Funds for Customer Experience Tracker FullStory

The round was led by growth equity firm Stripes, with participation from Dell Technologies Capital, GV (formerly Google Ventures), Kleiner Perkins and …

Funds for Customer Experience Tracker FullStory

April 29 2019

Atlanta, Georgia-based software company FullStory has raised $32m series C funding, which it will use to further develop its analytics suite for digital customer experiences.

Scott VoigtFullStory was founded in 2014 by former Google engineers, and captures clicks, mouse movements and other data such as failed clicks and scripts, to give clients usability insights for their web sites, ecommerce stores and apps. This includes ‘dead clicks’ (where users click but nothing happens in response); ‘rage clicks’ (multiple rapid clicks on an element) and ‘thrashing mice’ (when the mouse pointer is whipped angrily back and forth). Sessions can be replayed for the client to watch, and users can be identified with a profile card with name, email address, location, session history, screen resolution and other information.

CEO and cofounder Scott Voigt (pictured) comments: ‘Top-down tools simply weren’t built to provide the insights companies need to surface – and solve – problems in the increasingly complicated realm of customer experience. FullStory aims to tell you when, where, and why you are losing… revenue and operationalizes concern for the digital customer experience across organizations in a secure, scalable, and privacy-compliant way’.

The round was led by growth equity firm Stripes, with participation from Dell Technologies Capital, GV (formerly Google Ventures), Kleiner Perkins and Salesforce Ventures; and brings the firm’s total raised to date to $57m. Stripes partner Ron Shah will join FullStory’s Board of Directors with Dell Technologies Capital partner Creighton Hicks, who will serve as an observer. Shah comments: ‘We believe FullStory has the attributes of a company poised to succeed in their market as innovative executives across the world realize that digital experience is a critical-path to revenue, and increasingly the primary way in which they engage with their customers in the modern economy’.

FullStory employs around 175 people – up fourfold in two years – and says it plans to move to a new headquarters later this year. The firm is online at www.fullstory.com .

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Marketers positive about GDPR’s impact on email, as ROI increases, DMA reports

adds Marcus Gearey, chair of the DMA Email council’s research hub & Analytics manager, Zeta Global, “Less than a year into the new regime, …
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2In a year of change for many marketers, email remains the key strategic channel for most marketers – 91% rating it as important. Despite the challenges many felt with the incoming General Data Protection Regulation (GDPR), most marketers (56%) feel positive about the impact the new laws have had on their email campaigns – just a fifth feeling the contrary (20%). Resulting in a marked increase in the returns from email and other key measurement metrics for the channel.

According to the ‘Marketer Email Tracker 2019’ report released today by the DMA, in partnership with dotdigital, the new rules have had a positive impact on a range of email metrics and, despite the short-term difficulties, the long-term benefits are already being felt. Even though some marketing teams have observed decreases in list sizes, the majority of marketers reported an increase in email open rates (74%) and click-through rates (75%) in the past 12 months. Most organisations also saw a reduction in opt-out rates (41%) and spam complaints (55%) in comparison to previous years, further evidence of the GDPR effect.

“Before the GDPR’s implementation something not unlike panic had set in amongst many. Was legitimate interest or informed consent the best strategy – or both? Would marketing lists be decimated or would a new era of opted-in consumers show those lists to be largely chaff in the first place?” adds Marcus Gearey, chair of the DMA Email council’s research hub & Analytics manager, Zeta Global, “Less than a year into the new regime, marketers appear to have transitioned from wary to hopeful. Perhaps this will change when the ICO make their first big prosecution under the new legislation, but the disaster it was sometimes pitched as a precursor to, looks to have been wildly exaggerated.”

The value of email

The latest edition of the research reveals that almost two thirds (62%) of marketers are now confident in their organisation’s ability to measure return on investment (ROI) – the highest-ever proportion since the DMA started asking the question back in 2012. According to these marketers, the estimate of ROI for every £1 spent on email has increased by almost £10 since the previous study (pre-GDPR) – this is now set at £42.24, up from £32.28 in 2017.

Marketers’ confidence in calculating the lifetime value (LTV) of each individual email address also rose sharply over the last year, with nearly twice as many organisations able to calculate these figures now (41%) than before the new rules came into force (21%). The estimated LTV for each email also increased in 2018 to £37.32.

“Despite the challenges the GDPR have posed to those of us working in the data and marketing industry, it’s positive to see that sentiment towards the changes is already broadly positive,” said Rachel Aldighieri, MD of the DMA, “The good news for proponents of email marketing is that ROI is increasing; marketers are predicting an increase in investment in the channel; they’re also bullish about their ability to measure its effectiveness and their overall competence in the discipline of email marketing.”

Phil Draper, chief marketing officer at dotdigital, said: “What struck me most in this year’s figures is that email ROI has shot up from £32 to £42 – email’s return on investment is undeniable. As such, marketers are measuring ROI more than ever before; many are able to access the tools they need to quantify the impact of email. Moreover, marketers may have benefited from better quality email addresses in their arsenal, post GDPR.”

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Types of Ad Fraud

Ad fraud is a big, costly problem in our industry. To fight it, you have to understand the different forms it can take. The brand safety experts at Peer39 …

Ad fraud is a big, costly problem in our industry. To fight it, you have to understand the different forms it can take. The brand safety experts at Peer39 typically divide ad fraud into three categories: non-human traffic (i.e., bots); ads with zero chance of being seen (i.e., zero-percent viewability); and intentional misrepresentation. The imposters who are responsible for these kinds of fraud are savvy, and they are continually finding new and more sophisticated ways to make money by defrauding advertisers.


Here’s a closer look at some of the most common types of fraud:

Bot basics

General invalid traffic (GIVT)arescripts that run from a server such as Amazon Web Services or some other hosting provider. As their name implies, these bots are usually easy to identify because they have a static IP, user agent, and cookie ID. This makes fingerprinting them pretty easy using DSP auction logs or even web server logs to spot abnormally high clickthrough rate (CTR) or unexpected spikes in traffic that are the signatures of simple bots.

Sophisticated invalid traffic (SIVT)is not as easy to identify. These bots rotate user agents, using random proxies to rotate IP addresses, and they mimic normal “human” CTRs, so they are more challenging to detect. They are also now capable of completing complicated tasks like filling out forms or completing videos. Sophisticated bots can even put items in shopping carts and visit multiple sites to generate histories and cookies—making them look attractive to advertisers and publishers.

The unviewable

Ad stackingisacommon way that fraudulent publishers get credit for running an ad that is actually hidden behind other ads and not viewable. The publisher can thereby generate multiple impressions for a single page view, even when only the top ad in the “stack” is ever seen.

Site scams

Domain spoofingis a scheme employed bydeceitful publishers, ad exchanges, or networks to obscure the nature of their traffic to resemble legitimate websites. For example, an advertiser might sign off on a contract to run a campaign on a legitimate entertainment website with very high monthly traffic, but instead its ads end up on an unknown site. This practice is most prevalent in the programmatic space where publishers are sometimes allowed to declare their own domains and label their own site IDs. Spoofed domains are not just fake website addresses, they are also banner farms that contain bad content.

Ghost sitesare among the most difficultfraud methods for advertisers to spot. Fraudsters create content farms and use bots to mimic human traffic. The sites may then be introduced to a legitimate ad exchange, where ad impressions are made available for advertisers to buy programmatically. Exchanges usually spot these schemes quickly, but even a short lifespan can be profitable to the ghost site creators.

Zero-adsites arethosewhere advertising is forbidden, such as government or educational sites. But fraudsters still find ways to inject ads into them when a user downloads and installs a browser extension or app (such as a free PDF converter or browser toolbar) bundled with software that quietly injects unwanted ads into the user’s browser.

Fraud is lucrative

The scale of online ad fraud has a significant impact on advertising ROI and advertiser confidence because all those falsified impressions and clicks cost money without yielding conversions or revenue. It’s estimated that fraud consumes $1 of every $3 spent on digital advertising. In 2018, and advertisers lost an estimated $51 million every day to fraud, a figure that is expected to more than double by 2022. Time and time again, advertisers unwittingly reinvest in fraudulent inventory because it appears on reports to be driving results. Worst of all, ad fraud is not technically illegal, so there is minimal risk for bad actors.

Protection is possible

Because fraud schemes continually evolve, effective fraud prevention requires staying one step ahead of their game. Peer39 does this by tackling the problem from every angle, both before and after the buy. Peer39 pre-bid antifraud targeting helps marketers exclude fraud from the buy up front, eliminating zero-ad sites and other fake inventory. Peer39 post-buy solutions offer multichannel, AI-driven fraud detection and filtration that enables you to monitor viewability and detect bots and other invalid traffic threats—even difficult-to-detect schemes and domain spoofing.


Fraud isn’t going anywhere, but with vigilance, you can significantly reduce your exposure and protect your investment.

Contact a your account rep or a Peer39 account manager to keep your next campaign fraud-free.

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The Current Applications Of Artificial Intelligence In Mobile Advertising

It’s not just the mobile apps that are becoming more “intelligent”. Advertising encouraging us to interact and install those apps has made its way onto a …

Mobile advertising and AI.Getty Royalty Free.

The concept of self-programming computers was closer to science fiction than reality just ten years ago. Today, we feel comfortable conversing with smart personal assistant like Siri and keep wondering just how Spotify guessed what we like.

It’s not just the mobile apps that are becoming more “intelligent”. Advertising encouraging us to interact and install those apps has made its way onto a way new quality level as well. Thanks to advances in machine learning (ML), the baseline technology for AI, mobile advertising industry is now undergoing significant transformation.

AI can reduce mobile advertising fraud

In 2018, mobile ad fraud rates have doubled compared to the previous year. To tap into the expanding marketer’s ad budgets, hackers have created a host of new tricks to their playbook. According to Adjust data, the following mobile ad threats have prevailed:

SDK spoofing accounts represented 37% of ad fraud. In SDK Spoofing malicious code is injected in one app (the attacked) that simulates ad clicks, installs and other fake engagement and sends faulty signals to an attribution provider on behalf of the “victim” app. Such attacks can make a significant dent in an advertiser’s budget by forcing them to pay for installs that never actually took place.

Click injections accounted for 27% of attacks. Cybercriminals trigger clicks before the app installation is complete and receive credit for those installs as a result. Again, these can drain your ad budgets and dilute your ROI numbers.

Faked installs and click spam accounted for 20% and 16% of fraudulent activities respectively. E-commerce apps have been in the fraud limelight this year, with nearly two-fifths of all app installs being marked as “fake” or “spam”, followed closely by games and travel apps. Forrester further reports that 69% of marketers whose monthly digital advertising budgets run above $1 million admit that at least 20% of those budgets are drained by fraud on the mobile web.

If the issue is so big, why no one’s tackling it? Well, detecting ad fraud is a complex process that requires 24/7 monitoring and analysis of incoming data. And that’s where AI comes to the fore. Intelligent algorithms can operationalize large volumes of data at a pace far more accurate than any human analyst, spot abnormalities and trigger alerts for further investigation. What’s more promising is that with advances in deep learning, the new-gen AI-powered fraud systems will also become capable to self-tune their performance over time, learning to predict, detect and mitigate emerging threats.

AI brings increased efficiency and higher ROI for real-time ad bidding

One of the biggest selling points of “AI revolution” across multiple industries is the promise to automate and eliminate low-value business processes. Mobile advertising is no exception. Juniper Research predicts that by 2021, machine learning algorithms that increase efficiency across real-time bidding networks will drive an additional $42 billion in annual spend.

Again, thanks to robust analytical capabilities ML-algorithms can create the perfect recipe for your ad, displaying it at the right time to the right people. Google has already been experimenting with various optimizations for mobile search ads. The results so far are rather promising. Macy’s, for instance, has been leveraging inventory ads and displaying them to customers’ who recently checked-up on their products and are now in close geo-proximity to the store holding the goods they looked up a few hours ago.

AdTiming has been helping marketers refine their approach to in-app advertising. By leveraging and crunching data from over 1000 marketers, the startup has developed their recipe for best ad placements. “Prescriptive analytics will tell our users when is the best time to run their ads; what messaging to use and how frequently the ad needs to be displayed in order to meet their ROI while maintaining the set budget,” said Leo Yang, CEO of AdTiming.

Just how competitive AI-powered real-time ad bidding can be? A recent experiment conducted by a group of scientists on Taobao – China’s largest e-commerce platform – proves that algorithms are performing way better than humans.

For comparison:

  • Manual bidding campaigns brought in 100% ROI with 99.52% of budget spent.
  • Algorithmic bidding generated 340% ROI with 99.51% of budget spent.

It’s clear who’s the winner here.

AI enables advanced customer segmentation and ad targeting

Algorithms are better suited for detecting patterns than a human eye, especially when sent to deal with large volumes of data. They can effectively group and cluster that data to create rich user profiles for individual customers – based on their past interactions with your brand, their demographic data and online browsing behaviors.

This means that you are no longer targeting a broad demographic of “women (aged 25-35), based in the US”. You become capable to pursue more niche audiences, exhibiting rather specific behaviors e.g. regularly engaging with hair care products in the luxury segment on social media. This insight can be further applied by an AI system when entering an RTB auction to predict when your ad should be displayed in front of the consumer (matching your profile) and when it’s worth a pass.

The best part is that AI-powered advertising is no longer cost-prohibitive for smaller companies. With new solutions entering the market, it would be interesting to observe how the face of mobile advertising will change in 2019 and onward.

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Yotpo Updates Consumer Feedback Platform

Yotpo, providers of a customer content marketing platform, has unveiled a new suite of eCommerce solutions that leverages artificial intelligence to …

Yotpo, providers of a customer content marketing platform, has unveiled a new suite of eCommerce solutions that leverages artificial intelligence to collect and display user-generated content (UGC) likely to drive higher conversions based on past performance.

“Consumers crave authenticity from the brands they engage with, and nothing is more genuine than validation from fellow shoppers. Leveraging consumer behavior, sentiment, and purchase data, intelligent user-generated content gives marketers a much more effective way to capture attention, build trust, and accelerate sales,” said Tomer Tagrin, CEO and co-founder of Yotpo, in a statement.

Earlier this year, Yotpo unveiled Insights, which applies natural language processing, deep learning, and sentiment analysis to user reviews to turn feedback into customer experience data. The same AI engine powers Yotpo’s new eCommerce enhancements to enable the following:

  • More reviews: Machine learning optimizes email review request send times tailored to the customer, the company, and/or the industry. Companies can generate more reviews, fast, improving order-to-review conversion rates to 10 percent or more, compared to industry standards of 1 percent or 2 percent.
  • Better quality reviews: An AI assistant encourages customers to write high-quality, in-depth reviews by suggesting relevant topics to cover. Topics are suggested based on previous submissions and buyer search history. Initial results show that AI assistance yielded a 61 percent bump in topics within reviews.
  • Auto-review: Sentiment analysis detects positive or negative sentiment words and phrases within reviews with 92 percent accuracy, allowing marketers to auto-publish positive reviews while flagging ones that might need manual attention and response.
  • Personalized views: On product pages, shoppers can customize their UGC views to find specific information. In addition to a search-in-reviews function, visitors can filter reviews based on topics. Machine learning identifies the topics that had the most impact on purchase decisions.
  • Review highlights: Optimized for the mobile-first shopper, sentiment analysis pulls key snippets from top-performing reviews and displays them above the fold.
  • Best reviews for marketing: Yotpo dynamically pulls the best and most relevant copy and images in email promotional campaigns, cart abandonment emails, social ads, and more.

Specialty fashion retailer Yandy used Yotpo to revive flagging dynamic product ads (DPA) for Facebook. Tapping into Yotpo’s Facebook Ad integration, Yandy automatically synced five-star customer reviews that had influenced purchases. As a result, Yandy’s click-through rates tripled and cost-per-click decreased by 72 percent.

“We have spent less money than our standard DPA campaign but have driven 70 percent more purchases and seen a 78 percent higher [return on ad spend],” said Eric Polatty, digital marketing manager at Yandy, in a statement.


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