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Digital survival: The transformation imperative

 

Many discussions of digital transformation focus on marketing metrics such as page views, clicks, and even mentions in the press. Although marketing is important, this limited and unsophisticated view ignores underlying issues such as improving business speed, agility, and responsiveness to customers.

Only by rethinking business models can we meaningfully gain benefit from digital transformation in departments across the company including operations, supply chain, manufacturing, and customer service. Even finance and accounting should evolve as an organization responds to the changing expectations of modern consumers.

Genuine digital transformation – not the veneer of marketing, but the real thing – has become a business imperative. Over 50 percent of the Fortune 500 have disappeared in the last 15 years or so years, demonstrating the need for organizations to evolve.

Even when companies commit to a program of digital transformation, the results are often disappointing. Despite large investments in digital transformation, 59 percent of respondents in one survey companies reported that “digital transformation has not delivered high business impact at their organization.”

Against this backdrop of market confusion, I invited a digital transformation expert, practitioner, and author to participate as a guest on episode 208 of CXOTALK, a series of conversations with the world’s leading innovators.

Anurag Harsh is a founding executive of Ziff Davis (no relation to ZDNet), one of the largest publishers of technology content in the world. He is a prolific author (of multiple books) and puts theory into practice as a senior executive at Ziff Davis.

The conversation with Anurag Harsh spans the practical to the philosophical; the cultural and psychological to the technological. Among the wide-ranging topics, we discuss strategies for digital transformation, each targeting a specific business situation. From the structural swivel to the inverse acquisition, Anurag offers a prescription for many digital transformation situations.

Watch the video conversation embedded above, but also read the complete transcript of our talk. Here are edited excerpts and highlights:

Why is digital transformation so important?

In the last fifteen or sixteen years, more than half of all Fortune 500 companies have become insolvent, been acquired by another company, or stopped doing business altogether. And if you just look at last year, 50% of Fortune 500 companies declared a loss. So, the stride of transformation has become a revolution. Rivalries have deepened, and business models have been dislocated. The only constant is the growing severity of digital disruption.

Because of disruption, there’s despondency, and that’s compelling companies to want these digital initiatives. And they are investing a lot of money, which mostly results in disappointment due to the absence of concrete strategy. As markets shift downward, many companies try to counter the spiral by initiating frantic investments and digital initiatives. Some of them are hiring Chief Digital Officers, and some of them are looking at their CIOs and CMOs to counter these disruptive effects.

Describe your model for digital transformation?

There are five things that companies need to think about. These are terms I use a:

  • Structural swivel
  • Inverse acquisition
  • Offshoot
  • Coattail rider
  • Oiling the hinges

I will describe the first three now.

Structural swivel. If you talk to any CTO or CIO they have all legacy systems and techniques that can impede their ability to execute. By altering the company’s configuration to spotlight digital initiatives, executives can swiftly escalate the speed of transformation. It’s tactic that requires earmarking funds, and human resources to digital initiatives and placing digital executives in command of existing business processes.

For example, a local bank has started to swivel actively. Remember, this is the structural swivel we’re talking about.

It swiveled out of a conventional branch-driven model by venturing outside to recruit a CDO (Chief Digital Officer). The bank empowered this guy with complete corporate supervision, comprising all branches that were still the lion’s share of the bank’s income. All product, tech, sales outlets, and marketing units started reporting to the new CDO. To push for digital transformation, each regional division also hired a committed CDO at the same level as the local bank president. These changes were intended to assist the bank in obviously speeding and hastening its conversion to a soup-to-nuts digital enterprise and organizing a purely digital experience across all the business channels. That’s what I call a structural swivel.

Inverse acquisition. There are a lot of businesses that have unearthed quick wins ─ quick triumphs ─ by placing boundaries on the digital products so they can function autonomously and uninhibited by traditional processes. Just put them in a corner somewhere. It’s like, “Off you guys go!”

However, the moment a digital project shows its usefulness, shouldn’t subsequent tasks follow suit? Persevering or preserving the project’s autonomy restricts its influence on other businesses. So, one possibility is to absorb the traditional businesses into the new digital unit, spreading the transformation business-wide, and then compelling the rest of the company to abandon its archaic approaches. This is what I call “the inverse acquisition. ”

This tactic requires hard work. It comprises the comprehensive moving and resettlement of technology manifestos, company structures, and processes, and ultimately consumers from the traditional business to the new model. You must be cautious to ensure the company doesn’t collapse into disorder during the changeover.

I’ll give you an example. The British retail store, John Lewis, acquired buy.com.uk in 2001. It inherited vital technology and talent that it used to erect its only e-commerce business quickly. John Lewis later commenced a gigantic undertaking to reconstruct its web and e-commerce framework, which involved assimilating over 30 existing tech systems. And then they launched an e-commerce site around 2013, fully connected with their supply chain, delivery conduits, and the physical stores. The 10-year long dedicated effort increased its online sales by close to 30%. So, inverse acquisition. That works.

Offshoot. It’s unrealistic always to expect a new digital operation to absorb the traditional business, especially if the digital business is not yet developed sufficiently to absorb a larger unit. Also, it may focus on too dissimilar a fragment of the value chain.

In these cases, you can grow those ventures by segmenting the separate fragments into distinct businesses that can develop outside the principal line of business.

There’s an example here as well. BBVA Compass, a Spanish bank, had a software development division called Globalnet that they used to fuel their technology initiatives for over a decade. A few years ago, Globalnet, this little software development division, transformed into a company called BEEVA, which is an offshoot for creating and marketing business web services.

Although BEEVA powered the base technology for BBBA ─ the Spanish bank’s transition into digital banking ─ bank executives understood the software division’s innate potential. As an independent services business, BEEVA helps other banks do what BBBA has done using BEEVA’s groundbreaking cloud technology platform.

In this instance, a structural swivel or inverse acquisition would not have worked. Why? Because the bank was ultimately a financial services company and its software division BEEVA was a web services unit with functionality that was different from the bank’s core business.

So, that’s what I call “offshoot.”

What is authenticity in digital transformation?

Consumers expect authenticity from corporations and individuals alike. As consumer psychology changes, digital and marketing must enter a new era where human needs — values and connections — define success and failure. This is a call to action for marketers and advertising executives to change their perspective towards consumers.

Companies can no longer see consumers as gullible moneybags or conquests. They must see consumers as community members, as human beings, who crave trust.

You see the theme here? We’re talking about technology and digital, but what I’m getting at is connection. Consumers crave trust: predictability, transparency, respect. I call it the relationship era.

Your corporates value must resonate at every level of infrastructure. It has to emanate outwards to the company’s employees, customers, suppliers, stakeholders, neighbors, and even your relationship towards Earth! Merely projecting an image is akin to falsity. Companies must steadfastly practice what they preach.

The public today cares not only about the cost and quality of products and services. People also care about the values and conduct of the providers. Trust, reliability, ethics often supersede quality and affordability.

Thank you to my colleague, Lisbeth Shaw, for assistance with this post.

CXOTALK brings you the world’s most innovative business leaders, authors, and analysts for in-depth discussion unavailable anywhere else. Enjoy all our episodes.

(Cross-posted @ ZDNet | Beyond IT Failure Blog)

McKinsey: AI, jobs, and workforce automation

 

For business people, AI presents a variety of challenges. On a technology level, artificial intelligence and machine learning is complicated to develop and demands rich data sets to produce meaningful results. From a business perspective, many business leaders have difficulty figuring out where to apply AI and even how to start the machine intelligence journey.

Making matters worse, the constant drumbeat of AI hype from every technology vendor has created a continual barrage of noise confuses the market about the real possibilities of AI.

To cut through this noise, I have invited many world-leading practitioners to share their expertise as part of the CXOTALK series of conversations with innovators.

For episode 219 of CXOTALK, I spoke with Michael Chui, a Principal at the McKinsey Global Institute (MGI), and David Bray, an Eisenhower Fellow who is also CIO at the Federal Communications Commission.

The McKinsey Global Institute has released a variety of research reports on topics related to Ai, automation, and jobs. For example, see this article on the fundamentals of workplace automation.

As you can see in the graphic below, Chui and his team examined a variety of industries looking at the impact of automation, including AI, on the workforce.

CXOTALK McKinsey - automation and AI

Image from McKinsey Global Institute

Another fascinating graphic showing automation potential and wages for US jobs:

CXOTALK McKinsey - automation, AI, and wages

CXOTALK McKinsey - automation, AI, and wages

Image from McKinsey Global Institute

The conversation between Michael Chui and David Bray covered key points about the relationship of business and the workforce to automation and AI – including investment, planning, and even ethical considerations.

You can watch our entire conversation in the video embedded above. An edited partial transcript is available below and you can read the complete transcript at the CXOTALK site.

How should organizations think about investing in AI?

Michael Chiu: More organizations have started to understand the potential of data analytics. Executives are starting to understand that data and analytics are either becoming a basis of competition or a basis for offering the services and products that your customers, citizens, and stakeholders need.

While there are often real technology challenges, we often find the real barrier is the people stuff. How do you get from an interesting experiment to business-relevant insight? We could increase the conversion rate by X percentage if we used this next product to buy an algorithm and this data; we could reduce the maintenance costs, or increase the uptime of this whole good. We could, in fact, bring more people into this public service because we can find them better.

Getting from that insight to capture value at scale is where organizations are either stuck or falling. How do you bag that interesting insight, that thing that you capture, whether in it’s in the form of a machine learning algorithm, or other types of analytics, into the practices and processes of an organization, so it changes the way things operate at scale? To use a military metaphor: How do you steer that aircraft carrier? It’s as true for freight ships as it is for military ships. They are hard things to turn.

It’s the organizational challenge of understanding the mindsets, having the right talent in place, and then changing the practices at scale. That’s where we see a big difference between organizations who have just reached awareness and maybe done something interesting and ones who have radically changed their performance in a positive way through data, analytics, and AI.

What are the adoption problems around AI and machine learning?

David Bray: The real secret to success is changing what people do in an organization, that you can’t just roll out technology and say, “We’ve gone digital, but we didn’t change any of our business processes,” and expect to have any great outcomes. I have seen experiments that are isolated from the rest of public service; and they say, “Well look, we’re doing these experiments over here!” but they’re never translating to changing how you do the business of public service at scale.

Doing that requires not just technology, but understanding the narrative of how the current processes work, why they’re being done that way in an organization, and then what is the to-be state, and how are you going to be that leader that shepherds the change from the as-is to the to-be state? For public service, we probably lack conversations right now about how to deliver results differently and dramatically better to the public.

Artificial intelligence, in some respects, is just a continuation of predictive analytics, a continuation of big data, it is nothing new because technology always changes the art of the possible; this is just a new art of the possible.

I do think there’s an interesting thing in which it could offer a reflection of our biases through artificial intelligence. If we’re not careful, we’ll roll out artificial intelligence, populating it with data from humans, [and] we know humans have biases, and we’ll find out that the artificial intelligence itself, the machine learning itself, is biased. I think that’s a little bit more unique than just a predictive analytics bias or big data.

Which business areas most suited to AI?

Michael Chiu: When we surveyed about 600 different industry experts, every single one of those problems we identified, at least one expert suggested it was one of the top three problems that machine learning could help improve. And so, what that says is potential is just absolutely huge. There’s almost no problem where AI and machine learning potentially couldn’t change and improve performance.

A few things that come to mind: One is a lot of the most interesting and recent research has been in this field called “deep learning,” and that’s particularly suited for certain types of problems with pattern recognition, often images, etc. And so those problems that are like image recognition, pattern recognition, etc. are some of those that are quite amenable and interesting.

So again, regarding very specific types of problems, predictive maintenance is huge. The ability to keep something from breaking; rather than waiting until it breaks and then fixing it, the ability to predict when something’s going to break. Not only because it reduces the cost. More important, is the thing doesn’t go down. If you bring down a part of an assembly line, you bring down the entire factory or often the entire line.

To a certain extent, that is an example of pattern matching. Sensors are the signals that reflect that something’s going to break, informing you to do predictive maintenance. We find that across a huge number of specific industries that have these capital assets, whether it’s a generator, a building, an HDC system, or a vehicle, where if you’re able to predict ahead of time before something’s going to break, you should conduct some maintenance. That is one of the areas in which machine learning can be quite powerful.

Health care is another case of predictive maintenance but on the human capital asset. Then you can start to think, “Well gosh! I have the internet of things.” I have sensors on a patient’s body. Can I tell before they’re going to have a cardiac incident? Can I tell before someone’s going to have a diabetic incident? That they should take some actions which could be less expensive, and less invasive, than having it turn into an emergent case where they must go through a very expensive, painful, and urgent care type of situation?

Again, can you use machine learning make predictions? Those are some of the problems things that can potentially be solved better by using AI and machine learning.

David Bray: There are opportunities for artificial intelligence and machine learning to help the public. I think a lot is going to happen first in cities.

We’ve heard about smart cities. You can easily see better preventive maintenance on roads or power generation and then monitoring to avoid brownouts. I think the real practical, initial, early adoption of AI and machine learning is going to happen first at the city level. Then we’ve got to figure out how to best use it at the federal level.

CXOTALK brings together the most innovative leaders in the world for in-depth conversations about leadership and innovation. See the complete list of episodes.

(Cross-posted @ ZDNet | Beyond IT Failure Blog)

Artificial Intelligence: Legal, ethical, and policy issues

Artificial Intelligence Legal, ethical, and policy issues

Image from Wikimedia

Artificial intelligence has entered mainstream consciousness surrounded by marketing hype, jargon, inflated expectations, and fear. Given the importance of AI, we have started a new series of CXOTALK videos, speaking with experts in areas such as technology, data science, ethics, and public policy.

This series kicks off with episode number 203 of CXOTALK and a conversation between one of the top legal experts in the world on AI ethics and a respected expert on public policy.

Kay Firth-Butterfield is an attorney, author, judge, and public speaker on topics related to AI and ethics. Kay’s experience in this field is quite amazing as you can see on her LinkedIn page. David Bray is a frequent guest on CXOTALK. He is an Eisenhower Fellow, Visiting Executive In-Residence at Harvard, and Chief Information Officer at the Federal Communications Commission.

The conversation offers a fascinating look at the implications of AI for society. It explores issues such as the speed of change due to advances in computing technology; loss of control and privacy; job destruction due to automation; and advice on law and public policy related to technology and AI.

The video embedded below is a summary of the entire 45-minute conversation. You can watch the entire video and read a complete transcript at the CXOTALK site.

Here is an edited version of the transcript taken from this summary video:

Why should we care about the legal, policy, and ethical issues of AI?

Kay Firth-Butterfield: One of the things that stick out in my mind is some research that McKinsey did recently, where they describe AI as a contributing factor to the transformation of society. And I just want to quote what they’re saying about the transformation of our society: that it’s happening ten times faster, and at three hundred times the scale, or roughly three thousand times faster than the impact of the industrial revolution. And you know, a lot of people compare this revolution to the industrial revolution. But, I think it’s the speed and the real, core underpinning that AI is contributing to that transformation of our society that makes these discussions so important.

David Bray: It’s not just about handing over judgment and decisions to a machine that a human would do otherwise. It is about the loss of a locus of control, either a loss of a locus of control for the individual. So, when you’re in an autonomous car, you know, you are not driving; the car is driving unless you have the ability to stop within milliseconds that might not be possible. It’s really about are we handing over control to an entity that we are willing to trust that will be as fair, if not fairer than a human. And that’s where it gets to what Kay said with Europe.

So, I think it’s just the scale at which it may be used, and the scale and the impacts of the decisions. There’s always been the ability to tailor your experience even before the Internet regarding what services were provided to you. People were making sense by hand what things you should receive in the mail regarding ads, or what was called “automated data processing in the 1970’s.

Privacy laws came back in the 1970’s when you started doing automated data processing. And again, these machines were nowhere near as fast as what we have today, but that somehow there could be a correlation of “This person lives at this address; they’re getting this type of heart medication; they also are on this type of insurance.” At what point do you need to say, “Well, those are correlations you shouldn’t draw unless that person is giving consent?” So I think artificial intelligence, much like those things that came before, it’s just the scale and the impact of what this machine might be able to make decisions that will impact your life will be. So you’re right it’s the same trend. But, I think it’s the sheer scope and impact that I think we need to take into consideration.

Are scale and pervasiveness the driving forces?

Kay Firth-Butterfield: Obviously, you know the seminal quote from Stephen Hawking on the first of May, 2014, when he said that this could be the best thing that we’ve ever done or our last. And I think that captured the attention of the media. And where there were lots of us thinking about these things before, it’s become so much part of a more public conversation now.

That’s a really important thing. One of the questions we have been talking about is taking some control for ourselves as individuals. And unless we empower people to do that through education, then people are not going to be able to take back that power. And so, and also I think that there’s an issue with what we’re seeing in social media at the moment. I have seen a lot on Twitter in the last two days that people are saying, “Oh well move. We have to defend our privacy.” And there’s a lot of fear of surveillance ─ switching to Tor and more secure uses of email and things like that. That is not a positive sign for the way that some people in our society are thinking about artificial intelligence.

What about robots, jobs, and the impact on people?

Kay Firth-Butterfield: AI, in my view, is a technology that will benefit mankind or humankind enormously. And, there are some great challenges that we have as humans and for our planet that we really can’t solve without AI. And so, we certainly don’t want to see a groundswell of opinion against AI by people who are losing their jobs to it. We’ve all read the Oxford Martin study, and the Bank of America [Merrill Lynch] study that says that 47% and I think 52% of jobs in America currently done will go to automation in the next 15 or 20 years. But we have to think about the complexity of job loss because we don’t know what the future jobs are going to be. But what we do know is that as people lose their jobs, and some think that hasn’t been done in the past, we need, and can use AI to retool and re-skill those that workforce to create the jobs of the future.

David Bray: As jobs are lost because they can be automated, what do we as society owe those people whose jobs have been displaced, to help them retool, retrain as best as possible for something else. And the jury is out as to whether more jobs will be created vs. destroyed as a result of artificial intelligence. So, we need to monitor them and be aware of it. We must also be aware of there is what’s called the “unemployment effect” on people’s health, which is we humans need to have a purpose. And so, a future in which we don’t need to work because artificial intelligence is doing everything may not be a nirvana as it sounds like because we won’t find purposes. Or we may find purposes in avocations as opposed to vocations. But that’s a collective conversation we need to have, which is, “Where are we going together as a society? How can we make sure we bring as many people along?” As Kay said, ideally make it so they’re not as fearful of artificial intelligence.

Kay Firth-Butterfield: As a historian by background, I worry about the analogies with the industrial revolution because the industrial revolution hurt a lot of people over a long period. And yes, we came through it and we developed something better. But, it looks as if this industrial revolution will be much faster, and we need to prepare not to hurt as many people very quickly.

David Bray: So Kay’s right. It’s going to happen in a much shorter period. It may be as big if not bigger change. And so, having again that conversation about what do we, as a society, owe each other is key to have now because we don’t know! And none of us know if the job we’re currently doing today in two or three years will be done better by machines.

What advice do you have for people writing laws?

Kay Firth-Butterfield: Well I think the advice to lawyers is that very soon, you will be receiving… You will see those cases coming across your desk, and you need to get up to speed around artificial intelligence. And, what’s going on in artificial intelligence now, I think just going back to that job creation thing, there are going to be a lot of jobs around, so we’re not going to kill all the lawyers by automating them just yet because we are going to see experts needed in court. For example, instead of cross-examining a driver, we might have to cross-examine an algorithm, a.k.a. an expert on the system. If you are in any business, you need to be looking at what AI can do for you, and what the impact of AI will be on your business. So there are two pieces of that because I genuinely believe that AI will change everything. And if you don’t start looking now, you will be too far behind.

How about advice for policymakers?

David Bray: Cloud computing in some respects is the appetizer, artificial intelligence and the Internet of Everything is going to be the main course that we’re going to be consuming over the next five years. And, I don’t know if I can necessarily give advice necessarily to policymakers, but I’ll say what Kay said. Any organization and any entity should recognize that this will disrupt how you operate and it’s a question of whether or not you are very intentional about it. Or, someone else is going to do it to you. So, start on that journey now. Start having conversations.

There’s one thing I want call out, looking at the OpenAI effort and other efforts that are trying to make this open and available to people. Try to either begin experimenting or if you don’t have the time to experiment, maybe have some of your employees begin to experiment with what’s possible. Because we’re only going to get the expertise we need to know in this era through the experiments that we need to do with artificial intelligence.

Please see the list of upcoming CXOTALK episodes. Thank you to my colleague, Lisbeth Shaw, for assistance with this post.

(Cross-posted @ ZDNet | Beyond IT Failure Blog)

Digital experience and white glove customer service at Brooks Brothers

 

The Brooks Brothers clothing brand is an iconic name in American business. Founded in 1818, the company has outfitted 39 US presidents and prides itself on offering white glove service to its customers.

How does a 200-year old brand translate and deliver high-end service in the digital age, especially when in a price-sensitive consumer environment?

To explore this question, we invited Brooks Brothers Executive Vice President and Chief Information Officer, Sahal Laher, to be my guest on episode 200 of the CXOTALK series of conversations with innovators shaping the world.

The short video above was taken from a lengthy, in-depth discussion that you can watch on the CXOTALK site.

As CIO, Laher is responsible for implementing technology that enables a high-touch, seamless customer experience extending across all channels including brick-and-mortar stores, e-commerce, and mobile.

During our conversation, Laher emphasized three primary goals:

  • Deliver a consistent customer experience across all of Brooks Brothers sales channels
  • Make the customer experience simple and easy
  • Understand every Brooks Brothers customer and personalize the consumer experience to their specific needs

In the video embedded above, Laher explains the technologies and business goals that underlie Brooks Brothers ability to achieve these customer experience goals. He describes how the company put in place a strong “digital core,” which is now central to creating a 360-degree view of the customer.

Here is a transcript of the short video embedded at the top of this post.

200 years of white glove service

Michael Krigsman: White glove service as you described it, has been a centerpiece of Brook Brothers approach for 200 years. It sounds like what you’re doing is translating that into a multi-channel, or omnichannel, approach.

Sahal Laher:

That’s exactly right. I think that manifests itself in many different ways.

It requires that we have a consistent customer experience across channels, and that doesn’t apply just to personalization, but it really applies in general, where every company now needs to break down the silos between channels. Traditionally, retailers have thought in channels, and they’ve been organized in channels and had separate business units for online versus brick and mortar, versus factory, and what is very evident is that the customer doesn’t see it that way. The customer doesn’t think of channels. They think of it as Brooks Brothers.

Most importantly, I think people are looking at retailers and companies: they’re not easy to do business with. It has to be simple; it has to be intuitive. You know, you can’t have a very complex aggregation on your website, you can’t have extremely long and tedious checkout process, because we’ve all been to those websites, and lost motivation to complete the checkout.

If it’s not simple and you’re not easy to do business with, and you don’t have a supply chain that can fulfill in a fashion that is geared to give the people the product they want, when they want it, then you’re really going to be at a big disadvantage, and you really are going to go to another site where it’s easier to do business.

Michael Krigsman: How do you maintain that customer experience, especially going across multiple channels?

Sahal Laher:

The reality of the world we live in now is that it’s just not like it used to be in that, now we travel more. We may want to go to the store, not in our hometown, but where we work, or we might be on business at a conference, and we might want to go to a store.

What we’ve really been working really hard on in the last couple years is trying to figure out: If John Smith comes to the store, and he’s never been into that store before, but he’s been a customer for 10 years, we are missing the mark if we don’t give him personalized service based on the information we already know about him.

We will have turned data that we have into actual, actionable insights that you, the store associate, can use to have a more personalized conversation, as opposed to talking to everyone who walks into the store that you don’t know about the same five products in the Fall collection.

So that’s a very important piece of who we are, and, obviously replicating that requires a lot of translation of this data into insights. Everyone talks about “big data,” everyone talks about these buzzwords of “big data” and “machine learning” and so on, but this is really a case study where it’s the differentiator, and really in all industries, I think, can be a differentiator not just for personalization but for many different parts of your supply chain and the way that you go to market.

And, the way that the machine learning works, is we can do that on the fly, and we can do that for terabytes and terabytes of data, which, in the old days obviously was just not possible, right?

Even if we took every single black book, every single store associate’s black book from the old days, where they had customer service and all of that done in paper books, that’s already a lot of data. And now, you multiply it by, you know, everything like your online clickstream, right? So every time you go online and you’re navigating the website there’s a trail of breadcrumbs that every customer leaves behind regarding what have they browsed, what have they put in their cart and not bought, how much time have they spent looking at a particular item.

All of this information, when you aggregate it together, and you have a true big data strategy, that utilizes some of these next generation tools like machine learning and in-memory databases. And, we have the ability to replicate that service, and now, you can also make that available online, and you can make more thoughtful recommendations for you online, as opposed to showing everyone the same five products that have just come out as things that the might be interested in.

Building the 360-degree customer view

Michael Krigsman: Can you talk about the relationship between service, engagement, customer experience, this machine learning project, because it’s all part of a broader perspective?

Sahal Laher:

Absolutely. So, you know, I think, again, customers don’t think in channels, right? And so, regardless of what channel they are interacting with you on, they expect that you know… So if I went onto the website and I made a purchase, and I come into the store two weeks later, and you don’t have any information on my order, or don’t even have any information on what’s in my wardrobe, then you’re missing the mark.

So, you know, one of the first things we did a couple years ago was really working on creating this 360-degree view of the customer, which sounds fairly obvious and it sounds fairly intuitive. But the reality is very few people have that all in one place, because over time, it doesn’t matter how long you’ve been in business, and obviously, the longer you’ve been in business, you’re likely to have more silos of data. But even if you haven’t been in business for decades, and you’ve only been in business for a few years, nobody has just one sales system, right?

You always have at a very minimum have a point of sale system, and then you have a website. And then you need some kind of system for customer service, you may need some kind of system for your store associate, be it clienteling or looking at alterations, or made to measure, or whatever the case may be.

So, what we try to do is all of those systems that I named was one or more different databases when we started, and what we’ve worked to do is really bring all of this into a single database.

And that single database now has John Smith’s customer record, it has all his personal preferences, it has his e-commerce transactions, it has his in-store purchases, it has his alterations and measure information, and it also has any interaction that he’s had with our call center is all logged in one central place.

What that allows us to do is obviously elevate the level of service that we can provide, because regardless, again, of what channel is your preference to interact with us on any given day, we will be able to have a consistent view of who you are as a customer, and therefore we’ll be able to better service whatever needs you have on that particular day, and they won’t be these handoffs or, “Let me transfer you to the place you ordered that, let me transfer you to the call center or the e-commerce fulfillment team to look at where your order is in the fulfillment process.” It needs to be, again, simple, right? If it’s not simple and intuitive, people are going to get frustrated and go elsewhere.

The digital core

Michael Krigsman: We have another question from Twitter, and this is from Arsalan Khan, who’s wondering, as the technologies change, and as the environment around you, the customer environment, the competitive environment is changing, how do you plan? How do you go forward and consider this ongoing change in your business strategy?

Sahal Laher:

That’s a great question, and I’m glad that it was asked because one of the things we haven’t touched on so far is the need for a strong, what I call “digital core,” right?

What that entails is, do you have a strong supply chain that can allow you to fulfill orders any time, any way? That’s the bottom line, right? The customers want their stuff. They don’t care where it’s being shipped from, they don’t care how it’s being shipped, as long as you can honor your commitment to getting that particular merchandise to the customer on a date that’s promised, then you’re meeting the customer expectations.

So, that’s obviously very difficult, and when we talk about omnichannel, right? And we talked about the 360-degree view of the customer.

But another extremely important piece that we touched on very briefly was the silos across channels coming down. And as those silos come down, you know, this digital core becomes more and more important, because in the old days it was fine for you to have a website, and a website only having inventory to your e-commerce warehouse merchandise. But now, you need to make sure that you have, you know, it’s almost another 360-degree view, it’s also a 360-degree view of product and inventory. And looking at that across all of your channels.

So, you know, there’s obviously tools that allow you to allocate product, and to come up with these assortments, but there’s always going to be times when someone comes in and we don’t have that product, and how do we get you that product? We have fifty of those units in the warehouse that are available for e-comm orders, but it’s a shame if that inventory’s not available to in the store, or vice-versa.

That’s digital core and this is kind of, a little long-winded response to the question, but it’s an important context that I think needs to be provided, and if you don’t have that supply chain that’s dynamic and nimble, and you as a company are unable to react dynamically and real-time to customer demand, then you’ve missed the mark.

This excerpt is part of episode 200 of CXOTALK, which offers in-depth conversations with people shaping technology and the world. Check out the list of upcoming episodes.

(Cross-posted @ ZDNet | Beyond IT Failure Blog)