E-commerce
September 4, 2026
Are you wondering how to handle a customer disputing a delivery marked as completed but nowhere to be found? Proof of delivery, whether it is a signature, a photo, or a drop-off point, is often the key element to reassure them, but it does not always resolve the dispute if it is poorly explained.
The chatbot must retrieve this proof, present it clearly, and immediately open an investigation in case of doubt, thereby avoiding abruptly closing the case. In a competitive e-commerce environment, the speed of resolution is crucial to maintaining the brand's reputation.
Warning: presenting proof to the customer without empathy creates distrust and increases refund requests. The goal is not to win a debate, but to understand what actually happened in order to offer an appropriate solution, whether it is a reshipment or a hand-to-hand delivery.
So delivery: how do you retrieve a signature, photo, or drop-off point to validate a delivery? On the agenda:
Why is proof of delivery often a source of conflict with customers?
What proof should your virtual assistant search for to secure the transaction?
How do you translate complex proof into clear language for the customer?
What steps should be followed if the customer formally disputes the proof of delivery?
Which performance indicators should you track to improve your logistical processes?
Let's dive in for an in-depth analysis of the best practices.
Summary
Why is proof of delivery often a source of conflict with customers?
For the carrier, a signature or a photo definitively closes a file. For the customer, it is often the beginning of a new problem if they do not recognize the photo or if the signature does not match their name. This divergence of perception is at the heart of current disputes and explains why the simple transmission of proof sometimes fails to ease tensions.
The chatbot must recognize this fundamental difference between the carrier's procedural logic and the consumer's lived experience. It must never brutally pit digital proof against the customer's lived reality, which would create an immediate sense of injustice and could lead to an escalation to social media or public reviews.
A proof of delivery serves to understand what happened, not to close the conversation too quickly. It is a starting point for the investigation, not an end in itself. This is why nuance is essential in communication: the tone must remain collaborative to identify if the problem stems from a confusion of identity, a poorly reported drop-off, or a theft at pickup.
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What evidence should your virtual assistant look for to secure the transaction?
The virtual assistant must be configured to search for a wide range of evidence. This includes the recipient's digital signature, a delivery photo showing the precise location, or even the name of the designated drop-off point, such as a partner business or a neighbor. The richness of the available data directly determines the ability to resolve the dispute.
It must also verify crucial contextual data: the secure locker code, the precise geolocation provided by the carrier at the time of delivery, the exact delivery time, and any comments left on-site by the delivery driver. These metadata help reconstruct a reliable timeline of events.
Finally, verifying the delivery address, the chosen shipping method, and any notifications previously sent to the customer is essential. These elements make it possible to build a complete view before starting the discussion, ensuring that the customer has indeed received the information regarding the available delivery options.
How do you translate a complex proof into clear language for the client?
Clarity is key to defusing tensions. The chatbot must translate technical jargon or raw data into natural language that is understandable by everyone. Using overly technical vocabulary can create a gap between the company and the customer, thereby increasing the frustration felt during a dispute.
For example, it can explain: "The photo seems to show a delivery in front of a glass door" or "The delivery was recorded at 2:30 PM with a signature under the name of Mr. Dupont". It is crucial to avoid disclosing unnecessary data that could be confusing or misinterpreted by the end user.
The bot should never assume that the person who signed for or picked up the package is known to the customer. This caution avoids creating misunderstandings about the recipient's identity and maintains confidence in the procedure. The goal is to establish a factual and empathetic dialogue where every detail is contextualized to avoid any ambiguity.
What procedure should be followed if the customer formally contests the proof of delivery?
When the customer disputes, the chatbot must immediately switch to investigation mode. It is no longer a matter of providing proof, but of collecting useful information to validate or invalidate the claim. This transition in tone is essential to show the customer that their word counts as much as internal data.
The process includes checking with neighbors, the contacted relay point, the condition of the mailbox, and identifying a potential caretaker. The chatbot must also request a precise description of the disputed photo and the specific instructions given by the customer to refine the working hypothesis.
Then, depending on the carrier's procedure, it is necessary to either open a formal investigation or transfer the file if the situation exceeds its scope of action. The dispute must always be treated with absolute seriousness in order not to fuel the customer's anxiety and to restore their confidence in your ability to solve their problem.
What should be done when no proof is available from the carrier?
Not all carriers provide equal proof. In some cases, no photo or signature is available in the system. The chatbot must then be transparent and communicate this clearly to the customer without inventing proof in an attempt to buy time.
It must verify other available tracking elements: the confirmed address, estimated time, and description of delivery conditions. This allows for assessing whether the problem stems from a logistical error or a potential theft, by cross-referencing temporal and spatial data.
In these situations, the bot can prepare a structured inquiry request with the current status, exact address, and the customer's detailed account. This proactivity shows the customer that their request is being taken seriously despite the absence of immediate visual proof, while facilitating the work of subsequent logistics teams.
What logical workflow should be adopted to handle a proof of delivery request without blocking?
The conversation flow must be designed to retrieve the proof without denying the customer's problem. The first step is always to identify the order, the carrier involved, the address, and the current delivery status to contextualize the request before any search.
Next, the bot actively searches for available proof: signature, photo, delivery to a third party or relay point. This search phase is critical because it determines the direction of the following conversation and helps guide the response toward the most appropriate solution.
The chatbot must then explain the proof with caution and confidentiality. If the customer disputes it, it collects the verifications already carried out by the customer before transferring the disputed proof, untraceable packages, or damage cases to the appropriate entity for resolution, thus ensuring optimal fluidity in the process.
What templates can be used to explain the situation to the client with kindness?
Messages must be phrased to inspire confidence and transparency. To search for proof, the bot can say: "I will check if a proof of delivery is available for this order to provide you with the exact information." This phrasing reassures the user about the action in progress.
To explain the situation found, neutral phrasing must be used: "The carrier indicates a drop-off at [time], with an associated photo if it was captured by the delivery person." This avoids giving other subjective interpretations that could harm the neutrality of the service.
If the customer disputes this information, the response must open the door to action: "If this proof does not correspond to your situation, I can submit a formal dispute with all your verifications and observations." These formulations show that the bot is an ally in solving the problem and not an obstacle.
At what precise moment is it necessary to transfer a file to a human agent?
Transferring to a human is not a failure, but a strategic necessity in certain critical cases. Transfer becomes imperative if the customer firmly disputes the visual or textual evidence provided by the bot, or if the situation requires human expertise to resolve a complex disagreement.
It is also necessary to transfer cases when the parcel remains untraceable despite verifications, if the photo is inconsistent with the actual address, or if the recorded signature does not match any name known to the customer. These scenarios often require in-depth investigations that automation cannot conduct alone.
The transfer must be accompanied by a comprehensive summary including the order, carrier, status, detailed proof, delivery time, customer dispute, and verifications already performed. This allows the human agent to take over the case immediately without unnecessary repetition, ensuring a smooth handling process.
Which performance indicators should you track to improve your logistics processes?
To effectively manage this process, precise performance indicators related to proof must be tracked. This data helps identify the carriers or geographical areas that generate the most disputes, making it possible to adjust logistical partnerships and active protocols.
Key metrics include the number of proofs consulted per day, the rate of deliveries disputed after a proof has been provided, and the number of photos deemed inconsistent by customers. It is also necessary to track the volume of reported unknown signatures to identify recurring anomalies.
Finally, monitor the number of inquiries opened, the rate of packages found thanks to proof, and the number of refunds or reshipments triggered after an inquiry. These indicators help to adjust drop-off instructions and select higher-performing carriers to reduce the frequency of future disputes.
What critical errors should be avoided in evidence management by the chatbot?
The first mistake to avoid is concluding too quickly that the package is necessarily received because proof exists in the system. This denies the customer's experience and can worsen the situation by creating a feeling of total ignorance on the part of the company towards its own customer.
Another mistake is disclosing too much technical or sensitive information that can lead to confusion. An inconsistent photo must also not be ignored, as this signals a major logistical problem that could potentially have been avoided if it had been analyzed more thoroughly from the first alert.
Finally, delaying a legitimate investigation is a critical mistake. The chatbot must use the proof as a starting point for a resolution, not as an impassable wall. The goal is to resolve the dispute, not to win a logical argument or to protect the internal procedure at all costs to the detriment of the customer.
How does Qstomy optimize the retrieval and explanation of proofs of delivery?
Qstomy allows you to directly connect your chatbot to real-time data from orders, the product catalog, ongoing promotions, and production statuses. This integration ensures that the bot responds with absolute accuracy regarding the location and status of the package, while guaranteeing the consistency of the shared information.
It also connects to support rules and logistics validations to clearly answer each request. If the case exceeds automatic capabilities, Qstomy forwards sensitive files with a structured and actionable summary for the human team, thus facilitating the transition to expert handling.
The bot thereby helps the customer move forward without inventing fictional delays, discounts, or approvals. It relies on reliable sources to confirm each step, ensuring transparent management of proof of delivery. To learn more about managing lost carts, check out our guide on lost carts after changing devices and discover how to optimize your entire supply chain.
What checklist should you follow to validate your proof of delivery strategy before launch?
Before launching your proof of delivery strategy, make sure the virtual assistant is properly connected to all logistical data sources. Check that the conversation flows allow for collecting disputes without blocking the customer, by providing backup paths for complex cases.
It is also crucial to test the bot's ability to translate complex evidence into clear language for your customers, as this is often where the conflict resolution is played out. The quality of the interaction determines the user's final perception of customer service.
In brief
Qstomy: A precise and empathetic response.
Should you say when a response is generated by AI?
Be transparent about the use of AI to build trust, as explained in our guide on transparency in the use of AI.
When to export evidence for insurance?
To secure your data, consult our guide on exporting after-sales exchanges for insurance or accounting.
How to handle name errors on an order?
Quickly correct errors before they cause a blockage, as detailed in our article on managing incorrect names on orders.
To go further: How to create Q&A paths to guide a customer to the right product - Qstomy, AI Chatbot for beta products: collecting feedback and explaining limits - Qstomy, Exclusion of conversation data: responding clearly to opt-out requests - Qstomy.
Enzo
September 4, 2026
