2018 – The Most Popular Posts from AnthonySmoak.com

Two thousand eighteen was the third year in which I’ve been actively blogging here on AnthonySmoak.com. It’s interesting to look back on my first full year of activity in 2016 and compare the blog’s growth in views and visitors.  For my first year of posting back in 2016, I had a little under a 1,000 visitors for the whole year, as compared to 2018 where I had about 28.5 thousand visitors. The past year was also momentous for my Youtube channel as I hit the milestone 1,000 subscribers mark.

I thoroughly enjoy sharing what I learn with people and that’s what keeps me coming back to share more. The positive feedback and the comments I receive from readers and followers make this pastime of mine worth carrying on.

Since I finally have a good sample size of views to draw from, I wanted to share a year in review of the most popular articles here on AnthonySmoak.com for 2018. Although I have a number of Tableau video posts, my most popular posts are related to companies and/or strategy. To my surprise, there seems to be a sizable audience for technology related strategy at Wal-Mart.

I want business intelligence practitioners to come here for their Tableau fix, but I also want business school, management information systems students and anyone else occupying the dual spaces of business and technology (like I’ve done throughout my industry and consulting career) to read my posts on various companies and their uses of technology.

Without further ado, here are my 10 most popular blog posts over the past year.

1. More Than You Want to Know About Wal-Mart’s Technology Strategy Part 1

Popularity Index Score = 100

By far this was my most read blog article of the year and the most popular of all time. It is the first part of a three part series that I wrote that takes a look at a few different areas related to Wal-Mart’s use of technology. This post specifically relates to technology infrastructure and IT staffing.

2. Michael Porter’s Generic Cost Leadership Strategy Explained

Popularity Index Score = 90

This is the first of two posts where I cover the famous business professor’s generic strategies. In this specific post I describe the cost leadership strategy and its advantages and disadvantages. The cost leadership strategy is employed when a company aims to be the lowest cost producer in the market. It enables a business to reap higher than average profitability.

3. Michael Porter’s Generic Differentiation Strategy Explained

Popularity Index Score = 84

At the opposite side of the generic cost leadership strategy is the differentiation strategy. A differentiation strategy advocates that a business must offer products or services that are valuable and unique to buyers above and beyond a low price.

4. How to Conditionally Format Text Cell Color in Tableau

Popularity Index Score = 82

Of all of my Tableau related videos, this is my personal favorite. I spent many hours researching how to perform this trick for a thoroughly ungrateful party I should add (but I won’t get into that). Tableau is not Excel and table data should be used sparingly in Tableau, but if you have to display table data then do it with style. The upside of my struggle to solve this problem is that I was left with a great video to share with my followers. This video is the 2nd most viewed video on my Youtube channel.

5. How to Fix an Import Specification Error in Microsoft Access

Popularity Index Score = 75

This post hearkens back to my days as a data analyst for General Motors where I heavily used Microsoft Access. I have a love/hate relationship with Access in that it can be an effective tool for light data work but it has the ability to frustrate you with seemingly nonsensical errors. In this post I share my findings regarding how to overcome an Import Specification Error (Run-Time error ‘3625’).  One would think that “import steps” and an import specification can be referenced and used the same way in code, but that is not the case.

Articles 6 -10

6. Strategic Analysis of ADP (Popularity Index: 69)
7. Costco’s Underinvestment in Technology Leaves it Vulnerable to Disruption (Popularity Index: 68)
8. More Than You Want to Know About Wal-Mart’s Technology Strategy Part 2 (Popularity Index: 63)
9. The Definitive Walmart E-Commerce and Digital Strategy Post (Popularity Index: 45)
10. More Than You Want to Know About State Street Bank’s Technology Strategy Part 3
 (Popularity Index: 40)

I want to thank everyone who follows AnthonySmoak.com and who also subscribes to me on youtube for their visualization fix. May you have a prosperous year in the making!

Since I’m writing this post on the Martin Luther King holiday I’ll have to close with a quote from Dr. King.

Life’s most persistent and urgent question is, ‘What are you doing for others?” – Dr. MLK 

I repost some of my articles on Medium
And of course you can subscribe to my Youtube channel

Row and Column Highlighting in Tableau

In this post you’ll learn how to highlight values in your Tableau table using set actions. The dashboard in this video displays the number of total points scored by NBA teams by position in the 2017-2018 season. I will give you step by step instructions on how to implement row and column highlighting on this dataset downloaded from basketballreference.com.

I’ve only made a few minor tweaks but this technique was developed by Tableau Zen Master Matt Chambers. You can check out his blog at sirvizalot.com and follow him at Big shout out to Matt for sharing this technique with the Tableau community!

You can interact with my visualization on Tableau Public:

If you find this type of instruction valuable make sure to subscribe to my Youtube channel!

Make Flashy Maps in Tableau with Mapbox

The default maps in Tableau are just fine but sometimes you need to kick up the flamboyancy factor in your visuals. Integrating maps from Mapbox with Tableau is the perfect way to add some Liberace flash to your development game.

Mapbox is an open source mapping platform for custom designed maps.  By creating an account with Mapbox, you can either design your own maps on the platform or use their preset maps, which are all more impressive than the out of the box option in Tableau.

All you need to do is enter your generated API token (provided by Mapbox) into Tableau’s Map Services interface and you’ll have access to some pretty impressive mapping options.

If you’re interested in Business Intelligence & Tableau subscribe to my  Youtube channel.

 

 

Starbucks, Digital and Analytics: A Perfect Blend

Starbucks is a differentiator, an early adopter in regards to technology and a savvy user of data analytics. Employing the “Starbucks Experience” differentiation strategy (e.g., customer service, ambiance, interior aesthetics, prime locations), the company is able to command above market prices for a commodity product.

Surprisingly, for an organization that was not born in the digital era, Starbucks embraces new technology like a forward-looking digitally native company. The company has demonstrated a willingness to take risks on its road to digital maturity which helped it acquire a unique value proposition in loyalty and customer satisfaction. From a company strategy perspective, the organization is aware that early adopters can acquire advantages.

“In 1998, it was one of the first companies to launch a website; in 2002, it began offering WiFi to its customers, helping to start the transition from quick coffee stop to all-day hangout; and a full decade ago, Starbucks was establishing its social media presence. Now, while others are setting up mobile payment terminals and struggling to start a loyalty program, Starbucks is seeing 11 percent of its sales from mobile order and pay, and 14.2 million Starbucks Rewards members accounting for 37 percent of U.S. company-operated sales.” [1]

As the importance and impact of data has come to prominence in today’s business environment, the company relies heavily upon analytics and digital technologies to enhance business performance. Starbucks processes approximately 90 million transactions per week, [2] therefore the company has to have the requisite culture and resources in place to wring optimal value from data and analytics.

Starbucks Technology Leadership

Any company’s drive for a culture of digital, data and analytics is greatly influenced by senior leadership. As such, Starbuck’s senior leadership increasingly reflects a silicon valley background and mindset. Former chairman and CEO Howard Schultz has been referred to as the “Steve Jobs” of coffee. Schultz’s handpicked successor Kevin Johnson is a veteran technology player having previously served as CEO of Juniper Networks and as President of Microsoft’s Windows division.

Another high level technology hire was that of Gerri Martin-Flickinger who has held technology leadership roles at Adobe, VeriSign and McAfee Associates. Ms. Martin-Flickenger serves as the company’s first Chief Technology Officer, which is a title change from the previous CIO position held by Curt Garner. Incidentally Garner left Starbucks to become the CIO of Chipotle, as his new suitor was impressed with Starbuck’s use of online ordering and mobile pay capabilities.

The ultimate technology name drop belongs to Satya Nadella who serves on Starbuck’s board of directors (as of 2017) and is currently the CEO of Microsoft. Coincidentally, Starbucks is a prominent customer leveraging the Microsoft Azure cloud platform.

Mobile Order / Mobile Pay

One of Starbuck’s most laudable achievements has been its execution of mobile ordering and mobile pay via its iOS and Android applications. Customers who are short on time can pop open the Starbucks app on a smartphone and then place and pay for their order. Purchases lead to an accumulation of loyalty “stars” which can be redeemed for free products. This digital relationship has helped drive demand in physical stores and increase ticket spend per customer.

“They’re actually broadening the footprint of their stores with technology. If everything was a walk-in order, you’d only be able to sell what people could drive up and wait around to get. But by having mobile pay and drive thru, they can extend that store footprint out for miles.”
 [1]

This functionality has been so popular that it has caused operational challenges in the physical stores as customers have walked out after seeing large crowds waiting on mobile orders. The company has beefed up staffing and changed store layouts where applicable to accommodate the increased demand. Starbucks has also based loyalty program rewards on total spend as opposed to number of transactions. The latter approach allowed customers to game the system by sub-dividing orders into multiple transactions which caused bottlenecks.

 Future enhancements to the application include a personalization engine that will allow stores to target customers for differentiated treatment (e.g., birthday rewards, discounts for previously purchased items). To increase customer satisfaction and operational efficiencies, geolocation can be used to track a customer’s presence near the store and allow the point of sale terminal to pre-assemble the customer’s typical order. The barista can then confirm the order once the customer arrives and then submit at the press of a button.

Additionally, voice ordering capabilities via Amazon’s Alexa platform will allow customers to order and pay for food and drinks as if they were speaking to a human barista. The company demonstrated this capability at an investor conference when it placed an order for a “Double upside down macchiato half decaf with room and a splash of cream in a grande cup.” [3]

Due to the company’s investment in this digital capability, Starbucks is in a position to capitalize on its homegrown mobile order and payment processing technology via licensing to other retailers. The decision would have to be weighed against the advantages of keeping its technology and processes proprietary.

Geolocation

Starbucks is a savvy user of data and analytics to help determine where to place its next retail locations. The organization has a real estate analytics team (amongst many others) that spearheads its site selection strategy.

Using an in-house mapping and business intelligence platform called Atlas, the organization can combine data from various internal and external sources to create models which help drive decision making.

“Through a system called Atlas, Starbucks links to as many external and internal APIs as possible, connecting the data with R to build cannibalization models that can determine impact to existing stores if a new store enters the area. This model drives decision-making in cities across the US and world.” [4]

As part of its growth strategy, Starbucks is planning to open 3,000 stores in mainland China through 2022. That is approximately 600 stores per year, or 1 every 15 hours [6]. Choosing the correct site locations is absolutely critical to store success and the correct mix of data and analyses must be conducted to enable a successful rollout.

Starbucks weaves together various data points such as weather, auto traffic, consumer demographics, population density, income levels, coffee purchase patterns, current store locations and even levels of mobile phone ownership to construct its site selection strategy. The company analyzes all pertinent data points overlaid on a visualization map powered by a spatial data warehouse.

Weather and sales data individually can tell two different stories but blended together they can offer the company new insights. For example, if it’s forecasted to be hotter than average in a location, Starbucks can geo-design a localized promotion for cold beverages [6].

Store Labor Analytics

In a presentation given by Leslie Hampel (VP, Store Operations) at Dartmouth’s Tuck School of business, she described the process of balancing the company’s top down strategic store scheduling decisions with emergent strategies from store level managers. If an improvement suggestion is offered by one store for use in all others, then its impact has to be modeled on a statistically significant group of stores first.

Starbucks is very risk averse in regards to making labor related changes. If you consider that labor is about a third of Starbuck’s cost base, a decision that is wrong by 1% will be wrong by about 40 million dollars [7].

In addition to taking suggestions from stores, the company will use analytics to systematize best scheduling practices by examining the best operating 10% of stores. These localized processes are then tested on a statistically significant group of 100 stores. An internal analytics team will assess the impact of the changes to the company’s financials, as well as impacts to store sales and the customer experience. At the end of the 90 day testing period the new process will either be rolled out to multiple stores, tweaked and re-tested, or simply abandoned [7].

As an aside, it’s worth noting that the creation of Starbuck’s famous Frappuccino’s were the result of an emergent strategy from a store level manager in California. Although Starbucks stores are only supposed to sell company approved drinks, the manager sold them in her store against the company mandate. Despite corporate management’s initial reluctance to stock and sell the drink (until seeing the local sales data), Frappuccino’s are now a billion dollar business for Starbucks [8]. Always listen to your ground level employees, as they are the closest to the customer.

Starbucks & Bitcoin

No post about digital technology would be complete without a nod to Bitcoin.

To burnish its tech bona fides, Starbucks has been in talks with Microsoft, the parent of the New York Stock Exchange and the Boston Consulting Group to develop a crypto-initiative. Bakkt (pronounced “backed”) will allow consumers to store and convert digital currencies to dollars that can be used for in-store purchases.

The idea has the backing of major corporations and could help lend some Fortune 500 legitimacy to the somewhat murky and volatile world of cryptocurrencies. Although cryptocurrencies have yet to reach mainstream appeal, Starbucks is showing its willingness to be an early adopter of a new payments solution as it did with its mobile ordering and payment initiatives.

References:

[1] 5 Ways Starbucks is Innovating the Customer Experience. https://www.qsrmagazine.com/consumer-trends/5-ways-starbucks-innovating-customer-experience

[2] Starbucks’ CTO brews personalized experiences  https://www.cio.com/article/3050920/analytics/starbucks-cto-brews-personalized-experiences.html

[3] Starbucks Adds Voice Ordering to iPhone, Amazon Alexa http://fortune.com/2017/01/30/starbucks-alexa-voice-ordering/

[4] Data Analytics in the Real World: Starbucks https://www.northeastern.edu/levelblog/2016/03/04/data-analytics-in-the-real-world-starbucks/

[5] China is getting nearly 3,000 new Starbucks https://money.cnn.com/2018/05/16/news/companies/starbucks-in-china-store-expansion/index.html

[6] Esri 2014 UC: Starbucks Coffee and IT. Coffee beans and business strategy. https://www.esri.com/videos/watch?videoid=3654&isLegacy=true

[7] Using Data to Create & Maintain the Starbucks Experience https://www.youtube.com/watch?v=sUkQwhMwOig @32:42

[8] Rothaermel, Frank T. 2015. Strategic Management 2nd Edition. New York: McGrawHill, Irwin (2nd edition).

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