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: http://www.retailwire.com/discussion/did-amazon-just-patent-tech-that-could-end-showrooming-in-its-stores/

“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.

Sincerely,

Jeff

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: http://www.retailwire.com/discussion/will-a-new-tjx-concept-put-more-hurt-on-department-stores/

Bed Bath & Beyond’s Membership Model #retail #marketing #membership

Is this Everyday Low Price (EDLP) spun as a membership program?  Will the elimination 0f the 20% Coupon’s “scarcity,” remove BBBY’s greatest customer activation trigger, or will the $29 annual fee create the “shop here first” behavior that Amazon enjoys with its Prime Members? The Amazon Prime comparisons always gets me as none of these membership programs come with all of the other value-adds that Prime does, (i.e.video streaming, cheap unlimited music). To truly compete, I think more complementary partnerships are required to enhance the value of these initiatives (e.g. discounted Uber/Lyft rides, Care.com services, etc [requested through the BBBY app] ), plus the extension of this membership’s benefits to the other BBBY subsidiaries. All in all, I do like BBBY’s willingness to test bold initiatives like this.

Reference Articles:

Will customers give Amazon the keys to their smart homes?

“I completely believe that this is a concept that could see wide adoption. Airbnb has helped in resetting the idea of what personal space means and blockchain technology will allow for secure, immutable, one-time access to home IoT locks. Delivery person tracking and home tracking (cameras, mobile device and presence sensors) will play an over-the-top role for auditing behavior. Insurance will cover the rest.”  ~ Shawn Harris

Read full article: http://www.retailwire.com/discussion/will-customers-give-amazon-the-keys-to-their-smart-homes/

Consortium For Operational Excellence In Retailing (COER) @Wharton – Day 2 Quick Recap

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About Consortium For Operational Excellence In Retailing (COER)

Consortium for Operational Excellence in Retailing (COER) is focused on advancing retail operations from a combined academic and business perspective. We hold an annual conference in May, alternating between Harvard Business School and The Wharton School, where we present cutting edge academic research for participants to exchange ideas, thoughts, and challenges. COER attracts companies and academics from various parts of the world.

COER began as the Harvard/Wharton Merchandising Effectiveness Project in 1996, started by Marshall Fisher of The Wharton School and Ananth Raman of Harvard Business School. The academics in COER have published dozens of papers in leading journals and many case studies that are taught at top business school. The work produced by COER was summarized recently by Fisher and Raman in the book “The New Science of Retailing,” Harvard Business School Press. COER has facilitated the work of numerous doctoral students, many of whom currently are on the faculties of leading business schools.

COER grew out of the understanding that while the retail industry now has the analytical tools to make merchandising more effective, there are still many areas where academia can help to push the retail industry forward from an operational perspective.

Consortium For Operational Excellence In Retailing (COER) @Wharton – Day 2 Quick Recap

Session Eleven: Impact of Stockouts
Presentation by Ananth Raman
Key takeaways:
* In manufacturing, a 1% increase in historical in-stock is associated with a 12% increase in demand.w
* If you have a supplier that delivers and one that doesn’t, you’ll order more from the one that does, even if at a slightly higher cost. It’s like buying insurance.

Session Twelve: Consequences of Centralizing Hiring at a Retail Chain
Presentation by Tatiana Sandino of Harvard Business School
Key takeaways:
* Employee Departures:
** Centralized hiring results in a lower rate of employee departure in more busy stores.
** Centralized hiring results in a higher rate of employee departures when the store serves service‐sensitive customers.

* Store Performance:
**Centralized hiring is associated with greater sales in distant stores: 1% increase in sales/additional 10 miles away from HQ
** Centralized hiring is associated with lower performance where customer relations may be important:
*** 7.3% decrease in sales if store serves service‐sensitive customers.
*** 0.04 point decrease in (0‐1) mystery shopper score scale when store serves repeat customers.

Session Thirteen: Demand During Store Liquidation: Local Economic Factors
Presentation by Nathan Craig of Ohio State University
Key takeaways:
* Across retail segments, revenue and asset recovery rates during store liquidation are positively associated with local median household income
* Revenue and asset recovery rates are negatively associated with local consumer sentiment
* Initial inventory, last-year revenue, local median household income, local consumer sentiment, and chain effects explain much of the variance in liquidation revenue (R2 = 0.96)

Session Fourteen: How Retailers Respond to Demand Shocks
Presentation by Vishal Gaur
Key takeaways:
* High inventory turn (HIT) retailers are able to react much more quickly than low inventory turn (LIT) retailers. Margin impact is greater for LIT, than HIT.
* HIT retailers lever is quantity changes, LIT is price changes. HIT retailer have better sustained return on assets in shocks than LIT.
* …so tune inventory for turn, vs availability.

Session Fifteen: Consortium on Patient Experience (COPE)
Presentation by Ananth Raman
Key takeaways:
* Replicate COER for patient experience. More to come…

Session Sixteen: Spatial Competition and Preemptive Entry
Presentation by Fanyin Zheng of Columbia Business School
Key takeaways:
* Deciding on store location, based on the future entry of competition.

Session Sixteen: Using Peer Feedback in Performance Evaluation
Presentation by Serena Loftus of Tulane University
Key takeaways:
* Multi-source feedback has advantages over single source
** Manager note always working w/ subs
** Manager bad evaluation
** Bias of manager.

Session Seventeen: Mobile Technology location-based marketing in Retail
Presentation by Jose Guajardo of Haas School of Business, UC Berkeley
Key takeaways:
* Effectiveness: Non-GEO < GEO < Facebook * Distribution: Non-GEO > GEO > Facebook

Consortium For Operational Excellence In Retailing (COER) @Wharton – Day 1 Quick Recap

About Consortium For Operational Excellence In Retailing (COER)

Consortium for Operational Excellence in Retailing (COER) is focused on advancing retail operations from a combined academic and business perspective. We hold an annual conference in May, alternating between Harvard Business School and The Wharton School, where we present cutting edge academic research for participants to exchange ideas, thoughts, and challenges. COER attracts companies and academics from various parts of the world.

COER began as the Harvard/Wharton Merchandising Effectiveness Project in 1996, started by Marshall Fisher of The Wharton School and Ananth Raman of Harvard Business School. The academics in COER have published dozens of papers in leading journals and many case studies that are taught at top business school. The work produced by COER was summarized recently by Fisher and Raman in the book “The New Science of Retailing,” Harvard Business School Press. COER has facilitated the work of numerous doctoral students, many of whom currently are on the faculties of leading business schools.

COER grew out of the understanding that while the retail industry now has the analytical tools to make merchandising more effective, there are still many areas where academia can help to push the retail industry forward from an operational perspective.

Consortium For Operational Excellence In Retailing (COER) – Day 1 Quick Recap
Session One: Kicking the Growth Addiction
Presentation by Marshall Fisher, Vishal Gaur of Cornell University, and Herb Kleinberger of NYU
Key takeaways:
  • Growth (Open more stores!)
  • Denial (Should we open more stores?)
    • Stop opening stores when they stop providing a return.
  • Mature (We need to get more from existing stores.)
    • Drive out non-productive work.
    • Grow revenues faster than expenses. In business of scale, even a 1% or 2% increase can have significant bottom line impact if expenses are kept in check.
    • Focus on projects that return significant capital returns.

 

Presentation by Donald Ngwe of Harvard Business School and Paulo Campos of Zalora
Key takeaways:
  • Selectively inducing search friction on a retail website can increase margins by as much as 20%, without negatively affecting conversion. Cost neutral chance in selling strategy that promises significant potential returns.
  • Caveats: Increased search friction on a website may drive consumers to the competitors site Long-term performance may be harmed as consumers form expectations about an on-line store.
Presentation by Santiago Gallino of Tuck School of Business and Antonio Moreno of Kellogg School of Management
Key takeaway:
  • Retailers need to optimize on
    • 1) Price competition,
    • 2) Information & ratings,
    • 3) Fulfillment speed,
    • 4) Return policy, and
    • 5) Retailer brand promise.
Presentation by Chloe Kim, Marshall Fisher, and Xuanming Su, all of The Wharton School
Key takeaway:
  • A variant fulfillment model where the delivery trucks are positioned (parked) at optimal locations for customers to self pick-up. This study took at look deeper at what daily repositioning can do to sales. Using a random forest machine learning algorithm the team determined that the firm could realize a 25.6% increase in sales.
Session Five: Managing Customer Compatibility
Presentation by Ryan Buell of Harvard Business School
Key takeaways:
  • Customer satisfaction and loyalty are tightly aligned.
  • Greatest influence on customer satisfaction:
    • 1) The Customer – 94%. That’s right, it’s mostly out of your control.
    • 2) Employee – 2%
    • 3) Locations – 2%
    • 4) Processes – 1%
    • 5) Markets – 1%
  • If you are really good at 1 dimension which is most aligned with your brand promise, customer will typically apply positive attribution to your business’ other dimensions.
Presentation by Amitabh Sinha of Michigan Ross School of Business
Key takeaways:
  • Dynamic warehousing is the acquisition of warehousing space and services on-demand, in small increments, from a large pool of geographically spread warehouses, on a pay-as-you-go basis (OPEX).
  • However,  self-owned/operated networks may be cheaper depending on a number of variables. Lack of cost certainty. Systems integration.
  • There may be a optimal model that leverage both dynamic and self-owned/operated networks.
Session Seven: Data Driven Pricing
Presentation by Kris Ferreira of Harvard Business School
Key takeaways:
  • How can you combine predictive analytics to predict demand with prescriptive analytics to make tactical decisions?
  • There was a ~10% increase in revenue when models where applied.
Session Eight: The Effect of Social Influence on Demand
Presentation by Vishal Gaur of Cornell University
Key takeaways:
  • Two dimensions: Popularity “Share” rankings and Quality rankings.
  • Low quality (low reviews), should push Popularity “Share” rankings with customers.
  • High quality (high reviews), should push Quality rankings with customers.
Session Nine: Case Study—Coca Cola Vietnam
Presentation by Ananth Raman
Key takeaways:
  • Company’s can die from a thousand little cuts, not just one big wound.
  • Drive employee satisfaction
  • Drive lasting relationships with your customers.
  • Deliver what you say you are going to deliver.
Session Ten: A Conversation on Cyber Security
Ananth Raman in conversation with Kent Burnett of Dillard’s, Inc.
Key takeaways:
  • Security is a multi layered effort
  • Employees need to be trained and tested on security policy and procedures
  • It is an industry issue, which takes industry collaboration. Collaboration through organizations such as Retail Cyber Intelligence Sharing Center (R-CISC) – https://r-cisc.org/.

Consortium For Operational Excellence In Retailing (COER) @Wharton – Day 2 Quick Recap