Transcript
In a lot of cases, organizations cannot have huge dedicated CX teams, and in that case, it makes a lot of sense for the marketer to take the lead on that.
Hey everyone, Stuart here, and you are listening to the Flow State podcast. This is episode two of three with Jennifer Arnold. We are continuing our conversations about all things customer. We’re going to get straight back into it and talk about why as a marketer you should be thinking about customer experience. Do you think that’s an area that marketers should be looking after or is it something that should sit somewhere else?
I think I mean there are large organizations that have large dedicated CX teams, right, who have that responsibility for looking at that customer experience from the very initial touch point that a marketer is traditionally responsible for all the way through that ongoing support and renewal and retention types of elements. And that helps because again you’re that’s an organization that kind of sits above all the other groups—sales, marketing, support, customer success, onboarding, all of those teams—and bringing together a cohesive view of customer across that entire customer lifetime. This is important because a customer themselves should not necessarily be aware that they’re being handed from one team to another or one process to another. That should all be happening; it’s kind of like the duck with the feet furiously going below the surface from all the delivery, and then the customer is nice and smooth on the top.
But in a lot of cases, organizations can’t have huge dedicated CX teams, and in that case, it makes a lot of sense for the marketer to take the lead on that. This is because marketers are a key touch point when it comes to customer feedback, customer engagement, and having an understanding of the voice of customer as well. They need to be taking the customer’s perspective and not just looking at things from the inside out.
Marketers must understand the impact of what customers are looking for and how the company is serving them. That’s a really interesting perspective because often, in the businesses we work with, there is a gap where everyone is aware that somebody needs to own the customer voice or listen to the customers. However, it often falls through the looming gap between people in charge, people in marketing, people in sales, and sometimes some product people, leading to everyone staring at everyone else. It’s a problem, right? Big companies are so siloed, and those customer touch points are siloed. It takes somebody who can kind of sit above it all and pull all the pieces together.
This person needs to look at each step of that customer journey, who is touching them, what information is being gathered, and how feedback is obtained about the experience customers are having at each of those touch points. This isn’t just down to surveying the customer; if you survey the customer at every single touch point, you’re just going to piss them off very quickly. There are ways to get that information and pull it together to get a view of what that customer experience is without asking them. This includes looking at how the product is being used, what types of support calls are coming in, feedback going directly to the sales team or the customer success team, or through social listening in customer community groups, etc.. There is just a wealth of data sources that can be pulled together that track CX, but it’s a matter of somebody doing the work to pull it all together and try to make a single picture of what that looks like.
That is a lot harder than it sounds when you get into the detail of it. It relates to the data side, the sources of that data, and the fact that often, even like-for-like metrics aren’t actually like-for-like. It’s an area where you can get lost in finding, organizing, and trying to cleanse all the information before making any useful assessment of what’s happening.
Jen Davidson was quoted in the summary of a report saying, “It’s one thing to be strategically aligned but the operating model isn’t supporting where the strategy is going,” which is a tension point for marketing. This is particularly interesting if ownership of this is shoved onto your remit, but you don’t have the actual business structure behind you to deliver on it.
If someone is suddenly responsible for a customer in its entirety, the first thing is just understanding where the interactions are happening. This involves doing customer journey mapping. The major touch points are obvious: the initial marketing connection, where interest is shown, where a sale happens, where onboarding happens, and if there’s a support call. These are the traditional areas, but often there are gaps in between and lots of little touch points that are happening that often aren’t even recognized or being tracked, or clearly understood who is influencing what is happening in that engagement.
A customer journey mapping exercise with a big software company, just focused on the activation and onboarding stage, revealed five or seven teams playing some role in that. During the mapping, they found people all the way along who were unaware of things, such as who wrote a specific email or how long it had been going out. Teams were internally unaware, and they were finding gaps all over the place and a lack of alignment. Different types of customers in different regions create a whole level of complexity. For example, Asia-Pacific customers are much more difficult to support and manage given the complexity of the language across the 13 or 14 markets they are trying to serve generally. They cannot hook into global processes, necessitating a whole other customer journey map for them.
That is where the complexity lies. Unless you do that initial mapping and ask, “Just from the customer perspective, what are they trying to achieve at this point and who’s interacting with them?” you cannot start making improvements or identifying what customer feedback can be pulled at each point.
The value of a good customer journey map resonates because of this exact reason. Even with smaller, more startup-type businesses, the complexities exist. There is a tension between people needing to get on with their jobs and being free to sign up, do stuff, and activate things in all the tools they have. This creates a massive disconnect and starts to potentially damage communications with customers because people are just doing things behind the scenes without centralized oversight of that map.
For example, when looking at CRMs like HubSpot or Salesforce, people might say someone was testing an automated sequence of communication, but nobody wrote it down or told anyone, and it is just running in the background. Nobody has any idea because that person may have left, be on holiday, or changed jobs. Diligence around documentation and somebody maintaining the big picture is super important and often missing in businesses of all sizes.
The more a business moves down the automation path, the more critical this becomes. If nobody has oversight and it’s just an algorithm sorting out what happens next, issues can pop up. For example, a marketing or sales sequence might go out trying to upsell a customer who is currently in a major escalation or threatening legal action. The last thing they want is to be sold more.
This failure to understand how people actually like to be engaged in normal day-to-day life is where the promise of more technology falls over spectacularly. This relates to the mega SAS bubble from five or six years ago, where the promise was that you could just automate everything and get a tool to do stuff. This has led to expectations like a “crap chatbot” or outsourced customer service provided by poorly trained people who often lack support themselves. That whole way of dealing with things has made everyone very cynical.
In a previous SAS startup, people liked that they were actually talking to the founders when they used Intercom for chat support. Customers would often comment that they thought they would be talking to a robot. The fact that they were talking to the guys who were actually building the thing stood out and was not the expectation, which is a tragically low bar to have to hit.
To make a start on getting customer feedback, it is not that hard, as there is a lot of data available to tap into. However, if a company is going to run an NPS (Net Promoter Score) program, they should do it properly. The speaker is not a fan of NPS for a number of reasons, primarily because they have seen it “gamed” so often. For instance, a salesperson might nominate their “mate” at the account, who they know will say good things, rather than the actual user who may not be happy. If a company is going to use NPS, the speaker’s view is to get an independent party who can help so that it cannot be gamed or have someone put their finger on the scale.
NPS should also be used as an opportunity to ask some more intelligent questions that uncover a little bit more insight. Instead of just asking, “Are you happy or not? Rate this on a scale of 1 to 10,” companies should start a conversation about how the product is working for the customer. Questions should include: “What stood out for you? What worked well? What would you like to see more of?”. Asking these questions can open a can of worms because customers feel that if you ask them, they should see improvements in return. But if feedback is sought in a genuine way, making it clear that the company wants feedback, is making an effort to improve, and will focus on the most critical areas, it is productive.
NPS is often considered a too simple way to assess how well things are going. It is easy to do, and people fill it in because it is one question, but it is also retrospective. It is a good, quick touch point, but it will not give much insight; it only provides one data point to gauge if the company is generally on the right track. It shouldn’t be taken in isolation because on its own, it only suggests things were fine last month, which offers limited actionable information.
This connects to a discussion about the lack of ability to usefully predict future performance. Companies often sit on a lot of useful customer data but fail to leverage it for predictive purposes. If a huge company has been trading for 10 or 15 years, they should be able to pull together enough information to make useful predictions about where the business is going over the next 12 to 18 months. The lack of this happening, or being presented with confidence, is surprising, as it seems achievable based on the data companies possess.
This situation is likely to change due to AI discussions, specifically related to true machine learning and Predictive Analytics type of AI. This technology has existed for a long time, going back close to 10 years ago. The speaker recalls working with a software company that had a strong machine learning capability and was looking at customer churn with one of the big banks. In personal banking, they had a good idea when customers would churn because of certain behavioral triggers, such as customers drawing down their savings account to near zero or changing their mortgage. Once predicted, they could determine the “next best offer” or “next best action,” such as giving them a better mortgage rate.
This predictive work 10 years ago required a team of data scientists and CX behavioralists. Now, big software vendors are building a lot of that capability into their platforms in the e-commerce, CX, marketing automation, and sales automation spaces. They are putting tools like propensity modeling into the hands of salespersons and marketers. For example, if customers have certain products, the platforms can predict the next most likely products they will buy, allowing sales efforts to be focused there. From an e-commerce perspective, seasonal trends are visible. In the government space, it is easy to look at buying patterns around the sales cycle and where they might be disrupted by an election. The data is there, and while the work used to require heavy lifting, it is now easier because it is being built into the platforms people use.
A report, the mi3 report, noted that 50% of respondents in banking, finance, and insurance disagreed that CX is well orchestrated and messages are personalized. This is considered poor, given the information has been available for 10 or 15 years. These institutions are the definition of big, complex, siloed organizations with almost too much customer data. Turning a huge container ship quickly does not happen; they have a lot of legacy systems and are not known for being nimble.
However, big banks are investing heavily in this space. They are implementing big Customer Data Platform (CDP) programs, setting up dedicated CX teams, investing in AI tools, and building their own communication platforms so they are not reliant on third-party media. They are also building their own mobile tools and putting a lot more into those platforms. The speaker suggests they are sometimes judged harshly for the work they are doing.
The big banks, like NAB or Westpac, fund a lot of the start-up side of things, meaning a lot of innovation is happening outside the actual institutions through innovation funds and investments in “New Wave stuff”. An anecdotal example involved a startup helping kids under 14 manage money, which was backed by one of the big banks. Although the startup charged $65 a year for an account, the CBA (Commonwealth Bank of Australia) offered a free alternative, which is likely their new version of the “little bank” they used to give every school child. The speaker mentioned that their first savings vehicle was a book from a Co-op savings thing where someone would write down transactions.
Discussions are currently happening about setting up tiny share market accounts for nieces and nephews to start investing for their future instead of giving them cash (e.g., “here’s five Tesla shares”). There are now different options that make it easy to set this up. A product called Raiz automatically rounds up purchases (e.g., $5.57 rounds to $6), and the difference goes into a bucket to buy shares. This makes investing much easier and more accessible, whereas in the 80s or 90s, it required a trading person and involved phones and forms, creating an incredibly high barrier to entry. Younger people who cannot afford a house are more likely to buy ETFs to increase their wealth.
CommBank’s app called Pocket, an investment tool for ETFs and specific products, is seen as a customer success story because it made investing super easy and accessible. It offers seven different funds, similar to Raiz, where money is put in and taken out through an app. The speaker notes that it might be “too easy,” as users need to remember they can lose money.
The original point about opportunity applies heavily to healthcare, specifically the long-running theme of “one patient one record”. While it is a sensitive area with legitimate concerns about information access, the core goal in healthcare is better health outcomes and better predictions. The finance model is exactly the same: looking at financial health and future prospects and making that process accurate and easy. Currently, everything is separately managed, and finance, in particular, is very product structured (home loan bit, savings bit, current account bit, new products bit). Although employees in banking talk to each other, they might not talk in as productive a way as possible. However, finance is a very highly regulated and sensitive industry, so it is understandable that they are not rushing to change too much too quickly.
In medical fields, it is amazing to see new technologies being put into people’s hands. You can monitor your heart rate, and there are tools where you can cough into a smartphone to determine if you have COVID. But medical care is a highly regulated industry, and you do not want people self-diagnosing or building their own chemical mixes. The speaker jokingly mentioned an automated machine for drugs where you press buttons for symptoms, like “the next version of Dr Google”.
People are now “Dr chat G-P-T-ing” their symptoms instead of Doctor Googling them. This technology is also making it easier for GPs to get information, for people in medical research to collaborate globally, and for sharing data. Putting a lot of data in allows professionals to predict outcomes for patients or potential issues based on lifestyle behaviors or other medical issues.
Medicine is a very personalized area. When considering the customer experience and the patient experience, all the great technology is undermined if a patient has a “horrific experience” because they are not being treated like a human. There is treating the body, but also treating the mind, which is critical in medicine. This flips back to the need to speak to a human in an emotion-laden engagement where empathy and a personal experience are desired. If you are sick and need help, you want a personal touch.
That’s the end of the episode for today. Thank you for taking the time to listen. Next time, Jen is joining the conversation again, diving straight back into “data collection madness,” noting that just because we can measure something, doesn’t mean we should.