Category: Latest Trends (page 1 of 1)

Innovation in Retail from China and around the Globe

Retail is an amazing area for tech innovation: AI, Big Data, IoT, Drones, Autonomous Delivery Vehicles, Social Media, Influencers…It’s all there.

This week we had a very interesting retail industry session on the latest trends and innovations by Noah Herschman, our Online and Offline Retail Innovation Leader, and as we are working with many ISV partners developing solutions for retail customers, I thought I’d share my notes.

#1 Complex to Simple

The first trend is that retail per category goes from being very complex to become very simple. There are several brands capitalizing on this trend. As an example, if you search Amazon for mattresses you will get 30.000 results but Casper has simplified this to only 4 types, making it much easier for the customer to go through the different options.

Casper capitalizes on the retail category simplification trend. Only 4 choices, with easy-to-understand differences.

The same goes with shoes or with Harry’s for razors. Harry’s decided that they are not going to compete in the game of “more blades are better”, standardized on 4 blades and instead tried to offer a subscription service for shaving.

#2 Private Labels vs Direct to Consumer (D2C)

The store brands are growing; The private label brands of retailers are growing.
At the same time brands are increasingly selling directly to consumers. As an example, Nike is selling online directly to consumers and it even has products on its website which are not available through their channel/resellers. No one can predict where this will settle but at its extreme, we could see in the future well-established brands selling only D2C through their websites, to their customer base.

#3 Gamification Trend

Retail is trying to figure out how to use fun stuff and games to increase customer engagement and sales. Two examples in the gamification trends are Drest and Pinduoduo.

Drest, combines gaming with e-commerce. It allows its users to play the game of being a pro stylist via virtual goods of 200 luxury brands, including Gucci and Bottega Veneta. Users can purchase any of these items at the end. This trend is called RVR, which stands for Real Goods turned into Virtual and then purchased in Reality and you can read more about it here.

With Drest, digital clothing is one step closer to mainstream | Vogue  Business
Drest allows users to become pro-stylists using virtual luxury brand items.

Pinduoduo is the second biggest marketplace in China after Alibaba. Pinduoduo has created a game that helped the platform to sell agricultural products during the pandemic. You can play the Duoduo orchard game and have the chance to win 1kg of fruits.

The Pinduoduo food game lets you grow plants and gives you the opportunity to get free food in return.

#4 Visual AI Use Cases

Another key trend in retail, is visual search, with the use of AI technology. As an example, Syte, from Israel, has developed a Visual search solution.
It can find all the fashion products in a picture and the user can then purchase them online. Syte is focusing on home accessories and fashion items; see how it works here.

Syte’s visual search AI tool can discover all the fashion items from a photo.

Apparel Sizing, is a key focus area for innovative solutions, as in fashion there is a 35% return rate due to improper fitting. Here you can find several startups trying to solve this problem, such as Presize, Virtusize, and Bigthinx.

Facial recognition: The innovation in facial recognition for retail focuses on finding the buyer sentiment, finding shoplifters, creating a better checkout process. As an example, in China, there is a “Smile to Pay” solution, in which you just have to look in front of a camera to pay for your order in a retail shop, as your face is linked to your bank account.

KFC in China tests letting people pay by smiling - The Verge
KFC in China using facial recognition technology to let customers pay. [Image source]

Inventory tracking: There are solutions that let you use the phone to scan barcodes and find which products to restock in your store, prices, etc.

#5 Unstaffed Stores

In China, the unstaffed stores sector is taking off with China’s autonomous retail sector estimated to grow to 7.1 billion USD this year. There are mainly three categories of these: unstaffed shelves, vending machines, and unstaffed stores.

An example here is the startup F5 Future store, which raised 14.5M USD to build fully autonomous convenience stores in China. These stores are open 24×7 and are operated entirely by robotic arms.

An F5 future store in China
A robotic arm delivering food in an F5 Future store in China.

I would also add here the autonomous delivery vehicles, that have been used in China, especially since the Covid start, such as the Meituan’s one.

China's e-commerce giants deploy robots to deliver orders amid coronavirus  outbreak | South China Morning Post
Meituan’s autonomous delivery vehicle (Image Source:SCMP)

#6 Innovative Loyalty Programs

In retail, there are multiple types of Rich Customer data that can be collected:

  • Personally identifiable information (PII)
  • Demographic information (gender/age/income/ethnicity/wealth)
  • Psychographic information (hobbies/interests/music/movies)
  • Shopping Behaviors (categories, frequency, spend, paring)
  • Connections (social media connections of customers)

The two big problems here are:
How do you get this data? how do you convince customers to hand over their PII data?
– How do you entice customers to read your marketing messages (email/SMS/Viber/FB/WhatsApp etc)?

The 8 Types of Loyalty Programs

There are eight kinds of loyalty programs:

  1. Points Programs: E.g., the banks’ loyalty points program that give you 1 point for 1 USD spent.
  2. Tiering Programs: E.g., the airline companies loyalty programs with gold/silver cards for flyers.
  3. Charity Programs: Every time you purchase, a certain amount goes to a charity.
  4. Coalition Programs: Different partners under the same loyalty scheme,
  5. Game/Gamification Programs: E.g. the Pinduoduo’s game example above.
  6. Paid Programs (e.g. the Amazon Prime)
  7. Hybrid Programs (they offer multiple types of benefits, e.g. a 2% on gas, a 5% discount at a hotel, etc)
  8. Value-Added Programs (like Amazon Prime which offers free shipping but no discount deals, or the Nike program [see below])
  9. No loyalty programs: E.g., Apple and Microsoft don’t offer loyalty programs

The truth is that loyalty programs are saturated, so you really need to stand out to make an impact on your business. In the US, 14.8% are using loyalty memberships and 6.7% are active members.

Some interesting examples of loyalty programs approaches are the following:

Nike offers zero discounting. Instead, they have created a value-added loyalty program, the NIke Plus Membership, which offers: Free shipping, events, audio-guided runs, home workouts, training plans, and other perks they are giving for free. Nike tries to add value to its customers without discounting its products.

The Nike membership adds value to its customers without product discounts

2.Taylor Stich
Another interesting approach to creating loyalty, the crowd-sourcing loyalty. Taylor Stitch is doing crowd-funding for some of its new products and pre-funds various clothing styles.

Taylor Stitch crowdfunds some of its new product designs.

Klarna has launched a “Buy now, Pay Later“, loyalty program, which tries to undercut the high credit card interest fees for shoppers.

The Buy-Now-Pay-Later loyalty program of Klarna, called Vibe

4.Hong Kong MTR: (metro station of Hong Kong)
This is a loyalty system that Microsoft Consulting Services have built for the metro station of Hong Kong. It is a points program that covers transportation, the shopping malls that exist underway, the in-station shopping experience, and others. As an example, you can get a 30% discount at a Starbucks coffee, while waiting for your next train to arrive.

The MTR mobile loyalty program in Hong Kong

You can also watch the following video for a preview of how the MTR mobile works:

The MTR mobile loyalty program

#8 KOL – Online Influencers

KOL stands for key opinion leader, and it is mostly a term used in China. The term refers to the youtube/live-streaming/social media influencers who direct consumer behavior and sales.

The influencers in the US are driving/influencing around 10 billion USD of sales, while in China this is much higher, at around 340 billion USD until 2022. Alibaba has even a KOL platform, which lets influencers connect with brands.

Alibaba’s KOL platform connects influencers with Chinese brands

An example is the 11.11 global shopping festival. According to Forbes, live streaming drove 6 Billion USD in sales during the festival. 300 million Taobao users watched live streams.

Viya, is the most famous e-commerce influencer, and according to Bloomberg, the e-commerce live streaming queen.

Another top influencer is Austin Li. Here is what Forbes has to say for Viya’s and Austin Li’s performance during the double eleven shopping period.

A chart with 11.11 pre-sale results for Alibaba’s two top live streamers.
Austin Li drove sales of 100 million USD in one day through live streaming. Source: Forbes

The top KOLs operate multi-employee businesses to achieve these results. Have a look at the crew of Viya:

Simsim, is an example of a video e-commerce platform coming from India. Simsim sells fashion, beauty, and electronics products as well as health and wellness items. Simsim works with influencers known as community opinion leaders.

Fanjoy, is an e-commerce platform only driven by influencers. At Fanjoy, the influencers can create products based on their real-life experiences, interests, and expertise. E.g. fashion items, toys, and DIY items. Then, they send them as a subscription, surprise box to the customers.

Fanjoy lets influencers sell and send surprise boxes to their followers

Yunji, is another example of this area, coming from China. It has a business model that lies somewhere in between Costco’s and Amway’s.

#9 Farm-to-Table Supply Chain

The farm-to-table innovation trend in retail tries to solve the problem of how to get fresh food on the tables of consumers, with the use of technology.

Freshippo, the grocery chain store of Alibaba, is an example in China. You can trace all the history of the fresh products. You scan the barcode and you know the farm that the food was produced at, when it is harvested, the reputation of the farmer, and other info. Here is a video:

Pinduoduo is also an example. Pinduoduo digitalizes the agricultural products supply chain in various ways, such as visual AR for growing plants, or presell the products before they are planted.

Jutudi, in China, is a Farm-to-fridge service. It allows consumers to buy a plot of farmland and have the crops shipped to them after the harvest.

#10 Last-mile innovation

A key innovation trend here are the autonomous robots for delivery. E.g., Suning has autonomous delivery robots that can run for 10 hours, go door to door and deliver products. has a drone delivery service. delivers books using drones in Indonesia during pilot run -  FreightWaves

Kroger and Walmart are testing self-driving robots.

Nuro Kroger self-driving delivery grocery
Kroger’s self-driving robot delivers groceries in Arizona.

CVS tested prescription delivery in Houston.

Nuro's Delivery Bots Adding CVS Prescriptions To Grocery And Pizza Runs
Image Source: Forbes

Smart Lockers

Albertsons has launched two different takes on unattended pickup: A temperature-controlled locker pickup service and a robotic pickup kiosk.

A pickup kiosk from Albertson. Image Source:
Albertsons Pilots Temperature-Controlled Lockers to Expand Pickup Options
Albertons’ pickup temperature-controlled lockers

Visibility Tools for the last-mile delivery

The idea here is to track where your product is, at its last stage of delivery. There are several companies offering a visual map with the location of the delivery driver. FedEx, has launched the Fedex Insight web tool to allow delivery tracking. And Pitney Bowes does the same for parcel tracking.

#11 Big Data and Trend Prediction

Retailers have been gathering a huge amount of data. Just a small sampling of available retail data from the past 20 years: Product 360/customer 360/store 360/employee 360/supplier 360

Now, retailers gather even more data: structured/unstructured data, internal data ( pos transaction data, customer PII, website clickstream, promotions, and elasticity), and external data (online image data, market research, brand data, social media data, in-store image data.

With the new technologies and innovations, you can put all of these data on a datalake and find trends, predictions, and correlations. Potential outputs could be used for demand forecasting, private brand design, etc.

Another interesting use of data is the Social media photo scraping and classification. As an example, you can find what products influencers are using, and with AI image classification you can predict some trends. E.g., what is the next trendy color?

Actually, Microsoft has implemented such a project for a top FMCG brand worldwide, to predict the shapes in lipsticks in various countries of the world.

Service as a Software Business Model

You probably know the Software as a Service business model (SaaS). But there is a new kid in town! The Service as a Software business model is a new trend that emerges thanks to the advances of AI and Machine learning. Here are some thoughts on what opportunities this might bring in the next years.

What is Service as a Software?

It is the idea of finding a service executed by humans and then using software (mainly Machine Learning and AI), to automate it. Once it is automated, you can use Software (an AI bot) or Hardware (a Robot) to perform the task.

A couple of examples:

Example 1: Have Software deliver an Accounting Service

A team of accountants gets all the Expense Report receipts from 500 employees of a company. They do the data entry of the amounts, then decide on the category of the expense and then input the information in the accounting system.

In the Service as a Software model, you have to place a software “watching” the actions of the accountants and getting trained on what it should do. Once the model is trained, you can have the software bots doing the work and keep a couple of accountants to supervise them.

Image result for ui path expense report
UI Path’s software recognizing the fields in an Expense Report.

Some examples of software companies working on this are UIPath, Automation Anywhere and (our Greek) Softomotive.

Example 2: Have software deliver a Call Center Service

A bank uses a call center with hundreds of agents replying to customer issues on credit cards, accounts, bonus points, and others.

Image result for omilia demo
Omilia’s Conversation Intelligence platform, recognizing the intent of the spoken words.

In the Service as a Software model, you build a bot that uses Speech to Text and a domain ontology that understands the concepts that your customers are talking about. Then you have the software answer the calls and provide customer support. Omilia (another great example from Greece) is providing this conversation intelligence using AI very effectively, as recognized by Gartner and Forrester.

Example 3: Have a Robot execute a delivery service

Image result for starship estonia
The Starship robot delivering food in the streets of Esthonia
Image result for amazon drone

You place an order in an e-food restaurant and instead of having a human delivering the food to your place, you have a sweet robot doing the job. Starship is building such robots in Esthonia. And as you know, Amazon is experimenting with drones delivering parcels.

Example 4: Have a robot make coffees

Image result for cafex
The CafeX barista gets instructions via an app, on how to prepare your perfect coffee.

CafeX is disrupting the baristas’ service by having a robotic arm making and delivering coffees, with full customization on the preferences of the customer.

Can Software and AI be better than humans in service delivery?

One might think that nothing can replicate the effectiveness of a human in service delivery. But is this true? Let’s have a look at where we stand regarding the accuracy of humans and AI.

Speech Technology: The word error rate for speech to text software is now at 5.1%, the same as a human. This means that software and humans misinterpret around 5 words in every 100 words they listen to.

Image Recognition Technology: Since 2015, the error rate for image recognition in specific domains for software is 4.5%, while humans are at 5%.

Examining Possible Outcomes and Decision Making: Software wins Chess champions and Go champions. Given a specific set of rules in which an environment operates, it is very tough for a human to win software.

Will AI and Robots substitute humans?

In some areas yes, AI and robots are and will be able to completely substitute a human. In most areas, we will live together and humans will be the supervisors of the services delivered by AI software and robots. And there will always be some areas in which humans will do better (e.g, movie creation services, lawyer services, industry research services etc).

How Can Startups Capitalize on the trend of Service-as-a-Software?

There are two main ways here to disrupt a service area delivered by humans.

The first way is to create the AI/Machine Learning software technology that will deliver the service in a quality-to-price ratio that is better than humans and then sell this technology to businesses.

E.g., develop an AI system that uses image recognition and drones to paint a house. You can then go and sell this technology to every business that paints houses, so that they can become more efficient and deliver the service with a better margin, e.g. because they will not have to create 30-meter scaffolds to paint the exterior of a 10-floor building

Image result for drones painting buildings
Airbrush drone painting a house.

The second way is to use this technology yourself, set up a painting franchise and disrupt all the existing painting service companies. This could prove to scale faster for your business, rather than having to convince and educate every existing supplier of a service to use AI/ML software and hardware.

Ideas for areas for service-as-a-software disruption

Obviously it is hard to predict which service areas will be the next ones to be disrupted by AI software+robots but here are a few ideas, with some of them under development at the moment. These are in random industries and come both from the AI Software and AI+Robots world, just to make the point of the range of areas for disruption.

  • Cleaning the Windows of Skyscrapers and Hotels.
  • Automating Accounting Services in Companies (Expense Reports, Data Entries etc): Mostly solved by RPA companies.
  • Cooking and Food Preparation Services for restaurants: Actually, a Robot Pizza Startup worth now $2 billion USD. Let’s build a Greek Spinach Pie robot please…
  • Coffee Preparation: CafeX underway.
The Mark Skakr robot preparing your bio-juice.
  • Call-Centers: Disruption currently underway with Omilia and other vendors.
  • Holiday Planning: A travel bot could learn all your preferences and suggest the perfect weekend getaway with your family: Book you a flight after 10:00 am because you hate early morning flights, get you a 4-star hotel with average rate on of +9.0 next to a metro, buy 4 metro cards for 3 days, the tickets to the top 3 museums for your children and save you from 20 hours of searching for information on the internet to create the itinerary.
  • Online Exams and Certification Tests: Having an AI system to do the online-proctoring to check via a camera a student who takes an exam for certification.
  • Traffic Police Services (cameras for speed tickets, license plate recognition, insurance fees avoidance checks, etc): Already in use.
  • Security Services: AI software and anti-drone technology will be used to protect critical infrastructure better than the security guards can at the moment. Rada and Kasperky are working on it.
Kaspersky Antidrone technology
  • Trading: High-frequency trading algos using AI are now broadly used and account for the most transactions in the US stock market. They have already disrupted to a large extent the service provisioned by daily desk traders.
  • Music Creation Service: I would not see it far fetched in a few years to have an AI software replacing the job of a music composer and a music audio engineer/producer. It could learn from the patterns of the top hits in a country (most popular rhythms, scales, mix of genres) and produce the next country hit. It could even create a synthetic song to sing, that could be a mix of the 3 most popular singers in the genre. AI music creation is much easier done in electronic music at first. Here is an AI produced album.

Let me know what you think!