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Startups Helping Businesses Understand What Customers Want

This article first appeared and originally published at https://blogs.oracle.com/startup/post/startups-customer-experience.

The concept of personal customer service is nothing new. Good bartenders have always remembered their regular patrons’ favorite drinks. Shopkeepers are trained to anticipate their customers’ needs.

The skill of identifying customers’ wants and desires to drive sales remains important to businesses, and now there is a raft of high-tech tools to help them gain new insights.

Here’s how companies in the Oracle for Startups program are helping their clients deep dive into analytics so they can understand customer needs better than ever before. 

Helping companies listen to customers

How we speak gives listeners all sorts of clues about what we really want. DataKlout uses Voice AI to analyze customers’ intentions. Its next generation analysis software provides consumable insights for decision making and results.  For example, it can be used to identify customers’ positive reactions to marketing and sales calls, allowing a sales team to focus on closure, or be used to train employees to deliver more delightful customer service, among other use cases.

Using the tool helped a client cut the cost of customer acquisition by 75%, leading to a 500% increase in opportunities for closure in a tele-sales and tele-marketing campaign, while it also increased opportunities for a car insurance company, which used the tool to identify prospects from a cold calling campaign, resulting in a jump from 2% to 8%.

While DataKlout’s Voice AI gives its customers a new technique to understand their customers’ deepest desires, it intends on going further, by equipping its clients with another in-depth tool by integrating the facial expressions in a video calls.

Understanding the whole customer

Customers are complex creatures, making predicting our actions and needs difficult. FirstHive uses a machine-learning driven algorithm, allowing its platform to ingest data from nearly every kind of customer interaction and transaction, including ERP, CRM, website, social, PoS, app, and customer care groups.

It can even absorb offline and unstructured data like social comments. The tool then builds unified customer identities and makes recommendations on what the next best action should be to enhance the customer’s experience. 

The startup has worked with companies like Singapore Airlines and Unilever and has shown its tool can help enterprises earn a sixfold increase in their marketing ROI, with the right content being sent to razor sharp customer segments at just the right time. 

Similarly, Pryon helps employees of enterprises find important information easily so they can do their jobs, including customer service. The startup behind the technology that powers Alexa allows users to ask an assistant a colloquial question and receive an answer in just a second. The solution applyies natural language processing to unstructured content automatically ingested from a vast range of content types.

Using AI for super forecasting

As any good service provider knows, the best way to meet a customer’s needs is to anticipate them. (Just think of that brilliant bartender or stellar hotel worker.)

Complete Intelligence runs more than 15 billion data points through an AI platform, making trillions of calculations across 1,400 industry sectors. This allows it to provide its customers in industrial manufacturing as well as the oil and gas, chemicals, electronics, food and beverages industries with a fully automated, globally integrated artificial intelligence platform to help purchasing, supply chain planning, and revenue teams make accurate forecasts.

https://open.spotify.com/episode/1U9haAa5gc7pgBjl7tojyw

Helping startups meet customer needs

Oracle for Startups exists to support growing companies and help them serve their customers’ needs. We know startups need reliable cloud services; that’s why we offer them a 70% discount on OCI.

We know young companies need to embrace new tech tools and scale, which is why we have a dedicated CAT team to help with migration and other goals.

We also know marketing support and introductions to enterprise customers are invaluable, and we strive to make these perks of our program a reality.

We’re also hoping to know even more about our customers by launching a global customer survey. After all, who better to inform our strategy than the startups we serve?

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News Articles

Sustainable startups help companies go greener

This article first appeared and originally published at https://blogs.oracle.com/startup/post/sustainable-startups.

“What you do makes a difference, and you have to decide what kind of difference you want to make,” said Dr. Jane Goodall. 

From high tech waste management solutions to tools that make the making of consumer products more ethical, startups are innovating solutions for a greener future. To empower their sustainable solutions, many tap into the benefits of Oracle for Startups to gain exposure, more customers, and stronger infrastructure. 

Startups and enterprises working together can help the business world lead the change toward greater sustainability. While some businesses are naturally greener than others (and not all of them are focused on combating climate change), every company can make adjustments to reduce its carbon footprint or mitigate negative environmental effects. 

If everyone makes small changes, they end up making a big difference, or as Sir David Attenborough puts it: “If working apart, we are forces powerful enough to destabilize our planet, surely working together we are powerful enough to save it.”

This Earth Day, we’re celebrating how some of the more eco-focused members of Oracle for Startups help other businesses make a positive impact on the planet. 

Waste not, want not

One of the simplest ways to make a positive impact is to waste less. Startups help other businesses minimize waste and maximize efficiency in several ways. 
Complete Intelligence, for example, uses AI and strong predictive analytics to help companies waste less – whether that’s materials, time, or money.  “It might not be as intuitive a sustainability play as, say, building solar panels, but it is important nonetheless,” said analyst Jeremy Cox in a report about the startups bringing sustainability to energy and utilities.

Tracifier created a blockchain-based traceability application to reduce food fraud and, therefore, food waste. “Blockchain allows for an accurate and transparent record of each of several certification processes, making forgery nearly impossible,” said Mina Kordi, CEO and cofounder of the startup, which is based in Hamburg. 

Faradai (formerly Reengen) turned rooting out energy waste in stores, offices, and other commercial properties into a global business. Their hardware agnostic IoT platform analyzes sensor data to uncover energy and operational insights. One of our favorite success stories involves the company’s work with a bank that found that ATMs with high energy usage often had outdated exterior lighting. A simple change in lighting reduced the site’s energy consumption by 59%. 

Buying better

More consumers are making purchases based on ethics and environmental concerns, and startups are serving up clever tech to the companies selling us greener products from clothes to cars.

Circulor makes it easier for automobile giants and other businesses to spot the weakest links in their supply chains so they can improve them and attract conscientious consumers. The London-based startup specializes in tracking raw materials using blockchain and artificial intelligence. It provides ‘traceability-as-a-service’ to verify responsible sourcing, underpin effective recycling, and improve efficiency, so consumers can buy new products with confidence. 

When it comes to the fashion industry, green is the new black.

Fashion houses are keen to embrace the public’s appetite for everything eco-friendly. The blockchain-based supply chain transparency platform offered by retraced gives fashion brands a boost for their inventory efficiency and sustainability credentials. The German startup uses the Oracle Blockchain Platform to create a supply chain management tool, enabling companies to map and verify their data, including certified details about raw materials, textile manufacturers, fabric dyers, designers, craft people, factories, and sewers. As retraced gathers information, two things happen: brands can collect and analyze supply chain data, and a QR code is automatically generated, which consumers can scan to discover information about ethical sourcing and sustainability. 

Cleaning up

Startups are playing a vital part of cleaning our planet. Oceanworks is one of them. The startup is intent on banishing plastic from the ocean and is doing its bit by creating an online marketplace for recycled plastic materials and products. It has more than 100 customers and a supply capacity of more than 190,000 tons of ocean plastic a year from collection sites across six continents.

Based in Los Angeles, the cloud startup runs a track-and-trace application to certify that the plastic that manufacturers source really is recycled ocean plastic so their customer base (which includes Fortune 500 companies) can prove their eco credentials. 

Calling climate crusaders

If your startup business is on a mission to save the world, Oracle for Startups can help. We offer the technical tools and one-on-one mentoring startups need to make the world a better place. From free cloud credits and access to Oracle Blockchain to introductions to customers, Oracle for Startups offers the support your startup needs to make a real difference. 

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News Articles

The startups bringing sustainability to energy and utilities

This article first appeared and was originally published at https://blogs.oracle.com/startup/post/startups-sustainable-utilities.

Analyst Jeremy Cox was watching the United Nations Climate Change Conference news while researching his recent report on startups offering sustainability solutions for the energy and utility sector. The timing was especially fortuitous for another reason: Cox was about to welcome a grandchild.

Sustainability is top of mind when you start thinking about future generations. Cox took notes and quoted environmentalist Sir David Attenborough to lead off the report, “If working apart we are forces powerful enough to destabilize our planet, surely working together we are powerful enough to save it.”

What is more powerful than startups harnessing the cloud and enterprise expertise to bring their novel sustainability ideas to the market?

Here’s a look at the startups featured in the report from CX Create: 4 startups show the way to sustainability

How an empty garbage truck sparked a company

The idea for Evreka came after the founders noticed empty garbage trucks driving back to the depot. The drivers drove scheduled routes regardless of whether there was garbage to collect, wasting gasoline and staffing. Evreka’s first product was a sensor that attaches to garbage collection carts to show the cart’s position – ready to be emptied or not at the curb. The information helps waste collectors orchestrate efficient pickup schedules.

The founders have since expanded their vision to include tackling inefficiencies in the entire waste management lifecycle, looking to extract materials (other than the obvious recyclables) that could be sold to manufacturers and reused as raw ingredients for different industries. They offer the data as a SaaS solution. The platform runs on Oracle Cloud Infrastructure (OCI), a choice the company made because of “(Oracle’s) global reach, second-generation OCI technology, and expansion of highly secure data centers.”

The All-in-One Waste Management Platform from Evreka

Why predictive analytics is vital to powering sustainability

Complete Intelligence joined Oracle for Startups because founder Tony Nash looked to the Oracle cloud to help power his machine learning platform designed for smarter revenue, expense, cost, and investment planning decisions.

Being super agile is critical in today’s global business world, including as it relates to understanding, measuring, forecasting for sustainability. “What I discovered from talking with Tony was that strong predictive analytics helps waste less – whether that’s materials, time or money,” Cox says. “It might not be as intuitive a sustainability play as, say, building solar panels, but it is important nonetheless.”

An AI-powered global intelligence platform for strategic and tactical procurement and investment decisions

Watch our interview with founder Tony Nash and check out Cox’s deep dive on Complete Intelligence: 

An AI-Powered Global Intelligence Platform for Strategic and Tactical Procurement and Investment Decisions

Making the cloud greener

Cloud storage opens many opportunities for enterprises and startups to run more efficiently but sending all that data to the cloud and instantly making it available is an energy hog. Danish startup GroenSky is meeting that challenge with an approach that makes less energy-intensive archived storage just as appealing and easy to access as live storage.

Writes Cox, “GroenSky allows customers to choose how they store their files. Those that are only rarely accessed, typically around 80% or more, can be placed in archived storage that doesn’t consume power except when accessed or moved to regular, live storage. A real-time calculator allows customers to see how much CO2 they can save.”

GroenSky founder Pierre Bennorth Cox he chose Oracle Cloud Infrastructure because of its security features, global data centers, and commitment to power its cloud with 100% renewable energy by 2025.  

A Green Cloud to Massively Reduce the Carbon Footprint

Combing millions of data points to find the energy wasters

The founders of Faradai (also known as Reengen) have turned rooting out energy waste in stores, offices, and other commercial properties into a global business. Their hardware-agnostic IoT platform analyzes sensor data to uncover energy and operational insights.

One of our favorite success stories involves the company’s work with a bank that found that ATMs with high energy usage often had outdated exterior lighting. A simple change in lighting reduced the site’s energy consumption by 59%.

The company joined Oracle for Startups and told Cox how they’ve reaped the benefits. “Apart from the performance and security advantages of the Oracle Cloud Infrastructure,” Cox writes. “Oracle has been instrumental in opening doors to its large enterprise customers throughout the world. Faradai has also benefitted from further exposure by speaking at Oracle conferences in the Middle East. As (the founder) said, ‘we get great leverage in industrial B2B sales and have had a very positive reception from the Oracle sales teams and now work even closer with them.”

AI-powered Energy and Sustainability Intelligence Platform

How startups can build a greener future

We ask Cox to interview startups because he often finds insights that we haven’t discovered. This project was no different. “Each of the startups highlighted in this report is making a significant difference that benefits customers and society, providing real hope that we can all make a difference collectively.”

Are you building the next great sustainability solution? Join us to save 70% on cloud and scale your business with global connections. 

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Visual (Videos)

Oracle for Startups Featuring Complete Intelligence

Complete Intelligence is in partnership with Oracle for Startups, and here’s a Youtube interview featuring our CEO and founder, Tony Nash, where he explained what the company does and for whom. Get to know the technology behind the superforecasting for manufacturing firms and learn how CI helps them be more profitable specially in a highly volatile market like in the Covid pandemic. There’s also a section on how CI uses the Oracle Cloud Infrastructure to better serve its clients around the world.

 

The video above is published by Oracle.

 

❗️ Besides Oracle, Complete Intelligence is also in partnership with Bloomberg, Refinitiv, and Microsoft. Learn more about our Partnership program here.

❗️ Discover how CI can help your company in future cost projections, revenue forecasting, budgeting, and more. Book a demo here for your agile budgeting and forecasting.

 

Show Notes

 

WD: Can you tell me a little bit about what Complete Intelligence does and for who?

 

TN: We work with global manufacturers and we help them better understand their cost and revenue environment. We’ll work directly with their ERP data. Work with IT in the cloud and help them understand the forecast for their costs and for their revenues. So, they’re using their exact data in their exact environment to make great decisions for their clients.

 

WD: I’ve heard what you do referred to as super forecasting, which sounds so cool. Which industries
are best served by the super forecasting that Complete Intelligence offers?

 

TN: It’s mostly manufacturers. We work with chemicals firms, mining firms, electronics manufacturers, industrial manufacturers. So people who make stuff or people who work with firms who make stuff have to know how much that stuff’s gonna sell for, how much it’s gonna cost. Anybody who has risk associated with the future cost or future price, would need what we do to really help them de-risk their future decisions and their proactive planning processes.

 

WD: How are the forecasts that you provide impacted by volatility caused by unprecedented global events, say a pandemic?

 

TN: When Covid came around, when markets were hit dramatically in February, March and into April, we increased the frequency with which we update our forecast to our clients. But we also folded in a lot more volatility-specific algorithms, so that clients would understand what the path back would be like. In a normal year, let’s say the cost forecasts for a major manufacturing firm can be off by up to 30 percent. In some cases even more. So, if you’re planning those expenses and those budgets. You have a huge variance that you’ve got to pad in your budgets.

 

On average, we’re looking at a four to seven percent error rate. We’re helping people in a dramatic way to really de-risk their future outlook on the cost side. What we’re doing is a fully automated process. That guesswork of people sitting around the table saying, “let’s push this number up, let’s take this number down,” that’s a long budgeting process for people. And we really put that in the cloud. We have the machines learn and work through the data and calibrate and reduce that error for clients.

 

WD: Working with global markets and currencies, you must have massive data sets. Increasing the frequency of running those data sets probably requires quite a bit of computational power. How does Complete Intelligence manage that?

 

TN: Wee do that with cloud solutions. We work with OCI and the current generation of OCI to expand our computing capability. Many companies work across clouds. They work across on-perm and cloud and so we’re flexible with all of that. The frequency of those updates, the frequency with which clients want an updated view of the future for different companies changes. You have really fast moving companies who want that on a really high frequency basis. You have slower moving companies who are looking at it maybe monthly. That’s fine. We adjust to all of them.

 

WD: So, flexibility and multi-cloud are two really interesting considerations for dealing with enterprise customers like you do. What are some of the other unique challenges that face startups, like yours right now?

 

TN: With the pandemic, we’ve seen clients be very, very risk-averse. The the risk of taking on a new small company as a vendor is a problem for major companies. They’re trying to figure out how to adjust their business to an uncertain environment. For us, partnering with Oracle has helped to de-risk that decision for major companies. Oracle says Complete Intelligence has a viable solution, let’s talk about how we can help you. And the credibility that Oracle has when we go into a client is really really important for that situation.

 

WD: Aligning with a credible brand that’s been around for 40 years like Oracle is absolutely something that a startup can use to hack their growth. I’m curious about your use of Oracle Cloud and solutions that are open source Cloud native like Kubernetes. Can you talk a little bit about how you work with those Cloud Native Solutions?

 

TN: Kubernetes is a great one where our solution is containerized. We throw it onto Oracle Cloud and we can use it with clients. So, whether it’s the database we use, whether it’s the scheduling languages we use, whether it’s containerization, all of that is flexible on Oracle Cloud. And we can use the open source infrastructure that we have within our specific configuration on Oracle Cloud.

 

Over the last year, OCI has changed a lot in terms of enabling some of the very specific solutions that we’ve had. And very kind of high performance computing solutions that we’ve needed. Accommodation has really given us a lot of confidence with OCI.

 

WD: Your startup has had a pretty unique trajectory. You started the company in Asia and now you’re based in Houston, Texas. What inspired such a significant change?

 

TN: I guess the biggest thought behind there, is this is where the customers are. And to be honest this is where the talent is. The people who are doing the leading edge work in what we’re focused on are here. And the context around manufacturing and the need to automate some of the decisions around manufacturing really are happening in the U.S. and Europe, in a big way.

 

Of course that’s happening in Asia but it’s different in Asia. I spent 15 years in Asia. We conceived of and started Complete Intelligence there but we really utilized as much as we could there. And I came to a point where we just had to move the company to the U.S. to find the resources we need to build the company.

 

It’s been great moving to Texas, has been great. It’s a fantastic business environment. The manufacturing clients here are fantastic. Oil and gas is seeing a lot of headwinds right now which is a real opportunity for us.

 

WD: So the forecast is looking bright for Complete Intelligence?

 

TN: Oh absolutely. Again, with the right partners, we can move into the right clients and any startup trying to go it alone today is going to have a really hard time. It’s possible and it’s probable with the right amount of work put in, but building the right partnerships like our partnership with Oracle has been huge in helping us to accelerate our commercialization and our presence in the market.

 

WD: Absolutely and I know that if startups want to learn more about working with Oracle they can go to oracle.com/startup. If they want to learn more about the exciting work that Complete Intelligence is doing, where should they go?

 

TN: They can go to completeintel.com. We’ve got all of the resources there. We have a weekly newsletter. We have regular video interviews with industry experts, similar to what you’re doing. There are a lot of resources. Our twitter feed is complete_intel as well, there’s a lot there.

 

WD: Great, any secret market intelligence you want to share with our viewers?

 

TN: The changes we’ve seen over 2020 and the risk and volatility we’ve seen over 2020, unfortunately we don’t see a return to normal soon. The challenges that we’ve faced as startups and the challenges that our customers have faced in 2020 aren’t necessarily going away. This type of up and down environments and the persistence that we’ve had to have as startups, 2021 is not going to bring a normal back. We’ll see a little bit more, but as startups we’re going to have to continue to push very, very hard to get the mindshare within those endpoints.

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Podcasts

Behold the Power of Superforecasting

This podcast first appeared and originally published at https://soundcloud.com/user-454088293/behold-the-power-of-superforecasting on August 26, 2020.

 

In just 2 minutes, you’ll learn why superforecasting is so much better than forecasting. Hear how automated, data-intensive AI with no human bias can help make predictions and adjust strategy on the fly, and how startup Complete Intelligence is making it happen.

 

Is forecasting enough when you need to analyze and take action? Meet the startup that says “no.” What’s needed is superforecasting.

 

Hi, it’s Mike Stiles, and this is Meet the Startups for the week of August 26th, brought to you by Oracle for Startups.

 

How can you be happy with forecasting when there’s something out there called superforecasting?

 

Startup founder Tony Nash and his company, Complete Intelligence are making super forecasting possible with a highly automated, data intensive A.I. solution.

 

Part of what makes it so SUPER is there’s zero human bias. No spin or wishful thinking allowed.

 

Complete intelligence is helping organizations visualize financial data, make predictions and adjust strategy on the fly. That gets you things like smarter purchasing, better supply chain planning, smarter cost and revenue decisions.

 

But it’s intense.

 

More than 15 billion data points are run on Complete Intelligence’s platform every day.

 

To get where they needed to be on performance and price, the company moved from Google Cloud to Oracle Cloud. That did it. Computing is at peak performance and Complete Intelligence’s global customers are reaping the benefits. That’s super.

 

We asked Complete IntelligenceCEO Tony Nash what this pandemic has done to forecasting and supply chains.

 

We’ve seen a big shift in how managers are looking at their supply chains. As a result of Covid-19, companies are eager to understand their cost and revenue risks, things like concentration risk and the timing of their cost, that sort of thing. We’re helping our customers with timely and accurate information to make smarter cost and better revenue planning decisions.“

 

What startup doesn’t like better performance and lower costs? Oracle has a startup partnership for you at Oracle.com/startup.

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Podcasts

Stories from the Cloud: The Forecast Calls For…

Tony Nash joins veteran journalists Michael Hickins and Barbara Darrow at the Stories from the Cloud podcast to talk about the forecast calls for businesses, and how AI and machine learning can help in predicting the futures in budget forecasting. How does his company Complete Intelligence dramatically improve forecast accuracy of companies suffering from a huge 30% error rate. He also explained the AI technology behind the CI solutions and strategic toolkit, and how this practically applies to global companies. How can they benefit from this new technology to better be prepared in their budget planning and reducing risks in costs?

 

Stories from the Cloud description:

It’s not easy to predict the future. But when it comes to business and cash or financial forecasting tool or software, the right data and the right models are better than any crystal ball.

 

Tony Nash, CEO and founder of Complete Intelligence, explains how AI and the cloud are giving companies better cash forecasting software tools to see into their financial futures.

 

About Stories from the Cloud: Enterprises worldwide are turning to the cloud to help them thrive in an ever-more-competitive environment. In this podcast, veteran journalists Michael Hickins and Barbara Darrow chat with the people behind this massive digital transformation and the effects it has on their work and lives.

 

Show Notes

 

SFC: Hey, everybody, welcome back to Stories from the Cloud sponsored by Oracle. This week, I am here, as always, with Michael Hickins, formerly of The Wall Street Journal. I am Barbara Darrow. And our special guest today is Tony Nash. He’s the founder and CEO of Complete Intelligence. And this is a very interesting company. Tony, thanks for joining us. And can you just tell us a little bit about what the problem is that Complete Intelligence is attacking and who are your typical customers?

 

Tony: Sure. The problem we’re attacking is just really bad forecasting, really bad budget setting, really bad expectation setting within an enterprise environment. Companies have packed away data for the last 15, 20 years, but they’re not really using it effectively. We help people get very precise, very accurate views on costs and revenues over the next 12 to twenty four months so they can plan more precisely and tactically.

 

SFC: It sounds like a big part of the mission here is to clean up… Everybody talks about how great data is and how valuable it is. But I mean, it sounds like there’s a big problem with a lot of people’s data. And I’m wondering if you could give us an example of a company, let’s just say a car maker and what you can help them do in terms of tracking their past costs and forecasting their future costs.

 

Tony: So a lot of the problem that we see, let’s say, with the big auto manufacturer, is they have long-term supply relationships where prices are set, or they’ve had the same vendor for X number of years and they really don’t know if they’re getting a market cost, or they don’t have visibility into what are those upstream costs from that vendor. And so, we take data directly from their ERP system or their supply chain system or e-procurement system and we come up with very specific cost outlooks.

 

We do the same on the sales revenue side. But say for an automaker, a very specific cost outlook for the components and the elements that make up specific products. So we’ll do a bill of material level forecast for people so that they can understand where the cost for that specific product is going.

 

Before I started Complete Intelligence, I ran research for a company called The Economist and I ran Asia consulting for a company called IHS Markit. And my clients would come to me and say, there are two issues at both companies. Two issues. First is the business and financial forecasting tool or even strategic toolkit that people buy off the shelf has a high error rate. The second issue is the forecasts don’t have the level of context and specificity needed for people to actually make decisions. So what do you get? You get very generic data with imprecise forecasts coming in and then you get people building spreadsheets and exclusive models or specific models within even different departments and teams and everything within a company.

 

So there are very inconsistent ways of looking at the world. And so we provide people with a very consistent way and a very low error way of looking at the future trajectory of those costs and of those revenues.

 

SFC: So I’m curious, what is the what is the psychology of better business forecasting software? So on your customers and I’m thinking, if I’m a consumer, so this is maybe not a good analogy, but if I’m a consumer and I look at the actual costs are of a phone that I may have in my pocket, I may think, jeez, why making a thousand dollars for this? But then part of me says, such things mark up and well, I guess so. There are uncertainties in financial projections. So on me, I mean, I don’t need a financial projection software to tell me that the components of the pocket computer have don’t add up to what I paid for them. But I kind of understand that there needs to be money made along the way. I just I want it. Right. How does that translate on a B2B perspective? What are the people’s attitude about price and how do they react to the data that, as you said, I mean, heretofore, it’s kind of been unreliable.And all of a sudden, I think you say a lot of procurement projections have been around 30 percent, which is huge. Right. So how does that happen and how do people react to something that seems more trustworthy?

 

Tony: Well, I think that expectations depend on the level within a manufacturing company that you’re talking to. I think the more senior level somebody is, of course, they want predictability and quality within their supply chain, but they’re also responsible to investors and clients for both quality and cost. And so at a senior level, they would love to be able to take a very data driven approach to what’s going on. The lower you get within a manufacturing organization, this is where some of the softer factors start to come in. It’s also where a lot of the questionable models are put in as well.

 

Very few companies that we talk to actually monitor their internal error rates for their cost and revenue outlooks. So they’ll have a cost business forecasting software model or a revenue forecasting model that they rely on because they’ve used it for a long period of time, but they rarely, if ever, go back and look at the error rates that that model puts out. Because what’s happening is they’re manually adjusting data along the way. They’re not really looking at the model output except for that one time of the year that they’re doing their budget.

 

So there really isn’t accountability for the fairly rudimentary models that manufacturing companies are using today. What we do is we tell on ourselves. We give our clients our error rates every month because we know that no no business forecasting software model is perfect. So we want our clients to know what the error rate is so that they can understand within their decision making processes.

 

SFC: And it’s kind like a margin of error in a political poll?

 

Tony: Yeah, we use what’s called MAPE – mean absolute percent error. Most error calculations. You can game the pluses and minuses. So let’s say you were 10 percent off, 10 percent over last month and 12 percent under this month. OK. If you average those out, that’s one percent error. But if you look at that on an absolute percent error basis, that’s 11 percent error. So we gauge our error on an absolute percent error basis because it doesn’t matter if you’re over under, it’s still error.

 

SFC: Still wrong, right.

 

Tony: Yeah. So we tell on ourselves, to our clients because we’re accountable. We need to model the behavior that we see that those senior executives have with their investors and with their customers, right? An investment banking analyst doesn’t really care that it was a plus and a minus. They just care that it was wrong. And they’re going to hold those shares, that company accountable and they’re going to punish them in public markets.

 

So we want to give those executives much better data to make decisions, more precise decisions with lower error rates so they can get their budgeting right, so they can have the right cash set aside to do their transactions through the year, so they can work with demand plans and put our costs against their say volume, demand plans, those sorts of things.

 

SFC: I have to just ask I mean, Michael alluded to this earlier, but I want to dive into a little more. You had said somewhere else that most companies procurement projections are off by 30 percent. That’s a lot. I mean, I know people aren’t… I mean, how is that even possible?

 

Tony: It’s not a number that we’ve come up with. So first, I need to be clear that that’s not a number that we’ve come up with and that’s not a number that’s published anywhere. That’s a number that we consistently get as feedback from clients and from companies that we’re pitching. So that 30 percent is not our number. It’s a number that we’re told on a regular basis.

 

SFC: When you start pitching a client, obviously there’s a there’s a period where they’re just sort of doing a proof of concept. How long does that typically last before they go? You know what? This is really accurate. This can really help. Let’s go ahead and put this into production.

 

Tony: Well, I think typically, when we when we hit the right person who’s involved in, let’s say, category management or they actually own a PNL or they’re senior on the FPNA side or they’re digital transformation, those guys tend to get it pretty quickly, actually. And they realize there’s really not stuff out there similar to what we’re doing. But for people who observe it, it probably takes three months. So our pilots typically last three months.

 

And after three months, people see side by side how we’re performing and they’re usually convinced, partly because of the specificity of projection data that we can bring to to the table. Whereas maybe within companies they’re doing a say, a higher level look at things. We’re doing a very much a bottom up assessment of where costs will go from a very technical perspective, the types of databases we’re using, they’re structured in a way that those costs add up.

 

And we forecast at the outermost leaf node of, say, a bill material. So uncertainties in financial projections are solved. A bill Of material may have five or 10 or 50 levels. ut we go out to the outermost kind of item within that material level, and then we add those up as the components and the items stack up within that material. Let’s say it’s a mobile phone, you’ll have a screen, you’ll have internal components. You’ll have the case on the outside. All of this stuff, all of those things are subcomponents of a bill of material for that mobile phone.

 

SFC: So I am assuming that there is a big role here in what you’re doing with artificial intelligence, machine learning. But before we ask what that role is, can you talk about what you mean by those terms? Because we get a lot of different definitions and also differentiations between the two. So maybe talk to the normals here.

 

Tony: OK, so I hear a number of people talk about A.I. and they assume that it’s this thinking machine that does everything on its own and doesn’t need any human interaction. That stuff doesn’t exist. That’s called artificial general intelligence. That does not exist today.

 

It was explained to me a few years ago, and this is probably a bit broader than most people are used to, but artificial intelligence from a very broad technical perspective includes everything from a basic mathematical function on upward. When we get into the machine learning aspect of it, that is automated calculations, let’s say, OK. So automated calculations that a machine recognizes patterns over time and builds awareness based on those previous patterns and implies them on future activities, current or future activity.

 

So when we talk about A.I., we’re talking about learning from previous behavior and we’re talking about zero, and this is a key thing to understand, we have zero human intervention in our process. OK, of course, people are involved in the initial programming, that sort of thing. OK, but let’s say we have a platinum forecast that goes into some component that we’re forecasting out for somebody. We’re we’re not looking at the output of that forecast and go, “Hmmm. That doesn’t really look right to me. So I need to fiddle with it a little bit to make sure that it that it kind of looks right to me.” We don’t do that.

 

We don’t have a room of people sitting in somewhere in the Midwest or South Asia or whatever who manually manipulate stuff at all — from the time we download data, validate data, look for anomalies, process, forecast, all that stuff, and then upload — that entire process for us is automated.

 

When I started the company, what I told the team was, I don’t want people changing the forecast output because if we do that, then when we sit and talk to a client and say, hey, we have a forecast model, but then we go in and change it manually, we’re effectively lying to our customers. We’re saying we have a model, but then we’re just changing it on our own.

 

We want true kind of fidelity to what we’re doing. If we tell people we have an automated process, if we tell people we have a model, we really want the output to be model output without people getting involved.

 

So we’ve had a number of unconventional calls that went pretty far against consensus that the machines brought out that we wouldn’t have necessarily put on our own. And to be very honest, some of them were a little bit embarrassing when we put them out, but they ended up being right.

 

In 2019, the US dollar, if you look at, say, January 2019, the US dollar was supposed to continue to depreciate through the rest of the year. This was the consensus view of every currency forecaster out there. And I was speaking on one of the global finance TV stations telling them about our dollar outlook.

 

And I said, “look, you know, our view is that the dollar will stabilize in April, appreciate in May and accelerate in June.” And a global currency strategist literally laughed at me during that interview and said there’s no way that’s going to happen. In fact, that’s exactly what happened. Just sticking with currencies, and for people in manufacturing, we said that the Chinese Yuan, the CNY, the Renminbi would break seven. And I’m sure your listeners don’t necessarily pay attention to currency markets, but would break seven in July of 19. And actually it did in early August. So that was a very big call, non consensus call that we got months and months ahead of time and it would consistently would bear out within our forecast iterations after that. So we do the same in say metals with things like copper or soy or on the ag side.

 

On a monthly basis, on our base platform, we’re forecasting about 800 different items so people can subscribe just to our data subscription. And if they want to look at ag, commodities, metals, precious metals, whatever it is, equities, currencies, we have that as a baseline package subscription we can look at, people can look at. And that’s where we gauge a lot of our error so that we can tell on ourselves and tell clients where we got things right and where we got things wrong.

 

SFC: You know, if I were a client, I would I would ask, like, OK, is that because you were right and everyone else is wrong? Is that because you had more data sources than anyone else, or is it because of your algorithm or is it maybe because of both?

 

Tony: Yes, that would be my answer. We have over 15 billion items in our core platform. We’re running hundreds of millions of calculations whenever we rerun our forecasts. We can rerun a forecast of the entire global economy, which is every economy, every global trade lane, 200 currency pairs, 120 commodities and so on and so forth. We can do that in about forty seven minutes.

 

If somebody comes to us and says, we want to run a simulation to understand what’s going to happen in the global economy, we can introduce that in and we do these hundreds of millions of calculations very, very quickly. And that is important for us, because if one of our manufacturing clients, let’s say, last September, I don’t know if you remember, there was an attack on a Saudi oil refinery, one of the largest refineries in the world, and crude prices spiked by 18 percent in one day.

 

There were a number of companies who wanted to understand the impact of that crude spike on their cost base. They could come into our platform. They could click, they could tell us that they wanted to rerun their cost basis. And within an hour or two, depending on the size of their catalog, we could rerun their entire cost base for their business.

 

SFC: By the way, how dare you imply that our listeners are not forex experts attuned to every slight movement, especially there’s no baseball season. What else are we supposed to do? I wanted to ask you: to what extent is the performance of the cloud that you use, you know, important to the speed with which you can provide people with answers?

 

Tony: It’s very important, actually. Not every cloud provider allows every kind of software to work on their cloud. When we look at Oracle Cloud, for example, having the ability to run Kubernetes is a big deal, having the ability to run different types of database software, these sorts of things are a big deal. And so not all of these tools have been available on all of these clouds all the time. So the performance of the cloud, but also the tools that are allowed on these clouds are very, very important for us as we select cloud providers, but also as we deploy on client cloud. We can deploy our, let’s say, our CostFlow solution or our RevenueFlow solution on client clouds for security reasons or whatever. So we can just spin up an instance there as needed. It’s very important that those cloud providers allow the financial forecasting tools that we need to spin up an instant so that those enterprise clients can have the functionality they need.

 

SFC: So now I’m the one who’s going to insult our readers or listeners rather. For those of us who are not fully conversant on why it’s important to allow Kubernetes. Could you elaborate a little bit about that?

 

Tony: Well, for us, it has a lot to do with the scale of data that’s necessary and the intensity of computation that we need. It’s a specific type of strategic toolkit that we need to just get our work done. And it’s widely accepted and it’s one of the tools that we’ve chosen to use. So, for example, if Oracle didn’t allow that software, which actually it is something that Oracle has worked very hard to get online and allow that software to work there. But it is it is just one of the many tools that we use. But it’s a critical tool for us.

 

SFC: With your specialization being around cost, what have you looked at… Is cost relevant to your business and so on cloud? How so?

 

Tony: Yeah, of course it is. For us, it’s the entry cost, but it’s also the running cost for a cloud solution. And so that’s critically important for us. And not all cloud providers are created equally. So so we have to be very, very mindful of that as we deploy on a cloud for our own internal reasons, but also deploy on a client’s cloud because we want to make sure that they’re getting the most cost effective service and the best performance. Obviously, cost is not the only factor. So we need to help them understand that cost performance tradeoff if we’re going to deploy on their cloud.

 

SFC: Do you see this happening across all industries or just ones where, you know, the sort of national security concerns or food concerns, things that are clearly important in the case of some kind of emergency?

 

Tony: I see it happening maybe not across all industries, but across a lot of industries. So the electronics supply chain, for example, there’s been a lot of movement toward Mexico. You know, in 2018, the US imported more televisions from Mexico than from China for the first time in 20 some years. So those electronics supply chains and the increasing sophistication of those supply chains are moving. So that’s not necessarily sensitive electronics for, say, the Pentagon. That’s just a TV. Right. So we’re seeing things like office equipment, other things. You know, if you look at the top ten goods that the US receives from China, four of them are things like furniture and chairs and these sorts of things which can actually be made in other cheaper locations like Bangladesh or Vietnam and so on. Six of them are directly competitive with Mexico. So PCs, telecom equipment, all these other things.

 

So, you know, I actually think that much of what the US imports will be regionalized. Not all of it, of course, and not immediately. But I think there’s a real drive to reduce supply chain risk coming from boards and Coming from executive teams. And so I think we’ll really start to see that gain momentum really kind of toward the end of 2020 and into early 2021.

 

SFC: That is super interesting. Thank you for joining us. We’re kind of up against time, but I want to thank Tony for being on. I want to do a special shout out to Oracle for startups that works with cool companies like Complete Intelligence. Thanks for joining us. Please try to find Stories from the Cloud at on iTunes or wherever you get your podcasts and tune in again. Thanks, everybody.

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How to Make Cloud Pricing More Transparent

This article on “How to Make Cloud Pricing More Transparent” is originally published at https://www.eweek.com/cloud/how-to-make-cloud-pricing-more-transparent

 

eWEEK CLOUD PERSPECTIVE: It used to be nearly impossible to compare cloud costs because different providers typically have their own nomenclature for cloud features, define services differently and offer different tiers of services that don’t line up with one another. Forget apple-to-apple comparisons, cloud price bake-offs were more like contrasting apples to peach cobblers. But help is here.

 

Cloud has inspired almost as much evangelical fervor as open source computing, particularly in the heady 2000s. The advent of cloud computing seemed to render traditional enterprise software vendors as out-of-date as telegraph operators. The monolithic process of releasing software every 18 months wasn’t fast enough for business, running your own servers became as fashionable as generating your own electricity, and the expense involved restricted technology access to the wealthiest businesses.

 

Cloud computing represented a true democratization of enterprise IT, allowing small companies to compete with bigger rivals without breaking the bank to buy servers, storage and software. Tens of millions of dollars for the right to walk onto the playing field were no longer required.

 

The other promise of cloud computing was of a more transparent and equitable business model.

 

In one of my first interviews as an IT reporter, in 2003, I asked the chief technology officer of a large health IT organization to define enterprise software. “It’s when they can’t tell you the price of the software upfront,” he said.

 

Sure, this lack of transparency reflected the complexity of the forecasting applications on offer, but also showed that the dominant sales model gave more power to vendors than customers.

 

The emergence of profitable cloud-native businesses both threatened existing business models and inspired business transformation. The agility and innovation made possible by cloud computing inspired many businesses to move their IT stacks from their own server rooms or data centers to the cloud.

 

 

The law of universal gravitation as applied to the cloud

 

By 2020, however, the low-hanging fruit has been picked. Businesses have reaped the benefits of relatively lower costs and more frequent innovation. And with the lion’s share of IT spending at most companies moving into the cloud, cost – and cost transparency – matters. Yet, the transparency promised by the cloud revolution has largely failed to materialize.

 

As was the case with the previous generation of technology, obfuscation isn’t a bug, it’s a feature, and it begins with Newton’s Law of Universal Gravitation. Pricing structures at legacy cloud providers punish moving data from one cloud to another. By intentionally making the cost of putting data into their clouds as low as possible, while making it prohibitively expensive to move data out to interact with systems in different clouds—a concept known as data gravity—they are walling in their customers.  This is an explicit strategy to make their clouds “sticky” and keep forecasting applications from moving to other clouds.

 

But the reality is that businesses want and need to operate in different cloud environments for many reasons. Not to mention, who wouldn’t want to cut 10, 30, or even 80 percent of cloud costs if possible?

 

 

 

Newton’s law of motion applied to the cloud

 

It used to be nearly impossible to compare cloud costs because different providers typically have their own nomenclature for cloud features, define services differently and offer different tiers of services that don’t line up with one another. Forget apple-to-apple comparisons, cloud price bake-offs were more like contrasting apples to peach cobblers.

 

There is help available. For one example, Oracle Cloud Workload Cost Estimator is a new tool now available for obtaining empirical cost information. It lets customers assess comparative costs of Oracle Cloud Infrastructure and Amazon Web Services in as close to a real apples-to-apples comparison as possible.

 

The calculator prices not only computing and storage costs, but that of IOPS (data input/output per second), and data transmission out of the cloud as well. That last factor, also known as data egress, is usually a wild card because traditional cloud companies start charging a markup after a given amount of data flows out. So once you hit a monthly target—1GB for AWS, according to the cost estimator—data egress charges kick in. At Oracle the meter doesn’t start until after 10,000 times more data egress—or 10 TB—per month.

 

IT leaders can enter the parameters of proposed workloads and then run their own OCI vs. AWS comparisons. In the end, they may discover that one cloud provider offers services that are closer to Newton’s third law (that for every action in nature, there is an equal and opposite reaction) than to his first

 

 

 

A few examples

 

Cost and performance go hand in hand, especially as software-as-a-service providers rely on third parties to serve their software to customers. Data technology firm Complete Intelligence, for instance, provides real-time risk management and forecasting services for its customers. It needs to know how much it will spend providing that service on an ongoing basis, and also be sure that its customers get the responsive service their businesses need.

 

“For us, it’s the entry cost, but it’s also the running cost for a cloud solution. And so that’s critically important for us. And not all cloud providers are created equally,” said Tony Nash, CEO of the Houston-based company, which picked Oracle Cloud Infrastructure.

 

Another example of how modern businesses use the cloud is data integration provider Naveego. The company helps customers parse data from a myriad of sources. It cleans the data, deletes duplicates, provides a trail of sources, and then provides a clean golden record of data that is ready for analytics in real time.

 

“To do that, we run instances of our product in multiple availability zones. AWS charges for communications back and forth between those availability zones. Oracle doesn’t, and the cost difference ended up being huge for us. So, we decided to move our research and development, and some production, cloud tenancies to Oracle Cloud,” wrote Naveego CEO Katie Horvath in a blog post.

 

The company saved 60 percent on its costs since moving to the Oracle cloud, while being able to do more research and development. “Oracle’s claims that Oracle Cloud Infrastructure is 65 percent more cost effective on computers have also proven to be true for Naveego,” she says.

 

We’re starting a new decade on an awkward footing, and businesses need technology to help make smarter decisions. They may still want to fail fast, but they will also want to know what went wrong fast, what the fast road looks like to the promised land – and at long last, what it costs to get there. They’ve long known the cost of sending a telegram, and they can finally figure out the cost of using the cloud.

 

Michael Hickins is a former eWEEK and Wall Street Journal editor and reporter.