by William Walsh | Apr 30, 2024 | 2024
As previously published on 4/15/24 in MarTech
Creating memorable experiences goes beyond integrating AI. Focus on small touches, employee empowerment and anticipating customer needs.
What will customers remember after they experience your website, product, service or people? What will they tell their friends or colleagues about their experience — will they even mention it?
A tremendous amount of investment and resources are being focused on integrating AI into the customer experience, from automating responses to comments on social media to improving the call center experience. And who knows where OpenAI’s Voice Engine will eventually take us?
Do you think customers will talk about how they were “wowed” by their AI experience? I’m willing to bet they probably won’t notice. Whatever experience is created from AI will lean more toward “expected” than “exceeding.”
So, how do you create a customer experience worthy of conversation? I was determined to answer that question as I went on vacation a few weeks ago.
Discovering a 5-star customer experience
After seeing Dan Gingiss, author of “The Experience Maker,” at a recent event and with his book in hand, I headed to a resort off the coast of South Carolina to find out how it earned its AAA Five Diamond award and Forbes five-star rating.
It began with a room upgrade because I had booked our stay through American Express. I’ve had this perk for years, yet I rarely receive an upgrade at other hotels we’ve stayed at. And it wasn’t just any room; it was oceanfront with a million-dollar view. Boom, at least one star, maybe more, accounted for in less than 10 minutes.
The next day, at breakfast, we met Lincoln. Lincoln wasn’t just our server; he was our vacation coach. He asked us what we were hoping to get out of our vacation. He then reminded us to slow down, breathe and enjoy our time at the resort. It was a much-needed reminder that we were on vacation and to take time to reset, be present and enjoy our time together.
He also asked, with genuine sincerity, if there was anything he could do to make our stay more enjoyable, which we would later take him up on. Another star earned, thanks to Lincoln.
With our mindset adjusted to vacation mode, we set out to see the resort and walk on the beach. We went to the hotel room to find another surprise that really brought home the art of “wowing” your customers with the little things.
Anticipating and exceeding customer needs
My wife is a member of a neighborhood book club. She, like me, brought her book with her to read and enjoy our newly found free time. The book was left on the ledge of the bathtub and the person who provided the cleaning service to our room noticed that she didn’t have a bookmark.
There, carefully placed on top of the book, was a resort-branded gold tassel bookmark. Impressive! If that attention to detail doesn’t earn you a star, I don’t know what will.
A couple of mornings later, Lincoln was once again our breakfast server. This time, my wife was trying to decide between two tempting items on the menu. Lincoln offered to take the most appealing items from each and make a custom breakfast specifically for her. After all, he asked what he could do to make our stay more enjoyable. Mission accomplished.
His final act occurred on the last day of our stay. We mentioned that we’d be checking out later that day, and Lincoln prepared a “goodie bag” for our trip, complete with utensils. This is a perfect example of going “above and beyond.” We were impressed and touched.
During our stay, it was clear that the resort empowered its employees, from room service to spa to restaurants, to make decisions that benefit the guest experience. Taking a page out of Gingiss’s book, they noticed the little things, like a book without a bookmark.
Delivering exceptional experience through a customer-centric culture
As the bookmark illustrates, experiences can be shaped without even interacting with customers. The power of observation, anticipation of needs and the willingness to act all shape the experience without engagement.
The resort earned its high ratings because employees bought into its mission. They all lived it. It wasn’t one or two things or one or two people; it was everyone. They knew what business they were in and it wasn’t hospitality — it was the experience.
You just can’t tell a customer what your brand stands for. They have to experience it, just like our experience at the resort. We felt the service and experience that earned its well-deserved high rating.
Every touch point shapes the experience. You can’t fake it and customers can feel it. You also have to know what business you are really in. It isn’t a technology you’re selling, but what it does for the users. It’s what it enables or produces, not the feature or functionality.
CX beyond AI: Focus on delighting customers
AI-enabled customer experiences will not be about what the technology can do but what job it does for the user. As an emerging technology, I see early signs this thinking isn’t happening.
Currently, the application seems more about what it can do for the company. Perhaps that’s a valid first step, but it needs to quickly move to the customer experience.
It’s still too soon to know, but in the meantime, there are many things you can do without it. Focus on the small things that delight customers, train and empower your employees and find opportunities to exceed their expectations. Sounds simple and intuitive, but is it happening consistently and across the organization?
What did we tell our friends and family about our trip? Was it the food, weather, beach and the resort’s beauty? Perhaps a little. But what truly captured the essence of our experience was the story of the bookmark — proof of earnest attention to detail.
We didn’t even have to explain it; people just got it when they heard the story. That’s how you build a good reputation and earn high ratings.
by William Walsh | Mar 10, 2024 | 2024
As previously published on 3/7/24 in MarTech
Every year, B2B marketers gather in Scottsdale, Ariz., to mingle and talk shop. This year, they talked a lot about AI.
The B2B Marketing Exchange (B2BMX) conference takes place annually in late February or early March at The Phoenician hotel in Scottsdale, Ariz. I was in attendance, along with a good 250 to 300 marketers. It was my first event since before the COVID-19 pandemic. Toss in representatives from about 75 vendors and the event was thick with pitches, presentations and promotions.
My goal for the three-day event was to check out and learn more about new technologies from the sponsors and gain insight into the top issues that practitioners are wrestling with day-to-day.
Here are my takeaways:
The major themes of B2BMX 2024
Artificial intelligence was everywhere
AI was everywhere — the vendors in the exhibit areas, the presentations and in conversation. That said, I didn’t really see anything that knocked my socks off. A bunch of generative AI content tools were on the vendor floor and featured in presentations, but nothing that was game changing.
Vendor consolidation
Vendor consolidation was not necessarily an official event theme, but martech consolidation was a definite vibe. From what I heard from vendors and marketers, the day of reckoning is upon us. Marketers are drilling into ROI, contract terms, spend and functionality. Ironically, Scott Brinker’s Chiefmartec newsletter came out during the event and announced that SaaS martech stacks shrunk 8% from 2023 to 2024. I expect that number to be much higher next year.
My favorite sessions
Keynote
Dan Gingiss, author of “The Experience Maker,” presented a lively and entertaining discussion of his WISE framework for creating notable customer experiences. Here’s the key point from his presentation: If something is expected or “normal” do the opposite. But to do that, you have to make time for it. Too many people are just going through the motions. To create truly memorable experiences, you have to take the time to think about it. It doesn’t have to be complicated. It can be as simple as how you respond to a customer on the phone. In fact, one of his examples was hold music.
Favorite workshop session
My favorite workshop was called “From Strategy to Tech Stack.” I actually never got to see the presentation — they couldn’t get the projector to work. So it became a “fireside chat” with Megan Crone from Palo Alto Networks and Amy Holtzman from CHEQ. The topic centered around cybersecurity for marketers, specifically protecting pay-per-click campaigns from bots. We’ve long known that bots are a nuisance on blogs, etc., but AI bots have become much more sophisticated in evading standard bot protection mechanism like CAPTCHA.
Most interesting vendors
- Writer. Writer is probably the best LLM (generative AI content) provider of the bunch with an impressive client list to boot. The template-driven approach was smart and well thought out.
- CHEQ. CHEQ is the vendor I didn’t know I needed. It’s ugly out there, and getting more dangerous every month. This is the tool you need to protect your marketing investments.
- The B2BMX event app. To me, the most impressive technology I experienced was the event app itself. The app allows you to customize your agenda, reach out and connect with others, download the presentations, track your points for visiting with vendors and apply the points you earned to SWAG.
Best vendor SWAG
Speaking of SWAG…
I don’t know if it was the “best,” but I will say it was the most usual giveaway I’ve seen at an event: an eye mask. But here’s the funny thing, there is no company branding on it so I don’t remember the vendor. A stand out giveaway with no branding… hello, marketing?!
Final word
B2BMX is an event for practitioners: managers, senior managers and director level attendees. I was surprised that many of the sessions didn’t reveal new insights (particularly relating to ABM) despite the fact that I hadn’t been to a conference in nearly five years.
It left me with the impression that we are still chasing the shining new technology instead of performance improvement. As an example, the sessions with the highest attendance (from what I observed) all had AI somewhere in the title or description.
by William Walsh | Feb 17, 2024 | 2024
As previously published on 2/15/24 in MarTech
In the movie 2001: a Space Odyssey, the astronauts come to the frightening realization that Hal, the AI supercomputer, runs every aspect of the space station.
A discrepancy between the ground computer and Hal begins the process of the astronauts believing that Hal may be going rogue. When they ask Hal why there may be a difference between the two computer systems Hal responds, “It’s a human error, as it also is…”
Our first experience with AI began in late 2019 when we began experimenting with an IBM Watson application. We used an AI tool to profile the personalities of buyers to help a client narrow down the 17 personas that came from the product marketing group to 4 actionable personas.
From there, we built a business on the tool which we call personality based marketing. The enabling AI tools we use, Crystal Knows and xiQ, can quickly determine the personality of an individual using DISC segmentation.
Our second experience was right when Chat GPT 4.0 was launched and we added Jasper to help us with content and image generation. We used the tools to help generate content for websites and images to give our creatives a head start.
Honestly, we have had mixed results. The tool being prompt driven, made it challenging to get the output we desired. And given everything else going on in our business we really didn’t have the time to continue to learn and perfect the prompts. Plus,with each new release of the tool, brought a whole new set of best practices on prompting.
This has now led us to the third round of experimentation with an AI platform called Cassidy. The new tool is potentially a game changer for us and possibly the beginning of a modern day Hal. Unlike previous tools, Cassidy will learn about us on its own.
It has read our website, and it is integrated into our G-Suite, Chrome browser and Slack application. It will understand and communicate in our brand voice, act as an assistant, and execute its own workflows. For an organization like us, a fast efficient agency with little to no overhead, this can be a true game changer.
The future of AI is bright but it’s also going to be bumpy. We know there will be shortcomings in this new wave of technology. For example, based on what we have now observed using the first generation tools, we know that biases exist in the tools.
The second generation taught us that the output is only as good as the input. The age old saying of “garbage in, garbage out” is still true despite how smart the technology may be.
In fact, many believe that ChatGPT 4.0 is getting worse, not better, as it becomes more widely distributed. A discussion thread on Open AI Developer Forum from November entitled “ChatGPT is Getting Worse and Worse Every Day” includes 182 comments, all of them in agreement that things are, in fact, getting worse with each release.
Developers point to increasing error rates, low retention of previous commands and outputs, and general lack of support. Even going so far as to say the ChatGPT 3.5 performed better than the most current release of 4.0. So we can, for now, put aside our concerns that Hal is going to be taking control of the ship.
Be that as it may, we have built Business Development and Project Manager assistants with a knowledge base created by integrating our proposal and project drives. These AI assistants will be able to help our team increase efficiency and one day, automate the proposal writing process.
It’s also integrated into my Chrome browser so it’s reading my email, attending meetings, and archiving and indexing the websites I’m visiting. Perhaps someday soon, it will begin drafting and responding to my emails…on its own.
Until that day, it will require our time to shape it into what we want (or need) it to be. And that’s the point, we will make AI into what it will be, and many have done so already. And, even though we have moved from “machine learning” to machines that learn, it is a good reminder that “Artificial Intelligence” is actually better described as artificial human intelligence.
A technology built by humans, and trained on what humans have made, will not be perfect…just like its creators.
And in case you’re wondering, this post is 100% human generated.
by William Walsh | Jan 23, 2024 | 2024
As previously published on 1/19/24 in MarTech
Maybe one step in the right direction would be ensuring that AI content generators properly cite their sources.
“Students in Europe are just now writing research papers versus students in the U.K. and U.S. who start writing them in high school.”
This comment was made by a British professor to my son who is a graduate student in Italy. The professor went on to say that, as a result, he and other professors, are more lenient when it comes to plagiarism.
The point of view the professor expressed seemed similar to the current state of AI content generators. We are at the beginning of what will be a long road of generative AI tools producing content. There are lessons to be learned on how to use them correctly.
Last month, I wrote an article for MarTech that ended up appearing on a digital agency’s website, presented at first glance as their own content (it has since been removed). The article was based on research our firm conducted on the best in class social media practices of over 40 companies. For some reason, the website claimed that the article had been written by an AI bot, but also referenced MarTech as the site where it was first published.
Plagiarism and AI detection
As new AI tools are being rolled out, and “rolled in” to existing tools, the discussion is focused on how to regulate the content they generate. There are now numerous AI content detector tools that can be used to determine if content was created by AI, and if it was plagiarized. Consider this a public service announcement for marketers, college students and apparently, college presidents.
OpenAI and Microsoft are now being sued by the New York Times for training its AI machines using Times content. The issue is that Open AI and Microsoft used copyrighted content owned by the Times, without paying for the rights to use it. Many believe that the output of this lawsuit could decide the fate of AI.
The key point in this argument is that of input versus output. The argument for Open AI and Microsoft is the copyrighted material is being used for learning purposes (for their tools). This is something that proponents argue has been done for hundreds of years with humans. Attorneys are a great example. Trained on case history in law school, their written arguments are based on precedent.
Where things change is in the output. If the output of the tools produces content that has been lifted from New York Times articles verbatim, or without citing the source, then you have issues with copyright infringement and/or plagiarism.
Just cite your sources
Our firm invested in, and conducted the research, but the digital agency received the benefit of the insights without properly crediting the source, ostensibly using an AI bot to lift passages from my original article. Any new technology ushers in a time of uncertainty and unknown change. AI tools are disruptive and the ethical issues around their use, see the writers strike as more evidence, are complex.
But maybe, in some ways, it’s not that complicated after all. If we consider AI tools to be, in a sense, a “digital” student consuming vast amounts of content to become knowledgeable and useful, then maybe the issue of how to manage AI generated content isn’t that difficult.
If you prompt an AI tool to source the information it is using, it will return and answer within seconds, confirming it can track back to the original information it used to create the output.
As with the OpenAI/Microsoft case, this comes down to the output, and even more specifically, the user. If, like new students, the users are naive, and/or lackadaisical, you will limit the effectiveness of the tools and your team, and potentially, invite someone from the legal department to come for a visit.
On the other hand, if users are trained and treat the output of the tools like any other article or research paper they would write for high school or college, a huge productivity increase is possible. Simply prompting it to include the sources of the content used does the trick.
Perhaps, the more things change, the more they stay the same. Giving credit where credit is due has always been right, no matter what the situation…or technology.
by William Walsh | Jul 11, 2023 | 2023
As previously published on 6/28/23 in MarTech
By Scott Gillum
Estimated read time: 5 Minutes
What if they are wrong?
When responding to questions about AI replacing humans in certain roles, most ‘experts’ claim that AI will replace some jobs, but will be a much more valuable tool for augmenting human intelligence and ability.
In all of the hype associated with this latest technology wave, an important trend is occurring across industries that could significantly change the impact of AI – the retirement of the knowledge worker.
We need to look no further than the last wave of intelligent technology – the “internet of things” (IoT) to see the impact.
The term ‘Internet of Things’ was coined in 1999 by computer scientist, Kevin Ashton. While working at Procter & Gamble, Ashton proposed putting radio-frequency identification (RFID) chips on products to track them through a supply chain.
“Machines talking to machines” started rolling out in early/ mid 2010 making their way into manufacturing, precision agriculture, complex information networks, and for consumers in a new wave of wearables.
Now, having about a decade of experience of how IoT has impacted certain industries and markets, perhaps it can give us some interesting insights on the future of AI.
In 2010, Cisco launched the “Tomorrow Starts Here” IoT campaign at the time when communication networks were transitioning from hardware “stacks” to software development networks (SDN).
The change meant that in order for carriers to expand their bandwidth, they no longer needed to “rip and replace ” hardware. They only needed to upgrade the software. This transition began the era of machines monitoring their performance and communicating with each other, with the promise of one day producing self healing networks.
Over this same period, network engineers who ushered in the transition from an analog to digital began retiring. These experienced knowledge workers are often being replaced by technicians who understand the monitoring tools, but not necessarily, how the network works.
Over the last dozen years networks have grown in complexity to include cellular, and the number of connections has grown exponentially. To help manage this complexity, numerous monitoring tools have been developed and implemented.
The people on the other end reading the alerts see the obvious, but have a difficult time interpreting the issue, or what to prioritize. The reason is, the tool knows there is an issue but is not smart enough yet to know how to fix it or if it will take care of itself. Technicians end up chasing “ghost tickets,” alerts that have resolved themselves, resulting in lost productivity.
The same thing is repeating itself in marketing today. As one CMO told me; “I can find people who know the technologies all day long, but what I can’t find is someone who thinks strategically. Ask a marketing manager to set up the tools and run a campaign and they have no problem, but ask them to write a compelling value proposition or offer for the campaign, and they will struggle.”
It’s easy to get sucked into the tools. AI generators are really intriguing and can do some amazing things. But based on what we have seen, the tools are not smart enough to fully deliver on their promise…yet.
Here’s the warning from IoT – as tools become more knowledgeable, the workforce operating them is becoming less. It is leaving a knowledge gap. As that knowledge is transferred from worker to machine, we need to ask ourselves what we’ll be left with. Will there be enough experience and expertise in our workers to know if what comes out of the machine is accurate, factitious, or even dangerous.
In a recent WSJ article, Melissa Beebe, an oncology nurse, commented on how she relies on her observation skills to make life-or-death decisions. When an alert said her patient in the oncology unit of UC Davis Medical Center had sepsis, she was sure the AI tool monitoring the patient was wrong.
“I’ve been working with cancer patients for 15 years so I know a septic patient when I see one,” she said. “I knew this patient wasn’t septic.”
The alert correlates elevated white blood cell count with septic infection. It didn’t take into account that this particular patient had leukemia, which can cause similar blood counts. The algorithm, which was based on artificial intelligence, triggers the alert when it detects patterns that match previous patients with sepsis.
Unfortunately, hospital rules require nurses to follow protocols when a patient is flagged for sepsis. Beebe could override the AI model, if she gets doctor approval, but faces disciplinary action if she’s wrong. It’s easy to see the danger of removing human intelligence in this case, it also illustrates the risk associated with over relying on artificial intelligence.
AI will free us from low value tasks, and that is a good thing, but we need to redistribute that time to better developing our people, and our teams. The greatest benefit from these game changing technologies in the business to business environment will be realized when we combine equal amounts of human intelligence with machine intelligence.
