Category: Digital Transformation

IT-as-a-Service (ITaaS) is now a key driver of digital transformation in enterprises. ITaaS treats information technology as a commodity and provides the enterprise with scalable and flexible hardware, software, and support. Enterprises may subscribe to the quantum of resources they require, with easy scalability up and down to suit the changing business requirements.

Such a model offers tremendous benefits for the enterprise.

Scalability and Flexibility to Suit Today’s Business needs

The subscription-based model offered by ITaaS, with a monthly fee paid by the business depending on the number of users and systems in-use of the business, perfectly syncs with today’s business requirements.

For businesses in the growth pace, and for startups, the ready scalability on offer ensures steady growth of IT capabilities to match business growth, without sapping the enterprise energy and resources on provisioning for the same.

Even entrenched enterprises with a dedicated IT department, complete with on-premises servers find the scalability offered by ITaaS handy to augment resources when needed. The ITaaS provider, could, for instance, service end-users and application faults, leaving the in-house IT team to support the core infrastructure components.

The unbridled scalability also comes with flexibility. ITaaS is platform agnostic and offers a flexible delivery model. At the same time, it retains an element of standardization and predictability. The net result is enterprise consumers getting the best of both worlds, resilient to operate in any condition, and the highest levels of service.

ITaaS is delivered from the existing on-premise server rooms, through colocation from a hosted data center environment, or through a cloud-based model. It is possible to mix and match these delivery modes as well.

Implementing ITaaS, however, requires an overhaul of the existing IT ecosystem. While this comes with the usual pangs of change, it also presents an opportunity to overhaul the setup and make it more efficient. Several wasteful practices, and ad-hoc processes, accumulated over the years, may easily be done away with, in favor of the highly efficient ITaaS. Enterprises nevertheless do face some challenges in integrating cloud solutions with legacy systems., and making sure the applications are ‘cloud-friendly’ in terms of latency, and file structure.

As high as 91% of CIOs welcome the flexibility offered by ITaaS, confirming the fact the benefits of ITaaS are for real.

Expert Support Just a Call Away

The unprecedented scalability on offer is also backed up by 24×7 support, and access to expert technical resources. IN today’s age of talent crunch, such resources may not be easy to come by in-house, and even I available, may not make financial sense.

Most ITaaS operators also provide self-service support portals, allowing users to open support tickets, check the FAQs, follow guides and read knowledge base articles, all without involving the IT department. This greatly enhances functional efficiency and accelerates troubleshooting times.  This perfectly syncs with the emerging and hugely popular work from home concepts.

Minimal Upfront IT Investment

In today’s age of heightened competition, many CFO’s prefer to keep purse-strings close to their chest, and are reluctant to invest in anything more than what is absolutely necessary. They usually strike down IT investments which do not generate direct returns on investments, regardless of the scope of indirect benefits. IT as a Service, by allowing the enterprise to invest only for the exact amount of resources required, is music to the ears of such CFOs.

With ITaaS, the enterprise is spared from investing in unneeded infrastructure for future contingencies or be straddled with excess resources no longer required in the foreseeable future. Enterprises are also spared from the costly capital expenditure. With ITaaS, excess capacity, often considered part of the cost of doing business, is totally being eliminated. ITaaS expenditure is transparent, and easily attributable to the operational domain for which it is used. The costs for each transaction can be clearly attributed, and assigned to the end users. Shifting the costs from capital expenditure to operational expenditure also brings significant tax advantages to the table.

ITaaS also contributes to financial stability for the enterprise. With ItaaS, IT expenditure becomes a regular and predictable exercise, rather than one-off capital shocks.

Easy Availability of New Technology

The world of technology is in a state of flux, with new technologies emerging by the day. ITaaS offers enterprise the opportunity to experiment with the latest technology and implement what works for them. Enterprises are spared from spending a fortune up front on an uncertain technology.

More importantly, ITaaS guarantees up to date hardware, and also regular software patches and upgrades. Enterprises are free from the hassles of having to upgrade their IT infrastructure and can focus their attention on their core business process, or serve their customers.

Maintaining up to date systems is important both from a reliability and functional standpoint.

IT-as-a-Service improves network security in a big way.

Enterprises ignore network security at their own peril. The latest Verizon data breach report reveals 70% of all cyber attacks exploiting known vulnerabilities, and some vulnerabilities date back to 1999! ITaaS delivers a coordinated patch regime, ensuring systems are less vulnerable to attacks. ITaaS providers deploy service agents on the IT infrastructure estate of the company, to highlight known vulnerabilities and out-of-date software.  The ecosystem also gets the benefits of regular system backups, pre-deployment testing, and patch updates.

ITaaS also enables continuous monitoring of service, Unified monitoring solutions track utilization, key metrics related to network health, and undertake trend analysis. These solutions cross-correlate code-level and transaction-level performance with the health of the supporting infrastructures, to flag issues as soon as it emerges. Such proactive monitoring identifies the symptoms and indications of a fault before it escalates into a major issue.

The 2017 Global Technology, Media and Telecommunications Predictions Report by Deloitte estimates global spending on IT-as-a-Service to touch $550 billion by the end of 2018. Enterprises currently spent about 25% of their IT budgets on pay-per-use services, on average. Deloitte Global expects the figure to jump to about 35% this year, and to 50% by 2022.

ITaaS is clearly the future, with enterprises no longer having to overpay for things they don’t need. However, the extent to which it serves enterprise needs depends on the extent to which CIOs can align ITaaS with business needs.

Mobile phone user base around the world has been growing at an unprecedented rate. The mobile market shows a lot of growth potential and with that, the growth in the use of the app-based technology. 96% of the Gen Z  is hooked to smartphones, which implies that most of the interaction with the digital world happens via apps. Whether it be communicating, shopping, surfing news or listening to music, mobile apps made life easier for customers. This is why it makes sense for businesses to reach their customers where they are easily available, on their mobile phones.

Benefits of Apps for businesses:

• Mobile apps can help businesses manage their internal operations more efficiently

A business is only as good as the system it is created for its operation. Perhaps the pinnacle of technology innovation is how successful it is in making our everyday tasks easier and more efficient. When customer satisfaction increases, sales typically do too. In fact, according to SalesForce, 70 percent of buying experiences are influenced by how customers feel they’re being treated. The more interested and pleased people become with your product and your business, the greater will be the consumer demand. When customer satisfaction increases, sales typically do too.  Employees can also stay in continuous communication due to wireless networks and mobile platforms, improving the efficiency of the organization. There are several ways mobile communication can increase productivity. However, it is important to keep in mind the security risks that come with it. Thus, it is crucial to choose legitimate and qualified platforms and systems.

• Customizable User Interface

Apps give the sellers an opportunity to customize the user experience based on their customer’s search history and preferences over the app. Using app analytics platforms like Appsee or Firebase can immensely help businesses improve their in-app experience. These platforms provide an in-depth analysis of user behavior as well reports on crashes of your app. Fixing the crashes ensures ease to the customer and promises an increase in the number of app usage sessions for the business.

• Ease of access

Gone are the days when one liked to open the browser, type in the website URL, log into his/her account and then proceed with scanning and ultimately with the purchase. People want a user interface which is convenient, quick, and easy to access-a criterion that mobile apps fulfill. Mobile apps offer ease to customers, by being readily available to them with just one click.

• Ease of making payments

In countries like the US where credit card networks are much developed, mobile wallet adoption rate has seen an encouraging growth. With a push to digital wallets such as PayPal or Apple Pay along with brand-specific wallets such as Walmart Pay, payments over the apps have become a lot easier for users. Using these platforms, customers are good to go without the hassle of logging in and out, again and again to their bank accounts, as is the case with buying the websites.

Bizness Apps – “Mobile Apps For Businesses Made Easy” || Business Explainer Video by Flikli

•  Increased Security

As per the Global Consumer Fraud Survey by ACI Universal Payments, the rate of card frauds in the US spiked. This survey also shows that close to 40% of the card users have reduced their frequency of card use. This highlights a worrying point that the increase in the use of credit or debit cards and even online banking, increases the vulnerability of users to hacking and phishing. Apps enable the customers to surpass the need of quoting their financial details again and again by providing an easy in-app pay option by incorporating digital wallets on their platforms. Using apps combined with digital wallets helps to keep customer’s financial information safe and secured. With the increase in frauds in traditional ways of transacting, more and more customers will look to shift their preferences to app-based technology.

• Value Addition for Customers

Reward the app users with accruing reward points, for maximum app usage, which can be redeemed for further in-app purchases. Such loyalty programs ensure customer retention and help the businesses to clock higher sales. The Starbucks loyalty program is one such example.

• Ease of giving and receiving feedback

Since it is easier for the customers to provide feedback over the apps, businesses stand to gain a lot in terms of increased user visits by incorporating those feedbacks and improving customer satisfaction which is of prime importance in highly competitive markets.

Massive investments are being made in app-based technologies because everyone has realized that mobile phone users of today prefer apps over everything else, because of their sheer convenience. So it makes sense for businesses to be where they can fight off their competition and build a better market for themselves. There are still a lot of untapped potential and opportunity which can be seized only if they make while there is still time.

Staying ahead of the curve should be the mantra, which can only be done by adapting to the changing times and adopting new technologies.

With data now being a critical source of competitive advantage, enterprises are cutting across size and geographies seeking newer methods to identify and analyze the data they generate. Most enterprise decision makers are now familiar with intuitive graphs, pie-charts, and other forms of visualizations that try to make sense of sales, revenue, and other aspects of company operations. However, the usefulness of such data visualizations depends on the effectiveness of the data, or how the data is used to come up with conclusions. A balanced approach in data visualization and analytics is thus pivotal in formulating an effective data strategy.

Many enterprises confuse data analytics with data visualization. Both allow users to make sense of data and obtain the relevant metrics that helps in better decision making. In today’s age of information overload, where data generated is multiplying every 3 years, interpreting them turns out to be the need of the hour. On the other side, we have these forecasts and projections hinting at an exponential growth in revenue for the big data software market in the coming years. The confusion, however, stems from the fact that both data visualization and analytics represent data in visual interfaces.

While there is considerable overlap between the two, data analytics deals with data at a much deeper level, compared to visualization. An end-to-end business intelligence solution consists not just of the front end dashboard, which transforms data into a visual context, but also tools and algorithms at the backend.

Related Reading: Find out how enterprises are relying on business intelligence platforms to leverage data for driving innovation and growth.

Difference between Data Visualization and Data Analytics

Data visualization represents data in a visual context by making explicit the trends and patterns inherent in the data. Such pattern and trends may not be explicit in text-based data. Most tools allow the application of filters to manipulate the data as per user requirements. The traditional forms of visualization, in the form of charts, tables, line graphs, column charts, and many other forms, have of late been supplanted by highly insightful 3D visualizations.

Data analytics go a step deeper, identifying or discovering the trends and patterns inherent in the data. Data visualizations, while allowing users to make sense of the data, need not give the complete picture. Visualizations are only as effective as the data used to prepare the visualization in the first place. Feeding visualization engine with incomplete data will render half-baked, obsolete, or erroneous visualization.

Moreover, today’s enterprises gather data from multiple sources, and store data in multiple repositories, including many silos. In such a state of affairs, gathering comprehensive data for visualization is a tough ask. While visualization tools mostly deal with raw and unstructured data, end-to-end analytic tools employ data mining algorithms to cleanse the data, evaluates the cleansed data using different evaluation models and software tools, subject it to algorithms, and then decides how to display the results.

Data Integration as the first step of the process

The essential prerequisite of effective analysis is consolidating all data in one central place for effective analytics. While there are analytical engines capable of collecting data from multiple silos, consolidating data in one place enables a “single version of the truth,” preventing duplicating and contradicting data from distorting the visualizations. Until recently, many companies use to aggregate data manually, on an ad-hoc basis, as it was easier this way than invest time and effort in a solution for the same.

However, the sheer increase in the volume of data in recent times makes manual aggregation impossible. A number of software tools and platforms cater to the need, by providing automated solutions. The add-on benefit of such automated solutions is data cleansing, to eliminate misnamed, outdated, and messy data, inevitable in a set-up which involves disparate sources and users.

Data Analysis as the second step of the process

The logical step after aggregating and cleansing data is subjecting the data to analysis or performing calculations on the data. As today’s business environment has grown complex, data analysis also involves complex calculations. The need for speed introduced multi-stage formulas that perform a number of calculations simultaneously. Visualization tools focus on reporting data rather than analyzing it, and as such, most tools are limited, with restrictions in the possible aggregations per formula.

In contrast, truly end-to-end analytical solutions allow users to create complex formulas, working in separate sources. The software undertakes the required pre-calculations automatically, making life easy for the user. Businesses seeking to thrive in today’s fast-paced business environment need analytic tools which update data and facilitate collaboration in real-time. The leading analytics tool in the market today, such as IBM Cognos play into this need, by streamlining available data and leveraging plug-and-play interfaces to derive colorful dashboards.

Companies in the retail sector have already leveraged the power of data analytics to streamline their business processes and thus maximize revenue. Analytics and visualization have aided them discover patterns and actionable insights pertaining to customer behavior helping managers plan and develop initiatives. Find out how retailers are harnessing data analytics to aggregate their customer data for accentuating efficiency and profitability.

Comprehensive business intelligence analytics suites offer predictive modeling and other types of advanced analytics based on complex algorithms compiled using languages such as R and Python. Advanced data visualization, data warehousing and dashboards make up some of the key technologies used by business intelligence platforms currently. The best solutions offer unmatched flexibility to the user, with the ability to combine data any way the user requires or prefers.

Moreover, the latest analytical platforms apply modern tools such as natural language processing (NLP) and chatbots, making it easier for users to perform the required calculation or input their queries with ease. The latest advances, such as location-based intelligence increases the potential of analytics and actionability of the insights in a big way.

Data Analytics or Visualization: Which comes last?

While the most effective visualization is based on the data subject to analytics, visualization need not always be the end of the process or the culmination of the project. Many situations adopt data analytics and visualization in a cyclical spree.  

Consider the case of Zao, who runs a host of machine learning and predictive modeling applications to gauge the success of targeted email campaigns. Data visualization enters early in the process, with the analysts pulling out specific variables into a graph to identify any possible correlation, or to identify metrics such as mean and median averages, data spread and standard deviation metrics, to get a sense of the scope of the data.

Both data visualization and analytics deal with data. Visualization tools generate a beautiful and easy to comprehend report, but only robust backend capability, which handles the messy data and processes the data by applying advanced algorithms, gives an accurate report. Data analytics offers the complete picture, while visualization summarizes the available data in the best possible way. The best solutions co-opt both.

Your data is growing at exponential rates. The insights from data can help the managers and business owners make decisions that can improve turnaround times, efficiency and more.

Related Reading: Get an insight into the hows of using data analytics to scale and grow your business.

Fingent a leading custom software development company, we house a team of skilled business analysts and data visualization experts. With their expertise, we can offer you exceptional data visualization services. Our team can assist you in implementing robust data analytics software that seamlessly integrates data from various sources, providing rich and insightful visual solutions that uncover the true story behind your data. Partner with Fingent today to unlock the power of data visualization and make informed business decisions.

In 2017, technology played a crucial role in disrupting most of the brick & mortar based business models, and the real-estate industry was no different. Meanwhile, the national unemployment rate fell to a 16-year low of 4.3 percent in July 2017, and recruiting and retaining employees became increasingly difficult. In this environment, many companies have started riding the digitalization wave and consider technology to be a growth-driver.

Businesses are leveraging upcoming technology to boost their operational efficiency, staff productivity, stakeholder relationships, and in-turn sales, but they still have miles to go with respect to reaching a point of consolidation. It is, therefore, important to understand some of the major shifts in this space, to stay ahead of the curve.

Chatbots for superior customer service

Chatbots, also known as conversational agents, are essentially digital employees that can not only answer simple customer service questions instantly, but also seamlessly hand a customer over to live staffers when necessary.

According to a study by the Aspect Software Research, 44% of consumers said they would prefer to interact with a chatbot over a human customer service representative. Surprising? Not really. Chatbots can leverage internal and external databases to personalize interactions and provide specific data to each prospect, thereby resulting in higher lead conversions. They are available 24/7 to handle customer queries and can be programmed to push promotions or products depending on customer requirements. Also, by taking on routine, repetitive tasks, chatbots free time for real estate agents and other staff to focus on more complex tasks.

The universality of Mobile

In 2017, NAR reported that 58 percent of buyers found their home on mobile. Mobile usage is expected to continue to rise in 2018 as more and more Millennials and Generation Z buyers enter the market, and therefore it is not enough to have just a mobile-friendly website. The mobile app market is exploding; in 2017, there were 197 billion app downloads, and that number is expected to triple in the next three years. Real-estate agents are investing more in apps while looking to access IDX data from their phones or tablets. Estate agents are also looking to use mobile marketing to build an online presence, connect with local neighborhoods and create communities for a more targeted approach to lead generation.

Virtual walk-throughs to make the difference

AR and VR are two technologies that can take customer experience and involvement to the next level. A recent Goldman Sachs research report predicts that the VR and AR market in real estate will reach at least $80 billion by 2025. Real estate owners and developers can use a combination of AR and VR, known as mixed reality, to enable their clients to remotely view properties around the world. They can create a 360-degree immersive experience for the potential client and offer multiple finished site options. This can also help both agents and customers save time by speeding up the buying process. However, like any new technology, companies need to consider the return on investment from potential investments in these technologies. In addition, companies would benefit from understanding the flexibility and scalability of these technologies at various stages of use.

Can IoT have an impact?

Not only has IoT managed to become the buzzword after the “Cloud”, it has also managed to bring a revolution with Smart Homes that are intuitive and customizable enough to match the taste of the homeowner. Such homes come with everything that is electronic inside your house, connected to the internet and controllable with your handheld mobile devices. The emerging markets are using the potential of Smart Homes to increase comfort and to consolidate data to make homes eco-friendlier. By having IoT enabled homes, real estate companies will get another chance to rewrite how luxury is experienced daily. The sky is the limit for interior experts to make breathtaking functional homes using IoT.

Did anyone say Big Data & Analytics?

Big Data & Predictive Analytics is the key to every future product that a company would roll out because they know what their user specifically likes or dislikes and they have enough data points to ascertain their assumptions as facts. It will play an essential role in helping real estate companies improve deals and investments, mitigate risk, better understand tenants and their needs, and open up new profitable business possibilities. Such data would also help real estate companies target users with new real estate products which can increase sales exponentially. It would help build a loyal customer base as well.

We are still in the nascent stage of Predictive Analytics for real estate, but the opportunities for companies that embrace this technology in the right way is infinite.

Marketing automation and project management tools

According to a study, salespeople spend just one-third of their day actually talking to clients. They spend 21% of their day writing emails, 17% entering data, another 17% prospecting and researching leads, 12% going to internal meetings, and 12% scheduling calls. The automation of various tasks such as scheduling and the use of CRM tools can enable real estate agents to spend more time building relationships and less time on routine administration tasks. Tools like HubSpot and GetResponse can also help companies leverage online leads. The decisive driver for this trend is above all the possible simplification and acceleration of management processes.

Is Blockchain the future?

The Blockchain is basically used to validate all transactions and add the transactions to the stack. (If this sounds too complicated, head over to a Medium post here to read about Blockchain in more depth).

Imagine a user gets to sign all his documents digitally and doesn’t have to keep a physical record! Now, all this is possible using Blockchain, but this technology is in its nascent stage. As more buyers turn to cryptocurrency for their financial transactions, building owners will need to accept cryptocurrency to stay relevant.  There are also emerging services that provides landlords and tenants a way to pay and receive rent in cryptocurrency.

To Sum Up

We discussed many of the technology trends in the real estate industry, but let’s not forget about the flying saucers. Well! Yes, we are talking about drones. Drones are presently being used for security and surveillance purposes, and they are also being used by real estate companies – More and more real estate professionals are turning to drone photography and videos to better market their listings. Drone photography is especially useful for large, unique properties that don’t photograph well from the ground.

We must ensure that we embrace new technology as soon as possible because the time required for a new tech to become a common tool is really short. Therefore real estate companies should quickly leverage that little time to capitalize on their market share perfectly.

Bill Gates: ‘A.I. can be our friend’ | CNBC
“AI can be our friend,” says Gates, speaking with “Hamilton” composer Lin-Manuel Miranda and his wife, Melinda, at Hunter College in New York City on Tuesday.

5 Things CEOs Should Start (and Stop) Doing in 2018 | Inc.
With the new year comes a new set of business trends leaders need to be monitoring. What will deliver the biggest impact this year?

How Cloud Computing Is Changing Management | HBR
How will management be changed by the most impactful information technology of our time: cloud computing. What does it allow us to do differently, and how will that change the way we do things in the future?

Innovation shapes the future of transportation technologies | Bloomberg
Together with new software and hardware technologies, including machine learning, blockchain, and the Internet-of-Things, today’s emerging mobility innovations and technologies could allow consumers to move more seamlessly between different modes of transport, as well as improve how the system is managed and controlled, with significant benefits to those who live and work in cities.

Google’s Vision for Mainstreaming Machine Learning | The Next Platform
Google has been a vocal proponent of the idea of democratizing AI by making it easier for mainstream businesses to use.

Automation in the retail space improves efficiency, enhances the quality of service, and reduces the cost for all stakeholders. Retailers who realize this fact strive to offer innovative products and solutions, based on automation technology, to differentiate themselves from competitors.

Here are some of the ways in which retailers leverage automated solutions to optimize their operations and cut costs.

Digitalization of Tasks

Digitalization boosts the efficiency of manual and tedious, time-consuming internal tasks. While most progressive retailers have already applied IT applications and solutions in a big way, the emergence of IoT has given rise to newer solutions that take efficiency and quality improvements to a whole new level.

IoT based solutions like leveraging components such as RFID tags, NFC, sensors, and smart devices are primed up to disrupt the retail space in a big way. Retailers apply such solutions in almost all retail functionalities, including product search, checkout, analytics, inventory management, security, PoS, and vending machines.

NFC based payments systems, for instance, facilitate contactless payment systems, speeding up the process considerably, and doing away with queues.

Amazon GO, the checkout free, fully automated grocery store in Seattle offers a prototype of the future store. The store uses cameras and sensors to track what shoppers remove from and place back on the shelves. Amazon bills the customer automatically when they leave the store, using the credit card stored on file. Check out lines, often the major pain point in a retail store, become superfluous.

Starbucks’s “Order & Pay App”, allows customers to pre-order items when on the move. The order is ready when customers enter the store, sparing them the hassles of having to stand in a line. The app, in essence, replaces the POS terminal.

Retailers who invest in such solutions reap huge savings as a result of reduced employee and infrastructure costs. They also gain through increased sales resulting from increased customer satisfaction.

Automation of Key Processes

Automation of critical processes such as inventory control, filling out employee timesheets, invoicing, entering information from various PoS terminals to the accounting platform, financial management, point of sale transactions, etc., can improve efficiency and bring about big cost savings. An integrated point-of-sale system, for instance, spares the need to manually key in transaction information into the card reader or other systems.

Many retailers have identified robots as a key tool to automate their processes. Evidence on hand suggests big efficiency gains and cost savings through the implementation of such innovative automation solutions.

A case in point is Walmart that has patented the use of robots in retail stores. Robots offer an effective and reliable solution to key issues such as insufficient staffing during peak hours, cleaning messy aisles instantly, and identifying theft with precision. Robots are excellent workers, and an HR department’s dream-come-true; they do not need a vacation, never get sick, arrive at work 100% trained, and do not slack. The deployment of such robots has saved millions of dollars for Walmart, in the form of reduced HR costs.

Another success story that retailers can set as a benchmark is that of Amazon’s Kiva robots. Amazon deployed these robots across its warehouses, in 2014, to automate the retrieving and packaging process. Amazon has already saved about $22 million per fulfillment center by deploying these robots, according to Deutsche Bank.

There is the added benefit of automated technologies attracting youth. A recent study by Fisch Restaurant estimates nearly one out of three customers in the 18 to 24 age group prefer ordering from the drive-thru at restaurants because “they don’t feel like dealing with people.” Likewise, McDonald’s automated ordering kiosks are a rage across Europe, relegating manual ordering to the second spot.

Personalize Customer Experiences

A major objective of applying automation technology is to enhance customer experience. Customers are the lifeblood of any business. In today’s age where choice is plenty, customers who feel dissatisfied or have to put up with difficulties to complete the transaction will leave. As such, retailers strive to create high-value, personalized interactions with customers.

The first step towards facilitating the customer is to understand their preferences and requirements. Today’s customers demand highly personalized services. Research by TimeTrade reveals 36% of shoppers do not like to wait for items to ship,  30% of shoppers prefer advice on what products to buy, and 90% of customers will make a purchase when they receive assistance from a knowledgeable store associate.

Retailers may personalize each customer’s experience by harvesting the growing volumes of customer data available across social and other channels.

Retailers could use such data to offer location sensitive offers. In the past, retailers strove to deliver contextual product recommendations. Today, they strive to rationalize the shopping opportunity. 7-Eleven, the global convenience brand targets customers based on location and weather and delivers customized and time-sensitive offers. For instance, if it has started raining in the city or a specific area, the company may offer discounts on umbrellas to customers who are outside their homes, and in the vicinity of the stores.

Lowe is all set to introduce LoweBots – multilingual, autonomous customer assistance robots. The pilot program has been announced in 11 locations across the San Francisco Bay area.

Personalizing customer experience goes far beyond creating brand loyalty and increasing sales. By understanding the customer, the retailer can target and optimize the effort and investment on the customer, without wastage. For instance, if a customer is known to prefer shopping alone without the aid of a salesperson, the retailer can spare the time and cost associated with deploying the salesperson to serve the customer.

Integrate the digital ecosystem with the real-world shop

The most common manifestation of integration of the digital ecosystem with brick and mortar shops are customer apps which aid the shopper as they shop in the physical store. Such apps make shopping easier by listing detailed product features, offering demo videos, and providing customized geolocation-based offers. Such apps also facilitate omnichannel sales. The shopper, may, for instance, opt to shop at the store and get the product delivered through an e-commerce channel.

Retailers need to complement such apps by installing beacons to identify customers who enter the store premises. They could then personalize the experience based on the customer’s preference and demographics. For instance, leading brands such as Sephora and American Eagle identifies each customer to deliver discounts on their birthdays.

Retailers would also do well to take the transaction to the customer without them having to wait in a long, slow-moving checkout queue. Even if red-hot IoT based technologies such as “Amazon’s Go cashless store” is beyond a small retailer’s budget, they can still adopt the Tablet PoS system that allows their staff to check out customers in aisles, doing away with the cumbersome check-out queues. There could also be a facility for online app-based checkout.

Examples of retailers who have applied innovative new automation-based products to enhance customer experience are many. Westfield London, for instance, applied an RFID technology driven smart card system, enabling customers to park their cars without needing a ticket. This is a big improvement over the manual process of refunding parking tickets, sparing the hassle of customers having to present the parking tickets and claim a refund from the store.

Likewise, augmented reality chat bots not just enliven the customer service experience, but also offer greater insights into the customer, reducing the effort and time for the customer service agent.

Audit and Redesign Internal Procedures

A sure shot way to boost internal efficiency and reduce costs is by reducing internal processes by adopting Lean methodologies.

The prerequisite is to conduct a thorough audit to identify supply chain strengths and weaknesses and identify and quantify waste. Retailers can optimize inventory, and pinpoint other areas of improvements.

Constructing an end-to-end lean value stream flow, focused on minimalistic procedures, improves transparency, and makes it easier to review and eliminate avoidable procedures. It creates a standardized workflow with all glitches and kinks ironed out, while co-opting abnormal workflows, catering to all contingencies such as a power failure which results in many POS terminals going down.

Automated retail workflows come integrated with analytics and a CRM suite, creating a highly powerful system, and facilitating further cost-saving methods such as just-in-time inventory, and real-time stocking.

Optimize the Workforce

Automated algorithmic-based labor scheduling tools calculate store workload and optimize staffing, with a high level of accuracy. Custom made algorithms consider factors such as store format, operating hours, backroom configurations, labor regulations, ergonomic considerations, and all other factors, to schedule shifts seamlessly. Such automated systems optimize staff at peak hours and ensure they remain at their productive best. It also reduces absenteeism. The investment is such a solution also delivers a big motivation boost for the workers, reducing turnover rates and decreasing HR costs considerably.

An advanced workforce optimization software, powered by advanced analytics, integrates the customer journey with the employee’s schedule. For instance, if an employee has already struck up a relationship or a rapport with a customer, the employee is assigned to the same customer as far as possible. Such software also ensures high levels of compliance to deliver operational control and business confidence.

Optimize Merchandising

Retailers do not make money by blindly stockpiling inventory. The inventory has to sell. When introducing a new product, smart retailers ask pertinent questions such as “Will the product sell?” “Can the store make money selling the product?”, and so on.

Having introduced a product, a retailer’s prime focus is to optimize the handling of the product inventory; the ordering processes has to be streamlined by identifying the most optimal order-quantity and should be integrated with the sales forecast to eliminate stock-out situations. A good, automated inventory management suite automates all these processes, boosting efficiency and reducing costs.

Automating the merchandising and product exposure process offers retailers the infinite capabilities offered by machines, as opposed to the finite capabilities of a human employee. It also facilitates easy integration of disparate data sources, and comprehensive analytics based on live data. Customers who enter the store are presented with the most optimal inventory, in the best possible way. The analytic engine, could, for instance, draw up the price-point resulting in maximum sales, and tweak the pricing and merchandising strategy accordingly. The net result is the elimination of wasteful trial-and-error methods and saving time. It also ensures the exposure of more products to more people, increasing sales.

Embrace analytics to improve processes

The application of IoT based sensors has taken data analytics to a new level. Retail analytic solutions now aggregate data from video camera feeds, beacons, Wi-Fi, POS systems and other components, and subject such data to big data analytics.

Retailers could gain valuable insights from such analytics. They could measure various elements in the buying process, such as the products each customer inspected in detail, tried, and compared. This information, available in easily digestible reports and graphs, allows retailers to optimize their inventory, eliminating slow-moving inventory. They could also fine-tune prices to boost uptake of inventory. More importantly, such processes become automatic and precise, with a high degree of accuracy, reducing the costly trial-and-error methods usually associated with the processes.

Mindtree’s 2017 study, “Sixth Sense of Retail“, reveals how rapidly evolving digital trends such as social media, mobile applications, and automation reshape the way retailers engage with customers. About 51% of young shoppers (16-24) are comfortable with automated technologies and would visit robot-driven stores, but as high as 78% of older shoppers (55+ years of age) were apprehensive about this trend. Likewise, while 44% of men, cutting across age groups are comfortable with automated technologies, only 30% of women are similarly comfortable. As such, retailers would do well not to blindly embrace automation, just because someone else is also doing it. Rather, they should study their business, understand their customer, and apply automated technologies judiciously.

Improving the customer experience is the mantra for survival in today’s highly competitive business environment. More and more businesses have identified machine learning as a reliable tool towards this end.
Machine learning is in essence software coded differently to traditional software. Rather than a long list of if-then-else statements typical of traditional software, machine learning predicts what humans would do given a specific set of inputs.
Currently, marketers and others leverage machine learning to further customer experience through improved personalization, enhancing the computer vision, improving natural language, greater decision support, through analytics optimization, and augmented analytics.

1. Machine Learning Aids Personalisation

Today’s highly pampered customers prefer and even demand personalized engagement and experiences. Machine Learning facilitates it to the hilt. Data and analytics allow marketers to understand customer preferences. Using machine learning in combination with new data sources from the Internet of Things (IoT,) telematics, geolocation beacons, and social data improve the insights.
Several marketers now apply machine learning based algorithms to understand the nuances of their customer’s preferences and engage them on their terms. Marketers use such algorithms to develop highly relevant marketing campaigns, such as a matching audience profile with highly targeted video content. These steps improve the call-to-action.
Customers receive tailored offers rather than irrelevant non-contextual offers. Such non-contextualized offers have a very low probability of conversion.
Segmentation gets better. For instance, insurance companies do not have to go by general assumptions or time-honored conventions to offer the highest automobile insurance premiums to a 16-to-25-year-old male. They can factor in everything specifically related to the customer, and tailor the premium based on individual rather than class factors.
The creation of such relevant content is the godsend at a time when over 90% of online users in the U.S. and Europe feels advertising is more intrusive today compared to two years ago.

Related Infographic: Machine Learning- Deciphering the most Disruptive Innovation

2. Machine Learning Facilitates Computer Vision

Machine Learning technology detects everything and anything, from objects and people to complex scenes within the images and videos. Applying the technology to enhance the quality of digital assets is a sure-shot way to win the customer’s heart.
One big success story is Twitter’s Magic Pony, which leverages machine learning technology to make pixelated images sharper, and enhances the quality of video captured on mobile phones in poor lighting conditions. Apart from delighting the customer, the spin-off benefit of Twitter is lower data usage, and by extension improved streaming abilities.

3. Machine Learning Aids Natural Language Processing

The next big thing revolutionizing human interactions with computers is speech recognition technology. The ability of computers to recognize human speech and act on it not only spares the hassles of keyboard typing but also unlocks a host of new possibilities. While speech recognition technology has been around for a while, the application of advanced machine learning technologies has made the system highly accurate, with error rates far lower than humans. Google’s Cloud Speech API now recognizes over 80 languages and variants, with a high level of accuracy.
Marketers can, and are leveraging advanced linguistic data and cognitive technologies spawned by speech recognition capabilities to create highly engaging content, targeted at the customer. In a sense, it furthers the cause of personalization in a big way.
Marketers benefit from natural language capabilities in myriad other ways also. A case in point is the intuitive new tool launched by Relative Insight, a UK based start-up. The tool converts natural language into data, offering marketers a wealth of information to connect with specific audiences instantly and deeply.

4. Machine Learning Improves Decision Support

Machine learning allows the marketers to predict the future. The “machine” becomes capable enough to predict the customer’s likely course of action, based on the data at his disposal, and his present behavior. The market is now flooded with several digital tools and services which provide advanced recommendations on this front.
On the anvil is “copyless paste,” where machine learning will save users time by proactively offering to share information between apps. Marketers will leverage the concept further to offer proactive product suggestions. Integration with other systems also offers the scope for proactive and automatic delivery.

5. Machine Learning Facilitates Analytical Optimization

Businesses leverage the immense analytics opportunities offered by machine data to fine-tune their operations, deliver new business models, and offer new products and services in tune with customer demand. The insights gained, predict not just how a customer may behave or act, but also how the competition may move in the future.
One sector where machine learning algorithms are already in widespread use is the financial sector. Financial services companies use various machine learning algorithms such as random forest and gradient boosted models for a host of applications, from predicting the probability of being ranked at the top of aggregator portals to predict midterm cancellation rates on policies, and more. These applications have a direct bearing on customer satisfaction. For example, banks and financial institutions predict volumes for credit card lines, to adjust rates and terms, and thereby attract the right type and volume of customers for the specific product.

Related Reading: Top Artificial Intelligence Trends to Watch Out for In 2019

6. Machine Learning Facilitates Augmented Analytics

The scope of machine learning improves with the development of technology. Neural networks support better classification and forecasting, decision trees support more complex rule and relationship-based customer experience programs. All these improve the organization’s ability to support complex decisions, forecasts, and optimizations.
Augmented analytics, which co-opts these latest and emerging technologies, combines various elements of the ecosystem, such as data preparation, business intelligence, predictive analytics and machine learning capabilities into a single, automatic and seamless process. Enterprises would be able to cleanse their data easily, to uncover latent insights and patterns.
Today’s huge data create millions of variable combinations impossible to process manually or even with traditional tools. Augmented analytics, powered by machine learning, deliver quicker insights, reducing customer frustration.
What exists now is just the tip of the iceberg. The future holds a world of possibilities. A case in point is the fragmented nature of the machine learning ecosystem being all set for a big churn. Increased competition, the hyper-fast paced changes in technology, and the proliferation of big data at an alarming frequency force many open source machine learning libraries, algorithms and frameworks to join forces and deliver a better deal to their customers. The lower-level personalization commonplace today will make way for a more robust collaborative filtering, delivering a much higher degree of personalization and contextualization than present levels.
Side-by-side, the machine learning ecosystem is becoming increasingly easier to use, and more affordable. Hitherto, only enterprises with large analytics teams could really afford to play around with machine learning. The advent of various solutions delivered in a cloud-based subscription model makes the power of machine learning available to the masses, including start-ups, freelancers, and even individuals.
Marketers and brands can leverage the improved ecosystem to generate a better picture of their customers’ true context, and serve them better. Simply put, customers will get better food, movie, music, travel, product and purchase recommendations.

Related Reading: AI To Solve Today’s Retail Profit Problems

Artificial Intelligence (AI) is the simulation of human intelligence processes with the aid of machines. These machines primarily include computer systems and tools that carry out learning, problem-solving, reasoning and perception.

Customer service is one area where AI tools and technology can be used efficiently. As per a recent study, it is estimated that nearly 85% of the customer interactions will be handled without the presence of a human agent in next three years. There are approximately 38% of the enterprises that are using AI and the number will grow to 62% by the end of 2018. AI has the capability to accomplish the following set of tasks and activities in customer service and interactions.

Pre-emptive Action

AI embedded monitors can provide the customer service team with the ability to analyze the primary customer issues. The systems can offer real-time support to the customer for the resolution of such issues. The huge clusters of web applications can be gathered and studied to resolve the issues even before their occurrence. This empowers the business organizations to reduce the customer abandonment rates and enhance the level of customer engagement.

Messaging Applications

The use of messaging applications is not restricted to connecting with friends, family, and colleagues. The brands are using these applications for customer interaction and the usage is set to increase further. The use of social media applications and in-house messaging applications allows real-time interaction with the customers and thus resolve their queries and issues.

One-time Training

The expenditure on hiring the customer service agent and training the member is quite high. It is also necessary to organize training sessions at regular intervals. However, with the use of AI platforms, a one-time training is sufficient which in turn would bring down the costs to a huge margin.

Non-stop Services

Customer service agents cannot be made available for a non-stop period of time. The human resources work for specific hours in a day and may remain unavailable at the time of an incident. These limitations can be resolved by the use of automated support and customer service tools which interact with the customers at any hour of the day. The queries and incidents reported by the customers are automatically recorded and a suitable response is also provided that matches the reported query.

Self-service Options

A recent customer service study revealed that over 72% customers do not prefer phone calls for the resolution of their query or issue. Self-service options for customer service are on a rise and the use of chat-bots and forums have rapidly increased. AI has enabled the easy development and usage of such options. The companies that are still not utilizing such platforms will be required to incorporate the same in the future for enhanced customer relationship management and service.

Reliability & Scalability

Customer service and interaction have a great role to play in customer engagement and relationship management. According to a survey, 42% of the customers increased their purchase after a good customer service response. On the other hand, 52% customers reduced their engagement with the organization after a poor experience. AI platforms can enhance the reliability of service with their non-stop availability and unbiased customer interaction. The AI tools can also be scaled up or down as per the requirements. These applications can simultaneously handle a wide number of customers without any impact on the speed and performance.

Cost-Savings

There are advanced customer services that can be provided by artificial intelligence. The use of automated applications can reduce the costs. Resource and training costs along with the cost of the infrastructure are eliminated with the incorporation of AI tools.

It is often assumed that Artificial Intelligence will replace the jobs currently being handled by the human resources. There are over 10 million jobs that will be replaced by AI tools and applications in next ten years. However, in the case of customer service, automated assistance can only resolve 10-35% of the customer queries. Human assistance and capabilities will be required in the rest of the cases. The business organizations must research the AI applications and platforms that can be incorporated into their customer service interactions. 

Learning is highly evolving in all facets of life and so are the systems that provide learning opportunities. The definite way to address the changing and growing requirements of employees in the organization is to provide them a responsive Learning Management System that will offer the right flexibility and accessibility of the learning material on multiple devices without loss of information or tracking. The continuous learning keeps them involved, motivated and helps to retain the employees in the organization. However, before your organization jumps into implementing a responsive LMS, it is important to put some crucial forethought for selecting the right one that suits the preference of your employees.

Understanding devices

You will need to understand and evaluate the various devices that are available and will be used most frequently as a learning source for your employees.There are numerous devices available today with various screen sizes.  Pre-identifying these target devices helps in understanding the compatibility of the software and achieving better productivity. Lock the primary ones that you need to address and check if the responsive LMS works well, and adapts from landscape to portrait mode and back. Also, the user information should be tracked effortlessly so that the data isn’t lost when they switch between devices.

Easy Navigation

A responsive LMS should utilize the features that each device offers, such as touch, swipe, and scroll in smartphones. A highly intuitive UI with user-friendly approach will definitely make the learning experience more fun and enjoyable. Along with switching of the device resolution and screens, the content should also be formatted to mold into any device dimensions and screen resolutions.

Better Accessibility

Data packs that connect to the internet vary largely on devices and hence you need to consider your content likewise. Choose an LMS system that utilizes HTML5 that helps learners to download and access the learning material offline. Also, keep the bandwidth that each lesson will need in mind and use a cloud-based system that customizes the resolution and bandwidth as per usage.

Safe and Secure

No matter how good a responsive LMS system is, if it doesn’t offer security to user’s information, it simply won’t work. Select an LMS system that can protect and safeguard the personal data of learners. This may include securing email IDs, passwords, phone numbers, and even course content. This can be identified by checking what level of encrypted coding is used by the system and how secure is its server or cloud data storage.

User Interactivity

Responsive LMS system that allows the user to not just access material, but also lets them upload content in the form of pictures, videos, and more will definitely work well. It should have the ability to convert the content into a compatible format that will be streamlined and functional for all device types. Another feature that can enhance a responsive LMS is the addition of social elements. It is a great strategy to make the learners in the organization interact with each other with the help of discussion forums, chat plugins, ratings, and content sharing. It will not only help learners explore more but also help promote the material via their word of mouth.

Support Services

A more engaging and responsive LMS would always require monitoring and support over a static system. Check if you get the right support from the vendor and figure out their turnaround time. If your organization is huge, check if they can offer a dedicated support team on a contract.

A responsive LMS is a sure-shot way to go ahead if all these factors are kept in mind. If you are reading this to seek a vendor, contact us to learn more about the latest trends and practices in developing learning management systems and how these benefit learners and organizations today.

If you are planning to select Business Intelligence (BI) tool for your Big Data solutions, it is important to evaluate which one is the best suited and not best rated for your company. Selecting a right visualization tool that can help you get the most out of Big Data and has well-defined functions, is an important criterion of the process. So you should ask the following questions before selecting the best tool for your company.

1. What are you visualizing?

It is important to first understand why you are looking for the tool in the first place. If you are planning to visualize the internal data such as marketing, finance, etc. you should look for a tool that is in alignment with your management system. For example, if you are using SAP ECC/Net Weaver system for handling internal data, an SAP-based BI will work better for easy implementation and cost reduction on training. Similarly, if you are going to use the tool for a client, it is better to use something that is compatible with what your client is using.

2. How is the tool’s interface?

It is imperative that the tool has an easy to use Graphical User Interface (GUI). Tools are meant to save time and make the task easy.  A well-designed tool that offers access to various options can be put in the pipeline with ease. Check if it has nice graphics capabilities in case you need to visualize decision trees and so on.

3. Does it have the essential support for visual discovery?

Tools should provide the most basic support for visual discovery and query processing. This might include something as simple as comma-separated values file, text, Excel, and XML support. Apart from these basic things, you might need to check what programming language it supports. Your decision will rest on what your internal team is expert at handling. Your team can get to support for various well-known programming languages such as C++, Python, Java, and Perl.

The other thing to check is whether or not the visualization tool you are planning to use is compatible with the operating system you use. In case of cloud implementation, ask the cloud provider for an OS that is compatible with your visualization tool. If you are catering to a client, ensure that the OS you select is compatible with their systems too.

4. Is the price right?

It is no surprise that price plays an important role in finalizing a lot of things in any company. BI projects cost a lot and the cost will largely depend on a number of criteria such as the level of in-house expertise and the ultimate goal to be achieved. Visualization tools should not be judged on the basis of their price alone but compared with how big is the need and what is being provided.

A good way to make a decision is to try a free trial version of the software to check whether it works for you or not. The tool provider should offer good technical support along with the documentation that covers all aspects of the tool.

5. How flexible is the tool?

Big Data is evolving at a phenomenal rate and so is the technology around it. Make sure that the visualization tool that you are seeking is flexible enough to adapt to these changes. Ask the provider how easy it is to upgrade the tool so that you do not hit a roadblock and require a complete overhaul in the near future.

Understanding these points will help you start zeroing on a list of visualization tools but seeking the support of an experienced tool provider will help you finalize it. Look for someone like us who have an expertise in understanding the requirements of the client and providing a complete solution.