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It’s All In The Details: Some Thoughts For Executives In The Entertainment Industry

July 17, 2019 BI News and Info

In the first blog of this series, I discussed the entertainment industry’s mandate to hold their customers’ attention. In my second blog, I talked about the importance of creating experiences that extend beyond the entertainment venue. In particular, I mentioned Walt Disney’s genius for extending familiar stories into films and then into theme park experiences. However, perfecting the idea of an extended experience is an ongoing process, not just for Disney, but for everyone in the entertainment industry. Now I’ll reveal the secret for creating a very personal and memorable experience: “specificity.”

For example, when Disneyland opened in 1955, the thematic areas of their park were given rather generic names (Main Street USA, Adventureland, Frontierland, Fantasyland, and Tomorrowland). They did have some dark rides that were tied to specific films, like Snow White’s Adventure and Peter Pan’s Flight, but most of the attractions were also generic in nature, like Autotopia, Storybook Land Canal Boats, and Frontierland Shootin’ Exposition. Ultimately, Disney added some specificity to some of its nonspecific attractions, like Haunted Mansion, Pirates of the Caribbean, and Jungle Cruise, when they went the other direction and became films.

Needless to say, Disney has learned a lot about creating experiences in the last 65 years. The original theory was that generic concepts would appeal to a wider audience, but that turns out not to be the case. In practice, people are drawn to richly detailed experiences because it gives them more potential ways to relate. For example, look at the new Star Wars: Galaxy’s Edge, which opened recently at Disneyland in California and is opening soon at Walt Disney World in Florida. Disney has learned that the more detailed and immersive an experience, the more personal it will be for patrons and the more likely it is to attract repeat business.

Of course, other parks have learned this lesson, too. For example, Bricksburg at Legoland in Florida; Angry Birds Land at Särkänniemi in Tampere, Finland and Thorpe Park in Surrey, England; Sesame Place at SeaWorld in Florida; and of course, The Wizardly World of Harry Potter at Universal Studios in Florida and California. Theme park executives know if they can leverage intellectual property that customers have been exposed to outside the park, it will create a more meaningful experience inside the park because customers will be immersed in a very specific and familiar world.

Casino resorts also offer these rich, immersive adventures. Las Vegas has experiences that range from the ultra-kitsch to the truly opulent. You’ll find Paris, Venice, New York, and ancient Greece within five miles of each other, and casinos use intellectual property to draw players to slot machines. Today, the slots are more interactive than ever and often have dynamic slot symbols moving about the screens. But the biggest experiential distinction among slot machines is branding. Would you rather play Game of Thrones, Kiss, Pac-Man, Wizard of Oz, or the all-time favorite Wheel of Fortune machine? One machine, TMZ, immerses patrons in a personalized experience by taking their picture and using it as a slot symbol.

Just like cinemas and entertainment parks, casino resorts need to hold a patron’s attention, but casinos hold their attention with fantasy. People come to resorts with the dream of not only beating the odds but living like the truly wealthy in luxurious rooms surrounded by sumptuous pools, fountains, and gardens. They’re thrilled by close encounters with beautiful showgirls or their favorite musical performer, and they delight when magicians or acrobats are able to do the seemingly impossible.

Cirque du Soleil is an excellent example of this. It has more than 25 visually mesmerizing shows running around the globe. Seven of them are running in Las Vegas alone and two leverage familiar intellectual property (The Beatles Love and Michael Jackson One). And all the shows are created with extraordinary detail and specificity that delivers a personal, memorable experience. In fact, ever on the cutting edge, Cirque du Soleil has encouraged the use of cellphones and a cloud-based app during its performances of Toruk to improve audience participation.

BrendaEntertainmentBlogOneQuote 1024x576 It’s All In The Details: Some Thoughts For Executives In The Entertainment Industry

The future of technology-infused entertainment is limited only by the imagination.

Currently, Cirque du Soleil has more than 4,000 employees, but it wasn’t always that way. It began in 1984 with only 20 performers, a leaky tent, and considerable debt. The success of Cirque du Soleil is not due only to its attention to detail on stage but its exhaustive, detailed efforts behind the scenes, as well. For a great look at how that happens, check out its truly amazing, behind-the-scenes videos about its production of Ka.

In fact, it’s quite ironic that Las Vegas and other bastions of entertainment have a reputation for a reckless “anything goes” reputation. The reality is that these places are subject to extraordinary regulation and must be able to assure the health and safety of their guests and employees. Only an intelligent enterprise that has complete control of its licensing and regulations, as well as an adequate, certifiably qualified workforce, and the ability to procure the best possible health and safety equipment, can be certain of that.

Further, if casino resorts want to provide their customers with unique experiences, they obviously need the ability to treat each customer uniquely. People come to these resorts for many different reasons. It might be to gamble, but it could be for a convention, a reunion, a wedding, or just for the shows. Patrons might be regular high-rollers, casual visitors, or travelers looking for a once-in-a-lifetime experience, and they should all be treated distinctively. While this probably means gathering significant data about these customers, it’s also critical to assure them that their information will be safely guarded, as casinos take confidentially and customer data security very seriously.

Experiences don’t just happen, they’re created. It’s called “experience management” (XM), and there is no industry that requires zealous experience management more than the entertainment industry. Whether you’re a cinema, museum, concert venue, casino, amusement park, Renaissance fair, or circus, if you want to (figuratively) go from a leaky tent and handful of performers to a billion-dollar business, you need more than a C-suite; you need an X-suite – a group of executives who are focused on the experience of their customers.

Can I honestly say that every startup entertainment enterprise will become wildly successful strictly by focusing on experience management? No. But I can assure you that all highly successful entertainment enterprises are putting considerable focus on their experience management or they’re on the path to obsolescence.

For an in-depth look at how the x-suite is changing the way companies do business, read “Meet the ‘X-Suite’ – the job roles shaping Experience Management” by Qualtrics, an SAP company.

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FILTER() – When, Why, & How to Use It (in Power BI too)!

July 17, 2019 Self-Service BI

Below, you will find one of our all-time favorite Power BI techniques. In the ten (yes ten) years we’ve been operating this site, we’ve written over 1,000 articles on Power BI. Hard to imagine – even for us – but true.

Years ago, we first wrote up this technique in the context of Power Pivot – Power BI’s cousin and predecessor, which we like to call “Power BI in Excel.”

Since the two products share the same brains (DAX and M), this technique is more relevant today than ever. Everything below is 100% legit for Power BI – the same data model “shape,” the same DAX, etc. – but now you can use it to power up your Power BI visuals, and not just PivotTables. Enjoy! FILTER() – When, Why, & How to Use It (in Power BI too)!

A recent comment/question alerted me to the fact that I’ve never devoted a post just to this very useful (and often misunderstood) function. Time to correct that.

The #1 Reason to Use FILTER – When CALCULATE Breaks Down

Does this Mysterious Error Look Familiar?

Different Formula, Same Error

What’s Wrong With Those Formulas?

The thing both of those formulas have in common is that they are using a measure in the filter argument of the CALCULATE function. In both examples here, I’ve highlighted the offending measure in yellow.

CALCULATE([Sightings per Year], [Avg Sighting Length in Mins]>6)

CALCULATE([Sightings per Year],
Observations[TTL Min]>[Avg Sighting Length in Mins])

In the first formula, I was trying to use a measure on the left side of the comparison, and in the second, I was trying to use a measure on the right side of the comparison. Both are illegal.

CALCULATE expects its filter arguments to take the form of Column=Fixed Value, or >Fixed Value, <= Fixed Value, etc., where “Fixed Value” is a specific number (like 6), a specific text string (like “Regular”), or a specific date. So my first formula violates the rule that a column name is required on the left. And my second formula violates the rule where a fixed value (not an expression or a measure) is required on the right.

CALCULATE refuses to let you use variable expressions like measures in these filter arguments largely because “vanilla” CALCULATE is intended to always be fast, and once you start including expressions in these comparisons, your formulas might run a LOT slower. So this is a good rule really – it forces you to stop and think before accidentally doing something bad. The error message, of course, could and should be a lot better.

For a bit more explanation on this, see this brief post.

What’s the Solution?

If you look at those two illegal formulas above, they both reflect a perfectly valid intent. The first formula is attempting to ask for “how many sightings per year would I report if we just counted sightings that lasted more than 6 minutes” and the second is asking for “how many sightings per year are above average in length.”

I’m almost regretting my selection of those examples because they are a bit more complex than necessary to make the fundamental points. But hey, too late now to change them, so I’ll move quickly.

In the first example, the Avg Sighting Length measure is actually based on a column in my Observations table – each UFO sighting has a [TTL Min] column. So I could rewrite that filter in the calculate as Observations[TTL Min] > 6 and everything is fixed.

But let’s say I wanted to filter out entire States where the average sighting length was > 6. Since I don’t have a column in my States table that does that, it’s sensible to use the measure, and that forces me to use FILTER because FILTER does allow me to use measures in my comparisons:

CALCULATE([Sightings per Year],
FILTER(States, [Avg Sighting Length in Mins]>6)
)

See that? The highlighted section took one of the filter arguments to CALCULATE and replaced it with a call to the FILTER function. The syntax of FILTER is pretty simple but is explained below.

In my second example, where a measure was used on the right side of the comparison, the formula gets rewritten as:

CALCULATE([Sightings per Year],
FILTER(Observations,
Observations[TTL Min]>[Avg Sighting Length in Mins])
)

So there you go. When you want to use a measure, or an expression like AVERAGE(Observations[TTL Mins]), you have to call in the FILTER function. More details follow, starting with the simplest information and moving to the most subtle of characteristics.

How does FILTER() Work?

The syntax for the FILTER function is FILTER(TableToFilter, FilterExpression). Pretty simple.

For simple purposes, if you understand the gist of the above, and then points 1 and 2 below, you are good to go. If you want to understand more of the details over time, I recommend revisiting points 3-5.

FILTER() takes a TableToFilter and a FilterExpression and returns all rows from that TableToFilter that match the FilterExpression.
1. In the example above, TableToFilter is ALL(Periods)
2. and FilterExpression is Periods[Year]=MAX(Periods[Year])-1
FILTER() steps through the TableToFilter one row at a time.
1. And for each row, it evaluates the FilterExpression. If the expression evaluates to true, the row is “kept.” If not, it is filtered out.
2. Because FILTER() goes one row at a time, it can be quite slow if you use it against a large table. When I say “large” that is, of course, subjective. A few thousand rows are fine in my experience. A million is not. Do not use FILTER() against your fact table.
The FilterExpression typically takes the form of Table[Column] = <expression>
1. The comparison operator doesn’t have to be “=.” It can also be <, >, <=, >=, <>
2. The expression on the right-hand side of FilterExpression can be “rich.” This is VERY useful. In a simple CALCULATE, the right side of each filter expression has to be simple, like a literal number (9) or a string (“Standard”). The fact that FILTER() allows for rich expressions here is one of the most common reasons I use FILTER().
3. The Table[Column] in the filter expression is a column in the TableToFilter. If you are filtering the Periods table, it makes sense that you are testing some property of each row in Periods. I can’t think of a sensible reason to use a column here that is NOT from TableToFilter. (Insert “boot signal” here, maybe the Italians can address this).
FILTER() ignores everything else going on in your formula and acts completely on its own.
1. For example, our overall formula sets ALL(Periods) as the first argument to CALCULATE.
2. The FILTER()’s that come after that do NOT pay any attention to other arguments, however, including that ALL(Periods).
3. In other words, the FILTER() functions are still operating against the original filter context from the pivot! If the pivot is sliced to Year=2009, then the FILTER() function starts with the Periods table already pre-filtered to just 2009.
4. This is why each of my FILTER()’s uses ALL(Periods) for TableToFilter. I have to repeat the “expand” step so that my FILTER() is also working from a clean slate.
Even though each FILTER() operates on its own, their results then “stack up” in the overall formula.
1. Even though FILTER() RETURNS a set of rows that matched the FilterExpression, it actually REMOVES rows from the overall filter context.
2. This sounds tricky, but really, it isn’t.
3. Let’s say our TableToFilter contains 6 rows: A, B, C, D, E, and F.
4. And our overall formula contains two FILTER() clauses that both operate on the same TableToFilter, just like our overall formula near the beginning of this post.
5. Let’s also say that the first FILTER() returns rows A, B, C, and D.
6. And the second FILTER() returns rows C, D, E, and F.
7. The net result is that only rows C and D are left “alive” in the overall filter context of the formula.
8. So one way to think of this is that FILTER()s “stack up” on top of each other.
9. Another way to think of it is that even though the first filter RETURNED rows A, B, C, and D, its real effect was to REMOVE all other rows (E and F) from consideration.

FILTER() – When, Why, & How to Use It (in Power BI too)!

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Best Practices for Email Marketing Automation

July 17, 2019 CRM News and Info

Segment Your Lists and Provide Personalized Email Content

List segmentation is one of the principal benefits of email marketing automation and separates marketing automation platforms from email service providers (ESPs). Segmenting according to engagement level, areas of interest, and demographics allows email marketers to tailor their messaging to target their audiences with compelling content when and where they’re looking for it.

Compared with the traditional batch-and-blast approach used by ESPs, segmented sending produces exceptional open rates, engagement, and return on investment. It also results in fewer unsubscribes and spam complaints because the content being sent via email has been written for each specific audience — and increased relevance leads to increased resonance every time.

Yet, while segmentation is crucial in the modern digital environment, many companies continue to struggle to group their audiences properly. Here are three ways to ensure accurate audience segmentation and increased engagement through personalized content.

1) Learn Your Audience’s Pain Points

As marketers, sometimes we have a tendency to overcomplicate things, which is only natural considering the tools at our disposal. However, one proven way to learn more about your audience’s challenges and interests is to simply ask them! When you seek your initial or follow-up opt-in, include a checklist of 5-10 pain points or areas of interest for them to complete. Once armed with this valuable information, segment your lists accordingly and work on developing relevant content for all stages of the sales funnel.

2) Collect Website Visitor Data

The great thing about digital marketing is that skilled marketers are able to gather tons of meaningful data and then leverage that data to segment their marketing lists and deliver content that pushes prospects and customers through the sales cycle. First, you’ll have to create intuitive email reporting dashboards in your marketing automation platform so you can see which emails certain recipients are opening, reading, and engaging with. From there, you simply divide and conquer with compelling content delivered to the appropriate inbox.

3) Market Based on Location

Depending on the type and size of your company, knowing where your customers live, work, and shop can be a major competitive advantage because, more often than not, geography influences purchasing patterns. Email marketing based on location also helps senders optimize for multiple time zones, execute regional promotions, and deliver geo-targeted content to different audience segments. One particularly powerful way to do this is to send emails with testimonials from satisfied customers who live close to your intended recipients. Understanding where your customers and prospects live will help you understand what makes them tick and empower you to foster trust and nurture relationships.

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Goalkeeper Cat In Action

July 16, 2019 Humor

The ball will not get past this cat.

“Goalkeeper Cat Blocks Ball Every Time.”
Image courtesy of http://www.dailyhaha.com/_vids/Goalkeeper-Cat-Blocks-Ball-Every-Time.htm.

Video: Crowe CRM for Wealth Management for Microsoft Dynamics 365 Overview

July 16, 2019 Microsoft Dynamics CRM

To remain competitive and excel in an increasingly difficult financial advisory sector, wealth management firms need to streamline processes, increase advisor and client collaboration, and embrace a 360-degree view of a client’s finances.

Crowe CRM for Wealth Management, built within Microsoft Dynamics 365, provides financial advisors with a single, centralized view of client-related information and interactions. Through seamless integration with familiar Microsoft Office 365 applications such as Microsoft Word, Excel, and Outlook, or relationship tools like LinkedIn and Skype, Crowe CRM for Wealth Management helps you improve the client experience while also helping to increase your productivity.

Watch the full Crowe CRM for Wealth Management overview video.

Client Activity Dashboard

Each time you access Crowe CRM for Wealth Management, a personalized Client Activity Dashboard provides instant visibility into your upcoming scheduled activities. In addition, this dashboard surfaces actionable insights based on business intelligence all in one place. Identify clients you haven’t talked to in a long time or clients with upcoming birthdays in case you want to send a card with a personalized message or take the opportunity to remind them of an important investment opportunity.

Client Performance Dashboard

Switching to the Client Performance Dashboard, you can view client segments measured against key performance indicators. In this example, you see different variations of clients categorized by the amount of assets under management. Dashboards can be easily configured to provide you with a quick glance into the information most important to your business.

Navigation

Navigating through Crowe CRM for Wealth Management couldn’t be easier. From the main menu we can access all of the main areas of the system. Let’s start by looking at accounts.

By default, the Active Accounts List View is presented. Let’s find the Bradford and Buncheon household. They are one of our top clients, and I want to review their information before I meet with them.
To find a specific account, you can scroll down to it. Use the letters at the bottom or use the search bar at the top. To open an account record, just click on it. The account form provides a wealth of client information all on one screen.
The Summary tab displays basic household information, including a financial summary, family relationships, referrals, and other related connections– for example, an attorney used by the client. It also provides quick visibility into upcoming appointments as well as the days since the client was last contacted. A financial summary provides a quick glance into a client’s financial assets.
The Timeline tab allows you to record notes about clients and see all past interactions with them. You can also filter these activities and notes by date, making it easier to view relevant notes in a given time period.
The Financials tab offers a more detailed financial summary. The default grouping is based on assets and liabilities and provides a good picture of the client’s overall financial health. Financial information can be easily grouped and summarized in a variety of ways.
Investment Goals provides an easy way to capture your client’s risk tolerance while also tracking and prioritizing their long-term goals.
Contact Details display address information as well as contact preferences.
The Related tab provides additional options to navigate to related records, such as activities, leads or opportunities.

As you can see, the user interface is easy to navigate with a lot of information at your fingertips.

Making Personal Connections

Now let’s take a look at the Jerry and Selma Pierce account. I have an upcoming meeting with their son Brian, and I’d like to review his contact record prior to meetings.

On the Contact form, you can capture and view specific information about the contact. You can see personal information, a timeline of notes and activities, family connections, and who may have referred Brian to the firm.

The Details tab has additional information such as addresses, personal notes, employment details, and contact preferences related to marketing activities.

Similar to the Account screen, investment goals can be tracked at the individual level.

The LinkedIn tab pulls LinkedIn profile information right into the contact record.

The Icebreakers tab provides you with some great talking points you can use to build rapport and engage in warm introductions. It includes mutual connections and recent activities like shared posts or published articles.

You can use these updates to see how active your client or prospect is on LinkedIn and know when it’s a good time to reach out. The Get Introduced tab shows first, second, and third-level connections in your network that are also connected to a client or prospect. This empowers you to find a common connection or reach out to a colleague to get an introduction.

The Related Leads allows you to find individuals similar to the client or prospect you’re looking at. This helps you segment and expand your potential pool of prospects.

Crowe CRM for Wealth Management can help improve your clients’ experiences, increase advisor productivity, and provide effective ways to analyze your business and client needs.

Watch the full Crowe CRM for Wealth Management overview video.

If you are interested in learning more about what Crowe CRM for Wealth Management and Microsoft Dynamics 365 has to offer, visit us at www.crowecrm.com, email [email protected], or call 877-600-2253.

Find Crowe CRM for Wealth Management on the official Microsoft AppSource.

By Ryan Plourde, Crowe, a Microsoft Dynamics 365 Gold Partner www.CroweCRM.com

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Grocery chain Giant Eagle taps Grabango to pilot checkout-free experiences

July 16, 2019 Big Data

The next time you walk into one of Pittsburgh-based Giant Eagle’s more than 216 supermarkets, 202 GetGo convenience stores, or 56 Ricker’s stores, you might not have to stop by the checkout counter on your way out the door. The supermarket chain today announced that it has partnered with Grabango, a Silicon Valley startup founded by Pandora Radio cofounder and chief technology officer Will Glaser, to create a “no-wait” and checkout-free brick-and-mortar payment experience.

Glaser says that multiple configurations of Grabango’s platform are being tested at a pilot Giant Eagle location, including an app-based solution and another that allows customers to pay with cash or credit card.

“Giant Eagle has been a fantastic partner on this groundbreaking project,” said Glaser, who serves as Grabango’s CEO. “We are excited to be announcing our first checkout-free partnership with a major U.S. retailer. Together, we are offering a technology that is seamless and equitable for all.”

Giant Eagle director of corporate communications Dan Donovan added, “Giant Eagle is committed to advancing technologies that create an improved, stress-free shopping experience for our customers. In Grabango, we have found a partner who shares our commitment and who can help us bring this enhanced experience to life.”

It’s a big get for Grabango, given Giant Eagle’s profile. The grocery company was founded in 1931 and has locations in Western Pennsylvania, Ohio, Northern West Virginia, Maryland, and Indiana, and it ranked among the top 40 on Forbes’ largest corporation list with reported annual sales of roughly $ 8.9 billion.

“Giant Eagle is an ideal development partner for multiple reasons,” explained Glaser in a blog post. “First, the range of store sizes under one banner (3,000 sq. ft. GetGo convenience stores to [100,000 sq. ft.] Market District supermarkets) makes for an ideal proving ground for Grabango’s enterprise-class technology. Second, Giant Eagle’s … technology team firmly controls the technology that operates in their … retail locations and is capable of rapid deployment throughout the operation.”

Since its incorporation nearly four years ago, Grabango has amassed 17 patents related to checkout-free technologies and signed on clients that it claims serve over 600 million shoppers per year collectively. That momentum no doubt won over investors like Propel Venture Partners, Ridge Ventures, Abstract Ventures, Commerce Ventures, and Founders Fund, which in January contributed $ 12 million in a series A funding (bringing the company’s total raised to $ 18 million).

Grabango isn’t the only company that’s working on frictionless shopping experiences. In June, Japanese telecom company NTT East teamed up with startup Earth Eyes to create AI Guardsman, which attempts to prevent shoplifting by scanning camera feeds for suspicious activity. And in November, Intel and Tencent took the wraps off a pair of artificially intelligent (AI) products for retail that can track customers and perform complex object detection.

But according to Glaser, Grabango’s product is much more scalable than many of its competitors’ offerings. It’s designed to work in stores with more than 100,000 square feet of floor space, he said, and to track hundreds of shoppers at once. (Current store deployments range from 2,000 to 50,000 square feet.) Moreover, the system can keep tabs on “hundreds of thousands” of SKUs, obviating the need for store employees to manually inventory store stock, and it integrates with existing point-of-sale systems and store operations platforms.

Glaser said the goal is high fault tolerance, which is why the bulk of processing is performed on-premises by a dedicated PC that doesn’t require an internet connection. Grabango’s item-tracking cameras — which were designed in-house — are both lightweight and relatively inexpensive to manufacture, reducing the cost of install. Stores benefit from greater vision coverage, which helps Grabango generate better three-dimensional floor plans of stores and reduce the chance a purchase isn’t recognized as the result of occlusion.

The system’s machine learning algorithms — which were trained using reinforcement learning, an artificial intelligence (AI) training technique that uses rewards to drive agents toward goals — can recognize objects with 99.9% accuracy, Glaser said. That means they make a mistake one time out of 1,000.

These aren’t the only ways Grabango is setting itself apart from the competition. Unlike, say, Amazon Go, which requires customers to download an app and log into an account before they begin picking things off of shelves, Grabango-powered stores retain their existing cashier setups. Shoppers have two options: (1) checking out conventionally with cash, a credit card, a debit card, or SNAP benefits, or (2) opting into a cashierless experience with Grabango’s app. In the latter case, checkout is automatic — customers can walk out without scanning a barcode or confirming that they’ve finished shopping.

Grabango’s target market is exclusively the U.S., where Glaser said its focus will remain for the foreseeable future. The optimistic plan is to capture the top 30 grocers and top 10 convenience store chains in the U.S. as clients in the coming years.

To do so, it’ll have to go head to head with a bevy of new startups, most of which have opened proof-of-concept stores or partnered with brick-and-mortar retailers. Standard Cognition became the second company to open a cashierless store in San Francisco, following hot on the heels of Zippin in August. Trigo Vision recently inked a deal with Israel supermarket chain Shufersal for 272 cashierless stores and is reportedly in partnership talks with Tesco. And, of course, mega-retailer Amazon has Amazon Go locations in Seattle, Chicago, and San Francisco.

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Family Survives War, Pirates, Refugee Camp to Build Thriving International Bakery

July 16, 2019 NetSuite

Posted by Barney Beal, Content Director

Fleeing war-torn Vietnam, facing rough seas, pirates and capsized boats along the way before landing in a refugee camp, the story of the Ly family and the success of Sugar Bowl Bakery is a testament in perseverance, ambition and sound business sense.

After the fall of Saigon, the five Ly brothers realized they needed to leave the country with their families. In 1978, they boarded a small boat and set off. After six nights at sea, including three run-ins with pirates, the family landed in Malaysia and were placed in a Red Cross refugee camp where some remained for 9 months and others for as long as 24 months.

Bit by bit, the family emigrated to California, with everyone ultimately reuniting in San Francisco where they found jobs and began saving. In 1984, the family pooled its money and purchased a small doughnut shop called Sugar Bowl Bakery. That initial investment went on to become a business manufacturing a line of desserts at two 55,000-square-foot manufacturing facilities that are delivered to retailers like Costco, Safeway, Kroger and others across the United States.

From Doughnuts to Food Service to High Volume Manufacturing

As part of the purchase of the original Sugar Bowl Bakery, the sellers taught the Lys how to make the shop’s doughnuts, muffins and croissants. Led by Andrew Ly, whose ambition and drive helped him to learn English and graduate from San Francisco State University with a degree in accounting, the business began to expand, selling pastries to 7-Eleven and other doughnut shops in the Bay Area.

Later, when Andrew Ly began meeting with hotel managers, convention center staff and universities that were looking to outsource their bakery operations, he saw an opportunity and created a food service division, ultimately becoming the largest food service provider in the Bay Area, serving some of the hot new startups at the time like Google and Yahoo.

Years later, another opportunity came when a team of Costco buyers in the Bay Area came for a visit to Sugar Bowl Bakery. As they were walking through the processing area, they noticed a multi-layered Puff Dough Cookie. It was made with the highest quality with all butter and folded with more than 300 layers of doughs. Soon, Sugar Bowl Bakery named that cookie Palmier and was distributing to Costco stores across the US. The shift from food service to manufacturing a significant amount of a single product at scale, helped propel the business further.

Delivering at Scale

“It was a big change moving from food service to high volume manufacturing for retail,” said Michael Ly, general manager and vice president of Sugar Bowl Bakery. “In food service locally, a big customer might want 1,000 brownie bites an order. With Costco you might have to make 600,000 brownie bites consistently every week. It’s a much different business, a different level of scale.”

After Costco came other big accounts like Safeway, regional grocery chains, as well as other national retailers like Kroger and Walmart, requiring Sugar Bowl Bakery to expand beyond its initial commissary at multiple plants in San Francisco to its state-of-the-art manufacturing facilities in Hayward. Today it manufactures a couple million items a day, baking Madeleines, Brownie Bites, Palmiers, Duet Bites and Apple Fritters.

“Our go to market was to make much higher quality cookies in big volumes and sell them at a great value due to our ability to lower our manufacturing cost. We bought ingredients at high volume to receive good pricing” Ly said. “Consumers now buy our high-quality baked goods from retailers and get much greater value than what they’d get at a cafe. When we were in food service locally, we had more than 750 items, but it was very hard to scale due to complexity. We changed our business model to focus on just a few items at the lowest cost. We knew by doing that we could dominate our category. We have become the leader of high-quality bite-sized baked goods in the supermarket bakery perimeter area”

Sugar Bowl Bakery’s strategy paid off with each iteration of the company, achieving double digit growth for many years straight.

Moving from Navision to NetSuite

Amidst that growth, executives realized that the existing Microsoft NAV system the company was running was not going to be able to keep up due to the software’s complexity. A client server-based system, NAV required an in-house IT team to manage the system and, even then, frequently caused errors that were hard to fix. Additionally, because the company had done extensive customizations, it was reluctant to undergo upgrades that would require more resources and disrupt the business.

After an evaluation of SAP Business One, Sugar Bowl Bakery implemented NetSuite in 2013 due in large part to its cloud-based architecture and its simplicity.

“We knew we wanted to keep scaling and didn’t want the complexity of anything including the servers to be a bottleneck,” Ly said. “We didn’t want the ERP system to hold us back.”

With a modern, stable, cloud-based system, Sugar Bowl Bakery had unified, real-time insights into its business. It was able to transition from lot-based accounting to standard-based accounting, allowing company leadership to see, in real time, how the business is performing in detail.

In January of 2018, Sugar Bowl Bakery, one of NetSuite’s early food manufacturing clients, implemented the Advanced Manufacturing module.

“We have a lot of discipline on the manufacturing floor now,” Ly said. “Now we’re really into a stage where we can do continuous improvement and analysis related to KPIs to control cost on the manufacturing floor.”

With NetSuite as its system of record, delivering insights and shop floor discipline, Sugar Bowl Bakery is poised to expand further as it evaluates new manufacturing facilities to serve its East Coast customers and reduce costs.

Learn more about NetSuite software for food and beverage manufacturers.

Posted on Tue, July 16, 2019
by NetSuite filed under

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Radical Innovation And Machine Learning

July 16, 2019 SAP

Innovation may be the calling card of the future, but how can we determine that we’re transforming in the right way? Namely, how can we sustain innovation when the digital pace of change increases exponentially?

It begs the question: Are all the good ideas taken?

Innovation experts broke down these questions and more in an episode of the Game-Changing Predictive Machine Learning Radio series called The Machine Era: Disruptive Innovation and Ideation. Host Bonnie D. Graham led three panelists through a discussion focused on how we should innovate in the age of machine learning.

The featured experts were:

Bryan Mattimore: Co-founder and chief idea guy of The Growth Engine Company and Innovation Agency
Omar Maher: Practice lead for machine learning at ESRI and co-founder of two tech startups
John Schitka: Senior director (retired) of solution marketing at SAP

Check out the full replay online. Here are the highlights.

What holds us back from innovation?

Is it the tendency to get caught up in everyday tasks that keeps us from generating good ideas, or does today’s innovation overload keep us from even trying? The panelists dove into a debate about the biggest innovation inhibitor: Is it complacency or saturation?

Complacency of technique

Bryan believes it’s complacency, but added that it’s more about the method than the habit. While there are hundreds of new ideation techniques designed to help us innovate, we often fall back on the tried-and-true team brainstorming meetings. The problem is that these are no longer working.

“You have to introduce stimuli to people’s brains to get them to trigger new ideas,” he said. Open brainstorming sessions “may be okay in the beginning of a project, but, boy, as you move down the road, you’ve got to come up with new ways to trigger people to get new ideas.”

Fear of the unknown

John believes it’s neither complacency nor saturation and instead has more to do with comfort and fear of the unknown. “I’d prefer to go down a path that I know and that I’m comfortable with,” he said, “then walk into a dark room and not know what my feet are going to hit as I step forward.”

Lack of understanding

Omar agreed with both Bryan and John, but pointed out that “another factor is getting to know those new tools in the first place.” Many people hear about artificial intelligence, machine learning, and other hot-button technology topics in the news, but don’t really understand them. That’s the first major creativity roadblock before innovation can even begin. He noted, “One thing I really enjoy the most is demystifying those concepts – explaining them in simple English.”

How can we be intentional about innovation?

Einstein famously said that if he had an hour to work on a problem, he’d spend 55 minutes thinking about it and five minutes solving it. He believed that a well-stated problem is a problem well-solved. John, Omar, and Bryan investigated this approach to problem-solving, also known as the 80/20 rule, and discussed how it can lead to purposeful innovation.

Define the problem

John, a proponent of Einstein’s rule, recommended taking the time to define the problem before executing the solution. One of the biggest issues at the beginning of the innovation process is that having a vague idea of the final step. “What is the problem you’re actually trying to solve?” he asked. “Is it maybe multiple problems, and we’re going to have to break it up into smaller bite-sized pieces?” That’s where we have to start before we can get to creative innovation.

He likened this to using machine learning, saying that all data must be prepared and refined before it’s ready to be used in any kind of solution. “All the work and effort have to go into clearly defining a problem, so I understand what it is I’m trying to solve.”

Confirm the direction

Omar suggested a two-pronged approach. First, we must think about the business challenge as the primary piece of the puzzle. “Think about the problem from a business perspective rather than jumping to technology, which I find many people doing.” Breaking up the question, as John mentioned, is the next step to determine sub-problems. But the most critical piece is confirming the direction of the project. “I think finding the right question is the most important step in all of this.”

Consider the assumptions

Bryan advocates a calculated approach to innovation to garner measured results. One of the most effective ways to start the innovation process is to note all the assumptions about your problem. “List maybe 20 of those,” Bryan recommended. “What’s interesting is that you get the obvious assumptions at the beginning, and then it gets harder and harder, and then you get some real breakthroughs towards the end of that process. That’s why we push people to get 20.”

Then break down the reasoning behind each assumption and determine whether it’s true. For example, a hotel business might assume that it needs to acquire rooms to rent. However, businesses like Airbnb moved past that assumption and disrupted the entire market.

“This questioning of the basic assumptions is critical to the work we do,” he said. Sometimes the key to innovation is reframing our problems.

How can machine learning help us innovate?

Bonnie ended the show by asking the experts how we can use machine learning to innovate, especially in the future.

Data cleansing

As John mentioned earlier, it takes a lot of work to prepare data upfront before feeding it to machine learning technology. However, Omar predicted that it will be common to have data-cleansing programs that prepare the data with little or no human intervention. “There are techniques to do that today, and I think they are going to get more useful.”

Automated prompts

Bryan envisions a future that incorporates machine learning into the innovation process from the start by harnessing its power for question development. “Using predictive machine learning to generate some of those intriguing questions, they could be funneled to the global virtual ideation teams to come up with ideas, almost on a daily basis.”

He ended by asking: “How much more productive could we all be if we had this culture of curiosity?” By blending machine-learning question-generation with human creativity, we’re bound to increase our capacity for innovation.

Continual improvement

John noted that right now machine learning is a great tool, but it will continue to improve as the world changes. It’s been created to solve problems, but the creativity involved is still attached to the human way of thinking. “The ideation, the application of the tool, the coming up with the problems,” he remarked, “will still be very much human.”

Learn more

For more machine learning and innovation thought leadership, listen to the full replay online and check out another episode of the Coffee Break with Game-Changers series. Read these articles to learn more about machine learning.

And please listen to the replay of our “Pathways to the Intelligent Enterprise” Webinar, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling.

This article originally appeared on the SAP Analytics blog and is republished by permission.

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July 16, 2019 BI News and Info

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Thanks.

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Radical Innovation And Machine Learning

What holds us back from innovation?

Complacency of technique

Fear of the unknown

Lack of understanding

How can we be intentional about innovation?

Define the problem

Confirm the direction

Consider the assumptions

How can machine learning help us innovate?

Data cleansing

Automated prompts

Continual improvement

Learn more

Digitalist Magazine’s Top Posts Of The Week [July 15, 2019]

Digitalist Magazine’s Top Posts Of The Week [July 15, 2019]

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Unnecessary error messages

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