AI for Review Response in Automotive: Pitfalls, Best Practices & Examples

If you've ever had the pleasure of experiencing an artificial intelligence tool like ChatGPT, your initial reaction was probably in sync with the general consensus - a mixture of astonishment, wonder, and awe.

It’s undoubtedly impressive, and that is why Widewail is integrating it into our platform, with full adherence to Google's AI guidelines. What we are curious to explore in this report, specifically in the context of the quality of responses to reviews from automotive customers, is "where is the line? Where does the capability of AI-powered response break down? 

Drawing from our extensive experience in responding to over 1 million reviews, we have formed a hypothesis that, despite its sophistication, technology will inevitably fall short in delivering the industry-specific language and nuanced approach necessary for exceptional customer support. 

Can AI Respond to Dealership Sales/Service Reviews?

Widewail has always championed the concept that to engage with customers, with a level of quality that they deserve (while saving time and resources), a people + technology partnership is the best approach. 

The short version: Review response via artificial intelligence requires human review for optimal results. AI adds efficiency, while people add accuracy.

The long version: Today we will show you our estimation of that “line.” In the context of automotive, where does AI excel and where is it likely to need help from edits or review by a human being? 

Automotive AI Review Response Examples

We’ve run a variety of real automotive customer reviews through a basic AI model to find out what works out of the box - and what benefits from a manual review process. 

Each example features a generated response (some better than others depending on the content and scenario), followed by a final version after human revision. Examples are also categorized into common scenarios where AI responses alone can prove to be lacking. 

In each case, this is followed by notes and recommendations as to what actual members of our Review Response Team changed to improve the quality of the reply and its value to the original reviewer.

Scenario #1: Grammar

Customer Review, 5-Stars
“Very impressed with the sweet deal I got on my new Highlander last weekend. Both the sales and finance staff were knowledgeable and helpful, and I will highly recommend your dealership to friends and family.” - John Smith

Original AI Response
“We’re really glad to hear you got a sweet deal on your sweet ride, John. I thank you for your feedback, and we are so glad to hear that you had a great experience buying your new Highlander. I hope you will continue to visit us for all your automotive needs.”

After Human Review
“We’re really glad to hear you got a sweet deal on your new 2022 Toyota Highlander, John. Everyone at [DEALERSHIP] appreciates your feedback, and we are happy to hear that you had a positive experience. Please visit the team again soon if we can be of more help.”

Change Notes

• Repeated uses of an adjective (“sweet”) and noun (“glad”) were scaled back to one instance apiece.
• A combination of first and third-person tenses was made consistent.
• Additional vehicle details, and the dealership’s name, were added to the revised response for local SEO benefits. 

Without intuitive prompt engineering (again, the specific instructions given to an AI bot regarding how to respond to different types of reviews), the output may be repetitive, too long or too short, mis-capitalized, poorly punctuated, using incorrect first/third person tenses, or result in an unfortunate combination. 

The risk of grammatical errors is increased if your prompts pull in data from other sources, like a list of keywords chosen to be dynamically included for local SEO purposes, user-generated terms from the original review, make/model/trim details, or references to specific profit centers. 

Slang or spelling errors in the original review may also lead to confusion. It’s a stretch to assume generative AI will pick up on every possible nuance, or that errors in input won’t result in errors in output.

Whether you’ve programmed your AI (carefully tested best-case directions personally), or are using a vendor solution with customizable variables, the prompts have to be on point to ensure your outputs follow basic grammar constructs. And don't forget about the many unique benefits to using automotive customer survey software. It ain't all about reviews. 

Scenario #2: Tone

Customer Review, 5-Stars
“Joanne in sales recently helped me buy my first new car. She took the time to work with me and explain different options, listened to my needs, and the process was pretty much painless. I will be back again soon.” - Sally Jacobs 

Original AI Response
“We appreciate your honest review, Sally, and take feedback like this very seriously. Please let us know if [DEALERSHIP] can be of further assistance, and we appreciate your business.”

After Human Review
“We appreciate your review, Sally, and will let Joanne know her guidance was helpful when buying your first new car - congratulations! Glad to hear your needs were met and the experience went smoothly. From the team at [DEALERSHIP], thank you for your business.”

Change Notes

• The serious nature of the initial AI response output was altered to more appropriately match the tone of (what was actually) a positive review.
• Reference was added to it being the customer’s first new car purchase and the fact they felt listened to and had guidance from a mentioned salesperson. This benefits “personalization” and ensures the review comes across as non-automated and genuine.
• Repetition of the word “appreciate” was scaled back. 

AI systems can sometimes misinterpret customer feedback, leading to inaccurate conclusions or tone-deaf responses. Additionally, AI lacks the emotional intelligence required for handling sensitive customer issues, such as a complaint about a rescheduled service appointment or disappointing trade-in value. 

This may become an issue when there’s a largely negative review calling out a single positive part of the customer’s experience. If you can’t trust your AI solution to identify these subtle differences consistently, your response may look sloppy, insensitive or obviously automated. 

Likewise, a serious and overly professional response to a positive, lighthearted review can seem obtuse to readers and produce a less-than-ideal perception. 

In both cases, it will be obvious to the original reviewer (and prospective future customers looking for “social proof”) that it’s unlikely a real person was behind the response. 

Scenario #3: Personalization

Customer Review, 5-Stars
“I recently leased my third Lexus from [DEALERSHIP] and am absolutely thrilled with the experience. They feature a diverse inventory of vehicles, and their modern facilities offer many business and general amenities. My sales consultant, Allan Watson, was extremely attentive and knowledgeable. The finance department provided me with plenty of options to choose from without any pressure. Overall, I had an outstanding experience and would highly recommend their dealership.” - Anna McGuiness

Original AI Response:
“We are thrilled to hear about your exceptional experience at [DEALERSHIP]! Thank you for sharing your kind words about Allan Watson. He’ll be delighted to know he made a positive impact on your car-buying experience. We appreciate your business and look forward to serving you in the future. Safe travels in your new Lexus!”

After Human Review:
“Anna, we are overjoyed to hear about your exceptional leasing experience at [DEALERSHIP]! Thank you for sharing your kind words about Allan. He’ll be delighted to know he made a positive impact. We appreciate your thorough feedback on our wide selection of vehicles, efficient and knowledgeable team, clean and modern space, and transparent financing process. We wish you safe travels in your 2022 Lexus RX!”

Change Notes

• The response now correctly refers to a lease rather than a purchase.
• The reviewer’s name was added for increased personalization.
• The employee’s last name was omitted. This makes the response sound more natural, customized and less automated.
• A summary of the review’s details is added (as opposed to encapsulating everything as an “experience”), as is mention of the OEM and specific year/model for increased local SEO benefit.

When enough detail is present in a customer’s original review, poorly programmed AI, with unnecessarily generic or impersonalized responses, is almost tragic. It defeats the point of using artificial intelligence to save time in the first place - if the reply looks like it was simply copied and pasted.

Making references to details like make/models, types of service, etc. in your replies is often straightforward enough for AI to accomplish with the right sophistication in prompt engineering.

Likewise, including the customer’s name alone adds a level of personalization that can go a long way, and is easy for artificial intelligence to identify and parse.

The worst outcome possible might be AI getting the customer’s reason for visiting your dealership incorrect, as we’ve seen many times, and is evident in this example. Wouldn’t that look sloppy?

Scenario #4: Action Items

Customer Review, 1-Star
“I have a signed monthly lease agreement for $427.52. But two months in a row now I've received a bill for almost $550. That to me is straight up cheating your customers. I've called multiple times looking for answers and haven't gotten any yet because I'm speaking to the wrong people? Are you ducking me? Very disappointed.” - Richard Ashcroft

Original AI Response
“We apologize for any confusion or inconvenience caused by the billing issue you have experienced. Our goal is always to provide transparent and accurate billing to our customers. We take your concerns seriously and would like to resolve this billing issue as quickly as possible. Please reach out to our customer service team and provide them with your account information so we can investigate the matter further and provide you with a clear explanation of the charges. We value your business and hope to have the opportunity to regain your trust in our dealership.”

After Human Review
“Thank you for taking the time to provide feedback on your recent experience, Richard. We apologize for any confusion or inconvenience caused by the billing discrepancy. Please contact our F&I team directly at [PHONE] so we can address this issue as soon as possible.”

Change Notes

• Longer isn’t always better. The response has been shortened to omit specific criticisms and get right to an attempt at resolution and a tangible suggested next step.
• Rather than a main dealership or general customer service number, we’ve included F&I contact information for convenience and efficiency.
• The reviewer’s name was added for personalization. 

A review response strategy, fully dependent on AI, creates the potential for customer questions, requests, demands, suggestions or even compliments to fall through the cracks. No matter the level of automation sophistication, if a next step or necessary action item is obvious, the feedback has to make its way to a real human in the most relevant dealership profit center. This is a lot to expect of AI, and another example of human review being a best practice.

And, after an initial review has been left, should the onus of the actionable next step be lobbed right back in a disgruntled customer’s court with a tacked-on “Sorry, try calling us again at our main number”? We don’t see that ending well.

Without a process aimed at a swift resolution - backed up by human review, which is absolutely critical in this scenario -  programmed responses may do far more harm than good. Not only will the reviewer remain frustrated, but future prospects reading the exchange will likely get the sense that the situation remained unresolved, sowing doubt for months after the initial situation.

To ensure tangible next steps resolve customer issues, human review is a necessary component, as opposed to a quick tweak improving grammar or tone accuracy.  

Best Practices for Responding to Dealership Reviews Using AI

In the world of Automotive, there’s too much at stake when it comes to customer service and problem-solving across multiple departments to leave it up to the machines every time. At least for the foreseeable future.

Artificial intelligence, at the level of GPT, is a revolutionary advancement that is going to change the way we sell, market, service, create, research, communicate and… absolutely everything else. 

As tempting as this realization may be when considering adopting a review response strategy fully automated by AI, the above examples may have made it obvious that we have a ways to go before full automation is a wise option for dealerships. 

Widewail’s Recommendation

We endorse a hybrid approach, combining artificial intelligence efficiency gains with the accuracy assurances of human review.

1. Save valuable time with the generation of a smart suggestion or “version one” of a response.
2. Review the response with a human eye to make any necessary adjustments before final approval and publishing. 

We should set realistic expectations and not attempt to run before we’re walking. While still in its infancy, the best application of AI in Automotive review management strategies is as an assistant, an efficiency tool, an inspiration - as opposed to an effortless 360 solution.

AI for Automotive Review Response: Conclusions

These examples should get you thinking about the potential pros and cons of using AI as part of your dealership’s customer service methodology. Here are some of our key takeaways regarding AI and automotive reputation management:

• AI is a tremendous timesaver and efficiency gain when managing online reviews, which can help rooftops or dealership groups provide timely solutions to customers while reducing the workload on resources.
• Even in its most advanced incarnations, artificial intelligence may not be able to understand the nuanced meanings and undertones present in human language and cultural contexts, which can lead to embarrassing misinterpretations of customer reviews.
• AI models may struggle to understand and empathize with human emotions, which are often conveyed through language and tone.
• AI may have difficulty correctly identifying the make and model of a vehicle, employee names, profit centers and other rooftop details mentioned in a customer review - particularly if the review is short, uses emotional language or contains spelling errors or slang terms.
• Clever and comprehensive prompt engineering, and testing a variety of programming variables, is essential to give your AI solution the best chance of generating consistent usable responses.
• Combining the efficiency of AI as an assistant making response suggestions, with the safety of human review, is our recommended way to take advantage of the technology.

Summary

Artificial intelligence has become an essential tool for the automotive industry with a variety of uses and applications. As AI technology continues to evolve, we can expect to see even more innovative AI-powered solutions being developed to meet the ever-changing demands of customers.

That said, the potential exists for errors in interpreting language or sentiment, lack of contextual awareness, and the inability to handle complex or nuanced requests. Therefore, where review management is concerned, Widewail feels the safest and most promising applications of artificial intelligence should act in conjunction with human review and approval. 

AI is already terrific at automating mundane tasks, and we should continue to employ it innovatively. While we’re looking at the dawn of an exciting new age, however, there is too much at stake to leave customer service 100% in the hands of technology. 

We’re just not there yet, Skynet.