What is your customer data share?

By the year 2020, it is estimated that the average person will generate 1.5 GB worth of data per day. At that point, it won’t be just about your firm’s share of customer wallet; instead, you be asked… what’s your firm’s customer data share? Oh, and consider today who owns the majority of insight in to “your” customer.

[Infographic Credit: Domo]

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#010: This Game is Not One-Dimensional, You Have to Face the Customer with Shawn Harris, Global Innovation Strategy Lead, Zebra Technologies.

In this episode of The IoClothes Podcast, we speak with Shawn Harris, Global Innovation Strategy Lead for Zebra Technologies. The reality is, innovative products don’t just sell themselves and companies aren’t composed of just designers, developers and engineers. Someone has to interface with the customer, and keep the ship sailing along a strategic path, which includes profitability (that’s if you want to stay in business). Today, we shift gears and talk a bit about the struggles of retail, the importance of differentiating yourself in the marketplace and how are current relationship with MS Excel may be a sign of the future!

For more information on Zebra Technologies, check out their website at, on Twitter @ZebraTechnology and on Facebook @ZebraTechnologiesGlobal. You can also follow Shawn on Twitter @SmarterRetailer

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AI and the Final Judgement.

I believe that for the foreseeable future, human machine collaboration, which will lead to a new type of collective intelligence will be a requirement. Today, we are seeing many advances in the ability of machines to make high confidence predictions; in narrow use cases. This will certainly lead to significant shifts in tasks within job roles. However, where I think machines will lack for some time is in judgement. Judgement being the ability to consider multiple predictions, and come to sensible conclusions in context. This is where humans will need to remain in the loop, playing a key role in judgement. This human machine partnership will lead to a collective intelligence that will results in even higher confidence outcomes, than either could realize individually. However, this prescribed union could quickly find the machine taking more and more of the judgement role as well. Just consider Google’s search results; at this point, how many of us ever get to the second page of results. What’s happening here is that we are trusting Google algorithm’s judgement. We could debate the consequences of this for search results; as we endeavor to use AI in medicine, autonomous cars, and other decisioning based on potentially biased data, we will need to take a more deliberate approach to the policies and practices around final judgement.

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Startups at the Intersection of AI and Blockchain

  • Decentralized Intelligence: TraneAI (training AI in a decentralized way); Neureal (peer-to-peer AI supercomputing); SingularityNET (AI marketplace); Neuromation (synthetic datasets generation and algorithm training platform); AI Blockchain (multi-application intelligence); BurstIQ (healthcare data marketplace); AtMatrix (decentralized bots); OpenMined project (data marketplace to train machine learning locally);
  • Conversational Platform: Green Running (home energy virtual assistant); Talla (chatbot); (quantified biology and healthcare insights);
  • Prediction Platform: Augur (collective intelligence); Sharpe Capital (crowd-source sentiment predictions);
  • Intellectual Property: (IP discovery and mining);
  • Data provenance: KapeIQ (fraud detection on healthcare entities); Data Quarka (facts checking); Priops (data compliance); Signzy (KYC)
  • Trading: Euklid (bitcoin investments); EthVentures (investments on digital tokens). For other (theoretical) applications in finance, see Lipton (2017);
  • Insurance: (P2P insurance), Inari (general);
  • Miscellaneous: Social Coin (citizens’ reward systems); HealthyTail (pet analytics); Crowdz (e-commerce); DeepSee (media platform); ChainMind (cybersecurity).

Read the full article: The convergence of AI and Blockchain: what’s the deal? by Francesco Corea

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Lifelong Learning

I believe that lifelong learning is essential to career success. Technology is changing every industry, and each segment within. Today, you cannot rest on your undergraduate and graduate degrees, and on the job experiences alone. You have to go back to school. But, you don’t have to go back to a school. MOOCS, or Massively Open On-line Courses have transformed learning. Now, you can advance your knowledge, with courses from top universities; from the comfort of your couch. Platforms like,, getsmarter, and numerous others allow you to take many courses for free (audit), or for a relatively small fee earn a certificate of completion, which proves you’ve successful completed a given program. I am a huge proponent of these platforms. For the past year, I have been doing the micromasters in Supply Chain Management via MIT CTL & Edx, and soon will be starting a course in Artificial Intelligence via MIT CSAIL & Getsmarter. No question, it’s a lot of work, when you work and have a family, but it’s worth it. Stay hungry, stay foolish.

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The Technology Behind Alibaba

Highlights from a recent article in the South China Morning Post:

“The widespread application of cutting-edge artificial intelligence and machine learning by the e-commerce juggernaut Alibaba Group is borne out of necessity – the sheer volume of products that would be moved would make it practically impossible for employees to keep up, and is part of a broader push by China to embrace AI…Jack Ma, executive chairman of Alibaba Group,said in October that the company’s front end garners most of the attention even though it was strong in technology. “People tend to recognise our platforms and services, but overlook the technologies that make it happen””

Key Points:

  • “China has set a target to build a 1 trillion yuan (US$150 billion) AI industry by 2030…”
  • “…Smart Selection, an AI-powered recommendation algorithm will, in no small measure, help buyers to make a decision…compared with seasoned fashion industry professionals, big data and AI would excel in picking products such as clothes where there are numerous brands and variables if evaluated manually.”
  • “The recommendation algorithm is backed by the latest advances in deep learning and natural language processing, which is also used by Amazon in its recommendation engine and text-to-speech service Polly.”
  • “AI-powered customer service chatbot Dian Xiaomi is another of Alibaba’s tech tools to help make businesses smarter and more efficient…the chatbot can understand more than 90 per cent of customer enquiries and serve almost 3.5 million users a day.”
  • “…the more advanced “cloud” version [of the AI-powered customer service chatbot]… features capability to understand customers’ emotion through text analysis…”
  • “About 200 robots – automated guided vehicles – will work round the clock to deliver the orders placed at a newly opened automated warehouse operated by Alibaba’s delivery arm, Cainiao Network, in Huizhou…”
  • ““These 200 robots can process 1 million shipments per day,”…“They are three times more efficient than manual operations and need to be charged for just one hour after every six hours of use.”…“All the robots are automatically connected with each other and they assign shipping tasks themselves without a central control room.””
  • “The efficiency of the logistics service means customers could receive same-day deliveries – orders placed in the morning would be delivered to customers’ doorsteps in the afternoon.”
  • “…it was difficult to find people to work in warehouses the robots could easily cover the labour shortage. “We will have more such smart warehouses next year.””
  • “…, China’s second largest e-commerce giant, also has its own smart warehouse where only robots operate. Besides, the Alibaba rival launched JD-X in May, a logistics lab to develop robots, drones and smart warehouses.”
  • “, which offers drone delivery services in the western city of Xian and eastern China’s Suqian, said in August it would offer 100 million yuan to the winners of a competition to find the best solution for conducting widespread drone delivery services across China.”
  • “…Alibaba used drones to deliver packages over open water for the first time. The flying robots delivered six boxes…weighing a combined 12 kilograms…Alibaba said it would consider using the drones in the future to deliver high value products…”
  • “Alibaba will spend more than US$15 billion to open seven research labs in Beijing, Hangzhou, Singapore, Moscow, Tel Aviv, San Mateo, and Bellevue as part of the project. These will focus on areas that include machine learning, network security, visual computing and natural language processing.”

Read the full article: Alibaba lets AI, robots and drones do the heavy lifting on Singles’ Day

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Alibaba Found Lots of Love on Singles Day 11.11.17

The Chinese’s Guanggun Jie, or Singles Day, takes place each year on 11/11. A day set aside to celebrate being proud to be single, has become one of the largest consumer shopping days of the year, dominated by one ecommerce giant Alibaba. Last year (2016), Alibaba processed $17.8B in Gross Merchadise Volume (GMV), which doesn’t represent Alibaba’s corporate revenue, but instead the total value of the goods sold on the platform. Alibaba mostly earns revenue on advertising placements on the platform. Yesterday, for Singles Day 2017, a whopping $25.3B was purchased through the Alibaba platform. This represented a 39% YoY increase. Wow. However, what this also continues to demonstrate is the structural shift underway in retail, where one digital platform can attract and process more volume in a day, than most retailers do in a year. Think about it, today we’re talking about Alibaba, not the 140,000 brands and retailers who provided the products. Who owns the customer, really? That’s huge!

Below is an infographics published by Alibaba:

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In conclusion…

  • Retailers you can’t run from this. You can’t regard these times as being temporary in nature waiting for market improvements; this change is not cyclical, it’s structural. Focus on your value in the chain…
    • Coopetition, work with other retailers, including classic competitors.
    • Sell the operations, franchise…
    • Partner with new world distribution, let them aggregate … Facebook, Google, etc; you fulfill.
    • Become a fashion brand, move up the stack via well priced, on-trend private label.
  • I’m a big believer in focusing in on how to think vs. what to think. If you chase the what in these times, you’re reacting too late. If you know how to think, you have the opportunity to be a defining/benchmark setting retailer. Never forget, focus on your customer, not the product.
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Did Amazon just patent tech that could end showrooming in its stores?

“I would be shocked if Amazon implemented this tech as described. I do think they would implement the tech to monitor in-store web traffic to gain insights to make the overall shopping experience better. I would recommend other retailers do the same; many are still struggling to make sense of the data they have.” ~ Shawn Harris

Read the Full Article:

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Who Am I?

“I am who I am today, because of yesterday. I look forward to seeing who I will be tomorrow, because of today.” ~ Shawn Harris

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“Jeff, what does Day 2 look like?”

“It’s all about culture, culture, culture.” ~Shawn

Jeff Bezo’s 2016 Letter to Shareholders

April 12, 2017

“Jeff, what does Day 2 look like?”

That’s a question I just got at our most recent all-hands meeting. I’ve been reminding people that it’s Day 1 for a couple of decades. I work in an Amazon building named Day 1, and when I moved buildings, I took the name with me. I spend time thinking about this topic.

“Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it is always Day 1.”

To be sure, this kind of decline would happen in extreme slow motion. An established company might harvest Day 2 for decades, but the final result would still come.

I’m interested in the question, how do you fend off Day 2? What are the techniques and tactics? How do you keep the vitality of Day 1, even inside a large organization?

Such a question can’t have a simple answer. There will be many elements, multiple paths, and many traps. I don’t know the whole answer, but I may know bits of it. Here’s a starter pack of essentials for Day 1 defense: customer obsession, a skeptical view of proxies, the eager adoption of external trends, and high-velocity decision making.

True Customer Obsession

There are many ways to center a business. You can be competitor focused, you can be product focused, you can be technology focused, you can be business model focused, and there are more. But in my view, obsessive customer focus is by far the most protective of Day 1 vitality.

Why? There are many advantages to a customer-centric approach, but here’s the big one: customers are always beautifully, wonderfully dissatisfied, even when they report being happy and business is great. Even when they don’t yet know it, customers want something better, and your desire to delight customers will drive you to invent on their behalf. No customer ever asked Amazon to create the Prime membership program, but it sure turns out they wanted it, and I could give you many such examples.

Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight. A customer-obsessed culture best creates the conditions where all of that can happen.

Resist Proxies

As companies get larger and more complex, there’s a tendency to manage to proxies. This comes in many shapes and sizes, and it’s dangerous, subtle, and very Day 2.

A common example is process as proxy. Good process serves you so you can serve customers. But if you’re not watchful, the process can become the thing. This can happen very easily in large organizations. The process becomes the proxy for the result you want. You stop looking at outcomes and just make sure you’re doing the process right. Gulp. It’s not that rare to hear a junior leader defend a bad outcome with something like, “Well, we followed the process.” A more experienced leader will use it as an opportunity to investigate and improve the process. The process is not the thing. It’s always worth asking, do we own the process or does the process own us? In a Day 2 company, you might find it’s the second.

Another example: market research and customer surveys can become proxies for customers – something that’s especially dangerous when you’re inventing and designing products. “Fifty-five percent of beta testers report being satisfied with this feature. That is up from 47% in the first survey.” That’s hard to interpret and could unintentionally mislead.

Good inventors and designers deeply understand their customer. They spend tremendous energy developing that intuition. They study and understand many anecdotes rather than only the averages you’ll find on surveys. They live with the design.

I’m not against beta testing or surveys. But you, the product or service owner, must understand the customer, have a vision, and love the offering. Then, beta testing and research can help you find your blind spots. A remarkable customer experience starts with heart, intuition, curiosity, play, guts, taste. You won’t find any of it in a survey.

Embrace External Trends

The outside world can push you into Day 2 if you won’t or can’t embrace powerful trends quickly. If you fight them, you’re probably fighting the future. Embrace them and you have a tailwind.

These big trends are not that hard to spot (they get talked and written about a lot), but they can be strangely hard for large organizations to embrace. We’re in the middle of an obvious one right now: machine learning and artificial intelligence.

Over the past decades computers have broadly automated tasks that programmers could describe with clear rules and algorithms. Modern machine learning techniques now allow us to do the same for tasks where describing the precise rules is much harder.

At Amazon, we’ve been engaged in the practical application of machine learning for many years now. Some of this work is highly visible: our autonomous Prime Air delivery drones; the Amazon Go convenience store that uses machine vision to eliminate checkout lines; and Alexa, our cloud-based AI assistant. (We still struggle to keep Echo in stock, despite our best efforts. A high-quality problem, but a problem. We’re working on it.)

But much of what we do with machine learning happens beneath the surface. Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type – quietly but meaningfully improving core operations.

Inside AWS, we’re excited to lower the costs and barriers to machine learning and AI so organizations of all sizes can take advantage of these advanced techniques.

Using our pre-packaged versions of popular deep learning frameworks running on P2 compute instances (optimized for this workload), customers are already developing powerful systems ranging everywhere from early disease detection to increasing crop yields. And we’ve also made Amazon’s higher level services available in a convenient form. Amazon Lex (what’s inside Alexa), Amazon Polly, and Amazon Rekognition remove the heavy lifting from natural language understanding, speech generation, and image analysis. They can be accessed with simple API calls – no machine learning expertise required. Watch this space. Much more to come.

High-Velocity Decision Making

Day 2 companies make high-quality decisions, but they make high-quality decisions slowly. To keep the energy and dynamism of Day 1, you have to somehow make high-quality, high-velocity decisions. Easy for start-ups and very challenging for large organizations. The senior team at Amazon is determined to keep our decision-making velocity high. Speed matters in business – plus a high-velocity decision making environment is more fun too. We don’t know all the answers, but here are some thoughts.

First, never use a one-size-fits-all decision-making process. Many decisions are reversible, two-way doors. Those decisions can use a light-weight process. For those, so what if you’re wrong? I wrote about this in more detail in last year’s letter.

Second, most decisions should probably be made with somewhere around 70% of the information you wish you had. If you wait for 90%, in most cases, you’re probably being slow. Plus, either way, you need to be good at quickly recognizing and correcting bad decisions. If you’re good at course correcting, being wrong may be less costly than you think, whereas being slow is going to be expensive for sure.

Third, use the phrase “disagree and commit.” This phrase will save a lot of time. If you have conviction on a particular direction even though there’s no consensus, it’s helpful to say, “Look, I know we disagree on this but will you gamble with me on it? Disagree and commit?” By the time you’re at this point, no one can know the answer for sure, and you’ll probably get a quick yes.

This isn’t one way. If you’re the boss, you should do this too. I disagree and commit all the time. We recently greenlit a particular Amazon Studios original. I told the team my view: debatable whether it would be interesting enough, complicated to produce, the business terms aren’t that good, and we have lots of other opportunities. They had a completely different opinion and wanted to go ahead. I wrote back right away with “I disagree and commit and hope it becomes the most watched thing we’ve ever made.” Consider how much slower this decision cycle would have been if the team had actually had to convince me rather than simply get my commitment.

Note what this example is not: it’s not me thinking to myself “well, these guys are wrong and missing the point, but this isn’t worth me chasing.” It’s a genuine disagreement of opinion, a candid expression of my view, a chance for the team to weigh my view, and a quick, sincere commitment to go their way. And given that this team has already brought home 11 Emmys, 6 Golden Globes, and 3 Oscars, I’m just glad they let me in the room at all!

Fourth, recognize true misalignment issues early and escalate them immediately. Sometimes teams have different objectives and fundamentally different views. They are not aligned. No amount of discussion, no number of meetings will resolve that deep misalignment. Without escalation, the default dispute resolution mechanism for this scenario is exhaustion. Whoever has more stamina carries the decision.

I’ve seen many examples of sincere misalignment at Amazon over the years. When we decided to invite third party sellers to compete directly against us on our own product detail pages – that was a big one. Many smart, well-intentioned Amazonians were simply not at all aligned with the direction. The big decision set up hundreds of smaller decisions, many of which needed to be escalated to the senior team.

“You’ve worn me down” is an awful decision-making process. It’s slow and de-energizing. Go for quick escalation instead – it’s better.

So, have you settled only for decision quality, or are you mindful of decision velocity too? Are the world’s trends tailwinds for you? Are you falling prey to proxies, or do they serve you? And most important of all, are you delighting customers? We can have the scope and capabilities of a large company and the spirit and heart of a small one. But we have to choose it.

A huge thank you to each and every customer for allowing us to serve you, to our shareowners for your support, and to Amazonians everywhere for your hard work, your ingenuity, and your passion.

As always, I attach a copy of our original 1997 letter. It remains Day 1.



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Will a new TJX concept put more hurt on department stores?

“The key to TJX’s success is their merchants. They are constantly on the hunt for high-value, on-trend, opportunistic buys. This creates the treasure hunt, and a compelling reason to shop … frequently. I think TJX will launch a full assortment off-price furniture chain, instead of it just being a department in HomeGoods. It’s not department stores that should be worried, it’s full-priced traditional furniture stores who should keep their eyes wide open.” ~Shawn Harris

Read the Full Article:

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Do consumers want to follow grocers on social media?

“I think that the key reason people follow retailers and brands on social media is for reasons of lifestyle projection. Either the consumer is living, or wants to live, the brands ideals. For grocers, this would be things like healthy living and sustainability. With the brand ideals as the backdrop, consumers will become sticky if the social feed is educational, informative, entertaining, will save them time and/or money or is otherwise a utility — very much similarly to why consumers want and keep a mobile app installed.” ~ Shawn Harris

Read full article:

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Digitization and the Retail Revenue Reset #retail #economy #leadership

Retailers are feeling significant pressure as digital takes a greater and greater hold of the industry. Many say that digitization actually brings demonetization. This will result in a massive shift in the share pie for retailers. What once was a great traditional retail business, will become a much smaller primarily online business. I thought I’d take a stab at visualizing that. Thoughts?

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What is Supply Chain Management? Quick Definitions…

While Supply Chain Management is a new term (first coined in 1982 by Keith Oliver from Booz Allen Hamilton in an interview with the Financial Times), the concepts are ancient and date back to ancient Rome. The term “logistics” has its roots in the Roman military. Additional definitions:

  • Logistics involves… “managing the flow of information, cash and ideas through the coordination of supply chain processes and through the strategic addition of place, period and pattern values” – MIT Center for Transportation and Logistics
  • “Supply Chain Management deals with the management of materials, information and financial flows in a network consisting of suppliers, manufacturers, distributors, and customers” ‐ Stanford Supply Chain Forum
  • “Call it distribution or logistics or supply chain management. By whatever name it is the sinuous, gritty, and cumbersome process by which companies move materials, parts and products to customers” – Fortune 1994

According to the Council of Supply Chain Management Professionals…

  • Logistics management is that part of supply chain management that plans, implements, and controls the efficient, effective forward and reverse flow and storage of goods, services and related information between the point of origin and the point of consumption in order to meet customers’ requirements.
  • Supply chain management encompasses the planning and management of all activities involved in sourcing and procurement, conversion, and all logistics management activities. Importantly, it also includes coordination and collaboration with channel partners, which can be suppliers, intermediaries, third party service providers, and customers. In essence, supply chain management integrates supply and demand management within and across companies.
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Fundamental Models in Data Science

1. Classification (probability estimation or scoring): binary or categorical.Attempt to predict, for each individual in the population, which of a (small) set of classes this individual belongs to. Classification will bucket individuals, and scoring will provide quantification of likelihood of being in a particular bucket.

2. Regression (“value estimation”): numeric. attempts to estimate or predict, for each individual, the numerical value of some variable for that individual.

3. Similarity Matching: attempts to identify similar individuals based on data known about them.

4. Clustering: attempts to group individuals in a population together by their similarity, but not driven by any specific purpose.

5. Co-occurrence (also known as – frequent item mining, association rule discovery, and market-basket analysis): attempts to find associations between entities based on transactions involving them.

6. Profiling (also known as behavior description): attempts to characterize the typical behavior of an individual, group, or population.

7. Link prediction: attempts to predict connections between data items, usually suggesting that a link should exist, and possibly also estimating the strength of the link.

8. Data reduction: attempts to take a large set of data and replace it with a smaller set of data that contains much of the important information in the larger set.

9. Casual modeling: attempts to help us understand what events or actions actually influence others.

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Should Zappos take steps into the hospitality world?

“When people talk about Zappos, it’s not just about the great pair of shoes they scored, but the awesome customer service they received. It truly has become a tangible asset that is synonymous with the Zappos brand. In extending the brand to hotels and beyond, the customer satisfaction bar will be set high. Execution will be key to protecting the brand’s equity. Also I could see some Amazon (the parent company) products and services being a part of this.” ~Shawn Harris

Read full article:


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