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Monday’s Marketing Minute: Meta’s AI Play, CMOs Don’t Trust Their Data, CS to ‘Older’ Americans

Happy Monday, y’all! I hope everyone is ready for another productive week! I think today will be the last hot day of the year for us, temps are supposed to be in the high 80s. After today, temps will be in the low 80 or high 70s for the rest of the week and next. Fall is almost here! It’s honestly my favorite time of the year! I hope the weather and everyone else is wonderful where you are, here’s 3 stories that caught my eye:

 

So Meta is working on a new AI engine to try to catch OpenAI. According to this article, Meta’s first foray into AI, Llama2, was trained on 70 billion parameters. Sources in Meta say the new engine will be ‘several times’ larger, but that might not be enough, as the latest version of ChatGPT is said to be trained on well over a trillion parameters. Meta, Google and others will all be rushing to get new AI systems out over the next 6-18 months. As crazy as 2023 was for AI development, 2024 could be even moreso.

 

CMOs aren’t trusting the data they have to work with. In fact, a third of CMOs don’t trust their data OR their analysts, according to new research by AgilityPR. And I think this is a problem that will be made worse by AI, at least in the short term. After AI systems become more mature and robust in data management, they can become a much better asset to CMOs in this regard.

 

Ok I gotta be honest: The main reason why I included this next story was because it irked me that the article considers anyone over 45 to be an ‘older’ adult. Seriously???  Look we are getting old fast enough, I don’t need to hear that once you hit 45 it’s time to start sizing up rocking chairs for the front porch! Besides, have you tried to call into customer service lately? I don’t want to deal with the 45-60 minute or more wait times on a phone. I’d rather do the same thing in chat and wait for a ‘ding’ to tell me they are ready.  *shakes fist at clouds*

 

So that’s it for this week’s edition of the Monday Marketing Minute! On Wednesday, look for the unveiling of the Power List for the RETAIL industry! Last week we debuted the Power List for the Technology industry, and have received a lot of nice feedback from those chosen, and I can see they are getting some nice exposure from being on the Power List, which is great to see. So see you Weds for that, and a new post up tomorrow! Have a great week!

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Your Guide to Optimizing Online Customer Service in the Technology Industry

online customer serviceProviding exceptional online customer service is now essential for technology companies (really all companies in all industries) to satisfy and retain users in an increasingly digital world. However, delivering seamless support across websites, mobile apps, social media, webchat, help centers and emerging channels is complex. Yet at the same time, it is demanded by hyper-connected customers.

With customer expectations rising and new technologies advancing rapidly, support organizations are struggling to keep pace. Those that can, are reaping the rewards of higher levels of customer loyalty and profitability, while reducing customer service costs. While companies that aren’t reacting as quickly are being punished. As customers become more accustomed to using digital and online tools, they appreciate the speed and delivery of information and experiences. That expectation will extend to all elements of the online experience, including customer service and support.

This comprehensive guide explores proven strategies and best practices for technology companies to optimize online customer service delivery in today’s omnichannel environment.

Conduct In-Depth Customer Journey Mapping

Customer Journey Mapping involves mapping out the path that a customer takes from first becoming aware of your product, to purchasing it. This link gives you a good breakdown and definition of customer journey mapping. The advantage of mapping the customer journey from a support perspective is it helps you identify potential pain points in the purchase process for the customer. Once the potential problem areas are known, they can be addressed and responses can be proactively created. Considerations include:

  • Creating detailed journey maps for key processes including onboarding, adoption, training, troubleshooting and escalation. This helps you identify friction points at each step from the user perspective. Knowing those potential pain points makes it much easier to address them, leading to higher levels of customer loyalty.
  • Conducting extensive ethnographic research through surveys, interviews and observations to uncover unmet needs and grievances directly from customers. Customer feedback is vital to optimizing and improving support processes.
  • Completing comprehensive audits examining support metrics across platforms – response times, wait times, resolution quality, customer satisfaction (CSAT), Net Promoter Score (NPS) and more. Tracking metrics throughout the customer journey allows your technology company to identify strengths and weaknesses throughout the process.  Once identified, weaknesses can be corrected and strengths magnified.
  • Analyzing support case topics and types to reveal knowledge gaps. Expand help content to address common questions. This is where tracking satisfaction with support as well as time spent with agents can reveal potential problem areas. Consistent problems could point to a need to invest in training and/or the hiring of SMEs.

If leveraged correctly, these insights will inform an omnichannel customer service optimization roadmap tailored to your customers’ needs.

Design Integrated Omnichannel Experiences

Today’s consumers expect unified support experiences across the web, mobile apps, social media, email, chat, and emerging channels. Eliminate friction through thoughtful omnichannel design:

  • Define optimal roles for each channel based on strengths. Chat for convenience, phone for urgent issues, forums for troubleshooting. This also helps you consider the needs of the customer, for instance forums should have access to SMEs who can answer questions that are often involving a more complex issue. One answer from a SME could potentially deflect multiple CS calls, which would be a cost savings that would continue to be realized as long as the answer appeared on the forum.
  • Craft tight channel integration, such as website support forms prefilled with user data for contextual experiences. Make sure to collect only the data that customers have consented for collction, and carefully explain to customers which data will be collected. This helps address privacy concerns and works to establish trust.
  • Enable smooth cross-channel transitions, such as handing off conversations between agents without repetitive explanations. Remember that the customer assumes the entire interaction with CS is being conducted by a team that’s on the same page, not disjointed employees.  The customer doesn’t expect to have to start over with a fresh explanation of the problem every time a new agent is involved.
  • Connect steps into one seamless, consistent and personalized cross-channel customer journey.

Contextual, continuous omnichannel experiences satisfy users and improve efficiency while increasing loyalty.

Evaluate and Adopt New Service Channels

The digital service landscape evolves quickly. Continuously evaluate and pilot new channels and innovations such as:

  • Artificial Intelligence powered chatbots equipped with natural language capabilities, sentiment analysis and escalation for automated conversations.  This post has a detailed breakdown of how technology companies can leverage AI in customer support. AI can be used to deliver customer support directly, and it can also be leveraged to analyze customer data to enhance the support experience.
  • Augmented reality that allows remote visual issue diagnosis through overlays and annotations. This is especially useful in the field service spac, where a remote worker can use AR to diagnose and fix a problem directly, or be connected to a SME (Subject Matter Expert) who can help the technician deliver support on site, saving time for both the customer and service vendor.
  • In-product communications via embedded commentary forms or help widgets. QR are a useful example of this, allowing both the customer and the service technician to get relevant product information at the site.
  • Proactive assistance powered by machine learning models that predict issues. This ties into the first point about AI, machine-learning can also be utilized to predict potential problems, turning costly repairs into preventative maintenance.

Assess new technologies based on your capabilities and customers’ evolving needs. Adopt channels that provide high value.

Incentivize Use of Self-Service Options

Deflecting common inquiries to self-help resources reduces human support costs. Boost adoption by:

  • Identifying key opportunities to augment or replace live service, such as leveraging virtual agents or community forums.  Pro-Tip: Publish some of the most engaging forum answers on your website’s homepage. This is a great way to promote the availability of the forum as a way for the user to self-troubleshoot problems. Remember that one answer from a SME on a forum could help countless users solve the same problem. Every time a user can solve their own problem via a forum, that deflects a call or chat with a CS agent. Which saves you money.
  • Designing stellar help centers, FAQs, chat bots, online communities, and in-product self-service. Make sure every level of service is provided for the user. If the user needs 101-level help, provide FAQs, if they need a bit more instruction, give them easy access to chat bots and forums.
  • Driving awareness through promotions, in-product prompts, and informational content. Users can’t access help if they don’t know it’s available. Providing exceptional online customer service is about giving customers the ability to choose the type of experience that’s most helpful and convenient for them.
  • Continuously optimizing self-service content and functionality based on analytics and user feedback. Constantly monitor for roadblocks and bottlenecks in the process. Survey users post-ticket to identify areas for improvement.

Giving customers self-service options that work leads to higher customer loyalty and satisfaction, as well as reduces costs for your technology company.

Experiment, Iterate and Innovate

Customer service cannot remain stagnant as user expectations rapidly evolve. Continually optimize by:

  • A/B testing new features and support processes to improve key metrics. Leverage community forums and give the most active participants the chance to test new features. You will often find that frequent contributors to forums will jump at such an opportunity.
  • Monitoring emerging innovations in service delivery from competitors. Be aware of what the competition is doing, and what is working for them. This can give you ideas for improvement in your own CS processes.
  • Interviewing users to identify desired improvements and pain points. Lean on feedback from users throughout the customer support process. You will often find that users will be happy to provide feedback and stay in contact with your CS team to help implement their suggestions.
  • Proactively journey mapping to uncover friction opportunities. Find and identify pain points for users along the CS journey, and eliminate them.
  • Piloting enhancements, measuring impact based on data, and iterating. Pro-tip: Rollout changes first to your forum members or loyalty program members. If you will be providing a new features in your customer support efforts, give limited advance access to certain user segments. This will be viewed as a perk by the group, and will result in better feedback and higher levels of satisfaction and loyalty as a result.

The best brands continually and proactively adapt based on customer needs and feedback.

Achieving Excellent Online Customer Service

Providing superior omnichannel customer service is challenging yet invaluable. Applying the strategies outlined equips technology companies to meet, and even exceed, rising user expectations.

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AI-Powered Customer Support Solutions: Enhancing Customer Experience and Efficiency for Fortune 500 Companies

ai-powered customer support

As today’s business landscape continues to evolve at a breakneck pace, providing exceptional customer support is vital for Fortune 500 companies. Customers want more personalized support, faster. To address the needs of customers, forward-thinking CMOs are turning to AI-powered customer support solutions. Leveraging the capabilities of artificial intelligence (AI), these innovative solutions revolutionize customer interactions, enabling personalized experiences, seamless self-service, and streamlined operations. Let’s explore some of the benefits of AI-powered customer support, and look at some real-world examples of how companies are utilizing these emerging technologies to drive real business growth.

The Benefits of AI-Powered Customer Support

In an increasingly digital world, AI-powered customer support solutions offer many compelling benefits for Fortune 500 companies. First, AI chatbots and virtual assistants enable immediate and round-the-clock support, addressing customer queries and concerns in real-time. This availability boosts customer satisfaction and improves response times. While there can be some concerns over the quality of the support delivered via AI, these concerns should further abate over time as the technology matures and improves.

Secondly, AI enhances personalization by leveraging customer data and historical interactions. AI algorithms analyze vast amounts of information to deliver tailored recommendations and customized support, creating a more personalized experience for customers. This is an area where AI provides a level of support that humans simply cannot replicate. And as noted earlier, AI’s ability to process vast amounts of customer data and extrapolate relevant outcomes should only improve as the technology continues to mature.

Additionally, AI-powered customer support solutions drive efficiency by automating routine tasks and inquiries. Chatbots can handle repetitive questions, provide self-service options, and escalate complex issues to human agents when necessary. This automation optimizes resource allocation, reduces costs, and allows human agents to focus on more complex and value-added tasks. Also, this allows a company’s SMEs (Subject Matter Experts) to spend more time addressing support issues that require advanced expertise.

Examples of How Companies Are Currently Leveraging AI to Improve Customer Support

Let’s look at some quick examples of companies that are seeing real results from integrating AI into existing customer support processes:

  • Fintech startup Tomorrow reduced AHT (Average Handling Time) by 50%, dropping AHT from around 20 minutes down to 10 minutes.
  • According to Satu Karaksela, Service Manager (Digital Customer Service) at Finnair, the airliner realized a 30% improvement in response times for CS cases after adding AI to the mix.
  • Payment and transactions company Efecty realized a 48% reduction in calls to customer support in the first year it began utilizing a chatbot in its CS mix. You can take the internal cost your company assigns to each CS call and get a rough estimate of the potential savings that could be seen from a similar decrease at your own company.
  • Launched in 2018, Bank of America’s chatbot Erica has now helped over 32 million BoA customers with over one billion interactions.

The common theme here is utilizing AI to assist with the more basic customer support requests.  Think of AI as being able to handle the 101-level inquiries, and then handing off to a human agent if more detailed and specific assistance is needed. While this may reduce the need for less skilled customer support agents, it should increase the demand for more skilled SMEs.

 

Transforming Customer Experiences

AI-powered customer support solutions have the potential to transform customer experiences for Fortune 500 companies. This can be done by harnessing the power of AI, organizations can offer intuitive and seamless interactions across multiple channels, ensuring consistent support regardless of the customer touchpoint. As research shows, a consistent experience throughout the buying and/or support process is vital to achieving superior CX.

The use of natural language processing (NLP) and machine learning algorithms enables chatbots to understand and respond to customer inquiries in a human-like manner. These AI-powered systems continuously learn from interactions, improving their responses over time and providing a more conversational and engaging customer experience. It’s worth noting that as investment continues to flow into AI and related technologies, the automated experience that AI can provide will only improve. Keep this in mind when considering or analyzing the current landscape and offerings.

Furthermore, AI-driven personalization allows companies to anticipate customer needs and preferences. By analyzing past behaviors, purchase history, and demographic data, AI-powered solutions can deliver relevant product recommendations, promotions, and content tailored to individual customers, fostering a sense of connection and loyalty. Again, relevance will be key to driving customer satisfaction, and AI’s ability to give relevant interactions will only improve over time.

Streamlining Operations and Enhancing Efficiency

CMOs of Fortune 500 companies understand the importance of efficient operations and cost optimization. AI-powered customer support solutions offer significant opportunities in these areas. By automating processes and handling a high volume of inquiries, AI chatbots can significantly reduce response times, improve customer satisfaction, and enhance operational efficiency. The four case studies linked to earlier in the post were provided to illustrate this very point.

Moreover, AI-driven analytics provide valuable insights into customer behavior, sentiment, and trends. CMOs can leverage this data to identify pain points, understand customer preferences, and make data-driven decisions to refine products, services, and marketing strategies. This data-driven approach enables organizations to stay agile and responsive in meeting customer demands.

Integration and Implementation Considerations

While the benefits of AI-powered customer support solutions are clear, implementing such solutions requires careful consideration. CMOs should evaluate their organization’s specific needs, customer base, and existing infrastructure to determine the most suitable AI solution. Collaboration between marketing, IT, and customer support teams is essential for seamless integration and deployment. Integration is the key to driving success as well as initial adoption. AI, if used correctly, can integrate into existing business functions and enhance current customer support processes. Companies like yours can more easily justify an investment in AI technology if it can improve existing processes versus an investment that requires costly new processes be created to support its function.

Conclusion

In the competitive landscape of Fortune 500 companies, delivering exceptional customer support is a strategic imperative. AI-powered customer support solutions empower CMOs to quickly deliver assistance at scale to customers. This helps companies realize improved satisfaction scores, customer loyalty and reduced support costs.

Leveraging AI to enhance your company’s existing customer support efforts can not only save you money, it can grow your business as well.