Agent Spotlight: Ahmed Madni, Father of Two and Invisible Operator
January 18, 2023
March 24, 2023
5
min to Read
eCommerce
Between 3-5% of all eCommerce transactions end in a payment dispute. Whether it's a customer who is unhappy with a product or a service, or a shopper who is contesting a charge, finding an effective solution is essential to maintaining a healthy cash flow and recovering revenue.
In this blog post, we'll explore how Invisible can help you resolve both payment & chargeback disputes at scale and ultimately recover lost revenue.
A payment dispute in eCommerce occurs when a customer challenges a transaction, claiming it to be unauthorized, incorrect, or not up to their expectations. This can result in chargebacks or withheld payments that carry a two-fold impact: revenue is left on the table and the reputation of the business is damaged.
According to a report from PYMNTS, the revenue impact is significant for both small and large businesses. Merchants doing more than $1 billion in yearly business lose 0.51% of revenue to disputed transactions, the report found, and those doing $20m-$100m in sales lose 0.4%.
Alarmingly, the problem is getting worse, with the PYMNTS report finding that a quarter of merchants lost more revenue YoY, and even more reported an increase in disputed transactions from the previous year.
The report notes that businesses are being proactive in resolving chargebacks. For merchants performing thousands of transactions, it’s key to navigate disputes at scale.
Many are looking to third-party tools or building solutions in-house to do it. 93% of businesses doing $20m-100m in yearly sales have turned to third-party tools.
Although they are often used interchangeably, payment disputes and chargeback disputes aren’t the same things. Both types of disputes involve customers contesting a transaction; however, the processes and reasons behind them can vary.
A payment dispute typically occurs when a customer or client challenges a transaction, claiming it to be unauthorized, incorrect, or not up to their expectations. Payment disputes can arise due to a variety of reasons, such as:
In a payment dispute, the customer usually contacts the merchant directly to resolve the issue. The merchant can then investigate the dispute and determine the best course of action, such as offering a refund, providing additional documentation, or negotiating with the customer.
A chargeback dispute, on the other hand, occurs when a customer disputes a transaction directly with their bank or credit card issuer, rather than contacting the merchant. This process is more formal and involves the customer asking the bank or credit card issuer to reverse the transaction due to various reasons, such as:
When a chargeback dispute is initiated, the bank or credit card issuer investigates the claim and decides whether to grant a chargeback. If a chargeback is granted, the merchant is usually required to return the disputed amount, along with a chargeback fee.
All merchants experience both payment and chargeback disputes. The smartest merchants turn to a third-party solution that has the capability to tackle every dispute and win at scale.
Invisible offers a comprehensive, streamlined approach to payment & chargeback dispute resolution specifically designed for eCommerce. By leveraging cutting-edge technology and a team of skilled professionals, Invisible takes the stress out of managing disputes while increasing the likelihood of a favorable outcome.
How does it work? Let’s look at how we do this for merchants using Stripe.
When you resolve a dispute through Stripe, you need to submit evidence. Our trained operators do it on your behalf, submitting all possible details and documentation to help you make your case and speed up the time to resolution.
The evidence we submit for you includes communications with the other party and any relevant policy documents like your refunds and cancellation policy. The result: effective chargeback and payment dispute resolution at any scale.
A property tech company we’ve worked with for 2 years leans on Invisible to handle payment dispute resolution through Stripe. In that time, we have successfully resolved 9,000 Stripe disputes and counting, leading to over $160k in recouped revenue in 2022 alone.
Interested in learning more about how we can resolve disputes for your business? Get in touch.
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Between 3-5% of all eCommerce transactions end in a payment dispute. Whether it's a customer who is unhappy with a product or a service, or a shopper who is contesting a charge, finding an effective solution is essential to maintaining a healthy cash flow and recovering revenue.
In this blog post, we'll explore how Invisible can help you resolve both payment & chargeback disputes at scale and ultimately recover lost revenue.
A payment dispute in eCommerce occurs when a customer challenges a transaction, claiming it to be unauthorized, incorrect, or not up to their expectations. This can result in chargebacks or withheld payments that carry a two-fold impact: revenue is left on the table and the reputation of the business is damaged.
According to a report from PYMNTS, the revenue impact is significant for both small and large businesses. Merchants doing more than $1 billion in yearly business lose 0.51% of revenue to disputed transactions, the report found, and those doing $20m-$100m in sales lose 0.4%.
Alarmingly, the problem is getting worse, with the PYMNTS report finding that a quarter of merchants lost more revenue YoY, and even more reported an increase in disputed transactions from the previous year.
The report notes that businesses are being proactive in resolving chargebacks. For merchants performing thousands of transactions, it’s key to navigate disputes at scale.
Many are looking to third-party tools or building solutions in-house to do it. 93% of businesses doing $20m-100m in yearly sales have turned to third-party tools.
Although they are often used interchangeably, payment disputes and chargeback disputes aren’t the same things. Both types of disputes involve customers contesting a transaction; however, the processes and reasons behind them can vary.
A payment dispute typically occurs when a customer or client challenges a transaction, claiming it to be unauthorized, incorrect, or not up to their expectations. Payment disputes can arise due to a variety of reasons, such as:
In a payment dispute, the customer usually contacts the merchant directly to resolve the issue. The merchant can then investigate the dispute and determine the best course of action, such as offering a refund, providing additional documentation, or negotiating with the customer.
A chargeback dispute, on the other hand, occurs when a customer disputes a transaction directly with their bank or credit card issuer, rather than contacting the merchant. This process is more formal and involves the customer asking the bank or credit card issuer to reverse the transaction due to various reasons, such as:
When a chargeback dispute is initiated, the bank or credit card issuer investigates the claim and decides whether to grant a chargeback. If a chargeback is granted, the merchant is usually required to return the disputed amount, along with a chargeback fee.
All merchants experience both payment and chargeback disputes. The smartest merchants turn to a third-party solution that has the capability to tackle every dispute and win at scale.
Invisible offers a comprehensive, streamlined approach to payment & chargeback dispute resolution specifically designed for eCommerce. By leveraging cutting-edge technology and a team of skilled professionals, Invisible takes the stress out of managing disputes while increasing the likelihood of a favorable outcome.
How does it work? Let’s look at how we do this for merchants using Stripe.
When you resolve a dispute through Stripe, you need to submit evidence. Our trained operators do it on your behalf, submitting all possible details and documentation to help you make your case and speed up the time to resolution.
The evidence we submit for you includes communications with the other party and any relevant policy documents like your refunds and cancellation policy. The result: effective chargeback and payment dispute resolution at any scale.
A property tech company we’ve worked with for 2 years leans on Invisible to handle payment dispute resolution through Stripe. In that time, we have successfully resolved 9,000 Stripe disputes and counting, leading to over $160k in recouped revenue in 2022 alone.
Interested in learning more about how we can resolve disputes for your business? Get in touch.
LLM Task
Benchmark Dataset/Corpus
Common Metric
Dataset available at
Sentiment Analysis
SST-1/SST-2
Accuracy
https://huggingface
.co/datasets/sst2
Natural Language Inference / Recognizing Textual Entailment
Stanford Natural Language Inference Corpus (SNLI)
Accuracy
https://nlp.stanford.edu
projects/snli/
Named Entity Recognition
conll-2003
F1 Score
https://huggingface.co/
datasets/conll2003
Question Answering
SQuAD
F1 Score, Exact Match, ROUGE
https://rajpurkar.github.i
o/SQuAD-explorer/
Machine Translation
WMT
BLEU, METEOR
https://machinetranslate
.org/wmt
Text Summarization
CNN/Daily Mail Dataset
ROUGE
https://www.tensorflow
.org/datasets/catalog/
cnn_dailymail
Text Generation
WikiText
BLEU, ROUGE
Paraphrasing
MRPC
ROUGE, BLEU
https://www.microsoft.
com/en-us/download/details.a
spx?id=52398
Language Modelling
Penn Tree Bank
Perplexity
https://zenodo.org/recor
d/3910021#.ZB3qdHbP
23A
Bias Detection
StereoSet
Bias Score, Differential Performance
Table 1 - Example of some LLM tasks with common benchmark datasets and their respective metrics. Please note for many of these tasks, there are multiple benchmark datasets, some of which have not been mentioned here.
Metric
Usage
Pros
Cons
Accuracy
Measures the proportion of correct predictions made by the model compared to the total number of predictions.
Simple interpretability. Provides an overall measure of model performance.
Sensitive to dataset imbalances, which can make it not informative. Does not take into account false positives and false negatives.
Precision
Measures the proportion of true positives out of all positive predictions.
Useful when the cost of false positives is high. Measures the accuracy of positive predictions.
Does not take into account false negatives.Depends on other metrics to be informative (cannot be used alone).Sensitive to dataset imbalances.
Recall
Measures the proportion of true positives out of all actual positive instances.
Useful when the cost of false negatives is high.
Does not take into account false negatives.Depends on other metrics to be informative (cannot be used alone)and Sensitive to dataset imbalances.
F1 Score
Measures the harmonic mean of precision and recall.
Robust to imbalanced datasets.
Assumes equal importance of precision and recall.May not be suitable for multi-class classification problems with different class distributions.
Perplexity
Measures the model's uncertainty in predicting the next token (common in text generation tasks).
Interpretable as it provides a single value for model performance.
May not directly correlate with human judgment.
BLEU
Measures the similarity between machine-generated text and reference text.
Correlates well with human judgment.Easily interpretable for measuring translation quality.
Does not directly explain the performance on certain tasks (but correlates with human judgment).Lacks sensitivity to word order and semantic meaning.
ROUGE
Measures the similarity between machine-generated and human-generated text.
Has multiple variants to capture different aspects of similarity.
May not capture semantic similarity beyond n-grams or LCS.Limited to measuring surface-level overlap.
METEOR
Measures the similarity between machine-generated translations and reference translations.
Addresses some limitations of BLEU, such as recall and synonyms.
May have higher computational complexity compared to BLEU or ROUGE.Requires linguistic resources for matching, which may not be available for all languages.
Table 2 - Common LLM metrics, their usage as a measurement tool, and their pros and cons. Note that for some of these metrics there exist different versions. For example, some of the versions of ROUGE include ROUGE-N, ROUGE-L, and ROUGE-W. For context, ROUGE-N measures the overlap of sequences of n-length-words between the text reference and the model-generated text. ROUGE-L measures the overlap between the longest common subsequence of tokens in the reference text and generated text, regardless of order. ROUGE-W on the other hand, assigns weights (relative importances) to longer common sub-sequences of common tokens (similar to ROUGE-L but with added weights). A combination of the most relevant variants of a metric, like ROUGE is selected for comprehensive evaluation.
