Technologies in Retail

Big Data, Data Solutions, Healthcare, Retail

Trends and Industries: How Data Solutions upend existing sectors to new heights in 2023?

Trends and Industries: How Data Solutions upend existing sectors to new heights in 2023? 650 486 Exist Software Labs

The defining era of data is currently upon us. Business model threats and economic shocks are common. Power is changing wherever you look, including in the market, our technological infrastructure, and the interactions between companies and customers. Change and disruption have become the norm. Data Solutions have been useful in innovating the industry.

Data-savvy businesses are well-positioned to triumph in a winner-take-all market. In the past two years, the distance between analytics leaders and laggards has increased. Higher revenues and profitability can be found in companies that have undergone digital transformation, embraced innovation and agility, and developed a data-fluent culture. Those who were late to the game and who still adhere to antiquated tech stacks are struggling, if they are even still in operation.

So, when you create your data and analytics goals for 2023, these are the key trends to help you stay one step ahead of your competitors.

Healthcare

Data Analytics and Data Solutions can be used to improve patient outcomes, streamline clinical trial processes, and reduce healthcare costs. 

Some specific examples of how Analytics is being used in healthcare include:

  1. Improving patient outcomes: Analytics can be used to identify patterns and trends in patient data that can help healthcare providers make more informed decisions about treatment plans. For example, data from electronic health records (EHRs) can be analyzed to identify risk factors for certain conditions, such as heart disease or diabetes, and to determine the most effective treatments for those conditions.
  2. Streamlining clinical trial processes: Data Analytics can be used to improve the efficiency of clinical trials by allowing researchers to identify suitable candidates more quickly and by helping them to track the progress of trials more closely.
  3. Reducing healthcare costs: Analytics can be used to identify inefficiencies in healthcare systems and to help providers implement cost-saving measures. For example, data analysis can be used to identify patterns of overutilization or unnecessary testing, and to develop strategies for reducing these costs.

Financial services

Data Analytics can be used to detect fraud, assess risk, and personalized financial products and services. 

Some specific examples of how Data Analytics is being used in the financial industry include:

  1. Fraud Detection: Data Analytics can be used to identify patterns and anomalies in financial transactions that may indicate fraudulent activity. This can help financial institutions to prevent losses due to fraud and to protect their customers.
  2. Risk Assessment: Analytics can be used to assess the risk associated with various financial products and services. For example, data analysis can be used to assess the creditworthiness of borrowers or to identify potential risks in investment portfolios.
  3. Personalizing financial products and services: Analytics can be used to gain a deeper understanding of individual customers and to personalize financial products and services accordingly. For example, data analysis can be used to identify the financial needs and preferences of individual customers, and to offer customized financial products and services that are tailored to those needs.

Retail

Retail companies can use Data Analytics to optimize pricing, understand customer behavior, and personalize marketing efforts. 

Some specific examples of how Data Analytics is being used in the retail industry include:

  1. Prizing Optimization: Retail companies can use Data Analytics to identify patterns in customer behavior and to optimize their pricing strategies accordingly. For example, data analysis can determine the most effective price points for different products and identify opportunities for dynamic pricing (i.e., adjusting prices in real time based on demand).
  2. Understanding customer behavior: Analytics can be used to gain a deeper understanding of customer behavior and preferences. This can help retailers to make more informed decisions about the products and services they offer, and to identify opportunities for cross-selling and upselling.
  3. Personalizing marketing efforts: Analytics can be used to deliver more personalized and targeted marketing efforts to customers. For example, data analysis can be used to identify customer segments with similar characteristics and to develop customized marketing campaigns for each segment.
  4. Cost Reduction: Being able to have a JIT (Just in Time) procurement and storage of items which in turn increases/optimizes warehouse capacity and reduces spoilage, and improves logistics.

Manufacturing

Data Analytics can be used to optimize supply chain management, improve production efficiency, and reduce costs. 

Some specific examples of how Data Analytics is being used in the manufacturing industry include:

  1. Optimizing supply chain management: Analytics can be used to improve the efficiency of the supply chain by identifying bottlenecks and inefficiencies, and by developing strategies to address these issues.
  2. Reducing fuel consumption: Analytics can be used to identify patterns in fuel consumption and to identify opportunities for fuel savings. For example, data analysis can be used to identify the most fuel-efficient routes or to identify vehicles that are consuming more fuel than expected.
  3. Improving fleet management: Analytics can be used to improve the efficiency of fleet management by identifying patterns in vehicle maintenance and repair data, and by helping fleet managers to develop strategies to optimize vehicle utilization and reduce downtime.
  4. Forecast roadworthiness of vehicles: This can help set trends on when a vehicle would break down or need repairs based on utilization, road conditions, climate, and driving patterns.

Energy

Data Analytics can be used to optimize the production and distribution of energy, as well as to improve the efficiency of energy-consuming devices.

Some specific examples of how Analytics is being used in the energy industry include:

  1. Optimizing the production and distribution of energy: Analytics can be used to optimize the production and distribution of energy by identifying patterns in energy demand and by developing strategies to match supply with demand. For example, data analysis can be used to predict when energy demand is likely to be highest and to adjust energy production accordingly.
  2. Improving the efficiency of energy-consuming devices: Analytics can be used to identify patterns in energy consumption and to identify opportunities for energy savings. For example, data analysis can be used to identify devices that are consuming more energy than expected and to develop strategies to optimize their energy use.
  3. Monitoring and optimizing energy systems: Analytics can be used to monitor and optimize the performance of energy systems, such as power plants and transmission grids. Data analysis can be used to identify potential problems or inefficiencies and to develop strategies to address them.

Agriculture

Analytics can be used to optimize crop yields, improve the efficiency of agricultural processes, and reduce waste.

Some specific examples of how Data Analytics is being used in agriculture include:

  1. Optimizing crop yields: Analytics can be used to identify patterns in crop growth and to develop strategies to optimize crop yields. For example, data analysis can be used to identify the most suitable locations for growing different crops and to develop customized fertilization and irrigation plans.
  2. Improving the efficiency of agricultural processes: Data Analytics can be used to identify patterns in agricultural data and to develop strategies to optimize processes such as planting, fertilizing, and harvesting.
  3. Waste Reduction: Analytics can be used to identify patterns in food waste and to develop strategies to reduce waste. For example, data analysis can be used to identify the most common causes of food waste on farms and to develop strategies to address those issues.

These are just a few examples of the many industries that are likely to adopt Data Analytics technologies as part of their digital transformation efforts in the coming years. 

Other industries that are also likely to adopt Analytics Technologies include Government, Education, and Media, among others. In general, Data Analytics Technologies are being adopted across a wide range of industries because they can help organizations to gain insights from their data, make more informed decisions, and improve their operations. 

As more and more organizations recognize the value of Analytics, it’s likely that we’ll see even greater adoption of these technologies in the coming years.

web 800x507 Anahaw POS Version 3 Enhanced for a Future Ready Retail Management copy 768x487 1

Anahaw POS Version 3 – Enhanced for a Future-Ready Retail Management

Anahaw POS Version 3 – Enhanced for a Future-Ready Retail Management 768 487 Exist Software Labs

Six years have passed since we developed Exist Anahaw Retail Platform and pushed it into the market. The goal was to build a system that will enable the retail businesses in understanding their customers, have an efficient operation, and be steadfast in a fast-changing retail environment. Today, we are proud that Exist Anahaw achieved this and is continuously improving.

This 2021, we released Exist Anahaw Version 3. This newly updated system will continually push the platform’s boundaries by adapting modern technologies to further enhance technical capabilities by making it more customizable, providing more hardware support, and improving overall performance. By using Anahaw Version 3, both clients and those who are looking for a new platform will be able to use and check the right technology to improve their overall business operations.

Designed for medium to large retail enterprises, Anahaw is a fully integrated retail solution that provides today’s retailers with end-to-end functionality. But the journey is not done in innovating and giving a better experience to our clients.

Exist developed Anahaw Version 3 that serves as the upgraded and future-ready POS system.

Introducing Anahaw Version 3:

The old version was a browser-based software which is limiting and dependent. Our team decided to build Anahaw Version 3 using Java and upgraded it as a robust software that performs better memory handling and processing, can easily connect even with new POS peripherals, and can flexibly integrate APIs. 

We have also upgraded its local document database enhancing its performance. Now we also have local reports that you can use to check transactions made daily, from your sales, items sold and tenders accepted. This also includes BIR-required reports. 

POS software is now easier for everyone to audit and inspect. It has been updated with the latest BIR Requirement, and we can easily comply with the latest memorandums. By the way, we’ll have “Athlete’s Discount” available soon.

Anahaw Version 3 is now more intuitive as its interface has a cleaner and lighter feel so the person who operates it can easily navigate the POS application. POS functions are easily accessed with button selections. Users can perform transactions like scanning items, applying discounts, and tendering payments making it faster for the cashier to do the transaction. Buttons can easily be activated with the use of mnemonics or alt-key combinations. No more labeling of keys, no more memorizing of shortcuts. 

We have added a customer service feature to facilitate returns and replacement properly. This feature clearly defines the separation of POS transactions with your Customer Service transactions. As for your IT or DevOps, they can use Anahaw Version 3’s Command Center, to have updates or patches installed right from your headquarters, view the terminal’s current build number, and check the logs on transfers made between stores to headquarters and vice versa.

With the introduction of Exist Anahaw Version 3, our team is delighted to introduce this newly upgraded platform to all of our clients as we start our transition to fully deploy the new version this year. Exist will continue to support Anahaw Version 1 until this year and will then be tagged as a Legacy System by 2022 thus ending its lifecycle.

We are excited to see Exist Anahaw Version 3 rolled out to our clients and experience the difference! 

Update to ANAHAW VERSION 3 Today!

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Adapting Tomorrow’s Technology In Today’s Retail Business Webinar Highlights

Adapting Tomorrow’s Technology In Today’s Retail Business Webinar Highlights 768 487 Exist Software Labs

“It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change.” – Charles Darwin

According to Forbes and Microsoft, these are the main technologies in retail in 2020:

      • Chatbots
      • Mixed Commerce (e-commerce X in-store)
      • Loyalty on Mobile
      • Blockchain
      • Data-driven Business
      • Artificial Intelligence
      • Augmented Reality

Technology innovation is a key parameter for innovating your business right now. KPMG, a multinational audit firm, recently illustrated a graph of three (3) options to drive retail businesses.

Adapting 1

Keeping the business-as-usual model means that you are a legacy type of business. You are comfortable with the tried and tested model that you have right now that has worked in the past but is uncertain to work in the future. It is like ignoring the changes that are happening around you and how the consumer behaves. Remaining in this grey area despite all the shifts that are happening in the industry will lead to becoming irrelevant over time.

However, if you are thinking of leveraging or being the platform for retail, you are responding to the changes, and to different consumer behaviors. That is leveraging technology to respond to new demands, and anticipate those that might occur in the future, making sure that you are still at the growth path.

One example is how Jollibee dealt with the changes engendered by the pandemic.

Adapting 2

Jollibee has been opening a lot of stores locally and globally. They have been doing the same growth strategy for decades with less integration of technology on their operations compared to what they developed right now.

Jollibee built “cloud kitchens” to offset the losses that they had during this period. They have invested 7 billion pesos to set up these “cloud kitchens” to continue to bring their products to their customers without the need to build the usual dine-in, brick-and-mortar facilities. Instead, they built delivery outlets located in discreet, low rent sites that serve as hubs. Cloud kitchens are now available in the United States, the United Arab Emirates, and Singapore. This presented how important it is to be on the lookout for opportunities to innovate and adapt to the possibilities in the market using technology.

When you talk about innovation, you should not only talk about how you can provide your services online. Your business should be encompassing three (3) fronts and is able to empower these three. You have to be smart and have a data-driven strategy. Bringing your business online is just one part.

Customer

      • Seamless Online and In-Store experience (Web-mobile platform, In-store tech hub)
      • Data Source (I.e. CRM – Know your customer, Loyalty Engine – Reward your customer, A.I. – Understand your customer)

Employee

      • Empower your employees with the information. 
      • Seamless Online and In-Store experience both customer and business
      • (Mimic online experience in-store)
      • Data Source (I.e. Real-Time Inventory, Inventory Projections, Smart Suggestions, Special offers for targeted customers)

Partners

      • Engage and enable your partners
      • Data Source (I.e. Supply Chain Automation, Vendor Management System)

Technology innovation does not only cover how you can provide for your customers. It has to be perceived holistically.

So, How do you do it?

There are five (5) stages of an organization’s journey.

Business as Usual (No changes at all)

This is when you are not adopting any changes right now. You might be trying to get by with the tested business model. This puts your business into the risk of being forgotten, of failing, or of suffering drastic losses.

Experimentation (R&D)

You want to learn more and test something to see what fits your business.

Strategic Planning

It is when you are laying out a strategic plan that will fit your business from the data that you have gathered for the past months.

Organizational Roll Out

When you already have a strategic plan, you have to roll it throughout your organization to align every aspect of your digital strategy to every section of your organization.

Full Adaptation

Continuously innovating to see where else can you improve.

Five-step Iterative process

These are the processes to help you out with your journey of being part of the digital economy.

      1. Strategic Intent – you want to inject a digital strategy as part of your objective
      2. IT Feasibility Validation – checking if it is feasible to do in your organization in an IT perspective
      3. Business Unit Engagement – getting everyone on board
      4. Organization Injection – execution, making it part of your business culture
      5. Rapid Release of the Next Generation Business Services

It does not have to happen in one go. Even after releasing your first digital milestone, you may go back to the first step to see what else can be improved.

Case Studies and Insights

Walmart

(in an interview with then CTO, Jeremy King)

      • In 2018 Walmart and Microsoft announced a five-year partnership to drive digital transformation across Walmart, boost shopping speed, and empower retail associates.
      • Customer-facing technologies are in Microsoft’s Azure cloud
      • Machine learning as one of the most important technologies Walmart is using. 
      • Predictive Inventory accurately addresses one of the biggest consumer complaints, which is accurately providing the right availability of items that they have.

Starbucks

      • They incorporated Data Analytics into their Marketing and Sales efforts.
        • what kind of coffee their customers are ordering and adjust their offerings accordingly.
        • Personalize offers and marketing materials.
        • Increase sales and cut costs of ineffective ads and marketing
      • Starbucks Mobile app: Starbucks has been using reinforcement machine learning technology (ML) to provide a more personalized experience for customers who use the Starbucks mobile app
      • IoT – measure consumption of coffee beans in partnership with Microsoft
      • BlockChain – trace coffee beans from its source

Domino’s Pizza

In the mid-2000s, Domino’s was struggling with both brand image…they recognized the need for rock-solid digital strategy to improve customer engagement and overall brand image

      • Marketing and IT  aligned to communicate Domino’s digital transformation story to the users (consumers). This led to the social listening platform Think Oven, a social listening platform which allows Domino to get real-time feedback from their customers. This was launched in the mid-2000s, back when social media is starting to gain dominance.
      • After a successful rollout of their mobile app, they introduced Domino’s Anywhere: customers can order from a plethora of devices including Amazon Echo, Google Home, Siri, Smartwatches, Smart TV’s, Slack, Facebook Messenger, Twitter and more
      • Extensive use of Cloud technology via MS Azure’s PaaS, all of their core systems – digital ordering systems, ERP, back-office operations, and supply chain systems in the cloud.
      • Domino is planning to invest in conversational AI and cognitive technologies (NLP) to further enhance the user experience.
      • In 2017, Domino Pizza overtook Pizza Hut to become the largest pizza company in the world generating $12B in global revenue.

IKEA

Cultural Transformation (Interview with Chief Data Officer, Barbara Martin Coppola)

This is how they achieved a digital mindset:

      • “In order to be successful, digital needs to be embedded in every aspect of IKEA. Digital is a way of working, making decisions, and managing the company.”
      • “At IKEA we’ve divided our digital transformation into four main areas:
        • Meeting the customer
        • Empowering co-workers
        • Digital Foundation
        • Digital DNA”
      • “When speaking of digital transformations, it is imperative to think of it as a strategic paradigm shift, and culture can either enable that transformation or it can severely hinder it.”

Technologies per Business Size

Adapting 3

Companies or people before put up a small store, and they were only using cash register machines and accepting cash as payment. Now, this CRM is now being replaced by POS. Along with this, people are also changing and tend to use tools like GCash or PayMaya. 

On another part, people who are not able to put up a physical store make use of e-commerce platforms, like Lazada and Shopee, to make it available to the public.

During the pandemic period, people had to adapt and be more creative. They are utilizing Facebook as their channel. People who are doing transactions here usually start with a COD payment method until they have established their customers’ trust, but definitely switch to utilizing mobile wallets.

With all these models of small enterprises, inventory is done manually.

Adapting 4

Physical stores become more scattered within the city, across the city, or all over the country. With the utilization of POS, companies are able to control these brick and mortar stores and to communicate with applications in the headquarter. 

Some companies start as e-commerce. But eventually, these retailers would want physical presence and put up physical stores, putting up brick and mortar, and e-commerce to form their mixed channel of selling.

For databases, the structured data will require the use of SQL technologies, while document databases are used for non-structured or semi-structured data.

Examples:

      • SQL Technologies (PostgreSQL, MySQL, Oracle, MS SQL Server)
      • Document Database (MongoDB, CouchDB, CouchBase)
Adapting 5

The large enterprise model is similar to medium, but it presents more potential for the company to do more, and utilize more technologies. There will come a point in time that the company will need to use corporate applications/ERP. It handles all of the operations of the business.

In 2019, more brick and mortar stores adopted the use of kiosks to perform other functions.

Large enterprises also have plenty of vendors and need to have applications to manage their operations.

It is expected for such a size of businesses to have an executive information system in place, basically like business intelligence already. When a variety and volume of data come in, Big Data also does because it already has so much data to consider.

There are three (3) types of data analytics:

        • Descriptive (under business intelligence)
        • Predictive (statistical models; used for forecasting)
        • Prescriptive (artificial intelligence; machine learning; draws up specific recommendations)

Greenplum fits best if you want all of these. It is a data hub, a repository for all your data. It has AI and machine learning libraries.

Adapting 6

If your system is future-proof, it is capable of putting all of these technologies together.

DevOps should be in place if you are undergoing a digital transformation for a more seamless deployment.

Current to Tomorrow’s Technologies:

Artificial intelligence (AI)

Allows visual recognition or customized image recognition to fit the business needs, It detects and identifies people, emotions in images. In Amazon Go, a person can pick up an item from the store and immediately leave with it. The payment is automatically deducted from the person’s bank account. The same concept is also a running idea for hotels.

Retail use cases:

      • Amazon Go
      • Cardless Membership

Internet of Things (IoT)

A system of interrelated, non-traditional computing devices with unique identifiers, usually IP addresses. It also has the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

Retail use cases:

      • Amazon Go’s IoT sensors
      • AWM Smart Grocery
      • Smart Shelves/Shelf Tags
      • Thermostat Control
      • Flonomics’ counting system and retail analytics technology help retailers determine optimal staffing levels for different dates and times, improve marketing strategies, gauge traffic flow, enhance customer service, and more.

Chatbot

A software application used to conduct online conversations, like Alexa, Siri, and more. It provides live contact with a human. An example of this is Globe’s Dude, a chatbot to monitor employees’ health launched during the enhanced community quarantine.

Retail use cases:

      • Tommy Hilfiger Chatbot
        • recommends client based on the information provided
      • Support use
        • Answering queries – FAQs
        • Respond to customer concerns and informing the support team
      • Globe’s DUDE – monitor employee health

Augmented Reality

An interactive experience of a real-world environment where the objects that reside in the real world are enhanced by computer-generated perceptual information.

Retail use cases:

      • IKEA Place
      • Fitting Rooms App

Blockchain

Is a growing list of records, called blocks, that are linked using cryptography. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data (generally represented as a Merkle tree).

      • Retail use cases
      • tracking shipments, 
      • centralizing databases
      • stopping fraud and counterfeits
      • and increasing transparency

Key Takeaways:

      • Business strategy must include the ability to adapt/embrace change
      • Technology adoption is a major contributor to long term success
      • Digital Transformation will help define and execute the digital strategy road map
      • Choose the technology that best fits your strategy

Get to know ANAHAW, our retail solution specially built to adapt to modern retail business requirements, and to specifically work on providing you an edge over the rest.