Atom Tickets was struggling with chargebacks; their chargeback rate was very high, which decreased their revenue and order volume. There isn’t a team focused on preventing fraud – while there are stakeholders across departments, fraud is a one-man army led by Trust & Safety Specialist Aaron Rennell. Managing all of Atom Tickets’ fraud was already a big job but it got even trickier for Aaron when Atom Tickets experienced spikes in activity during blockbuster movie ticket sales. The significant increase in online movie sales for the company also brought in an uptick in fraudulent purchases for blockbuster must-see movies. Given the increase in potentially fraudulent purposes, they needed a solution that would automate and streamline fraud prevention to make it manageable, and help them greatly reduce their chargeback rate.

DoorDash, a technology company that enables merchants to reach consumers via delivery, was facing a significant challenge with fraudsters. These fraudsters were using stolen credit cards and reselling DoorDash as a service illegally. They would advertise online through various platforms, claiming to be selling DoorDash at a significant discount and convincing consumers to make purchases through them. This left DoorDash in a position of having to reimburse the victim (either directly or via chargeback) whose credit card was stolen after the victim disputed the charge. DoorDash was also experiencing chargebacks due to the charges on those stolen credit cards, and their rules-based fraud prevention needed to be regularly updated to stave them off, consuming time and resources. In these early days of DoorDash, no automation was in place and most fraud prevention was done via manual review. DoorDash needed a solution that could proactively detect and prevent these fraudsters before they could make it onto the platform to do damage.

As Favor Delivery expanded, they experienced an increase in the number of chargebacks. The growth of fraudulent accounts and account takeover (ATO) attempts were becoming more frequent. Favor Delivery was using their internal heuristic system to manually search for fraud, which wasn’t scalable and couldn’t keep up with the volume of incoming orders. They needed a proactive solution that could automate and keep them ahead of fraud – not struggling to keep up with it.

HelloFresh, the world’s leading meal kit company, faced a significant challenge with users exploiting their promotional offers, which was hurting their bottom line. The company initially tried to tackle these challenges internally through manual review processes in spreadsheets, but quickly found that they didn’t have the breadth of data they needed to effectively detect which customers were exploiting their system. The team decided it was crucial to seek out a more effective and efficient solution on the market instead of building their own capabilities. They were looking for a flexible model that could adapt to each of their market’s unique needs, responsive and knowledgeable customer support, and an adjustable pricing model.

CoinJar, a well-established digital currency exchange, was facing challenges with identity fraud, chargebacks, and account takeover. Being an online-only platform, it was crucial for CoinJar to have an effective online identity assurance program. The irreversible nature of crypto transactions and the anonymity provided by digital currencies made it a prime target for fraudsters. The company needed a solution that could adapt in real-time and provide effective fraud prevention.

GetMyBoat, the world's largest boat rental and charter marketplace, was facing a significant challenge with fraudsters. These cybercriminals were coordinating efforts to exploit the platform for their own financial gain. They would transact in small amounts to test the system and then use associated accounts to complete higher value reservations. The company was also experiencing an increase in chargebacks, which were costing the business resources, time, and money. These chargebacks were in the form of friendly fraud, where customers try to get a refund for a service they already used, and common fraud, through stolen credit card credentials. The company needed a solution to combat these issues and turned to Sift for help.

The major American airline was facing a high volume of Visa disputes, which were becoming valid chargebacks. Over the first three months, the company had over 4,700 Visa disputes initiated against them, representing $1.8 million. This was causing significant financial loss and operational challenges for the airline. The company needed a solution to reduce the number of disputes and prevent them from becoming valid chargebacks.

The luxury apparel and accessories company was experiencing unprecedented growth in both physical locations and e-commerce volume. With this growth came an influx of transactions and a surge of disputes. By early 2015, the company faced a dilemma: should they expand and adjust to manage customer disputes with manual processes, or find an automated dispute management solution to streamline the process? Managing disputes manually would require the creation of a new department, and all of the hiring, training, and onboarding required to furnish said department. With the EMV liability shift deadline growing ever closer, the team knew they couldn’t gain the expertise needed before disputes became an even bigger problem.

Taptap Send, a global remittance service, was facing an increase in fraudulent payments made with stolen credit cards as their business grew. They needed a streamlined fraud prevention solution that was nimble enough to scale across international lines and quick enough to meet customers’ needs. The market for global remittances, which accounts for over $500 billion annually, is dominated by traditional services that are expensive, can take days to arrive, and have limited reach in rural areas. Taptap Send was committed to providing their customers with a speedy, secure, and hassle-free user experience.

Uphold, a multi-asset digital money platform, was facing challenges in ensuring the trustworthiness of new users while reducing friction throughout the customer journey. The company needed accurate risk assessments of the actions taken on their site. This meant deploying additional friction points and manual review before Uphold would allow a customer to transact. Uphold needed a fraud prevention solution that highlighted the riskiness of every action taken on their site and that simplified the review process for their fraud analysts, allowing them to quickly identify linked fraud behaviors between accounts so they could stop fraud fast.

Qube Money, a banking and budgeting app, was facing issues with identity theft and account takeover fraud. Fraudsters were stealing identities and setting up accounts on Qube. International transactions also posed a risk due to more complicated chargeback processes. In the early stages of the startup, the app experienced a fraudulent attack by a fraud ring, costing the company tens of thousands of dollars. As an early-stage startup, they knew they couldn’t afford to have more fraud like this happen on their app.

Banxa, a fast-growing public payments and compliance infrastructure provider for the digital asset industry, faced a significant challenge when its business volume increased by 30x. The company encountered multiple fraud scenarios, including fake profile creation, card fraud, scams, and chargebacks. Initially, Banxa had set up their own fraud function from scratch, handling everything manually when volumes were manageable. However, as Banxa began to grow, this basic model became too limited for their needs. It introduced unwanted friction for trusted customers and became riskier when incorporating multiple variables and increased velocity. So when Banxa’s volume spiked 30x, their fraud rate rose alongside it. The team knew they needed to implement something quickly to support their scaling business, which is where Sift came in.

The email marketing platform was facing a significant challenge due to the susceptibility of the marketing technology industry to fraud attacks. The scale and severity of spam and scams were increasing, putting the onus on sending providers to protect the health of their network. As the company began to scale their business faster than their manual vetting processes would allow, they needed a solution that could keep up. They were looking for a solution that offered uptime, affordability at scale, model customization, data sharing and app integrations, and the ability to automate common support tasks such as account disablement.

When Studypool first launched, the platform saw users who were taking advantage of tutors by posting questions and later filing chargebacks, in an attempt to get free study help. Some users also tried to game the system by creating fake student accounts so they could pay themselves and later file a chargeback, ultimately getting their money back and a payout from Studypool. At the time, their internal fraud prevention tools couldn’t keep up with the types of fraud surfacing on the platform. The tools were only able to track IP addresses and weren’t accurate or reliable, so Studypool decided to look for a better solution.

When Swan Bitcoin first launched, the platform experienced an influx of fraudulent transfers, chargebacks, and identity reuse that ultimately led to a significant loss rate. The team identified multiple accounts formed under the same identity, spamming SMS with an influx of phone numbers that made it difficult to keep up with. And although implementing an upfront passwordless login system helped reduce fraud rates, the platform was not entirely free and clear of fraud. Swan recognized they needed a more robust fraud prevention solution to stay a step ahead of the sophisticated tactics they were facing. But being in the security-conscious Bitcoin space, Swan also wanted to be sensitive to their customers’ privacy. Because of these concerns, one-time emails and burner phone numbers are more popular with their users—and likely to be key indicators of fraud in many industries. But for Swan, they could simply be the byproduct of cautious but trusted users looking to protect their identities. This made it especially important for Swan to take into account a larger range of signals, and not apply undue friction to every customer.

PartnerStack, a partner relationship management platform, was initially focused on transactional fraud on the end-customer side. However, they soon started to see fraudulent behavior on the partner side as well. They were faced with self-referral abuse, in which sellers tried to impersonate themselves and use their own link to generate revenue, at the expense of PartnerStack. Fraudsters were also attempting to game the rewards system by signing up for as many programs as possible. As these more advanced forms of fraud began popping up, the team recognized they needed a more sophisticated solution. Their original fraud platform was outdated, limited to email address and IP signals, and simply couldn’t keep up with the abuse they were battling. As a publicly-accessible marketplace, it was crucial for PartnerStack to maintain a trustworthy and secure platform—for customers, partners, and their own bottom line.

Food delivery businesses face several challenges including cost control, customer service, logistics, staffing, and unpredictable spikes in order volume. These issues are compounded when processing and delivering food orders. Delivery speed is the number one factor in customer satisfaction, with 60% of customers across all markets citing it as a key factor. The quality of perishable items is significantly affected by an increase in travel time, even if only a few minutes difference. While speed is a vital component, it is also essential to ensure the safety of delivery people on the road. Additionally, companies must ensure that they are equipped to deal with spikes in order volume, which may be predictable or unpredictable. Finally, manually planning routes is inefficient and costly.

John Lewis, one of the UK's largest omnichannel retailers, aimed to seamlessly link online shopping with the traditional shop experience while still providing exceptional service to customers. The company pioneered its first click and collect service in 2008, enabling shoppers to choose from over 200,000 products on johnlewis.com for free delivery to local John Lewis or Waitrose shops. However, the company faced challenges in improving the cross-channel shopping experience, enabling click and collect ordering, streamlining the delivery of large products to customer addresses, and providing detailed tracking information.

Polybags Ltd, a leading UK site for sourcing polythene bags and film, was using an enterprise resource planning (ERP) system provided by Kerridge Commercial Systems (KCS). The ERP system was directly integrated through a bespoke piece of software with a single carrier. However, when that carrier ceased to exist, Polybags faced a serious dilemma. The company needed a solution that would protect against overreliance on a single carrier and provide options to easily bring on more carriers without having to develop their own bespoke link.

brands4friends, Germany’s first and largest shopping club for fashion & lifestyle products, was looking to optimize its shipping processes to aid international expansion. The company was using seven shipping modules from various providers, which was time-consuming and inefficient for the logistics department. The company also wanted to increase transparency in the supply chain, monitor shipping companies, and improve the shipping experience for customers.

River Island, a global leader in stylish, affordable fashion, was facing challenges with its carrier management system. The system was manually intensive, requiring personnel to assign each parcel to a carrier on an individual basis when processing an order. Moreover, the system wasn’t aligned with physical stores, and it couldn’t handle carrier automation or package assignment. The retailer was looking for a more efficient approach to meet the demands of shifting consumer behaviour, especially across borders.

Gorgeous Shop, a luxury haircare and beauty retailer, was facing challenges with its logistics operations. The company was relying on a process of manual address data entry, which required order information to be imported into bespoke systems that varied from one courier to another. This led to picking errors, addressing errors, and manifesting issues. The company was seeking to minimize these inefficiencies and free up resources that would be better spent on sales and marketing efforts. Additionally, the company was looking to drive customer retention and loyalty by providing consistent, reliable, and cost-effective delivery options.

Halfords, a company offering 30,000 product lines related to automobiles, leisure, cycling, and more, wanted to keep up with the changing consumer behaviors as online shopping becomes more prevalent. The company aimed to launch a service that allows consumers to order goods online and collect them from a local store at no extra delivery cost. This was in response to the changing shopping habits of their customers who increasingly move between Halfords stores and the brand’s website. The challenge was to implement this without incurring any additional implementation, resource, or cost requirements.

figleaves.com, a luxury lingerie and clothing retailer, was facing challenges in improving customer satisfaction and expanding its range of delivery options. The company's in-house warehouse management system was integrated with two carriers, both offering next-day service and a more economical three- to five-day delivery option. However, customer feedback revealed that users wanted a wider range of delivery options, as well as improved online tracking. Introducing extra services would mean that each new carrier would need to be integrated separately into both the figleaves.com front end and the company’s back office system, which would incur significant costs and have an impact on time and resource. The company's goal was to find a way to introduce a greater choice of delivery options with minimal administrative burden.

The Indian broadcast satellite service provider was facing major issues with managing appointment bookings and the fleet of service agents. They did not receive real-time updates of booked appointments, agents’ whereabouts, delivery of service, and customer feedback. The lack of an automated system was causing delays in their daily business operations and was affecting their efficiency and customer satisfaction.

Papa Gino's Pizzeria was facing several challenges with its delivery system. The customer experience was compromised due to lack of visibility and consistency around delivery times. The company was struggling with forecasting driver requirements accurately and managing driver retention. They were eager to prioritize self-delivery to maintain a relationship with their customers. However, manual intervention when dispatching to various delivery partners was slow and laborious. They were unable to achieve the desired unit economies for profitable delivery. They had limited metrics to optimize fleet performance. A significant increase in delivery volume was having a detrimental effect on delivery success rates. Too many real-time decisions had to be made by the in-house operations team.

DeliverThat was ready to increase its delivery success rate and onboard new partners, but its past software providers limited its growth capacity. The company needed to find a logistics management system to free their tech team and the CEO from manual, tedious and time-consuming data entry. The current software system provided no transparency and nonexistent customer support. The company was also facing high development costs, advertising costs, human resources challenges, and issues with sources of capital.

The Fortune 50 general retailer, with nearly 50 supply chain facilities and 2,000 stores across the United States, was facing challenges in optimizing and innovating their freight operations across vendor to distribution center routes. The retailer was experiencing waves of canceled shipments due to a spike in freight demand in 2021. Many carriers were unable to fulfill the increasing volumes well above committed contract volumes, leading to increased volume hitting the spot market. This resulted in underperforming lanes that remained business critical, but drove up operating costs. Reliable capacity was needed to support peak retail seasons.

The metal and glass manufacturer, with operations across nearly 90 facilities in over 22 countries, was facing frequent and costly repricing due to shifting market conditions and expiring contracts. Rates were climbing and so were RFP administration costs. Within six months, teams faced four repricing exercises that tripled costs. The need to transport products—often with less than 24 hours notice—remained. While longer-term contracts were being negotiated, the manufacturer turned to the spot market for last-minute coverage. Shipping costs quickly added up and service quality started to slide.

A multinational industrial goods company faced challenges in delivering supply chain products to their customers, often within 24 hours’ notice. The volume, timing, and destination of these products were often unpredictable. The company also had to retrieve the products from their customers’ locations and then dynamically route them to servicing centers, stocking locations, or other customers’ operations. Traditional RFPs were not suitable for their operations due to the unpredictable volume requirements and lane pairings. Turning to the spot market for thousands of loads was unsustainable in the long run, requiring immense administrative effort and cost.