It’s time for New York businesses to put a ring on it.
Monogamy
Married households aggregate wealth faster than those headed by a single person. Our superpower as a species is cooperation. When we agree to fully cooperate with someone … wonderful things can happen. In addition to building wealth (the means), you can achieve the ends, producing things that look, smell, and feel like you who are good citizens — why we are here. But I digress.
Firms who are in transactions businesses, like retail, are serially dating. Being single, while it has its moments, is exhausting and expensive. Equinox, Tinder, vodka, and hangovers all tax your time and well-being. Firms that are single and constantly need to be attractive to strangers to repopulate their customer base, are valued at a multiple of profits. Firms in monogamous relationships (recurring revenue) are valued at a multiple of revenues.
You still ain’t stankin’ enough for me
Beyoncé
Single Ladies
The Jackson Administration will Put a Ring On It and Implement New York Ring Prime
Many have predicted the death of brick-and-mortar stores but physical stores can be a huge asset because they have the power to deepen the customer’s relationship with a brand, increasing their lifetime value.
Smith said that the next round of stimulus needs to support the small businesses that still remain underserved by traditional financial institutions — and that new financial technology software and services can help.
Robert Smith
“We need to continue to rally as Americans to come with real, lasting, scalable solutions to enable the communities that are getting hit first, hardest, and probably will take the longest to recover with solutions that will help these communities thrive again,” Smith told NBC’s Chuck Todd.
Smith called for an infusion of cash into community development financial institutions and for a new wave of technology tools to support transparency and facilitate operations among these urban rural communities that aren’t served by large banking institutions.
The Jackson Administration will invest in Storytelling and Augmented Reality for local retail.
The above object was brought for $1.00 and later sold on E-bay for $162.50.
What happens when you hire creative writers to make up stories about cheap trinkets, and they post these stories along with the items online for sale? This was exactly what Rob Walker and Joshua Glenn did in 2009 as part of their storytelling experiment, Significant Objects. From the Significant Objects website:
Significant Objects, a literary and anthropological experiment devised by Rob Walker and Joshua Glenn, demonstrated that the effect of narrative on any given object’s subjective value can be measured objectively.
The project auctioned off thrift-store objects via eBay; for item descriptions, short stories purpose-written by over 200 contributing writers…. The objects, purchased for $1.25 apiece on average, sold for nearly $8,000.00 in total.
Stories change the way our brain works and potentially change our brain’s chemistry.
Empathic storytelling changes brain chemistry
The Jackson Administration will make real investments in locally-owned businesses, and not just give lip service.
Andrew Cuomo has given New Yorkers socialism for the very rich, and rugged individualism for everyone else
In all, New York’s 118 billionaires have seen their net worth increase by $77bn since coronavirus hit the United States. Those billionaire gains in just three months are more than five times the size of the state’s entire projected budget shortfall of $14bn.
Cuomo offered the wealthiest man in the world $3 billion instead of organically growing the New York economy in an equitable way.
Bezos, Amazon’s 56-year-old founder and the world’s richest person, has seen his fortune swell $74 billion in 2020 to $189.3 billion, despite the U.S. entering its worst economic downturn since the Great Depression. He’s now personally worth more than the market valuation of giants such as Exxon Mobil Corp., Nike Inc. and McDonald’s Corp.
We will also encourage Americans to buy more products manufactured in the United States and for New Yorkers to purchase more locally made products.
We will develop a search engine that will have products ads with place of manufacturing information. This is to provide the data so that we can all buy more US-made and locally-made products and create more good jobs.
Today on Google, when someone searches for anything related to a product name, Google automatically populates most of the above-the-fold space on the Search Engine Result Page (SERP) with Product Listing Ads (PLAs).
What big-brand Pay-Per-Click (PPC) managers are finding out is that their PLAs are doing extremely well but this is at the expense of their regular text ads such as AdWords.
Frank DuBois
Product ads such as PLAs are proving to be very effective and we propose to create a search engine that will have products ads with place of manufacturing information. This information will be like the Kogod Made In America Auto Index assembled by Frank DuBois at American University’s Kogod School of Bussiness.
Counties and parishes with a greater concentration of small, locally-owned businesses have healthier populations — with lower rates of mortality, obesity and diabetes — than do those that rely on large companies with “absentee” owners.
Applying Artificial Intelligence to Local Retail
Amazon Doesn’t Just Want to Dominate the Market—It Wants to Become the Market
The company is a radically new kind of monopoly with ambitions that dwarf those of earlier empires.
Setting up shop on Amazon’s platform has helped Gazelle Sports stabilize its sales. But it’s also put the company on a treacherous footing. Amazon touts its platform as a place where entrepreneurs can “pursue their dreams.” Yet studies indicate that the relationship is often predatory.
As a local retailer, if you think keeping up with Amazon is expensive and time-consuming, consider the alternative: extinction.
Today’s retailers face a number of complex and emerging challenges.
Thanks to lower overhead and higher volume, online behemoths like Amazon can deliver products faster and at a lower price, driving smaller retailers out of business.
In order to compete, retailers need a new approach – and the fresh technologies that go along with it.
Personalized Promotion & Product Recommendation Engines
Delivering real-time recommendations to online shoppers is a proven way to maximize revenue. It improves the customer experience and increases sales. According to McKinsey & Company, 35 percent of what consumers purchase on Amazon and 75 percent of what they watch on Netflix come from product recommendations.
However, shoppers expect finely tuned product recommendations and react poorly to one-size-fits-all or uninformed recommendations (e.g., “I’ve already bought that. Why are they showing it to me again?”). To be effective, recommendations must be personalized based on the individual consumer’s preferences, shopping history, interests and needs – in addition to what’s already in their current shopping cart.
Real-time recommendations require data products that connect masses of complex buyer and product data (and connected data in general) to gain insight into customer needs and product trends.
This cannot be achieved with relational database (RDBMS) technology; the SQL queries are too complex and take too long to provide recommendations in real time. The same goes for big data processing technologies like Hadoop and Spark. These technologies work well for something like email recommendations – which are delivered once a day – but they are not real time.
The Challenges with Traditional Systems
By design, a graph database quickly query customers’ past purchases, as well as instantly capture any new interests shown in their current online visit, both of which are essential for making real-time recommendation engines.
Because relationships are treated as first-class entities in a graph database, retailers can connect customers’ browsing history with their purchasing history, as well as their offline product and brand interactions.
This enables a real-time product recommendation algorithm to utilize a customer’s past and present choices to offer personalized promotion recommendation. No offline pre-compute is necessary, eliminating the associated delay.
Furthermore, in order to counter dynamic pricing from the likes of Amazon, retailers need the ability to change pricing and promotions at any level of a product hierarchy in real time. For example, they must be able to mark down all 60-inch televisions by 10% for the next two hours if the right economic and competitive factors indicate such a move is necessary.
Similarly, retailers must be able to implement competing promotions. They might reduce all smartphone prices except Apple iPhones due to Apple’s strict pricing guidelines.
Real-time promotions such as these involve complex rules that are made simple when handled by a graph database. The database may hold millions of relationships that have only one parent node. With a graph database, retailers can change one relationship type rather than a thousand products and all their prices.
Case Study: Walmart
Walmart became the world’s largest retailer by understanding its customers’ needs better than any competitor. An important tool in achieving that understanding is graph database technology.
Walmart’s Brazilian e-commerce group wanted to understand the behavior and preferences of its online buyers with enough speed and in enough depth to make real-time, personalized “you may also like” recommendations. However, Walmart quickly recognized that it would be difficult to deliver such functionality using traditional relational database technology.
“A relational database wasn’t satisfying our requirements about performance and simplicity, due to the complexity of our queries,” said Marcos Wada, software developer at Walmart.
To address this, Marcos’s team decided to use the leading graph database. Matching a customer’s historical and session data is trivial for graph databases, enabling them to easily outperform relational and other NoSQL data products.
Walmart deployed graph database technology in its remarketing application run by the company’s e-commerce IT team based in Brazil, and it has been using graph database technology in production since early 2013. It enables Walmart to understand online shoppers’ behavior, as well as the relationship between customers and products.
As a result, the retailer has also been able to up- and cross-sell major product lines in core markets.
“With [graph database technology] we could substitute a complex batch process that we used to prepare our relational database with a simple and real-time graph database. We could build a simple and real-time recommendation system with low latency queries,” Marcos said.
Case Study: Top-Ten Retail Company
One Top 10 US-based, bricks-and-mortar retailer turned to graph database technology after its burgeoning online operation was almost overwhelmed by the volume of customer traffic it attracted on Cyber Monday 2015.
The company was running its site on an IBM DB2 relational database, and on Cyber Monday 2015, it offered an across-the-board 15% discount to site visitors. While the retailer had pulled in more customers than any other bricks-and-mortar rival – one of the project’s target metrics – the price paid was unacceptable: The site’s checkout function kept working that day, but 90% of customer traffic was delayed.
As one senior company executive said: “We pushed a lot of guests to the site and we were very successful in terms of volume. But the reality was we got significantly more traffic than we ever projected, and we couldn’t handle it. We protected checkout so the site functioned. But we disappointed way too many guests, and that’s never okay, period.”
The biggest bottleneck was the crucial but complex personalized promotions process, where the company invites shoppers to add last-minute extras to their online cart. To flash up exactly the right recommendations requires software that can instantly analyze the shopper’s cart contents and their buying history, and dig through 15-30 layers of data – such as promotion types, qualifying manufacturers, product names and categories – all in real time.
This proved beyond a conventional relational database like DB2. So, the retailer considered graph database technology, which is optimized to rapidly carry out such complex searches among masses of connected data.
The company already knew its biggest rival, Walmart, had turned to graph database technology to provide the best web experience for its customers (see case study above), so in mid-2016 the company rolled out both a new graph database technology-based front-end and backend to its website, transforming the company’s real-time personalized promotions engine and online cart promotion calculations.
Graph database technology now processes 90% of the retailer’s 35 million-plus daily transactions – which involve between three and 22 hops across different layers of data – in 4 milliseconds or less. And during Q4 2016 – the vital Christmas retail period – the company’s digital sales rose 34% to a record high, helped by the friction-free graph database technology solution.
Conclusion
An effective product recommendation engine can’t be half-baked or only partially efficient. Either recommendations (or promotions) are timely and relevant or they convince your would-be customers that your e-commerce site only offers stale, pre-computed suggestions.
The only way to craft truly personalized promotions or product recommendations – that consider not only past buying history and current session data – is to use graph technology to power your recommendation engine.
StartUpTown 