should not be a basis for automatically regarding an influencer as fake. In particular, you should focus more greatly on the engagement quality for the telltale signs of fraudulent accounts.

Related: 5 trends we will see in Influencer Marketing by 2022

 

Startups

Southeast Asia insurtech Igloo increases its Series B to $46M • TechCrunch

Published

on

Igloo, a Singapore-based insurtech focused on underserved communities in Southeast Asia, announced it has raised a Series B extension of $27 million, bringing the round’s total to $46 million. The first tranche of $19 million was announced in March, and led by Cathay innovation with participation from ACA and returning investors OpenSpace.

The newest round was led by the InsuResilience Investment Fund II, which was launched by the German development bank KfW for the German Federal Ministry for Economic Cooperation and is managed by impact investor BlueOrchard. Other lead investors were the Women’s World Banking Asset Management (WAM), FinnFund, La Maison and returning investors Cathay Innovation.

Igloo develops its insurance products and then partners with insurers who underwrite their policies. Igloo currently works with 20 global, regional and local insurers across Southeast Asia. It distributes its insurance products through partnerships, and is partnered with over 55 companies in 7 countries. It now offers 15 products, including policies for gig workers, gamers, cars and farmers in Vietnam, and says it has facilitated more than 300 million policies and increased gross written premiums by 30 times since 2019.

Co-founder and CEO Raunak Mehta told TechCrunch that Igloo decided to raise a Series B extension because of investor interest after the first tranche of funds. The extension will give the startup a multiyear runway and will be used for hiring, infrastructure and merger and acquisitions opportunities.

Mehta said that the penetration rate of insurance in much of Southeast Asia is low, less than $100 USD per capita across Indonesia, Vietnam and the Philippines. Igloo was created to make insurance more affordable and relevant to the needs of communities in Southeast Asia. Igloo distributes insurance products that range from 2 cents USD for phone screen protection to $600 USD for comprehensive motor insurance.

Igloo provides the tech stack for its products across Southeast Asia, which Mehta says means the entire insurance value chain, from product discovery to claims, is available on one platform. This makes it faster for it to brings the policies it distributes to market more quickly, and significantly reduce the operational cost of claims.

Mehta said more than 80% of claims are currently managed in an automated or semi-automated way, and that big data management, along with machine learning and artificial intelligence, has enabled it to reduce anti-selection risks, false positives and fraudulent claims. By bringing down the cost of managing claims, Igloo is able to offer lower premium to customers.

An example of Igloo’s insurance policies include ones for gig economy riders that it sells through its partnership with Foodpanda in Thailand, Singapore and the Philippines, and Lozi and Ahamove Vietnam. Its policy for Foodpanda, called PandaCare, includes motor, personal accident and hospitalization income protection for workers.

Another, more recent one, is is Weather Index Insurance product in Vietnam. The policy uses blockchain-backed smart contracts and automates claims payouts by using pre-assigned values for crop losses caused by weather and other natural events. Igloo says the Weather Index Insurance is Vietnam’s first parametric insurance (or a policy that agrees to make pre-agreed payouts based on trigger events like a flood) and its first integration of smart contracts into insurance.

Igloo also provides products that Mehta says directly or indirectly benefits women, through a partnership with Philinsure in the Philippines. They have distributed more than 5 million policies that cover credit default, personal accident, family relief and natural calamity support to women micro-entrepreneurs and their families. In Vietnam, more than 65% of the agents who use Igloo’s Ignite digital platform to sell insurance policies are women, and they are also the main beneficiaries of the Weather Index Insurance product.

The insurtech’s distribution partners include telecoms like Telkomsel, AIS and Mobifone, and e-commerce platforms like Shopee, Lazada, Bukalapak and JD.ID. It also works with financial service providers, like AEON, Gcash and UnionBank, to sell policies for their customer base, and provides products for insuring goods in transit and protecting fleet drivers through logistics platforms like Ahamove, Shippit, Loship and Locad.

Other Southeast Asia-based insurtechs that want to increase insurance penetration in the region and have raised large Series B rounds include Indonesia’s Fuse and PasarPolis and Thailand’s Sunday.

Source link

Continue Reading

Startups

Cyber Monday shopping expected to set record but annual growth has slowed | Adobe

Published

on

Cyber Monday shopping sales hit at least $6.3 billion through part of the day in the U.S. today, according to the latest online shopping data from Adobe Analytics.

It’s not unusual for Cyber Monday and Black Friday online shopping results to break records, but it this economic climate it’s encouraging to see it happen. Still, growth has slowed from 2021 and 2020 holiday seasons.

Consumers spent $6.3 billion up through 3:00 pm Pacific time for Cyber Monday. Adobe expects that when the final tally is in, consumers will spend between $11.2 billion and $11.6 billion for the day, making Cyber Monday the biggest online shopping day of the year (and of all time).

Today, the top 15 hot sellers (not in ranked order) have included Legos, Hatchimals, Disney Encanto, Pokémon cards, Bluey, Dyson products, strollers, Apple Watches, drones, and digital cameras. Gaming consoles also remain popular, along with games including Mario Party, FIFA 23, Madden 23 and Call of Duty: Modern Warfare II.

Over the past weekend, the top sellers were included Hot Wheels, Cocomelon, Bluey, Disney Encanto, L.O.L. Surprise dolls, Roblox, and Fortnite in the toys category. Nintendo Switch, Xbox Series X and PlayStation 5 remain the top selling gaming consoles, with popular games including FIFA 23, God of War Ragnarök, Call of Duty: Modern Warfare II, Madden 23, and NBA 2k23. Other hot sellers included Apple iPads, Apple MacBooks, digital cameras, Roku devices, drones, gift cards and Instapots.

Black Friday online shopping sales were $9.12 billion, up 2.3% from a year ago, and Thanksgiving itself came in at $5.29 billion, up 2.9% from a year ago. Those were above Adobe’s projections. Last year, consumers spent $10.7 billion on Cyber Monday.

Strong consumer spend has been driven by net-new demand, and not just higher prices. The Adobe Digital Price Index, which tracks online prices across 18 product categories (complements the Bureau of Labor Statistics’ Consumer Price Index, which also includes prices for offline only products and services like gasoline and rent) shows that prices online have been nearly flat in recent months (down 0.7% YoY in October 2022).

Adobe Analytics says Cyber Monday will set a record.

Adobe’s numbers are not adjusted for inflation, but if online inflation were factored in, there would still be growth in underlying consumer demand, the company said.

On a category basis, toys were a major growth driver in the days leading up to Cyber Monday, with online sales up 452% over the average day in October 2022. Appliances (up 305%) and baby/toddler products (up 289%) also saw strong demand, in addition to electronics (up 276%) and apparel (up 258%).

Shoppers will find record discounts today for computers (peaking at 27% off listed price). Deals will also be found in nearly all categories tracked, including apparel (19%), toys (33%), electronics (25%), sporting goods (16%), televisions (15%), and furniture (11%). Those looking to buy an appliance should consider waiting until Thursday (December 1), when discounts are set to peak at 18% on average.

Weekend spending remained strong

Consumers spent over a Black Friday’s worth of ecommerce over the weekend at $9.55 billion, up 4.4% YoY ($4.59 billion on November 26, up 2.6% YoY / $4.96 billion on November, up 6.1% YoY). Season-to-date (November 1 to November 27), consumers have spent a total of $96.42 billion online, up 2.1% YoY.

And while the big days (Thanksgiving Day, Black Friday) have reached new heights, consumers spent at record levels all season. Since November 1, shoppers spent over $2 billion every single day, with 19 days above $3 billion in online spend. Broad, early discounts were the main drivers for the shift in consumer spending.

“Shoppers have seen massive discounts this past week, which is the exact opposite situation from last season when supply chain constraints kept prices elevated,” said Vivek Pandya, lead analyst at Adobe Digital Insights, in a statement. “While discounting will have an impact on margins for retailers, it is also driving a level of demand that can help brands build long-term loyalty and net some short-term gains.”

Additional Adobe Analytics Insights

Over the weekend, online sales of toys were up 383% (compared to average daily sales for the category in October 2022), with baby toys seeing strong demand (up 252%). Other categories that surged over the weekend include jewelry (up 230%), sporting goods (up 239%), and apparel (up 217%).

With online spending hitting new records and inflation impacting consumers, flexible payments have become a big story this season. In the last week (November 21 to November 27), “buy now, pay later” orders have risen 68% and revenue has increased 72%, when compared to the week prior.

Over the weekend, smartphones drove over half of online sales for the first time (52%, up from 48% last year). Adobe expects mobile shopping to dip on Cyber Monday however, based on historical trends. Many people are back at work and using laptops, which will be the preferred device for shopping online.

Forecast for Cyber Week

Adobe expects Cyber Week (the five days from Thanksgiving Day through Cyber Monday) to generate $34.8 billion in online spend, up 2.8% YoY, and represent 16.3% share of the full November through December holiday season.

Cyber Monday is expected to remain the season’s and year’s biggest online shopping day, bringing in between $11.2 billion and $11.6 billion. Black Friday generated a record $9.12 billion in online spend, up 2.3% YoY, while Thanksgiving brought $5.29 billion in online spend, up 2.9% YoY.

Adobe analyzes direct consumer transactions online. The analysis covers over one trillion visits to U.S. retail sites, 100 million SKUs, and 18 product categories.

Source link

Continue Reading

Startups

Snowflake 101: 5 ways to build a secure data cloud 

Published

on

Today, Snowflake is the favorite for all things data. The company started as a simple data warehouse platform a decade ago but has since evolved into an all-encompassing data cloud supporting a wide range of workloads, including that of a data lake

More than 6,000 enterprises currently trust Snowflake to handle their data workloads and produce insights and applications for business growth. They jointly have more than 250 petabytes of data on the data cloud, with more than 515 million data workloads running each day.

Now, when the scale is this big, cybersecurity concerns are bound to come across. Snowflake recognizes this and offers scalable security and access control features that ensure the highest levels of security for not only accounts and users but also the data they store. However, organizations can miss out on certain basics, leaving data clouds partially secure. 

Here are some quick tips to fill these gaps and build a secure enterprise data cloud.

Event

Intelligent Security Summit

Learn the critical role of AI & ML in cybersecurity and industry specific case studies on December 8. Register for your free pass today.


Register Now

1. Make your connection secure

First of all, all organizations using Snowflake, regardless of size, should focus on using secured networks and SSL/TLS protocols to prevent network-level threats. According to Matt Vogt, VP for global solution architecture at Immuta, a good way to start would be connecting to Snowflake over a private IP address using cloud service providers’ private connectivity such as AWS PrivateLink or Azure Private Link. This will create private VPC endpoints that allow direct, secure connectivity between your AWS/Azure VPCs and the Snowflake VPC without traversing the public Internet. In addition to this, network access controls, such as IP filtering, can also be used for third-party integrations, further strengthening security.

2. Protect source data

While Snowflake offers multiple layers of protection – like time travel and fail-safe – for data that has already been ingested, these tools cannot help if the source data itself is missing, corrupted or compromised (like malicious encrypted for ransom) in any way. This kind of issue, as Clumio’s VP of product Chadd Kenney suggests, can only be addressed by adopting measures to protect the data when it is resident in an object storage repository such as Amazon S3 – before ingest. Further, to protect against logical deletes, it is advisable to maintain continuous, immutable, and preferably air-gapped backups that are instantly recoverable into Snowpipe.

3. Consider SCIM with multi-factor authentication

Enterprises should use SCIM (system for cross-domain identity management) to help facilitate automated provisioning and management of user identities and groups (i.e. roles used for authorizing access to objects like tables, views, and functions) in Snowflake. This makes user data more secure and simplifies the user experience by reducing the role of local system accounts. Plus, by using SCIM where possible, enterprises will also get the option to configure SCIM providers to synchronize users and roles with active directory users and groups.

On top of this, enterprises also should use multi-factor authentication to set up an additional layer of security. Depending on the interface used, such as client applications using drivers, Snowflake UI, or Snowpipe, the platform can support multiple authentication methods, including username/password, OAuth, keypair, external browser, federated authentication using SAML and Okta native authentication. If there’s support for multiple methods, the company recommends giving top preference to OAuth (either snowflake OAuth or external OAuth) followed by external browser authentication and Okta native authentication and key pair authentication.

4. Column-level access control

Organizations should use Snowflake’s dynamic data masking and external tokenization capabilities to restrict certain users’ access to sensitive information in certain columns. For instance, dynamic data masking, which can dynamically obfuscate column data based on who’s querying it, can be used to restrict the visibility of columns based on the user’s country, like a U.S. employee can only view the U.S. order data, while French employees can only view order data from France.

Both features are pretty effective, but they use masking policies to work. To make the most of it, organizations should first determine whether they want to centralize masking policy management or decentralize it to individual database-owning teams, depending on their needs. Plus, they would also have to use invoker_role() in policy conditions to enable unauthorized users to view aggregate data on protected columns while keeping individual data hidden.

5. Implement a unified audit model

Finally, organizations should not forget to implement a unified audit model to ensure transparency of the policies being implemented. This will help them actively monitor policy changes, like who created what policy that granted user X or group Y access to certain data, and is as critical as monitoring query and data access patterns. 

To view account usage patterns, use system-defined, read-only shared database named SNOWFLAKE. It has a schema named ACCOUNT_USAGE containing views that provide access to one year of audit logs.

Source link

Continue Reading

Trending

URGENT: CYBER SECURITY UPDATE