customer loyalty story

How a Personal Interaction builds Customer Loyalty

I’m as surprised as anyone by this customer loyalty story. Recently, I tried to purchase a few sets of mini crib sheets (as it turns out, new parents need more than I imagined!). When I visited the Hello Spud‘s website I could only find one design. Although I was disappointed, I bought what I could.

What happened next is the story of one small business doing CX right. This note came with the sheets I received in the mail:

customer loyalty personal note

Prompted by what I suspect is web analytics insights, the company co-founder proactively reached out to me to help meet my needs and buy more of her products. Because of the data, she knew I did not have choice online. Sending me this personal note created a wow moment for me that put me on the path to engagement and loyalty.

Customer Loyalty Starts with Contextual Awareness

Melanie managed the delivery touch point utilizing contextual and customer data and creating a value proposition for her customer.  While brands today aim to use data across channels (web to mail), few are able to put it in action. The company founder understood who I was as a customer. Her customer experience data informed her about my on-site behavior, my needs, and my problem. She was able to act on it with a personal, relevant note and offer. Not only that, she created loyalty BEFORE I even used her product.

She used order and inventory data and reached out to me armed with information to resolve my problem (over time).  By doing that,  she converted me to an engaged HelloSpud customer, rather than a lost one. This shows how good data and the right approach to using it can create customer loyalty.

Customer Loyalty Comes from a Customer-Centric Priority

A customer-centric methodology is key to the successful outcome of my interaction with Hello Spud. It is the reason this story appears here, and not among the CX Big Fails! The company did not send an automated response. It did not deliver a message stating “sorry we couldn’t help you, would you like something else.” Instead, the company co-founder reached out to me personally across multiple channels (a handwritten note, followed by personal emails). She even offered to bring me samples so I wouldn’t have to wait until the next production run in January! This type of engagement puts customer-centric theories into practice. The brand created customer loyalty by making their customer a priority.

It is clear that customer loyalty matters to this small brand operating in a crowded field. Hello Spud is using data and outreach to create customer loyalty on the individual level and to grow an engaged customer base on the wider level.

Trust Breeds Customer Loyalty and Brand Advocacy

Hello Spud did something truly impressive. They made me a loyal customer by making me wait! The co-founder’s personal commitment to me made me feel connected to her success, almost like I was part of her team. I trust her commitment to me because of the way she communicated it.

In that state of trust, she then took the opportunity to engage me in a new way by recruiting me to support her business by sharing a review. This was a bold move (remember, I came to the brand seeking a product that they could not provide, and that their competition could).

Because the brand’s customer-centric culture was in place and supported by her action as a brand representative, it was a smart risk to take. The review I leave will be from a loyal customer.

Send us your questions on how to create a customer-centric culture.

 

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*All opinions expressed on the DoingCXRight Blog and site pages are the authors’ alone and do not reflect the opinions of or imply the endorsement of employers or other organizations.

 

drowning in data no insights

A Lot of Data, Not Enough Insight

A month ago I saw a Forrester presentation on Customer Experience measurement that began with a great quote from the Global Bank: “We are drowning in data and starving for insight.”

Aren’t we all?

Most organizations have more data than they ever could have wanted, but that data is either sitting idle in databases or cloud environments, or it is used sub-optimally. Why is it that WeWork can build a tool to manage its 350 properties as a website and digitally view every detail about every building, but Fairway regularly emails me with first time user coupons that I am not eligible for as an existing customer?

Make Data Usable

The answer to this question is fundamentally simple, but practically complex. The first step is to centralize and clean the data so it can be used in an actionable way to extract insights. For existing companies this requires organizational redesign. That makes this step complex, political, and difficult to execute.

In one case, a major brand acquired three small start-ups with the business strategy to grow its customer base. The brand worked on learning how the three customer segments feel and what each segment wants in order to optimize the brand’s offering with three different products. Even though this was the correct first step, the strategy did not progress well. The company was not ready to centralize the customer insights systems and the teams of the three distinct brands they had acquired. Each start-up had its own customer database and customer definitions. None was open about giving access to that data. Thus no insights were derived from any of the three brands.

This case only scratches the surface of how companies miss opportunities with data. Accessing and aggregating data is an essential first step for all organizations, but that is not enough to derive insights. Even after teams and data are centralized and aggregated, insights are not available until the definitions of the data are aligned. How is a customer defined? How far back should the data go? What spend per customer makes that customer “valuable”?

Get Everyone in the Room

Organizations must answer these and many more questions in order to make available the capability of data insights. Often, companies complete step one, aggregate the data, but fail to analyze it and define the key parameters of it. Why? The answers should come from the consumers of the insights, not the technology teams building the insights. And those people are not in the room. Until there is a real engagement by the business and a collaboration among the teams, no one is getting any insights from their data.

Democratize Data

The last step of getting insight out of data might seem the simplest, but it is often missing. The quality of insights is directly correlated to the quality of the questions asked from the data. I will repeat that. The questions asked of data are the engine of the insights derived from data. This is where democratizing the cleaned data with a very user friendly UI is key. Good questions are rarely formed on the spot. As business challenges arise and new situations emerge, questions come out. It is important that access to the data is readily available (no coding or SQL skills necessary!!) to all so the end users can run reports and get the answers they need – and can act upon – in real time.

Successful brands like WeWork turn data into a tool. When companies perceive data as a tool, they create real value for customers. And when they fail to make the difficult steps of organizational redesign and pay for cleaning the data, we receive those coupons we can’t cash in.

View more of our conversations about data, and send us your questions about how to democratize and optimize data to improve customer experience in your organization.

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*All opinions expressed on the DoingCXRight Blog and site pages are the authors’ alone and do not reflect the opinions of or imply the endorsement of employers or other organizations

data tips for customer experience

Lessons Learned at the Forrester Conference: “Data is the New Sexy”

Once a year I look for an event or a conference to attend where I can learn something new and get better at what I do. This year I attended the Forrester Global Council Meeting and the CXNYC2018 Forum in New York.  Usually, the big win from events like this is the opportunity to network and meet new contacts. This year, though, the Council meeting felt like school – which I loved. These are the aha moments I am eager to share with you.

“Stop decorating. Start renovating.”

Do not build a CX strategy that is disconnected from your business strategy and that nobody knows. Don’t maintain a VOC program that tries to fix journeys that were never built with the customer in mind. And stop obsessing over NPS scores versus improving the customer experience.

Instead, focus on your customer needs and what your customers perceive as value, then build your competitive advantage around that. Listen to your employees, who often have the best ideas. Create customer business value. Then execute, execute, execute!

Research is a real thing!

There are many tools customer experience professionals can use to conduct customer research. Depending on which phase of your discovery you are in, or how strategic or tactical the question you are working on answering is, you can use different tools. The broader the question you are asking, the more qualitative your methods should be.

At the discovery phase, when you are looking to find what problems exist, you can do interviews, diary studies or ethnography. If the problems are defined and you need to find the best way to solve those problems, you can get more specific with surveys and usability testing. If you are looking to evaluate a solution that you have built, you can do A/B/multivariate testing and cognitive walkthroughs.

“Data is the new Sexy!”

Customer obsession is nothing more than a dream if you lack the analytics to drive it. You achieve productive customer insights only when you are able to capture and analyze data across channels. CX insight professionals need to be comfortable looking at data from online to offline channels, and they need to derive insights from known data to anonymous data.

Customer analytics methods are interconnected and have dependencies that must be kept in mind. It is impossible to get to customer lifetime value without a solid grasp on customer churn. Understanding the sequence and educating your executives about the complexity and funding required to get end-to-end insights from data is imperative to your organization’s success and your customers’ satisfaction.  Without data, your strategy is based on opinion. You need a data-led strategy to survive.

Now start aggregating data!

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*All opinions expressed on the DoingCXRight Blog and site pages are the authors’ alone and do not reflect the opinions of or imply the endorsement of employers or other organizations.

What Is Big Data and Why Should I Care?

What is Big Data and Why Should I Care?

Big data has become part of our daily language. We read about big data. We see companies that are “experts in big data.” LinkedIn is filled with big data engineers and analysts. But what is big data, where did it come from, and why is it widely available now when it wasn’t ten years ago?

Big data is actually exactly what it sounds like. Big. Data. It is comprised of an enormous amount of 0s and 1s that carry all kinds of meaning. The volume and complexity of the data sets is so large that the Excel or Access analytical tools that we are used to no longer allow us to understand or manipulate the data. Big data is not new. It has always been available.

The big news about big data is that, now, the data no longer needs to be structured in order to be analyzed (read: less work for all of us to “prep” the data for analysis) and it is available real time vs. in weeks (read: no more need to schmooze your IT contacts to run a report for and send it a week later). I remember the days when I was in the banking industry and needed to analyze a set of trades within a month. It would take a week just to find who to talk to, build the relationship, explain what I need, and then another week to get my answers.

All of this inefficiency has been eliminated. Now, we have access to a real-time, friendly system that can answer questions about transactions as they happen. The enabler for all of this is new data storage options. In the past, it was impossible to store data in a flexible way. With the current advancements, that is now possible, making big data widely available.

This is the baseline answer to the “what is big data” question. Josh Ferguson, CTO of Mode Analytics, dives deeper to explain. “Big data is the broad name given to challenges and opportunities we have as data about every aspect of our lives becomes available. It’s not just about data though; it also includes the people, processes, and analysis that turn data into meaning.” In other words, just because we have more data, does not mean we have all the answers we need.

It is necessary to derive insights and information from the data. This is where most companies fail. They aggregate more and more data and never operationalize it. Sometimes they do not even report it. So much data is sitting dormant across all industries because there is no one engaging with it in an analytical way. Consider airlines and kiosk data. Airlines collect performance/usability data (how many days/hours the kiosk was working and/or was used), usability/UX data (which screen of the interface gets abandoned by the customer and how often), and a variety of transaction data (how quick was the kiosk response when people engaged with it). Does this data help airlines on its own, even if the data was collected for 12 months? Absolutely not.

Here is why this matters for customer experience professionals: because we can be the masters of data and control the insights and the messaging that comes from the insights. As customer experience leaders, we can recommend which kiosks should be REMOVED from the system because they barely get used (i.e. save total costs). We can build a dynamic maintenance contract to have maintenance performed only AFTER a certain usage number is reached vs. a static every 3 months maintenance cycle for all kiosks (i.e. lower maintenance costs). Consider how we can use insights that show when customers drop off. Finding that information allows a CX expert to fix the problem and increase the self-service conversion. That means more savings for the company! If a company has service agreements with partners, transaction speed data makes it possible to manage those SLAs much better. In other words, big data with the right critical thinker on top is a source of immense power and leverage. And that is why we should all care about big data.

So, the next time one of those companies approaches your organization saying it will empower your data, ask them who will be extracting the insights from that power, and make sure to build an in-house team of very smart people who can do the magic for you. Once that is set up, start asking good questions and fuel the engine of competitive advantage you can build with big data!

Learn more about Big Data

Rutgers University is offering a deep dive class on Big Data that gives you more of the tools you need to capitalize on this powerful resource. They have been kind enough to offer DoingCXRight readers 20% off the cost of the class. Sign up here to grab your discount and connect to the class.
*All opinions expressed on the DoingCXRight Blog and site pages are the authors’ alone and do not reflect the opinions of or imply the endorsement of employers or other organizations.

How to Prepare for AI: Dispatches from CR Summit, Charleston

On the eve of the #CRSummit in Charleston, customer experience leaders from various industries held the first AI Committee meeting. AI is a challenging topic to cover since it has varied customer experience applications depending on a brand’s growth cycle, customer base and business challenges. Companies like eBay place big bets on AI, while others use natural language processing (AI) only to build smart chatbots.

Regardless of their approaches all companies have one thing in common – they all need to prepare for AI implementation by having a comprehensive data strategy with flexible architecture and a lot of storage. This is the missing piece for most companies. Organizations have different reasons for lacking intelligent data. Some brands are too young and have homegrown systems that need major overhauls to even scale for the growth of the companies. Others have more robust data repositories, but have been built without the customer as the common unit.

There is a third scenario: companies that have third party CRM systems that also host the data. This makes it almost impossible to have the end-to-end data to use for building personalized experiences. It is important to learn the necessary foundations so when you meet with sales reps you can recognize the option that will fit your technology needs.

Another foundational and somewhat counter-intuitive aspect of applying AI is the need for humans. The biggest misconception about AI is that it will “remove jobs”. Meanwhile, customer experience leaders are all struggling to persuade CFOs to fund new teams of data scientists, people who would tag existing data, or people who watch for the “triggers” to use the data. Once this is done, brands will need data councils to add new elements or to design new uses of AI. Companies will always need more people to manage AI effectively.

Lastly, we all want to build solutions that will save operating costs today and enable a future customer experience transformation. So when we build, we need to think about scaling and building further, with the customer at the center.

There are still questions we need to answer. How do we begin the process (especially without much funding)? How can we make AI a reality and not just talk about it? What are the tradeoffs (if any) that we will have to make in the process?

Stay tuned for the next post on AI.

 

 

*All opinions expressed on the DoingCXRight Blog and site pages are the authors’ alone and do not reflect the opinions of or imply the endorsement of employers or other organizations.

Autonomous Customers, Traveler Privacy and More Questions for CX Professionals in a Changing World

“As we move toward a more automated culture, most travelers will adapt to a Jetsonian, automated lifestyle.  Every industry we know will be disrupted.  For those of us in aviation, this signals the shift from aviation as a service industry to a transactional one that is potentially devoid of the personal touches that made the romance of flight an event.”

As I am boarding my flight to Denver today to speak at the AAAE Conference on “Autonomous Airports,” I can’t help but question, what does autonomous airport really mean.  The customer experience value of an airport itself is not autonomous.  Rather, the emerging autonomous airport experience aims to give birth to, enable and empower autonomous customers.

That brings about even more questions for CX professionals, particular customer experience professionals in the aviation world.

What is an autonomous customer?

The autonomous customer uses his/her time better and has more of it. Today we have a “holding room” at airport gates. Holding room… even the term itself sounds limiting.

What is a customer supposed to do in a holding room?  Be on hold?

Autonomous airports are open spaces with no physical or process boundaries between the individual customer touch points (check-in, bag drop, etc.).  As a result, there also is no barrier between crewmember and customer. Eliminating barriers in autonomous airports shifts the power from the airport procedures and processes to the traveler. This makes travel more enjoyable.

Because of this customer experience-driven design, the autonomous customer can go through the experience at his/her own pace.  The autonomous customer is not “held” anywhere. The airport becomes a menu of tools and services that the autonomous customer is empowered to choose to use or not. Who would not want to do that?

What about Grandma’s journey?

Autonomous airports enable both customers and crewmembers. A roving crew has access to much more information and tools on the go that enable them to take care of the needs of all customers of all ages, particularly those who do not want to or are unable to do so themselves.

Maybe the first time, Grandma will be intimidated (although not all grandmas are alike!) by the autonomous airport environment, but she will quickly get used to and appreciate the self-driving device that can whisk her and her bags from one gate to another in a few minutes.

What about my privacy? Does autonomy mean my airline knows everything about me?

Autonomy is also about accountability.  On both sides. Customers want information and adequate services at the right times.  It is impossible for any brand to deliver that without access to certain customer information or preferences.

Customers also want seamless journeys across the airport. To design that airlines and airports need access to certain customer history. For example, if you want the airline to wait for the customer one extra minute at the gate, the airline needs to know that the customer is physically at the airport. Even more so, the airline should know whether the customer has passed security already.

In the case of JetBlue’s autonomous airport CX design, Bag Buddy, one of my ideas, was designed to pick up customer bags at their homes and transport them directly to their destinations. That seamless movement of objects and people lays on the foundations of data sharing. More specifically, it rests on good data that is appropriate and useful in delivering the experience customers want.

Questions remain, and as CX experts continue to design autonomous airports and meet the needs of the autonomous customer, new questions will arise.  For now, let me demystify the autonomous airport for you. At the heart of the autonomous airport, from the CX perspective, is the information that will allow the airport as a physical asset to expand its boundaries and reach people’s homes. Data allows physical boundaries to merge and creates one big experience of transporting people and their belongings across space. That is a future we all want, Jetsons fans or not.

 

 

*All opinions expressed on the DoingCXRight Blog and site pages are the authors’ alone and do not reflect the opinions of or imply the endorsement of employers or other organizations.

Is AI Really The Answer?

Earlier this week we shared some of the pitfalls of implementing  self-service and highlighted the importance of strategic and empathetic implementation.  AI (artificial intelligence) is one of the self-service tools in the customer service professional toolbox today. It is also one of the new buzzwords, together with blockchain (for the curious ones  – my favorite explanations of blockchain are this video and this article).

The primary current positioning of AI is in call centers. The value proposition is that with AI, companies will empower customer service employees to make better decisions/recommendations, thus increasing employee engagement. Additionally, through AI, organizations will achieve significant ROI by automating the role of the customer agent (in JetBlue’s case, the crewmember) and scaling customer support without incremental headcount.

So what exactly is AI? Do you really understand what this technology can and cannot do? If you do not, keep reading as all working professionals today should understand at least the basics of AI. If you are like me, you probably get 100 sales emails every week telling you that you are running late and must leverage AI in your call centers. But do you really need AI? What problem do YOU need to solve? And is AI the best way to do that for your company?

Last month I was invited by Execs In the Know to join their AI Advisory Committee with the below mandate:

“The final group output will come in the form of a report. The exact nature of the report will be determined and framed by the group, but may include areas such as:

  • A summary of ways AI is enhancing CX channels
  • Best practices on where and how to start
  • Trends and technology in AI to improve service
  • ROI of current AI customer service initiatives
  • Perspectives and predictions about the future of AI and customer service”

This is no small mandate and it is encouraging that we have a group of professionals who are examining these questions before we all get ahead of ourselves with AI and compromise the ROI we all want so much.

The most common use of AI is ML (machine learning).  Basically, this is data mining and predictive learning on steroids that enables a computer to make decisions and interact with a human. With that basic understanding I already have a few questions to all the companies that are calling, emailing, inmailing etc., to offer me AI enabled solutions. Who, or rather what, is enabling those solutions? Is it my company’s data? Because if it is, I have a lot more work to do internally before I respond to those sales pieces.

Erik Brynjolfsson and  Andrew Mcafee provide a comprehensive explanation of what AI is and what it is not in their publication The Business of Artificial Intelligence. In it they state that AI technology is ready for implementation in the business world and that “[t]he bottleneck now is in management, implementation, and business imagination.” I do have the business imagination, but I also am taking my time to know exactly what capability I am buying with AI. It might be cheaper to streamline processes and fix existing software tools or integrations to enable my employees to deliver excellent customer service, instead of paying for yet another software integration that makes decisions based on my bad data (since I would have prioritized the purchase of the expensive AI solution over cleaning my existing data).

Although it is clear that AI will be a solution that needs demonstrated ROI and employee adoption for success while it is learning, it is also clear that we cannot wait too long to get comfortable with this new technology. As Erik and Andrew say, one thing is pretty sure: “[o]ver the next decade, AI won’t replace managers, but managers who use AI will replace those who don’t”.

That is the exact reason I chose to join the AI Advisory Committee. More to come!

 

 

*All opinions expressed on the DoingCXRight Blog and site pages are the authors’ alone and do not reflect the opinions of or imply the endorsement of employers or other organizations.