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Oracle Endeca Commerce – An Overview

Oracle Endeca Commerce is designed to empower retailers to deliver a superior selling experience that will drive more clicks and conversions.

Oracle Endeca Commerce is an eCommerce search engine that is renowned to provide personalized search experience and is a pioneer of the faceted search feature that helps users to find the exact product they are looking for. On-Site Search for any eCommerce application is very vital as it is found that 60% of the users leave the site after a poor search experience.

Oracle Endeca Commerce brings to the table the following endeca search features which make it a leader among all other search engine providers.

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The CIO’s Quick Guide To Taking The Machine Learning Dive

CIO’s all over the globe are advancing their Machine Learning plans. The debate over Machine Learning as a boon or bane has long ended. Machine Learning has emerged as a victor with tangible benefits that CIOs want to embrace with all their might.

The ServiceNow Global CIO Point of View has found that 53% of CIOs surveyed have specified Machine Learning as their focal point. The survey also revealed that C-suite is leaning on ML as a catalyst for Digital Transformation.

But, for the CIO who is used to steering a mammoth organization with legacy systems, ML might appear to be quite a slippery ground.

An uncertainty about where to start, how to start and how to integrate the short-term with the long-term strategy is bound to kick in.

That uncertainty is what exactly what we are trying to dispel with this blog.

This is a CIO’s quick guide to getting started with Machine Learning. Here you go.

1.Identify Areas Of Application

Machine Learning implementation is possible only if you have enough data to build data models for predictions. Data from diverse sources like structured databases and unstructured databases must be consolidated and also cleansed for uniformity. It helps identify the exact operations or areas where the data lake can be used. For instance, integrating ML into accounting, marketing, HR, sales, customer service, etc.

2.Start Small, Scale Fast

Machine Learning is not a magic wand that can transform an entire function overnight through automation. Like building a skyscraper, an ML model is built brick by brick through continuous learning. A logical way to start ML would be to run smaller projects as ML experiments to test feasibility. Once the feasibility is proved, the RoI can be measured for scaling the data model across a function.

For instance, what kind of questions is repeated by customers when they ring up your company? If a pattern can be detected, a virtual assistant like a chatbot can be trained to automate the interactions which will reduce the efforts and time spent by personnel on addressing recurring questions with standard replies.

3.Ensure Data Integrity

The predictions you would get out of your ML system is directly proportional to the integrity of your data. You present the system with dirty data, you are bound to get wrong predictions that will make the whole ML system ineffective.

It is no surprises that data-driven enterprises have already invested resources to create new data strategies by harmonizing ERP systems, standardizing data definitions and cleansing data. The unified data strategy would help them look at their business from a 30,000 feet height with the ability to zoom down to 3-inch detailing.

So cleansing your data and making it ready for the ML system to weave through and arrive at accurate decisions is a prerequisite to taste success in machine learning.

4.Set Up A Data Team

Machine learning requires the expertise of a team with diverse skills. A single software engineer with graduation in mathematics and science is not going to help you scale your ML implementation. You will need to assemble a team comprising of data scientists, Big Data Architect, Systems Analyst and maybe a Business Analyst too.

Each team member will handle specific tasks ranging from setting up the data pipeline to teach the ML system to provide accurate predictions. That said, you cannot plan on hiring a single person who can do everything to set up the ML system as well have domain expertise to train the data model.

5.Build Domain Expertise

Although we are building a system that can automate tasks and make accurate predictions, the system first needs to be taught with basic data called test data. A domain expert who knows the in-out of the industry and its way of working must train the system with data models.

For instance, if you are automating the task of taking customer support calls for a software, you have to teach the ML system how the software functions under various scenarios and also the various scenarios when things go wrong. Without domain expertise, the system is bound to run into several wrong predictions.

6.Craft Accurate Data Models

Accurate data models are what enables Artificial Intelligence to reach its maximum potential. The machine learning system has to be fed with testing data from which it can learn to infer information as well make predictions. Such test data must a population of data that represents all possible scenarios that the ML system would have to confront.

7.RoI doesn’t happen overnight

Be informed that while ML is a phenomenal technology, results don’t appear overnight. It takes some time before the system is wholly ready to make predictions with substantial accuracy. The accuracy of predictions improves with time as the system continuously learns from recurring input and responses given to it.

Final Words

Globally CIO’s are getting serious about Machine Learning and the positive impact it can bring to their businesses. PwC’s Digital IQ Survey 2017 found that 63% of executives are betting big on Artificial Intelligence as disruptive technologies. Machine Learning, being an arm of Artificial Intelligence is a surefire priority for CIOs. But, understanding the technology, its requirements and the immediate agenda is a tricky affair. We have tried to simplify that transition with this blog.

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Magento 2.2 Key features That Will Make Lives of B2B Commerce Merchants Easier

Magento rolled out the 2.2 version on October 2nd 2017. Here is a gist of the functional fixes and enhancement updates that came out with it.


Magento has sharpened its stance as a B2B friendly eCommerce platform. The 2.2.x update comes with a host of key upgrades that will take Magento users to the next level of their efficiency.

Upgraded technology stack

The whole tech stack of Magento 2.2 is made cutting-edge with support for PHP 7.1 and Varnish 5, along with Redis 3.2 and MySQL 5.7. All third-party libraries like Zend and Symfony that Magento relies on heavily for heavily have been upgraded to the latest stable version.

Also, support for PHP 5.6, Solr and Varnish 3 have been discontinued. Solr will continue to remain the Magento code base until further version releases/upgrades happen.

In the earlier versions a mapper_parsing_exception was displayed when Elasticsearch was enabled. Now that the latest 2.2.3 comes with support for Elasticsearch 5.x that error is a thing of the past.

Advanced Reporting

Magento 2.2 is geared up to tell you how your business is performing from multiple aspects, including: order fulfillment, products, customer data and much more. Magento collects data and passes it on to Magento Business Intelligence for analytics. Magento BI churns out at least 20 reports and 3 well-decked dashboards full of data for proactive decision-making.

To be eligible to harness all the goodness of Magento Business Intelligence you must be the valid owner of the website and the website should be HTTPS-enabled with a SSL certificate. Also, you must have subscribed to the Advanced Reporting module.

Security Upgrades

Unserialize() calls have been removed to prevent code execution attacks. The Hashing algorithm (SHA-256) has also been strengthened for better security of the platform. Protection against XSS attacks have also been upped.


Bug fixes, refinements & improvisations all of which makes Magento 2.2 a worthy upgrade for B2B commerce.

Receive and Manage Quote Requests

Out of the box quote management and customer negotiation capabilities. Merchants can monitor all open quotes through quote management panel where quote details, history logs, and communications data are recorded.

Payment on credit option

A credit payment has been added along with PayPal and credit card payment modes. Configurable credit options will allow merchants to set up Minimum and maximum order limits, region-based credit restrictions, monitor customer credit lines, etc.

Advanced Account Management Tools

B2B customers can manage their own company accounts, categorize and organize customer information, import and export customer lists and also designate sales executives to specific accounts.


A handful of features that will help B2B merchants place orders quickly and easily with minimal steps. Several areas where time lag was felt like, shipping, ordering, catalog management etc. have been improved for better productivity.

Improved Shipping Options

Temendo based multi-carrier shipping and out of the box fulfillment that will enable merchants to achieve cost efficiencies. The new update will provide rules-based shipping option along with the ability to manage loading & dispatching of SKUs from anywhere.

Requisition Lists

All frequently ordered products are populated into lists for quick ordering. Customers can select desired products from the requisition lists, edit the quantity and finalize the requisition easily & quickly. Multiple lists based for various purchase scenarios can also be created.

Customized Catalogs

Merchants can create customized catalogs and price lists based on products, product categories & customers. Each catalog pricing can be narrowed down to the each product level for better control.


A handful of other features targeted at making

APIs for B2B

Web APIs for easier & simpler ERP or PIM integration available for new company, credit lines, shared catalogs, quotes, and requisition lists. A REST API to add video to a product description is also part of the new release.

Pipeline Deployment

An automated pipeline deployment process that avoids downtimes, quickens the overall configuration process.

Community Support

Magento’s thriving community of engineers, developers, users and business owners who share quick hacks and remedies for sticky situations.


Well, that’s not it. Magento 2.2 has still more in substance as a refined eCommerce platform. These are a gist of features that shine as the best among all.

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7 Questions To Ask Your Magento B2B Commerce Development Agency

Great businesses are built on the foundation of partnerships. Choosing the right partner for Magento B2B commerce development can be a tricky affair. There are tens of hundreds of development agencies that claim to be good.

But, how do you zero in on a Magento B2B commerce development agency that can deliver what it promises? After all, there is time, money and expensive resources at stake.

Here are 7 questions that will help you appraise your options better and pick at the right Magento B2B commerce development agency.

1.Are You A Full-service Firm?

A full-service firm is an agency that offers end-to-end design, development & deployment services. Compared to a one-man shop, they have a team of professionals who can handle diverse roles and responsibilities with ease. It brings down drastically the delays and inefficiencies that a single person development team would be prone to.

2.What Is The Size Of Your Magento B2B Commerce Team?

Taking sides with a full-service firm is a wise choice, but, do they have sufficient Magento expertise? Ask the Magento B2B Commerce development company the size and breadth of experience of their Magento team. Ideally, they must have a dedicated team made up of experienced and certified Magento developers.

There are four types of Magento certifications:

  1. Magento Certified Solution Specialist
  2. Front End Developer Certification
  3. Certified Developer
  4. Developer Plus

Depending on your project requirement, you can look for a Magento development company that has a team with adequate certifications.

Certified Magento developers will adhere to code standards that will run smoothly without the hindrance of bugs. And, the experience means that work gets done faster with results.

3.Can You List Some Magento B2B Commerce Achievements Of Your Firm?

This is just like asking a candidate about his/her past achievements in an interview. The motive is obvious. Has this company helped its clients overcome critical challenges with its Magento B2B commerce development services?

A company which does not have much to talk about is not an ideal candidate. If they can demonstrate the list problems they solved, the innovative solutions they devised and the final results they delivered, the company is an ideal option to go with. In fact, you can head straight to their website’s case studies section to find all the information to get a heads-up.

4.What is your project management process?

Agile. Agile. Agile.an Agile project management is the perfect partner you can ask for. The Project Management Institute reports that at least 71% of the organizations use an Agile approach for project management. Why Agile project management?

Because it is inherently beneficial in delivering benefits like:

  1. Better project control
  2. Reduced project risks
  3. Faster ROI
  4. Stable releases
  5. Continuous testing

Agile project management methodology ensures that your project turnaround is minimal, which brings us to the next question. So a Magento development company that follows Agile methodology should be your natural choice.

5.What is your average turnaround time?

There is no definite answer to this. Each company would have a different timeline. For a Magento development company with immense B2B expertise and experience, it shouldn’t take long. However, don’t be misled by the assumption that a company which delivers within a short phase of life is good to go it.

Your focus should be in finding out whether the company has a solid process of sticking to a timeline. It is here that a company following an Agile process would prove beneficial. They run sprints with established goals that push the project towards completion at a constant pace.

6.How would I stay in the loop about the project update?

As a stakeholder, you have the right to be updated on the project status, the plans set for the next sprint, bugs that need to be fixed and so on. This is because the project would be usually handled by a person other than the business development executive who attended to your inquiry.

In a well-established firm, there would be a dedicated project manager who would be the one-point contact for everything related to the product. The PM would be responsible for sending status reports, following up bug fixes, attending to client calls for a revision in project requirements, timelines and so on.

7.What if something goes wrong?

Things rarely go wrong with professional firms. But, when they do, there is always a backup plan in place that ensures that things are reset to resume progress. Before engaging the Magento B2B commerce development company, ask for their Escalation Matrix.

An Escalation matrix it the list of people and their contacts who should be alerted when something doesn’t work the way it was supposed to work. The matrix would include everyone from project manager to the director depending on the level of escalation. Without an escalation matrix, holding the firm accountable for its shortcomings would be a problem.

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What makes Magento 2.0 a winner for B2B commerce?

Magento is one among the top 3 most used eCommerce platforms in the world. Magento is such a sturdy platform that even global brands like Nike, Ford Motor Co., Procter & Gamble Co., 3M, Cisco and the likes are using it for their B2B commerce operations.

Here is a look at how much volume of commerce these brands are transacting using Magento:

Companies and their annual B2B Commerce sales for 2015:

  • Ford Motor Co. – $16.71 billion
  • Nike – $13.2 billion
  • Procter & Gamble Co. – $8.97 billion
  • 3M – $8.5billion

Apart from these giant brands, there are also 650,000+ Magento stores functioning in the B2B space.

So, what makes Magento so lucrative an option for these brands?

B2B Commerce-friendly features of Magento B2B

Magento Commerce is engineered to empower B2B enterprises to deliver a modern, intuitive and smooth purchasing process. For B2B enterprises that are already used to the offline route, Magento also paves the way for the digital transformation of legacy commerce systems.

Here are some highlight features of Magento that makes it a winner for B2B commerce.

Customized Pricing made easy

In B2B merchandising, each customer is unique and so are their wants. They want the products to be showcased differently and also priced to suit their budget limits.

Spreadsheets and word processors cannot help achieve a level of efficiency that B2B marketers would expect. Magento B2B Commerce helps assign custom catalogs and price rates for each individual customer that takes care of the entire purchase journey – from proforma invoice to final invoice.

Comprehensive Company Account Management

Magento Commerce enables traversing the many levels of procurement layers found in enterprises. Customers can also track order statuses, punch new orders, monitor credit lines, assign buyer roles and permissions and much more using built-in self-service tools.

One-touch Bulk ordering

Unlike B2C commerce where the orders are transactional in nature, B2B buying usually happens in bulk quantities. Magento makes it easy to process bulk orders. Additionally, there is also an option to set up bulk discounts. The eCommerce platform has flexible rules for a shopping cart, catalog management and product management which makes it easy to process bulk orders seamlessly.

Quick Ordering

B2B businesses need to address customer requisitions on the fly. The luxury of time to create fresh orders for similar orders or replenishments is not there. Magento’s quick ordering facility ensures that orders can be quickly created from saved shopping lists or pre-built templates.

Simple, functional & Interactive CMS

Magento is one of the best eCommerce platforms that gives maximum flexibility and visual-friendliness in merchandising. The Visual Merchandiser feature enables B2B merchants to showcase their products with proper categorization, visual elegance and simple navigation. All this and much more can be done with minimal steps like drag-and-drop.

Mobile-readiness for the mobile generation

Mobile commerce has become a mandate than an option for B2B players. According to Hybris report, 54% of B2B customers are using mobile devices to research and buy products online. Magento Commerce users are primarily business owners who want to make purchase requisitions on the go, from the place where they are, with the device they have. Magento’s mobile-readiness also helps deliver an advantage SEO that is native to mobile-responsive websites.

Built-in reports for quick decision making

With 75 built-in reports and many other customizable report templates for, Magento Commerce is a B2B retailer’s best friend. The interactive report presentation in the form of charts and graphs helps make quick decisions out of an otherwise mathematical mayhem of numbers.

Final Thoughts

All these features and the recent updates rolled out with Magento 2.2 release makes Magento an unbeatable winner for B2B commerce selling. It makes the lives of merchants as well as customer simpler and easier.

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How Chatbot Development Can Rethink Customer Service in eCommerce?

“Should you make your own chatbot for eCommerce?”

Yes, we say!

Chatbots are touted as one of the trending eCommerce trends for 2018 and beyond. It is obvious why retailers are pouring time and money into building a chatbot that will reimagine the quality of their customer service.

The 3Cinteractive Chatbot Progress Report has found that about “77% of consumers surveyed said positive interactions with chatbots make them purchase more often.”

Whether it is for increasing revenue or to render a better customer service or even to automate internal operations, chatbots for eCommerce have plenty of applications.

Why Retailers Should build an ecommerce chatbot?

They personalize the customer experience

From first visit to checkout, an eCommerce chatbot can be a virtual personal assistant with a human logic and machine memory. To quantify the possibilities, an intelligent chatbot will be able to drive conversions as high as 30% through chatbot powered visitor engagement (Source: VentureHarbour).

Machine learning and NLP capabilities would help build the best chatbot for ecommerce that can enable to chatbot to analyze the customer’s past preferences and predict their next purchase intent and also offer tailor-made product recommendations. Advanced data crunching technologies like Big Data would further empower chatbots to deliver a targeted customer experience that can heighten brand engagement.

Chatbots will help overcome one of the biggest challenges of traditional marketing tactics like email marketing. These tactics often greeted the customer with out-of-context information or overly promotional content. A chatbot can supply the customer with content that is more relevant to the customer’s preferences. In fact, studies have proven that customers are ready to spend as much as $314 on products suggested by chatbots.

Multi-visitor Engagement

Unlike human personnel, chatbots can assist multiple visitors simultaneously. They can provide canned responses to recurring queries or even provide dynamic responses tailor-made to individual customers based on their transaction history.

Chatbot development services This delivers a two-fold advantage for eCommerce retailers. Consistent interaction with customers will help keep bounce rate to a bare minimum. Secondly, visitors who have their queries attended to in a personalized manner have a higher probability of converting into paying customers. Global Think Tank Gartner has predicted that by 2019, 20 Percent of User Interactions With Smartphones Will Take Place via VPAs (Virtual Personal Assistants).

Data mining for customer sentiment analysis

Is the customer’s query filled with angst or is it a common query that most users tend to ask? An intelligent chatbot would be able to do an accurate sentiment analysis by deducing the text pattern to rate the query as positive, negative or neutral.

Data mining customer interaction and the text inputs exchanged between the customer and the chatbot would help in predicting customer sentiment. It would aid in rendering a predictive customer service that will cement brand loyalty.

Quick resolutions for customer queries

Chatbots can be trained Entity Extraction from text. Entity extraction from text means identifying information like person’s name, location, store name, device name, unique identification number, etc. This would help the chatbot answer the customer queries proactively without asking more questions.

For instance, a customer who cites the unique identification number of his device can be served by the chatbot in his native language. More information like whether past service history of the product, warranty period, nearest authorized service centre can be provided by the chatbot without pestering the customer for too much of the information.

Anomaly Detection

Retailers are plagued with the risk of fraudulent transactions schemed by miscreants who leverage loopholes in the system. For example, a flawed return policy which allows the customer to return a purchased item even after its warranty period. Or a faulty delivery process that leads to more returns or failed delivery attempts.

Using anomaly detection, a chatbot would be able to single out such instances for further investigation. This would enable the retailer to perfect the selling process that is free of revenue leaks. The benefit that chatbots provide above manual analysis is that, unlike a human data scientist, they have the capability to predict such scenarios before they happen.

Closing In

With Machine Learning and Artificial Learning maturing as affordable technologies, the cost of building a chatbot has also reduced considerably. Retailers can bank on chatbots to take their business volumes to new heights. Chatbots are the most easily relatable manifestation in which retailers can use these technologies.

From cementing solid customer relationships to taking the heavy burden of customer service away from personnel, chabots can deliver high on several areas where constant challenges plague retailers. Partnering with a reliable chatbots development company who can provide the know-how and the technical assistance is a must-have.

From gaining insights to delivering instant value, a chabot can be an everlasting source of value for eCommerce retailers.

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100 eCommerce metrics to Drive 10x Growth in 2018

The most successful online stores track their eCommerce metrics with an obsessive interest. That is what gives them 360-degree control of their business. But, it can be hard to zero in on the right metrics that will give your efforts a proper direction. That is why we compiled this list of 100 eCommerce metrics.

As the old saying goes, information is power, but it is those who use it properly, truly prosper. If you are in the business of selling products online, be it B2C or B2B or B2B2C, here are some 100 eCommerce metrics you can track and help your business grow by 10x or beyond in 2018.

At Ziffity we believe that eCommerce metrics is a lifeline for your business, the more you use them to measure, act and evolve, the better your eCommerce business performs, be it Operations, Marketing, Sales or Customer Service.


Daily, weekly, monthly, quarterly

  1. Total Turnover
  2. No: of orders
  3. Average order value
  4. New vs existing customer sales


  1. Total Turnover
  2. Average margin
  3. Conversion rate
  4. Shopping cart abandonment rate

Overall/category Level

  1. Total cart purchased vs abandoned
  2. Market share
  3. Product affinity analysis

Top 10 Things to look out for

  1. Top 10 abandoned products
  2. Top 10 lowest margin making products
  3. Top 10 products to stock up
  4. Top 10 products to discontinue
  5. Top 10 out-of-stock products which were searched


  1. Top 5 pages where sessions are abandoned
  2. Competitive pricing
  3. Top searched product/category
  4. Average revenue per customer


Monthly Marketing Spend

  1. Marketing spend vs rev
  2. Marketing spend vs net profit
  3. Sales driven by personalization campaigns
  4. Pay-per-click metrics (impression/CTR/etc)
  5. Ad spend mobile/web
  6. Revenue mobile/web
  7. Engagement rate mobile/web
  8. Total value offered in promotions
  9. $ Value offered by promotion type

Website Traffic

  1. Site traffic (overall/by source)
  2. Unique visitors versus returning visitors
  3. Average user dwell time
  4. Page views per visit
  5. Top product pages by views
  6. Organic search metrics

Website Traffic

  1. Newsletter subscribers
  2. Social followers
  3. Social engagement metrics

Website Traffic

  1. Revenue by Source (email/PPC/social/etc)
  2. Day part monitoring
  3. Total sales from referral sites
  4. Customer acquisition by campaign/promotion
  5. Sales by campaign/promotion
  6. % of shopping cart revived thru follow-ups
  7. Sales from revived shopping cart
  8. Sales by demographics (sex/age/interest)
  9. Next quarter top selling products (predictive)

Top 10

  1. Top 10 products whose price can be raised
  2. Top 10 products whose needs reduction
  3. Top 10 lowest rated products
  4. Top 10 content by dwell time
  5. Top 10 referral sites
  6. Top 10 highly competitive products
  7. Top 10 products which needs to be abandoned
  8. Top 10 trending products (aggregate of sale/pageviews/reviews)

Campaign Responses

  1. Email campaign metrics (open/CTR/etc)
  2. Engagement rate with personalization campaigns
  3. Product reviews (day/month)
  4. Drop-offs by stage (journey)
  5. Drop-offs by stage (journey) by product
  6. Average Clicks to Buy (CTB)
  7. Heat maps for all pages (find patterns)
  8. Top 5 journeys used for buying
  9. Least used journeys for buying
  10. Last interaction attribution model
  11. First interaction attribution model
  12. Assisted interaction
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How Blockchain Technology Can Deliver Value To The Retail Industry

Blockchain’s immutable ledger, cryptography and decentralized database bring to retailers several benefits at all stages, all the way from farm to customer doorstep.

Omni-channel commerce has leapfrogged into a trillion dollar industry. It has turned the world into a small retail village where consumers are able to reach out and pick any product of their choice without any difficulty.  

But, this convenience has brought a long list of operational challenges for retailers.

Some evident challenges include:

  • A restricted view of the supply chain due to several intermediaries
  • Ensuring product authenticity amidst Counterfeit products, stolen goods and fake labels
  • Complicated shipping and logistics processes that are repetitive in nature

Blockchain As A Value Driver For The Retail Industry

Blockchain can provide the retail sector with several bold and innovative use cases that will help address the above-mentioned challenges.

Ensuring Transparency In SCM

In Supply Chain Management, traceability of inventory is critical for pricing and profitability. The existing systems require retailers to compile information from multiple sources like manufacturer, supplier, 3rd party logistic players, etc. to know the real-time location of inventory.

Due to the manual processes involved and lack of real-time data capture, inventory planning ends up being a guesswork which ultimately leads to a phenomenon called the ‘Bullwhip effect’.

Bullwhip effect is caused when the retailer raises too many orders for a product thus causing piling up of inventory and increase in inventory carrying costs like warehouse rent, carriage, octroi, etc.

Blockchain can prevent the occurrence of Bullwhip effect and similar SCM inefficiencies by providing suppliers, retailers and logistic players with real-time digital information. This will help improve inventory demand forecast accuracy and reduce dead stockpiling or stock-out situations.

Identifying Stolen Or Counterfeit Goods

According to the Organisation for Economic Co-operation and Development’s Trade in Counterfeit and Pirated Goods publication, “5% of goods imported into the European Union are fakes.” This amounts to a staggering USD 461 billion.

Luxury items like fashion clothing, premium watches, electronic gadgets, home appliances, toys, etc. are the most counterfeited goods.

Blockchain can help customers and retailers ascertain product authenticity and quality at all stages of its transit and distribution. All product information, including ingredients, item number, place of origin, etc. can be tracked in the uneditable digital ledger.

This availability of information will improve customer trust and also help retailers prevent counterfeit goods from taking a share of their revenue.

Uncomplicating Paper-based Shipping Procedures

Sending a consignment from one port to another involves processing a bulk stack of paperwork like Bill of Lading, Certificate of Origin, Letter of Credit, etc. This paperwork has to be scrutinized, approved and re-approved at several junctures by shippers as well as port authorities. This processing delays the clearance of the goods often reducing their shelf-life and putting retailers at the risk of missing a timely entry into the market.

Blockchain with its decentralized record keeping can remove the need for this paperwork. It will create a common platform for scrutiny and exchange of confidential records related to the consignment. This will help accelerate the pace at which goods can be shipped to ports and subsequently cleared from customs to retailers.

The Way Forward

Blockchain is a relatively new concept. Retail is one among the many industries where it will radically change the way everyday business is carried out.

Retailers who are proactive in adopting Blockchain will be able to attain an early-mover advantage. Of course, there are inherent challenges like resistance to organizational change, migrating massive data volumes from legacy systems, training stakeholders and so on.

However, the benefits that immutable digital ledger can bring to a retail business outweighs all these initial hiccups. It is time for retailers to move fast and embrace Blockchain as the future way of doing business.

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How Machine Learning Can be a Game Changer for eCommerce

Machine learning in eCommerce can help reduce cart abandonment, increase time spent on the website, maximize conversion rates and serve plenty of other benefits.

Sounds too optimistic? Think about this. eCommerce stores are basically online shopping malls. They produce tons of data on a real-time basis that can give straightforward hints about customer preferences and behavior.

Machine Learning algorithms can analyze and make meaningful conclusions from massive amounts of data that humans, even with supercomputers cannot sit through. In the eCommerce industry, machine learning can help fill improve the customer experience at several junctures.

The opportunity is so promising that retail giants like Amazon and Walmart are leaning on machine learning to enhance omnichannel customer shopping experiences.

A machine learning system can replace an entire human-powered process, thus, removing the possibilities of errors, predicting anomalies and accelerating decision-making; all at fractional costs.

Here are some ways how Machine Learning combined with Artificial Intelligence will help eCommerce stores like Amazon, Macy’s, eBay, Flipkart and the sorts maximize their sales volumes.

Autonomous Chatbots

We live in the era of conversational commerce. Customers are increasingly communicating with brands through instant messaging apps and social media platforms before buying a product. Even a large chunk of after-sales service begins with a conversation.

Chatbots powered with Machine Learning and Natural Language Understanding (NLU) capabilities can engage in real-time conversations with customers. They can help customers pick the right product that best suits customer preferences.

eBay’s ShopBot is a classic example of chatbots in eCommerce.

Autonomous chatbots can deliver a personal shopping experience to customers by giving personal attention. They become personal shopping assistants who continuously learn from customer inputs and deliver accurate responses that are closely aligned with the customer’s historical interests and requests.

As they say in the business world, “A well-attended customer is one who returns for more shopping, which translates into a bigger customer base and larger sales volumes.”

Dynamic Product Suggestions

Upselling and Cross-selling are two strategies that almost every eCommerce store deploys to maximize their revenue. But, they cannot be easily implemented. Manual picking of products for can cost expensive man hours and is literally impossible. And, there is also the inherent risk that the manual suggestions may not be best fit for the customer at all.

Machine Learning can pitch in here with its swift data analytic abilities and zero in on products that customers might be interested to buy. They can give dynamic product suggestions that help customers ‘complete the look’ or bundle together products for convenient use. Machine Learning helps offer multiple bundle suggestions to customers, thus persuading them to buy more than what they had initially planned for.

This ultimately swells the sales volume for the eCommerce business without any additional cost or manpower requirements.

Market-driven anchor pricing

Online retailers have to rely on extensive anchor pricing strategies to drive sales. They have to slash margins and fluctuate prices in a moment’s notice to stay competitive and to retain their customers.

There are also holiday season sales which makes it extra difficult to fix market-driven prices. Often, when there is no other go, eCommerce store owners resort to anchor pricing based on a guesswork. Sometimes it works, most often, it fails.

Machine Learning can take away the guesswork in anchor pricing by giving accurate inputs that are aligned with market trends. The ML system gives adequate weightage based on preset parameters, like which competitor pricing should be given more weightage, what products should be prioritized, etc.

It gathers, analyses and throws out data that helps retailers fix prices with a competitive advantage. To deliver accurate results in this model, Machine Learning systems follow the IFTTT (If This Then That) principle which works as below:

Airlines, online travel operators, online hotel booking websites were the first to embrace Machine Learning for perfecting their pricing strategies.

Real-time Data Analytics

Data is like fuel for eCommerce industry. It helps create the right product mix, pricing and allied services that will maximize conversions. While analytic tools like Big Data are gaining momentum, there is still a gaping hole that the eCommerce industry is staring at.

It is still difficult to put finger on the right data and derive meaningful patterns from it. It is here that Machine Learning pitches in. it helps segregate, sort, group, cluster and analyze data in various forms which simplifies decision-making. Some metrics also aid in the long-term planning of the business thus helping strengthen profitability from the grassroots level.

Here is some real-time data analytics measure that Machine Learning can provide:

  • Customer Segmentation
  • Churn prediction
  • Cart abandonment reasons
  • Sentiment analysis
  • Inventory management & forecasting
  • Anticipatory shipping and planning

Bringing It All Together

Machine Learning will make computers intelligent and powerful than they are today. Computer systems will evolve through continuous learning based on constant flow of data from humans. At some time in the future, as most scientific visionaries like Stephen Hawking and Elon Musk opine, machines will have an upper hand on humans.

eCommerce will gain significantly from Machine Learning. It will help predict customer behavior, understand real-time data and take right decisions at the right time that will lead to more sales volumes.

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6 Solid Reasons why Every Business Needs A Chatbot

Look at these two seemingly unrelated facts from the mobile world.

  1. Mobile-first has eventually become commonplace.
  2. 6 out of Top 10 mobile apps are messaging apps.

Mobile-driven conversations are driving businesses, fostering an on-demand culture and doing everything else that was deemed impossible a decade ago.

There is another trend that is gaining in strength. Chatbot driven commerce. Mobile and chatbots form the perfect channel for businesses to reach their customers easily, quickly and cost-effectively.

Chatbots make it easy for users to find the right information quickly and easily, just like asking a friend.

As Mark Zuckerberg said in the Facebook Messenger Bot launch event, “You should message a business just the way you would message a friend.”

There are two primary forms of engagement that chatbots can drive for a business:

  1. Task-oriented engagement
  2. Data-driven or Predictive engagement

Task-oriented engagement
Chatbots that get things done for users. In task-oriented engagement, the user gives the input to the bot which the bot reads (& understands). The bot does the task of connecting with the app or the data backend to fetch information and perform the task as requested by the user.

Data-driven or & Predictive engagement
Data-driven & predictive engagement chatbots work similar to Google Assistant or Amazon’s Alexa. They constantly learn from the user inputs and detect patterns. Their intelligence has great application in eCommerce where customer requirements can be predicted to drive more conversions.

Why are chatbots essential for businesses?

Solving customer queries quickly should be the prime focus of all businesses. Especially in today’s digitally-connected world, a business that is quick to respond earns a positive brand image in the customer’s mind. While human customer service assistants are awesome at their work, there are inherent limitations which chatbots can eliminate.

Chatbots can unlock a business’ ability to provide interactive, personalized and proactive customer service that humans are yet to become capable of. And, they can be scaled at the drop of a hat unlike recruiting and setting up an entire customer support team.

Here are some more reasons why chatbots are essential for every business that want to serve its customers better:

#1. They raise business awareness
Your customers could interact with your business through diverse channels – website, mobile app or social media. A chatbot can deliver a consistent and personalized interaction through all these channels. Chatbots help present or make your product/service reach in front of prospective customers at the most opportune moment.
#2. They facilitate customer acquisition
Chatbots have a conversational interface. There is a place to give text input which they can understand using NLP (Natural Language Processing) capabilities. The conversational interface helps collect queries from customers and serve them better, instantly. For instance, a messenger bot can make a booking and confirm it with an email instantly without much ado.
#3. They simplify business transactions
Recurring transactions like account checks, balance confirmations, stock alerts, etc. can be automated by a chatbot. Their sturdy machine memory backend helps collect information quickly and deliver them to customers without the delay that is inherent to manual processes.
#4. They provide real-time support
Desperate calls to customer services often tend to get ugly when there is no immediate resolution. Sometimes all that the customer needs would be a simple confirmation or a quick answer on how to fix something. Chatbots can provide that information on a real-time basis, and with minimal steps without going about in rounds.
#5. They are easily scalable
Chatbots are fundamentally computer programs. They can be scaled quickly by expanding the software and hardware requirements. This makes chatbots a perfect ally for startups who are just growing and enterprises that are already millions of transactions.
#6. They help in customer data mining
All the input, the questions, the remarks, the frequency of questions – all the data that the chatbot receives from users can be collected and archived for analytics. This customer data mining helps in understanding the needs and aspirations of customers better.

What next?

In today’s hyper-connected mobile world, chatbots can be a welcome change for customer servicing. They can bring about a new range of speed, accuracy and intelligence in customer servicing that will eliminate all the shortcomings of a manual process.

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