“Ice-T? Why would they have Ice-T at a conference dedicated to customer experience?” Well by the conclusion of my two days at the CMSWire CONNECT conference in Austin last week I had my answer.
But more on Ice-T later.
First off a nod of thanks to my CMSWire editors Dom Nicastro and Siobhan Fagan for arranging for me to attend the conference as a CMSWire contributor. It was great to spend time in person with people I’ve been writing for over the last six years. It was also an excellent opportunity to meet other members of the CMSWire team too.
Early encounter with AI personas
My two days started off with attending a very thought-provoking Breakfast Briefing from Erin Reilly of the University of Texas on The Rise of Virtual Beings and how they are transforming the customer experience. I must admit I hadn’t given much thought to the use of three-dimensional avatars beyond gaming applications, but her examples of AI-driven personas certainly gave me pause to think about how the digital landscape is continuing to evolve.
Metrics of CX – data and experience
In his discussion on The Customer of the Future, the University of Texas’s Art Markman discussed the application of cognitive psychology to customer behavior and how we measure it. One point that really caught my attention was the observation that
“If we spend time just looking at data we start to think that every interaction is a digital experience. We need to look beyond that and embrace the real-life experiences and engagements.”
This really resonated as I’ve had several conversations recently about the relative importance of quantitative and qualitative metrics when it comes to determining the quality of content-driven interactions. This drove home that both are equally important. As one later speaker put it, and I’m afraid I missed taking a note of who it was, it’s no good knowing the ‘What’ if we don’t know the ‘Why.’
Katrina Taylor of LuLu Lemon also summed it up nicely in her excellent presentation on Human-Centered Design for Omni-Channel Delivery when she stated:
“You can go through all the data in the world, but you will learn way more in a 30-minute conversation with those on the front line who interface with customers.”
Intelligent content drives personalization
I must admit my heart gave a little jump to hear Matthew Shaeffer from REI talk about the need for intelligent content in his talk on Modernizing the CX Stack. His observation that Intelligent CX needs to be a series of uniquely assembled interactions driven by content that is structurally rich, and semantically categorized was great to hear. Engineering content in such a way is key to delivering the granular levels of personalized interactions most companies are looking to achieve, and it was great to hear of a major retailer adopting this approach.
Tarunam Verma from Lowes made a smart observation during his presentation on Hyper-Personalization that what we think of as personalization isn’t just about applying technology, in reality, it’s a mix of culture, mindset, and the technology.
Is AI really Augmented Intelligence rather than Artificial Intelligence?
The second day of the conference had a strong theme around the impact of Artificial Intelligence (AI) and Machine Learning (ML) with some excellent observations and talking points discussed throughout the day. Here’s a snapshot of some of the ones that caught my attention:
“Use AI in support of creativity not instead of creativity” – Karna Crawford (ex-Ford)
“AI is most useful currently as a back-office application that detects operational inefficiencies.” / “There are two waves of AI: (1) recommendation engines – which are established, and (2) generative – which we are still trying to figure out.” / “Don’t implement AI just for the sake of it, know what problem you are trying to solve. / AI requires us to rethink how we do things.” – Daniel Wu (J.P. Morgan)
“Think of AI as ‘Augmented Intelligence” that helps us do our tasks better, not ‘Artificial Intelligence’ that will replace us.” – Raj Krishan (Microsoft).
One of the best questions of the day came from CMSWire facilitator Kate Cox who posed her panelists a pretty philosophical question:
“If I use AI to craft an email and you use AI to read it, are we actually communicating?”
Ice-T on Walls and Boats
Which brings us back to Ice-T as the conference closing keynote. I wasna sure what a former gangster, turned rapper, turned actor would have to say that would be relevant to an audience full of technologists. In fact, I was in two minds about staying, thinking I’d leave a bit early to get ahead of the Austin downtown Friday traffic exodus; but I’m glad I did as he delivered one of the best conference keynotes I’ve seen.
It was entertaining, full of amazing stories, and above all an inspirational discussion on handling change. Here are just a few of his observations that I jotted down:
“There are walls – things that you can’t change – and obstacles that look like walls. Get over the obstacles by talking to people that have already got over them. But then make sure to put in the work that they put in.”
“Don’t ever get annoyed at the lack of results from the work you didn’t do.”
“Anything you do you bring your perspective to it. That’s your value. Make it your own thing.”
“Don’t complain, just figure stuff out.”
“Take opportunity when it turns up. A lot of times the opportunity is right in front of you. Just get in that boat, at least for long enough to say ‘I don’t like it.’ If you don’t you’ll never know.”
And if the CONNECT conference was one thing, it was a great opportunity to learn from, meet, and network with a whole raft of new people. Thanks to all I chatted with be it after presentations, at vendor booths, or over coffee or meal breaks.
“Account number, please.” Three simple words tell me I am about to have a less-than-optimal customer experience.
It’s an all too familiar scenario: I call up a company, and the phone system asks me to punch in my account number to verify who I am. And then every time I get passed on to another step in the process, I am asked again to give my account number and verify my name.
Derailed by data silos
It’s an obvious tell that the company I’m calling has its customer data siloed in systems that don’t talk to each other. Once I punch my account number into a supposedly automated system, that data, and the customer profile associated with it, should travel with me on every subsequent interaction, no matter who is handling my call at the moment or which department that person works in.
Isn’t that the promise that customer data platforms (CDP) are supposed to deliver? After all, CDP technology is designed to provide a persistent, unified customer database to other systems across the enterprise. But is that really what’s happening? Too often, companies see CDPs solely as marketing tools, and as a consequence keep them siloed within specific marketing-driven operational functions. These companies use CDPs to drive marketing campaigns, not to improve the customer experience.
Look at your company the way customers do
When marketers talk about the omnichannel experience, they are usually referring to the various channels through which they deliver their messages: websites, social media, email, etc. It’s an inside-out viewpoint built around a broadcast model. They are failing to look at their company the way their customers do — as a single entity.
When customers engage with you, they don’t do so because they are anxious to consume your latest marketing message, they do so because they want a question answered. They don’t want to passively consume, they want to engage in some sort of conversational relationship that will provide value and help them.
More to the point, they don’t care which functional group or department the information they need comes from. They don’t know your business-unit structure or your operational hierarchy. To them, your company is a single entity, and every interaction with that entity is a reflection of your brand experience.
Asking a customer to supply the same information, again and again, is a bad brand experience.
If your CDP acts solely as a siloed enabler of marketing campaigns and doesn’t improve the customer experience, then it is failing.
My Saturday mornings used to be full of artificial intelligence (AI). Thanks to the TV shows I watched and the comics and books I read, I grew up expecting to live in a world of robots that could think and talk, vehicles of all sizes that would whisk me off to far-away destinations with no need for drivers or pilots, and computers that would respond to voice commands and know the answer to just about everything.
I may not yet have that robot butler, and my first experience with a self-driving car left me more apprehensive than impressed, but in other ways artificial intelligence is now part of my everyday existence, and in ways that I don’t even think about.
One of the first things I do each morning is ask Siri for the day’s weather forecast and then check to make sure that my Nest thermostat is reacting accordingly. During the day, Pandora’s predictive analytics choose my music, and in the evening Netflix serves up my favorite shows and movies. My books arrive courtesy of Amazon, and there’s a fair chance that some of those purchases were driven by recommendations generated via AI.
And now everyday I see several posts about content generated by the AI driven chatbot ChapGT (most of which seems very repetitive to me), while my artist friends debate the ethics of AI generated art (or is it even art at all).
It seems to me that we are on the edge of a potential leap forward in the application of AI, or perhaps more accurately we are making noticeable strides in the application of Machine Learning (ML) rather than true AI.
Outdated practices hampers AI advances
What we have today is just a small representation of the promise of AI, and that promise has not yet been realized.
Many companies and organizations still use older technology and systems that get in the way of a truly seamless AI customer experience. When the systems we already have don’t interact, and companies continue to build point-solution silos, duplicate processes across business units, or fail to take a holistic view of their data, content, and technology assets, then AI systems will continue to pull from a restricted set of information.
Over the past several years, as I have talked and worked with companies that are pursuing AI initiatives, I have noticed that the majority of those projects fail for a common reason; AI needs intelligent content. It may not be the only reason, but it’s definitely a common denominator.
AI needs intelligent content
No artificial intelligence proof of concept, pilot program, or full implementation will scale without the fuel that connects systems to users — content. And not just any content, but the right content at the right time to answer a question or move through a process. AI can help automate mundane tasks and free up humans to be more creative, but it needs the underpinning of data in context — and that is content, specifically content that is intelligent. According to Ann Rockley and Charles Cooper, intelligent content is “content that’s structurally rich and semantically categorized and therefore automatically discoverable, reusable, reconfigurable, and adaptable.” [Ann Rockley and Charles Cooper: Managing Enterprise Content: A Unified Content Strategy, Berkeley: New Riders, 2012]
The way we deliver and interact with content is changing. It used to be good enough to create large monolithic pieces of content: manuals, white papers, print brochures, etc. and publish them in either a traditional broadcast model or a passive mode. We would then hope that, in the best case, we could drive our customers to find our content or, in the worst case, that whoever needed it would stumbled across it via search or navigation.
With the rise of new delivery channels and AI-driven algorithms, that has changed. We no longer want to just consume content, we want to have conversations with it. The broadcast model has changed to an invoke-and-respond model. To meet the needs of the new delivery models like AI, our content needs to be active and delivered proactively. We need to build intelligent content that supports an advanced publishing process that leverages data and metadata, coordinates content efforts across departmental silos, and makes smart use of technology, including, increasingly, artificial intelligence and machine learning.
In addition to Rockley and Cooper’s definition of intelligent content, our content should also be modular, coherent, self-aware, and quantum. Here are definitions of those four characteristics:
Modular: existing in smaller, self-contained units of information that address single topics.
Coherent: defined, described, and managed through a common content model so that it can be moved across systems.
Self-Aware: connected with semantics, taxonomy, structure, and context.
Quantum: made up of content segments that can exist in multiple states and systems at the same time.
Intelligent content with a common content and semantics model that allows systems to talk the same language when moving content across silos may be the key to unlocking the technology disconnect that is holding AI back from even greater acceptance.
A recent conversation about emerging technologies reminded me of an interesting infographic that I saw posted and reposted a lot a few years back, It showcased the “30 Technologies of the Next Decade.” It’s an impressive list of where digital transformation is taking us and how the customer experience will change in the relatively near term.
One thing that’s clear: our technology stack is continuing to go through a period of dramatic change.
Over recent years I’ve also been privy to the plans of some major organizations across a wide range of industries. These plans map out their aspirational goals for addressing the challenges such change will bring. I’ve seen a lot of systems and architecture diagrams, proofs of concept, and prototypes demonstrated with varying degrees of success. And, with a few exceptions, they all share a common weakness.
I estimate that at least half of those 30 technologies depend on content — be it written, graphical, video, audio, animation, or developing media like augmented reality — to deliver the customer experience. Yet many of those future-looking plans that embrace those technologies suffer from what I term “The Content Fallacy.” An unstated belief that “content just happens.”
Content needs to be engineered
A common trope when talking about the impact of digital transformation is to focus on the end result. This is good; we all need a shared vision. But the road to achieving that vision is often built on a common understanding of what foundations are needed to build that road. And the one I believe is largely missing is the concept of content engineering. To achieve any sort of personalized, high-quality experience across a growing number of delivery channels, you need to think up front as to what sort of content you will need and how it will be engineered to achieve those goals.
To give a real-world example, when discussing with a client how they would meet a C-suite level mandate for personalizing the customer experience as part of their digital transformation strategy, we discovered that to meet all the different vectors of marketing campaigns, product types, customer segments, industries, languages, and delivery channels they were targeting, they would be potentially delivering over 18,000 variants of one piece of content.
They had assumed that because they already had the baseline content, they could just feed it into their new systems and it would be delivered in the format the customer needed. But content for a website is not the same content you need for a smartphone or watch. The content you have is most likely not written or structured for the question-context-interpretation-answer model you need for a chatbot or voice assistant. If your customer communications have primarily been text-based, then they will probably not work alongside visuals or provide the right context and enhancement for an augmented reality experience.
The six facets of content engineering
Content engineering is a six-faceted approach to thinking and designing your content for the emerging digital transformation experience. Each of those facets can be defined as follows:
Model: a representation of the types of content you create, including their elements, attributes, and interdependent relationships.
Metadata: information that helps applications, authors, systems, and robots use content in a smart way.
Mark-Up: a way to identify the structure and context of the content outside the content itself.
Schema: a form of metadata that provides meaning and relationships to content. Schema often involves published standard vocabularies for describing concepts with standardized terms. Examples of XML schemas include DocBook, DITA, and TEI.
Taxonomy: a map of related concepts which are applied to content, often as tags. Enables and supports features such as related content reuse, navigation, search, and personalization.
Topology: the art of developing common organizational structures and containers across content management and publishing systems.
By taking an engineering approach, content moves away from being something that just happens (and then often doesn’t deliver the expected results) to becoming the foundational fuel to power digital transformation and deliver those exciting new multi-channel experiences we are all looking forward to.
Admit it, we all do it. I’m talking about how whenever we post something online, we can’t help but check back later to see how it was received. Thumbs up, likes, retweets, comments, downloads, page views. We all love metrics, whether it’s just “did anyone like the picture of my cat I posted on Instagram yesterday” all the way up to complex reports about web traffic, journey flow, click-through rates, and all that good stuff it takes a data scientist to sift through. We have so much data available about customer interactions that the true meaning is often forgotten.
The problem is that most of the metrics record what someone did in the past — typically an interaction with your content by either clicking a button or following a link. They don’t tell us why the person did what they did.
And knowing why is the most important part of understanding the customer journey.
Getting to the why (and why not) of customer behavior
There is an excellent video from Adobe entitled Click, Baby, Click that shows how reacting to clicks without knowing what is driving them can lead to an incorrect interpretation of customer demand. If you haven’t seen it, I highly recommend watching it — it’s a fun lesson you won’t forget.
So if action-based metrics don’t provide the information you need, do time-based metrics give a better picture of what’s driving customer behavior? They are probably a step in the right direction, but they have the same underlying issue — they still reflect past actions. You may now know how long someone interacted with your messaging but not why. For instance, time-on-page can be a false indicator: is someone engaged because your content is good and they enjoy reading it, or is it so obtuse that they have to keep plowing through it to find the answers they want?
Most people come to websites or interact with apps for one of two reasons: to get answers to questions or to complete a transaction. So maybe we should be measuring how well we achieve those two things. Instead of having page-based analytics, shouldn’t we be focused on content and transaction-based analytics combined with search analysis and time reporting to determine how easily, or quickly, customers achieve their goals?
On top of wanting to know what people do during a customer engagement and why they do it, it’s equally important to know why someone didn’t do what you wanted them to do. Why is no one clicking on that beautifully designed call-to-action button? Why isn’t anyone finding high-value content that would help them? This is where tools like heat maps can help you track where people engage with your designs.
So if the current metrics are a snapshot of past physical actions, how do you realign for a future where interactions migrate from the physical to the digital or to even more esoteric forms of interaction?
Think about the growing use of voice-based assistants such as Siri and Alexa. How will you measure audio interactions?
In many ways we already do, but for a different need. When you call a telephone helpline or get passed to a call center representative with a message that says “your call may be recorded for training purposes,” chances are high that training is low down on the list of why the call is being recorded. Call centers have long used technology to record, index, and analyze customer interactions not just for what was said, but also for the way it was said in terms of tone and inflection.
Sentiment analysis may drive the next generation of metrics for voice-assistant-driven interfaces, not only allowing you to understand what a customer asked for and wanted but also, with the application of machine learning, allowing you to start to understand not just how someone feels about an interaction but also what it was they were hoping to achieve in the first place.
Once you understand intent, as opposed to past actions, you can start to deliver predictive customer experiences and look forward instead of backward.
How can we help you?
The only true indication of a successful customer experience is whether you helped the customer do what they needed to do in a quick, intuitive, and helpful way? Did you make their day easier or answer their question?
The more you remove friction from the customer experience, the more likely those customers are to return and want to engage with you again.
That’s a well-worn saying — one that carries a degree of truth. But how do you know whether your customers are truly satisfied? Measuring something as emotional as an experience can be as much of an art as it is a science.
Why do people do what they do?
Sure, we have tools and metrics — surveys, Net Promoter Scores, the number of likes and followers — as well as behavioral analytics such as time on page and click-through and abandonment rates. And we use them to try and determine satisfaction levels. These are indicators of what some people do, but they don’t tell you why they do it.
Are you measuring based on what you believe your customers want as opposed to what they actually need?
Customers don’t come to your website or digital platform actively seeking out your latest marketing messages. They come because they have things they need to do. Those things can range from making a purchase to setting up an account to changing account information to paying bills. Therefore, efforts to measure the success of a customer experience must be based primarily on how easy it is to accomplish those tasks, with less of an emphasis on how often users click your call-to-action buttons.
One area with a long history of helping customers get stuff done is the customer-support call center. In recent years, I’ve heard many companies talk about a customer-experience success metric as being the number of calls that get deflected from the support center to a self-help portal on the website. That doesn’t measure customer-experience success; it just measures a process change. If you don’t have the right content on the self-help portal, and if that content isn’t easily accessible and navigable, then you may be delivering a worse experience when you send people to the self-help portal.
The word deflected makes me shudder because it implies (at least to me) that the company is actively avoiding engaging with its customers.
Customers want answers
This is especially worrying when research shows that what most customers want when they engage with a company are answers to questions. For instance, research by the Search Engine Journal showed that the top five content types that customers look for on a website can be summarized as follows:
Answers to the five W’s (who, what, when, where, and why)
How-to guides or instructions
Definitions (especially of complex terms)
Prices and cost breakdowns
That research confirms that customers want easy access to answers from any part of your organization. It’s no longer true (if it ever really was) that marketing provides one type of information and customer support provides another.
In the business-to-business environment, there is strong evidence that customer-experience needs are driving cross-functional convergence of content. A leading software company reported that over three-quarters of the visitors to its main websites want to look at technical content about the use and implementation of its products. Therefore, they now include metrics for what were traditionally seen as support functions in their overall customer-experience reporting.
Take a holistic approach
Do you measure in isolation as opposed to holistically?
In general, the metrics used for measuring customer experience still tend to be the indicators of success (or failure) for individual operational departments or groups. Rarely, if ever, are they looked at in a holistic way to provide an overall measurement of customer satisfaction. It’s possible that you could be scoring highly in specific categories but still delivering a poor overall customer experience because the journey is disconnected.
By looking at customer-related metrics as part of an overall ecosystem and not as separate performance indicators, you can develop a clearer picture of a customer’s overall journey.
Take two astrophysicists, an Apollo engineer, a guy who designed parts of the International Space Station, a professor of robotics, and a random science fiction writer, and what do you have? It sounds like a dream sequence from the TV show, “The Big Bang Theory,” or the start of a science nerd joke. In fact, it was the make-up of a talk panel at a recent science fiction convention where I was one of the guests. The panel was ostensibly meant to be a retrospective look back at the days of Apollo, but like many such conversations, it soon turned to thinking about the future, which led to the subject of machine learning (ML) driven artificial intelligence (AI) and its current capabilities.
I expected an enthusiastic discourse, and so I was surprised when most of these actual rocket scientists seemed more ambivalent about the technology and its potential impacts.
A couple of observations caught my attention enough to tweet them out at the time:
“ML is great at recognizing patterns but not much else.”
“ML assumes tomorrow is going to be the same as today.”
Yet it seems these technologies are being received more enthusiastically elsewhere. Nearly every customer experience discussion and the majority of CX projects my team is engaged in these days includes some mention of machine learning and artificial intelligence (and often the two are used synonymously although they are different). Which got me thinking, how do the somewhat downbeat observations of a panel of space experts play into the world of customer data, and the ways we try to infer context from it?
‘ML Is Great at Recognizing Patterns but Not Much Else’
Machine learning is usually defined as “a set of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions.” It’s a subset of artificial intelligence that relies on patterns and inference to drive conclusions. In other words, as the scientists observed, it’s great at doing what it is meant to: Pattern recognition.
That means it can see what is happening in a data set, but not why it’s happening. That still (at the moment anyway) needs human interaction to derive context based on experience, knowledge, and a degree of intuition.
Machine learning can greatly reduce the workload and automate the process of recognizing patterns of behavior in large sets of customer data, but it is not a magic panacea for developing an understanding of why customers do what they do.
‘ML Assumes Tomorrow Is Going to Be the Same as Today’
The data we get from machine learning is a reflection of what happened the day the data was captured. For the purpose of pattern matching, there is an underlying assumption that the next set of data is going to be similar enough for the patterns and models it recognized to still be applicable.
Machine learning is not a predictive tool. It is a great way to analyze a lot of data and an efficient way to learn about repetitive behavior. But that’s it. The danger can be we take that baseline and believe that is how things will always be. Our customers acted that way yesterday, so they will act the same way tomorrow. If that was truly the case, to paraphrase Henry Ford’s observation, we’d still be riding horses. ML does not take into account the impact of disruptive social or technological influences. Overreliance on technologies like ML without understanding their role in developing a broader understanding of our customers can be just as much a blocker to delivering a good customer experience as any older system or technology.
We’ve Got a Long Way to Go With Machine Learning
When my wife and I get into my car on a Saturday morning, the ML system connected to my phone that analyzes my movements assumes we are heading for our favorite local diner. While that’s true around 80% of the time, on the odd weekend we head off in another direction, and the phone and GPS literally get lost for a while.
We have a long way to go (both figuratively and literally) with machine learning before it drives a true artificial intelligence-driven customer experience.
“Welcome back, Mr. Porter. Great to see you again.”
Those were the first words I heard as I walked into my hotel after a day on-site at a client’s office. It’s always nice to be greeted in a hotel, especially one where you stay on a semi-regular basis. The greeting got me thinking, is there any better example of ‘baked in” customer experience than the hospitality industry? It is literally the key ingredient of the business (although I’ve stayed at my share of hotels where you’d think otherwise).
Technology Isn’t a Band Aid for Bad CX
How can we take the hospitality mindset and thread it through our digital transformation projects? I recently came across a sponsored post on my Twitter feed that declared, “Experience is everything,” and that we should “know what your customers are feeling so we can turn discomfort into delight.” It was of course a campaign to promote yet another experience management platform.
First, we need to realize that applying new technology isn’t the key to improving the customer experience. It should be an enabling tool, but if we don’t have the customer service culture in place, it’s a waste of time and resources.
But the Twitter campaign did get two things right: yes, experience is everything, in fact, it’s the key area of competitive advantage in today’s marketplace; and yes, we need to lead with empathy. But if you are starting with the assumption that your customers are already in some sort of discomfort when dealing with your company, then you have a systemic issue. Applying technology as some sort of band-aid will not improve the situation.
With Customer Experience, Actions Speak Louder Than Words
Like the hospitality industry, thinking about the customer experience has to be woven into everything a company and its employees do, irrespective of their job-title or function, and it must come from the top. I once worked with a company where there was a lot of talk about “the customer” but very little seemed to be done to actually address known product issues. In fact, they had a reputation for ignoring their customers. The internal dialog seemed at odds with the external perception. Until one day someone said, “you do know what the CEO means when he uses the word customer? He means the shareholders and analysts, he means his personal ‘customers.’ He doesn’t mean the people who actually use our products.” That was like a lightbulb going off — it explained everything about that company’s culture and business model.
Compared this with another company I worked with. On the surface, it lacked the various functional roles you would expect would be necessary to deliver on the promise of customer experience. There was no customer support desk or call-center, no customer engagement manager, etc. In fact, no one had the word ‘customer’ in their job-title. Yet it has a top-ranked reputation for service with the people who actually buy and use their products. In fact, it also has a minimal marketing staff, because its customer’s word-of-mouth recommendations are such an effective marketing tool. The reason is that the idea that everyone in that company is part of the customer experience is central to the company’s culture, communicated from the CEO outwards.
That CEO understands you don’t have to be actually directly interacting with a customer to impact the experience. If the company has a customer-led culture it will be reflected outwards. Working in the accounts department sending out invoices? Think about how those invoices read to the customers. How easy is it for them to pay their bills? Designing a product? Think about how easy it will be to use. Putting together a website? Do you understand what your customers want to do when they engage with you online?
Customer engagement is a holistic experience. The customer’s don’t know, nor care, who works in what department. The response to any customer engagement, no matter where it originates, or where it’s picked up, should never be “Sorry that’s not my job.” It’s everyone’s job. If an individual can’t immediately help solve an issue, then build a culture where they take the details, find the person to solve the issue, and then follow up. (Never underestimate the power of following up.)
The Foundations of Customer Driven Cultures
I’ve outlined before what I believe are the three essential parts of planning any CX-related transformation project. They still stand as the core to baking in a leading customer-driven culture that will avoid the discomfort:
Know your customer.
Follow your customer.
Understand your customer.
But if you do find there are the occasional instances of discomfort (and to some extent they are inevitable), then rather than throwing technology at it I’d add a fourth and fifth edict:
Help your customer do what they need to do.
Follow up and build a relationship with your customer.
The woman on stage proudly told the conference audience how her team had spent three days to find just the right kitten for Emily.
Emily was a single working mother in her early 30s who lived with her 4-year-old daughter in a two-bedroom apartment. She was on a limited budget and often pressed for time. She also loved cats. Hence the search for the perfect kitten. The thing was, Emily didn’t exist. Emily was a persona dreamed up by the marketing team. The aim of the team was to create a series of recipes that used the company’s products — a series of recipes just for Emily. And they spent (wasted) three days looking for a photo of a kitten to accompany a made-up person.
I’ll be honest. I have a few problems with Emily — and others just like her.
Personas with too narrow a focus
By focusing on an individual as a persona you can narrow your focus too much and miss a large percentage of the customers and prospects who might benefit from your message. By creating messages “just for Emily,” the team was ignoring a wider need for anyone who wanted to create quick, nutritious meals on a limited budget. Personas should be focused on addressing customer needs, not on developing fictional characters.
It’s a marketing point of view
Often, as with Emily, personas are developed by the marketing team with little or no interaction with actual customers. Marketing teams are often organizationally isolated from everyday interaction with customers, which can lead to personas that reflect what the marketing team thinks customers are looking for, rather than what customers actually need and how they go about finding information. It is essential that your marketing team take into account real-life customer experiences and needs.
Customers are changing
I have seen many personas documented along the lines of “Emily goes to the website to do initial research, checks reviews on mobile, and uses the app to purchase.”
The customer experience evolves rapidly. I know my digital behavior patterns have changed over the last 12 months. You need to keep up with these changes. How often do you review personas to ensure that they keep up with new technologies and changes in how customers interact with your brand?
Still part of the “Sell and Forget” model
Historically, personas have focused on the buying behavior of a given set of potential customers. They are designed to drive people along the traditional sales funnel from awareness to lead to prospect to sale. But that only represents a small part of a customer’s overall interaction with a company.
How do personas fit with the continuous customer journey?
Once prospects become customers they shouldn’t be forgotten and neither should the relevant personas. How do your personas interact with your brand from delivery of the product through owning, operating, and getting support? Do you understand the full customer life-cycle of your personas and how their journey across every interaction with your company is connected and mapped?
Get that right and the satisfied customer persona can be your best advocate to generate even more business.
Was the kitten really necessary?
When you are developing needs-driven personas to help you understand customer behavior, your process needs to be systematic, efficient, and based on data. Building an emotional backstory for a character is all well and good if you are working on your latest novel, but it can be a time-consuming misdirection in developing effective customer-driven personas. How many customer interviews could that marketing team have done during the time it took to find the perfect photo of Fluffy?