It’s a frustrating and all-too-familiar experience. You need a quick answer on your recent order from a favorite retailer. You spot an online chat support icon on their website, eagerly click it, and type out a question. A few seconds later, the chatbot responds with a cheerful, “Hello! How can I assist you today?” 

You brush off the fact that you need to repeat the question, still hoping for a quick response. Instead, the chatbot spits out a generic answer that doesn’t quite fit your query. You rephrase the question, but the bot keeps coming back with irrelevant information. Impatience mounts as you now try to outsmart the algorithm. Finally, after multiple failed attempts, the chatbot offers to connect you with a live agent. 

Relieved, you look for a quick handover to a human representative. But then, the dreaded message appears: “Our agents are currently unavailable. Please expect a response within 48 hours.” What should have been a simple, quick interaction requires more patience and persistence than it should.  

While automation and AI promise to improve customer service, they often fall short, leaving customers exasperated. How can brands use these technologies effectively without compromising the service customers expect?

Customer Service Automation Has Its Challenges 

By identifying the key challenges involved in automation for customer service, we can address them and develop the best ways to take advantage of the technology.  

1. Lack of Personalization

One of the most common issues with automated customer service is the lack of personalization. Chatbots and automated systems often provide generic responses that do not cater to individual customer needs. And while high personalization may not be necessary for every inquiry, a complete lack of it can lead to frustration for customers.  

2. Miscommunication and Misunderstanding

Automated systems rely on pre-programmed responses and keyword recognition, which can lead to miscommunication. If a customer’s query doesn’t match the bot’s expected input, the response can be irrelevant or incorrect, accelerating customers’ dissatisfaction. 

3. Delayed Human Interaction

Most customers who use chat functions are looking for quick help. They don’t want to wait for an email response. And they surely don’t want to endure a lengthy voice message warning not to skip ahead because “menu options have changed.” Long wait times defeat the very purpose of the chat.  

4. Over-reliance on Automation

As automated platforms become more available, some companies rely too heavily on them and may try to use automation as a complete replacement for human interaction. This can be a mistake, especially for complex issues that require a nuanced understanding and personal contact.  

5. Technical Glitches

Like any technology, automated systems can experience technical issues. Downtime, glitches, and errors can disrupt the customer service process, leaving customers stranded without the help they need. Problems happen, so it’s important to monitor the platforms to ensure downtime is kept to a minimum. 

Advantages of Automation in Customer Service 

Leveraging automation for customer service is a double-edged sword. When managed well, the challenges are minimal, and automation can empower the customer service experience. There are clear advantages in the use of automation in customer service when implemented correctly. 

1. Efficiency and Speed

Automation can handle a high volume of inquiries simultaneously, providing instant responses to common questions and freeing up human agents to deal with more complex issues. 

2. Cost-Effectiveness

It should never replace the human touch, but sensible use of automation reduces the need for a large customer service team, which saves costs.  

3. Consistency

Automation systems ensure that customers receive consistent responses, which helps to maintain a uniform brand voice and customer experience. 

4. 24/7 Availability

An automated system can operate around the clock, which is a huge advantage for any customer-focused company. Night-owl customers can get their support at any time of day or night.  

To navigate these challenges and harness the benefits of automation, here is a quick step-by-step guide to effective customer service automation.

Eight Steps to Effective Customer Service Automation 

1. Identify and Segment Customer Queries

Analyze the types of inquiries the customer service team receives and categorize them based on complexity. Use automation for simple, repetitive tasks and reserve human agents for more complex issues. 

2. Develop a Robust Knowledge Base

Create a comprehensive and easily accessible knowledge base that both chatbots and customers can use. This will enable the chatbot to provide accurate and relevant information.  

3. Implement Smart Routing

Ensure your chatbot can recognize when an issue needs human intervention and route it to the appropriate agent promptly. Smart routing minimizes delays and enhances the customer experience. 

4. Train Your Chatbots

The smarter your chatbot becomes, the more accurately it understands and responds to customer queries. Machine learning continuously updates and trains your chatbots with updated or specific information and scenarios for better responses. 

5. Monitor and Optimize

Regularly monitor the performance of automated platforms. They should be considered living, breathing systems. Consistently gather feedback from customers and agents to identify areas for improvement. Most importantly, use analytics to track key metrics such as response time, resolution time, and customer satisfaction. 

6. Blend Automation with Human Touch

For us at Mod Op, the balance between automation and human interaction is a core principle. It’s best to use automation for straightforward tasks and to ensure human agents are available for more nuanced, complex matters. This approach will provide efficiency without sacrificing personalization. 

7. Communicate Clearly

Clear and transparent communication helps set expectations with your customers. Don’t be shy about acknowledging what your automated system can and cannot do. Provide clear instructions on how to escalate issues to a human agent if needed. 

8. Maintain a Human Element in the Automation

While you shouldn’t try to position your chat bot as a real person, you should maintain a friendly and empathetic tone in the automated responses. When possible, personalize those automated messages to enhance customer experience and them customers feel valued.  

When done well, customer service automation can be a game-changer that brings together efficiency, consistency, and availability. But it’s critical to manage these systems carefully to avoid turning a quick question into a marathon of frustration. The key is finding that sweet spot between automation and human interaction, and when that happens, customers will walk away from every interaction feeling satisfied and valued.  

In the age of AI-generated content, how can creatives stay ahead of the curve and ensure their human touch remains irreplaceable?
As the Technology Director for our award-winning creative team, Aaron Grando is working to answer this question for our team and the industry.

 How is AI Impacting the Creative Design Process?  

Aaron recently participated in a panel hosted by DesignPhiladelphia, Philly Tech Week and the W Hotel. The panel discussion, moderated by Technical.ly’s Danya Henninger, focused on how creatives incorporate AI into their processes, how brands approach AI and how creative teams can embrace technology without sacrificing the human aspect that has been fundamental to many businesses and careers. 

It was a thought-provoking discussion with a lively audience of 75-100 creative professionals. They talked about many different topics, ranging from practical use cases to more theoretical issues like creative worker rights in a world where automated content farms are pumping out content at an unprecedented rate. 

I connected with Aaron to explore this topic and some insights uncovered during the event. Here’s a look at what he shared. 

AI and its impact on the creative design process is a complex topic that invites various perspectives. Can you provide more information about the panel at Philly Tech Week? 

The discussion started with each member of the five-person panel giving a bit of background on how we’re applying AI tech to our work. The panel consisted of individuals with diverse creative backgrounds, including architecture, industrial design, interior design, and furniture design, and me, the agency creative.  

There were commonalities between the tools we use (everyone’s using ChatGPT, and most are exploring image generation). Still, applying those tools within each field was different, with everyone figuring out the best ways to apply AI to their jobs differently. That was a bit of an eye-opener. There is no one way of doing things right now. 

Based on what you heard during the panel, how do you envision AI impacting the landscape of the creative industry? 

My co-panelists all spoke about niche problems within their line of work that they found AI helpful in solving. We’re in a period of emergence, as in “emergent behavior”—giving people a sandbox and seeing what they build. We have this new primitive intelligence that we can build into how we do things. So, in the short term, I think we will keep seeing rapid, inventive new ways of applying this technology to many different problems. 

Several of the panelists were independent or working with a very small team. They talked about how they’re looking at AI as an enabler and a playing-field leveler, something that lets them get more done with less time. That makes a lot of sense to me. I could see freelancers and independents taking advantage of their ability to move faster and make quicker decisions than larger organizations. 

Can you provide practical examples of AI use in creative processes from the discussion? 

One I loved was an industrial designer using AI image generators to transform clean 3D renderings of furniture he was designing into pencil sketches. It seems backward, right? But really, it’s not these days. 

Most of us start and end our process working entirely with digital tools. Pencil-on-paper drawings are a luxury that you don’t always have the time or talent on hand to produce. But, in this designer’s case, the pencil sketches show his work to prospective buyers in a way that resonates better than the hard, clean lines of a rendering. 

That’s a great example of using AI to serve customers better. Overall, how are clients reacting to the integration of AI in creative services? Are they embracing it or skeptical? 

Clients were a huge part of the discussion. There was a consensus that we still need to demonstrate the value of individual humans and the human organizations we belong to beyond how we currently use AI.  

The panelists were divided on using AI for client work but were all considering implementing it for their businesses. A few panelists emphasized the importance of clear communication with clients when it comes to the use of AI. They also highlighted the growing challenge of explaining AI integration as it becomes more prevalent in our work software. 

Our clients were as interested in embracing generative AI as we are. Many clients I work with every day are curious and open to how we can apply it in creative advertising. 

With AI evolving rapidly, how can creative teams keep up with the technology without losing their edge? 

Well, what is creative, right? We don’t produce creativity in a vacuum. It has always had inputs. Strategy, brand, objectives and then the human experience, inspiration and talent that creatives bring are all distinct parts of the equation. And now AI is another input. Or inputs. It’s not monolithic. Ideally, it’s a focusing lens on those inputs. 

As for edge, we creatives need to be careful not to leave it all up to the tech. For the most part, everyone currently uses the same tools, such as ChatGPT and DALL-E. If you outsource too much of your creative process to AI, you’ll end up with the lowest-common-denominator work.  

This gets at something we’re working on internally here at Mod Op: an AI assistant fine-tuned to the creative inputs—strategy, brand, objectives, content and voice—for the brands they’re working on. It gives our strategists and creatives access to in-house tech that other agencies don’t have!  

It seems like the conversation was quite thought-provoking. Do you have any final thoughts? 

It was great to get out there and talk shop with other creatives about this stuff that is so new and going to be such a huge part of the next few years. All the panelists agreed that we could have quickly gone another two or three hours. There’s so much potential to impact our business, and the panel discussion only touched the surface. Let’s hope for a follow-up!

About the Author  

Anna Julow Roolf is VP of PR at Crenshaw Communications, a Mod Op company. A natural communicator and skilled operations professional, Anna is passionate about bridging the gap between creativity and technology. She brings more than a decade of experience in the B2B PR industry, including leadership roles in both agency and SaaS startup environments, working with brands like Act-On, Pelican Products and Zoom.   

“In the world of AI, good data is our compass. With it, we can confidently navigate the future, instead of fearing it.”

 

Every day, someone says that generative AI, data science and artificial intelligence are changing your world, and you should take action. It could be a media headline, a conference speaker, or a popular online post. Generative AI will greatly affect our lives. However, leaders often lack clear guidance and next steps. 

Many business leaders find messaging on AI and data science confusing, technical, and lacking clear guidance. It often creates stress for leaders and their organizations because they feel they should be doing something—but what?  

Rather than stress about the future of AI, one thing an organization can do today is strategically focus on its data strategy. The fact is that a lack of quality data causes most organizations to struggle to meet business needs. That’s even without AI in the picture. 

Good data quality is crucial in AI. It’s the foundation that will make it faster and more affordable to adopt. Without quality data, you’ll find it nearly impossible to effectively execute AI in your organization.  

Marketing Illustrated: A Case In Point 

Let’s evaluate an email marketing use case as an example. Email marketing is a powerful tool for marketers across both B2C and B2B. Companies regularly use email campaigns to accomplish a range of objectives, such as enhancing brand awareness and boosting sales. 

Marketers have optimized everything from email copywriting to audience segmentation. However, most marketing automations are not leveraging dynamic Customer 360 data. 

For example, imagine a marketer begins a campaign by claiming their brand is superior. They also offer a limited-time discount on a new product. But they send the email to a list that’s so diverse, it includes customers and prospects that it really shouldn’t. 

This includes the following recipients: 

• Customer A – A long-time customer who has experienced issues with a product. They have contacted customer support multiple times within the past two months. They are not very happy with the brand right now. 
• Customer B – Has been purchasing products for several years. They highly appreciate new product launches and frequently make purchases, sometimes beyond their financial means. The fact is they are behind on their bills and often make many returns, causing a financial loss to the company. 
• Customer C – A prospect on the email list who has recently visited the website. If analyzed, behavioral data would show there is a high likelihood of conversion. However, they are on product pages that are different than the one discounted in the campaign. 
• Customer D – A prospect with behavioral data that indicates there is a high likelihood they will not purchase from the company. 

A lack of true, dynamic Customer 360 data limits the marketer’s options. Their latest email campaign will reach inboxes, but it may not work or could even backfire and have a negative impact. The question is, “Can AI address this issue?” 

AI can improve marketing automation when used with a strong Customer 360 data strategy and a reliable governance process. This combination allows for high-quality and timely data. 

However, AI is not a magic bullet. It can’t fix data access and quality problems. AI can’t automatically tie together customer support and finance systems.  

To prepare for a future with AI, focus on your data now. This will help you adapt to the upcoming world of AI and data analytics. 

AI With Quality Data 

When a company has good data, using AI becomes practical and affordable, creating new opportunities. AI can analyze the tone in customer support conversations. It can determine if someone who recently had issues is satisfied or dissatisfied with the brand. This could result in predictive machine learning driving simple decisions about whether to market to them. 

Using behavioral data, generative AI can create personalized content to improve the likelihood of converting specific individuals or defined personas. AI can predict profitable customers and if customers with valid cards are more likely to convert. Of course, this is all based on Customer 360 data being accurate and accessible to AI models. 

Where To Begin 

To prepare your data for AI, choose a domain like Customer 360 or Supplier 360 and ask some questions: 

• Do you have a defined set of attributes for the domain?  
• Do you have a system of record that is your trusted source for all the attributes in that domain?  
• If you have multiple methods of record, can you access a Customer 360 view across those systems? 
• What is the quality of your data from an accuracy and completeness perspective?

If there are gaps in your responses to these questions, it indicates that there are tasks requiring your attention and action. The starting point is defining the domain, understanding where the data sits, and lastly understanding the quality drivers behind it. 

That will be the starting point for your strategy. This will result in a working strategy, which includes making sure your marketing stack can produce the correct data. To improve data, make it accessible, and integrate investments in data quality into regular business operations. 

Who Can Provide Help 

Business leaders often seek technology vendors to solve their data challenges by purchasing software that can address all their problems.  The list of vendors outside their doors is growing daily.  Tech leaders agree that they must deal with strategy and governance before adding more technology to the mix. 

Listen to vendors to understand their approaches to data and data designed to support AI efforts. Use this knowledge to solve strategy and governance problems first. Then, approach your stack architecture with the assumption that a single vendor cannot solve everything. 

Lastly, contemplate the process and culture changes you will need to successfully implement your data strategy. If your organization needs help, hire a digital strategy firm or consultancy that works with any vendor. 

About the Author 

Derick Schaefer is the Senior Vice President of Technology at Mod Op Strategic Consulting. He focuses on using technology and process to develop customer-focused business strategies. Previously, he was the Chief Technology Officer at Trintech Inc. and held VP roles at Digital Insight and NCR. Derick founded a successful startup named Synthesis and also spent over 10 years working at Microsoft.