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Same-day delivery apps need more than speed to survive post-pandemic



We have entered a whole new era of e-commerce centered on speed and convenience. Business leaders are being forced to prioritize delivery capabilities and push for more accelerated delivery services.

“Fast/reliable delivery” was the most important online shopping attribute among the more than 8,500 consumers queried for PwC’s June 2021 Global Consumer Insights Pulse Survey, making it clear that delivery services will only become more crucial across the e-commerce landscape.

Now that consumers have grown accustomed to same-day (and same-hour) delivery service models, customer expectations for delivery options will only increase.

In fact, according to a recent report from the mobile app intelligence platform SensorTower, the top food delivery apps saw continued growth in January and February 2021, with installs up 14% year over year. And yet, despite climbing user growth, DoorDash, Uber Eats and GrubHub remain unprofitable. So how can business leaders design rapid delivery models that meet consumer expectations — and still make money?

If your delivery service results in a poor customer experience, you’ll be less likely to win customer loyalty just because you offer faster delivery.

The challenge: Delivery apps need more than speed to drive profitability

To remain competitive, delivery apps are rethinking their services and broadening their offerings.

“Amazon powers next-day delivery,” Raj Beri, Uber’s global head of grocery and new verticals, said in May. “We’re going to power next-hour commerce.”

But speeding up the delivery process won’t necessarily drive revenue. More importantly, if your delivery service results in a poor customer experience, you’ll be less likely to win customer loyalty just because you offer faster delivery.

The primary challenge faced by delivery apps, or any e-commerce company looking to add delivery services as part of its offerings, is building a foundation that enables not only speed and convenience for the customer, but one that takes into account all aspects of the customer experience. For example, when delivering food, the business responsible for the delivery must make sure the food is handled safely and remain free of any contaminants. The temperature — whether hot or cold — must be maintained throughout the delivery process and the order itself must be correct.

The solution: Same-day delivery relies on sophisticated technology platforms

The “Uberization” of everything, combined with dramatically elevated consumer expectations, will take much more than a delivery app and fleet of drivers for businesses to be profitable. To follow through on the promise of same-day delivery services, a number of things need to happen without any missteps between when an order is placed and when it shows up at the customer’s door. The more complex the product being delivered, the more difficult the delivery process becomes.

To enable same-day delivery services while also reaching profitability, a delivery app must take into account the technology needed to meet customer expectations. It involves much more than simply designing an app and growing user numbers. A truly successful same-day delivery model that provides an exceptional customer experience relies on a sophisticated software platform that can simultaneously manage various aspects of the customer journey, all while making it appear seamless from the customer’s point of view.

Profitable delivery services are built on automated systems powered by artificial intelligence systems and robotics. The technology must come first, before the app and before user growth. Any other delivery business model is putting the cart before the horse.

Domino’s Pizza is a brand that has perfected the delivery process and vastly improved the overall customer experience by making technology core to their business model. The key moment came when the brand defined itself as an e-commerce company that sells pizza. It committed to data applications and implemented a robotics technology platform that enabled electronic delivery systems that added speed and efficiency to the delivery process. In April, Domino’s began rolling out a robot car delivery service to select customers in Houston via Nuro.

GrubHub is also taking steps to integrate robotic capabilities into its delivery process. According to recent reports, the company announced it would be adding self-driving units that deploy drone-like robots to deliver food to college students. The program, which will roll out on a limited number of U.S. college campuses this fall, aims to reduce delivery times and, hopefully, costs.

This focus on technology is crucial in the world of delivery apps, or for any businesses forced to compete in the newly emerging category of next-hour commerce. The key to building a successful, profitable business model is to invest in technology platforms that can connect all components of the customer journey, from opening an app and clicking on a product to purchasing the product and scheduling the delivery, and beyond.

Same-day delivery: Where to go from here

In a world where everyone wants to open an app on their phone and have whatever it is they need to be delivered within an hour, it’s tempting for business leaders to focus on the delivery app itself, whether they are building their own or partnering with another company. But focusing on the app is a shortsighted view of same-day delivery models.

Instead, business leaders must use a wide-angle lens and consider every single aspect of their customer journey: How do customers engage with their business? How do customers search for and find the products they offer? What does it take to complete an order and what conditions must be met before the order can be delivered? Also, what happens after the order to ensure it went smoothly and to the customer’s satisfaction?

Some businesses are finding success partnering with delivery apps, but this comes with the risk of putting your brand’s reputation in the hands of another company that acts as a frontline employee with customers. Other companies are adding delivery service options to their current e-commerce model, relying on third-party software that can be plugged into an existing technology stack. Unfortunately, this comes with limitations and is not viable for regulated businesses that include multiple components.

The only way to ensure a seamless customer experience on top of same-day delivery services is to build a proprietary software platform that puts the technology at the heart of your business, which allows you to automate key processes, adding speed and convenience to your delivery model. It also makes it possible to integrate robotic systems that can expedite orders, include artificial intelligence protocols that can accelerate business growth, and scale your delivery model as your business expands.

Thriving in the new era of e-commerce

“Next-hour delivery” is a catchy tagline that is sure to gain traction among consumers, but whether it will help drive profitability remains to be seen. As the CEO of a firm that has built a profitable business model centered on same-day delivery services, I’m skeptical that the promise of next-hour delivery will drive more revenue if the technology powering the delivery systems lacks automation, artificial intelligence and robotics.

It’s true that businesses will be forced to compete on same-day delivery. But another truth that has emerged since the pandemic is that this new era of e-commerce comes with heightened customer expectations that won’t be met on speed alone. Consumer satisfaction hinges on more than the amount of time it takes to move an order from an app to the customer’s door.

To succeed in the delivery service market, business leaders must ask themselves a number of questions: Which parts of their business are needed to complete a same-day delivery order? Is the ordering process intuitive? Can the order and delivery be monitored by the customer? Is the order correct when it arrives? Does it meet the customer’s expectations?

And, most importantly, is their business built on a technology platform that can support the entire customer journey and delivery model, from product discovery and purchase to same-day delivery and beyond? The businesses that answer yes to these questions are the ones I expect to thrive in the post-pandemic world.

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Artificial Intelligence

The Ways Machine Learning Companies Can Redefine Insurance



Most insurance companies tend to process only a small part of their data — around 10 to 15%. The rest of the data in their databases are not being processed adequately, meaning that they are probably missing on insights in the data they keep but never analyze.

However, in order to analyze the unstructured data that will help you bring on better business decisions and prevent intruder attacks, the use of advanced technology is needed. Machine learning comes to the scene here because it is able to analyze lots of structured, semi-structured, or completely unstructured data the insurance companies tend to store in their databases.

The benefits of machine learning are numerous:

● Understanding risk

● Understanding premium leakage

● Managing expenses

● Subrogation

● Litigation

● Fraud detection

Since insurance companies deal with a lot of sensitive data and assets, they need to have an efficient way of finding any fraudulent activities and preventing them. This will increase their trustworthiness in the eyes of current and potential clients.

Stick with us while we explain the possible challenges when it comes to machine learning before we jump to explaining how machine learning companies can be of use for insurance services providers.

Challenges of Applying Machine Learning

Just like any other new thing you are trying to apply and implement for the first time, machine learning also brings some specific challenges. The most important ones are listed and explained down below.

Every system needs to be trained and fed with data that stimulate and support various scenarios. But since it is impossible to cover every single scenario, it leads to the system having certain unavoidable loopholes.

For example, if the insurers are looking for an AI-powered system to implement in billing, it will require them to have a separate training system. This is where the issue comes up — you need to provide the aforementioned data in order to train the AI system, and sometimes that is not physically possible.

Data sources

In machine learning, the quantity of data you provide will play a great role in training the AI system. The more data you feed into the system, the better predictive models can be created. However, let’s not disregard the fact that not only the quantity but quality of data is also very important.

If you feed the system with bad data, the predictive models will not be of any value. The sources of the data need to be representative and relevant, to avoid any bias in the future.

One of the biggest challenges with machine learning is that it can be very hard to predict and calculate the expected ROI (return on investment). This happens because machine learning is a continuous process, so if you dig up some findings at the early stage of the project and calculate the budget you’ll need, this may not be relevant at later stages of the project.

This is because there might be some new findings in the process that will request additional funding. These new findings may influence the ROI.

Pros of Machine Learning

After explaining the potential challenges when it comes to machine learning, it is time to explain the pros of applying machine learning in insurance processes. Here are some of the areas where machine learning is being used in insurance:

Lapse management – Machine learning plays a great role in finding out what policies in insurance are very likely to lapse, so it helps to identify them and find a way to prevent them from lapsing.

Recommendation tool – Machine learning can analyze all the individual insurances and automatically provide the best one for the given situation.

Property analysis – If you are using machine learning in property insurance, you can utilize it to identify the areas that will potentially need maintenance. You can also use AI to schedule any maintenance in the future.

Fraud detection – Probably one of the biggest pros of machine learning and the reason why most insurance companies want to use AI. Fraud detection and prevention play a vital role in insurance due to the fact that insurance companies deal with a lot of personal data.

Personalization – AI can be used to create personalized offers for policyholders. This can improve customers’ experience because the offer will be based on their past history with the insurance provider, so it will be customized to their habits and possibilities.

Prediction – Machine learning can be used for various statistical purposes, like predicting certain types of behavior in the future. You can use it to create models regarding prices, budgeting, risks, etc. The possibilities are really endless.

As you can see, machine learning is used not only for fraud detection and underwriting — there are so many other useful features machine learning is being used for in insurance.

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Artificial Intelligence

5 fintech startups that made a splash at FinovateFall



Fintech startups are increasingly leaning on automation and artificial intelligence (AI) to develop new technologies for financial services institutions as lenders look to increase efficiencies across their organizations. Thirty-five fintech startups demonstrated their budding technology at FinovateFall on Tuesday in New York. The Auto Finance News editorial team compiled five that made an impression during […]

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Longtime VC, and happy Miami transplant, David Blumberg has a new $225 million fund



Blumberg Capital, founded in 1991 by investor David Blumberg, has just closed its fifth early-stage venture fund with $225 million, a vehicle that Blumberg says was oversubscribed — he planned to raise $200 million — and that has already been used to invest in 16 startups around the world (the firm has small offices in San Francisco, New York, Tel Aviv and Miami, where Blumberg moved his family last year).

We caught up with him earlier this week to talk shop and he sounded almost ecstatic about the current market, which has evidently been good for returns, with Blumberg Capital’s biggest hits tied to Nutanix (it claims a 68x return), DoubleVerify (a 98x return at IPO in April, the firm says), Katapult (which went public via SPAC in July), Addepar (currently valued above $2 billion) and Braze (it submitted its S-1 in June).

We also talked a bit about his new life in Florida, which he was quick to note is “not a clone of Silicon Valley.” Not last, he told us why he thinks we’re in a “golden era of applying intelligence to every business,” from mining to the business of athletic performance.

More from our conversation, edited lightly for length and clarity, follows:

TC: What are you funding right now?

DB: Our last 30 to 40 deals have basically been about big data that’s been analyzed by artificial intelligence of some sort, then riding in a better wrapper of software process automation on rails of internet and mobility. Okay, that’s a lot of buzzwords.

TC: Yes.

DB: What I’m saying is that this ability to take raw information data that’s either been sitting around and not analyzed, or from new sources of data like sensors or social media or many other places, then analyze it and take it to all these businesses that have been there forever, is beginning to [have] incremental [impacts] that may sound small [but add up].

One of our [unannounced] companies applies AI to mining — lithium mining and gold and copper — so miners don’t waste their time before finding the richest vein of deposit. We partner with mining owners and we bring extra data that they don’t have access to — some is proprietary, some is public — and because we’re experts at the AI modeling of it, we can apply it to their geography and geology, and as part of the business model, we take part of the mine in return.

TC: So your fund now owns not just equity but part of a mine?

DB: This is evidently done a lot in what’s called E&P, exploration and production, in the oil and gas industry, and we’re just following a time-tested model, where some of the service providers put in value and take out a share. So as we see it, it aligns our interests and the better we do for them, the better they do.

TC: This fund is around the same size of your fourth fund, which closed with $207 million in 2017. How do you think about check sizes in this market?

DB: We write checks of $1 million to $6 million generally. We could go down a little bit for something in a seed where we can’t get more of a slice, but we like to have large ownership up front. We found that to have a fund return at least 3x — and our funds seem to be returning much more than that — [we need to be math-minded about things].

We have 36 companies in our portfolio typically, and 20% of them fail, 20% of them are our superstars and 60% are kind of medium. Of those superstars, six of them have to return $100 million each in a $200 million fund to make it a $600 million return, and to get six companies to [produce a] $100 million [for us] they have to reach a billion dollars in value, where we own 10% at the end.

TC You’re buying 10% and maintaining your pro rata or this is after being diluted over numerous rounds?

DB: It’s more like we want 15% to 20% of a company and it gets [diluted] down to 10%. And it’s been working. Some of our funds are way above that number.

TC: Are all four of your earlier funds in the black?

DB: Yes. I love to say this: We have never, ever lost money for our fund investors.

TC: You were among a handful of VCs who were cited quite a lot last year for hightailing it out of the Bay Area for Miami. One year into the move, how is it going?

DB: It is not a clone of Silicon Valley. They are different and add value each in their own way. But Florida is a great place for our family to be and I find for our business, it’s going to be great as well. I can be on the phone to Israel and New York without any time zone-related problems. Some of our companies are moving here, including one from Israel recently, one from San Francisco and one from Texas. A lot of our LPs are moving here or live here already. We can also get up and down to South America for distribution deals more easily.

If we need to get to California or New York, airplanes still work, too, so it hasn’t been a negative at all. I’m going to a JPMorgan event tonight for a bunch of tech founders where there should be 150 people.

TC: That sounds great, though how did you feel about summer in Miami?

DB: We were in France.

Pictured above, from left to right: Firm founder David Blumberg, managing director Yodfat Harel Buchris, COO Steve Gillan and managing director Bruce Taragin.

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Comparing SD-WAN and MPLS for IoT Applications



The requirements of business networks have changed mightily since the dot com era. Back then, company networks were primarily concerned with data communications and workstation connectivity. Today, businesses make more demands on network bandwidth than ever before.

Fifteen years ago, most businesses relied on the public switched telephone network (PSTN) to make calls. Now, much of this traffic has migrated to Voice over Internet Protocol (VoIP) applications. More recently, IoT has irrevocably transformed network communications as businesses integrate IoT devices into their networks.

As we know, IoT devices come in all shapes and sizes. From cameras to radio frequency identification (RFID) chips to digital signage, IoT devices bolster productivity and improve communications. But network designers and administrators find themselves scrambling to accommodate these devices on networks not built for IoT applications. Look no further than IPv4 exhaustion.

IoT endpoints can be highly interactive (viz., smart security systems) or completely passive (viz., RFID chips). Your company’s networks must accommodate not only host both IoT and NoT (non-IoT) devices but various IoT technologies including Bluetooth, Zigbee, and Near-field Communication (NFC).

Likely, your company utilizes either software-defined wide area network (SD-WAN) or Multiprotocol Label Switching (MPLS) for business communications. How do these architectures benefit IoT applications? Let’s dive into the debate of SD-WAN vs MPLS for IoT applications. 

MPLS Versus SD-WAN for IoT

To be clear, SD-WAN is a software-delivered strategy providing multi-cloud connectivity to users. MPLS, on the other hand, utilizes routing tables and label switch routers (LSR). MPLS is a well-established technology, introduced in the 1990s. SD-WAN is a relatively new approach, rapidly superseding MPLS because of its (typically) lower cost and intrinsic flexibility.

Cisco originated MPLS, handing off the technology to the Internet Engineering Task Force (IETF) for open-source standardization. MPLS is a hardware-based solution, dependent on very large-scale integration (VLSI) circuits. SD-WAN, as its name implies, uses software to route traffic over Internet broadband. SD-WAN is highly adaptable to a multi-cloud environment; MPLS is not.

SD-WAN has been described as “a backbone for IoT deployments.” Conversely, MPLS — widely deployed by telcos — is “ill-equipped” to serve the breadth of endpoints over an IoT system. Let’s see why network architects prefer SD-WAN over MPLS for IoT.

IoT Considerations When Comparing MPLS and SD-WAN

While carrier-based MPLS is a tried-and-true technology with an outstanding quality of service (QoS), interconnectivity is limited to a private dedicated network. Once a business expands beyond the MPLS footprint of the carrier, costs, scalability, and deployment time become untenable. Imagine the cost of connecting to every IoT device in your network using private MPLS circuits.

SD-WAN, as mentioned, connects to IoT endpoints using the public Internet. Internet broadband is much more cost-effective than carrier VLSI circuits. Moreover, SD-WAN is a cloud-based solution addressing software as a service (SaaS) connectivity. MPLS does not work in a multi-cloud environment without backhauling, impacting QoS considerations like latency, jitter, and packet loss.

Happily, for enterprises deeply invested in MPLS, many SD-WAN solutions allow for connectivity over MPLS while diverting less sensitive traffic to the Internet. Thus, businesses can maintain service level agreement (SLA) guarantees for prioritized data while utilizing SD-WAN for non-time reliant or broadband (viz., 25 Mbps upstream/3 Mbps down) IoT applications.

When assessing a network’s suitability for IoT devices, network architects evaluate the following:

  • How many IoT endpoints will be deployed?
  • Is the endpoint mobile or fixed?
  • Does the network control activity at the endpoint or is the device passive?
  • What security protocols does the endpoint require?
  • Does the device generate data requiring real-time analysis?
  • Is the device battery-powered or does it require external power (viz., PoE; Power over Ethernet)?
  • What are the IoT network reliability requirements?
  • Do IoT devices need application customization or configuration?
  • What are endpoint data and traffic management and monitoring requirements?
  • How much does large-scale IoT connectivity cost? 

While some IoT devices have specific upstream connectivity needs (viz., video surveillance), many IoT use cases require only infrequent connectivity with relatively small bandwidth. In such non-mission-critical cases, deployment of Low Power Wide Area Networks (LPWAN) or NarrowBand IoT (NB-IoT) is appropriate.

In some applications, standardized cellular technologies can be used for connectivity. However, past mobile solutions relied on obsolete 3G network protocols. As we know, mobile network operators (MNOs) are rapidly decommissioning their 3G networks to make room for LTE and 5G.

Thus, future mobile interoperability using 3G standards will soon end. For networks requiring mobile capabilities and low data throughput, options include long-range WAN (LoRaWAN), DASH7, and the proprietary Sigfox. But these technologies do not address the connectivity needs of data-intensive computing applications generating datasets from multiple locations and in various forms.

So, to state the obvious, a company’s unique needs — reliability, latency, security, data management — dictate how IoT is administered over business networks.

Is SD-WAN or MPLS Better for IoT?

Undoubtedly, SD-WAN is the choice for most businesses integrating IoT endpoints into their company’s networks. A crucial consideration for choosing SD-WAN for IoT is that it allows network administrators to centralize access, enabling them to manage all connected endpoints — including BYOD devices — from one location.

For those wondering about the network security of SD-WANs and the cloud, new technologies have emerged to address this concern. Next-generation firewalls (NGFWs) and secure access service edge (SASE) architecture allow network administrators granular visibility to SD-WANs. This decentralized approach to network security uses software upgrades, not new hardware, to lower cybersecurity costs.

Those interested in an SD-WAN solution for their business needs can choose from multiple vendors. They should make their selection(s) based on their IoT application requirements and performance criteria (viz., reliability, network capacity, data rates, payload sizes, etc.) An SD-WAN solution avoids both vendor- and carrier- lock-ins, making it a versatile option for IoT.

Also Read How IoT is Linked With Amazon Echo and Google Home

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