6 E-Commerce Personalization Trends

 

According to research conducted by BigCommerce, 80% of people in the U.S. make purchases online at least once a month. While the majority of people shop on large marketplace websites like Amazon, roughly 30 to 40% also purchase from online retailers like web stores, independent boutiques, and category-specific e-retailers. This year we’ll see several e-commerce trends emerge with the potential to transform how we shop and communicate with online stores. 

 

1) Mobile first 

In 2016, mobile commerce sales accounted for over $25 billion in sales, an increase of more than 25%. This figure is predicted to continue to increase, reaching over $30 billion in 2017, and $42 billion by 2020. Mobile traffic has overtaken the desktop. Google is working on a new, mobile-first web index and marketing strategy. According to these trends, it’s critical to business to put mobile first, rather than simply optimizing for mobile.

 

2) Digital payments

Mobile payments are on the rise as smartphone users adjust to the convenience of contactless payments. Starbucks was an innovator in mobile payments, becoming one of the first to offer products based on an online payment model. In 2016, an increasing number of customers adopted digital payment methods, using their phones, cards and even watches to complete contactless payments. PureNet predicts that every customer will expect to be able to complete their transaction using a digital wallet in real time.

 

3) Personalization and Customization

The amount of data available increasingly allows content, ads, and products to be shown to the right people at the right time, across the web and social networks. The continuing sophistication of personalized recommendations will be trending for ecommerce this year. Retailers will embrace personalized experiences – even using e-mail marketing to cater personalized emails to consumers, rendering unique customer experiences. and Artificial Intelligence by starting to collect behavioral data like browsing history, geo location, and social media from website visitors and developing highly targeted campaigns based on customer segments and previous online experiences.

 

4) Fast delivery

Ecommerce delivery timescales have reduced drastically and become much faster, boosting customer relationships with ecommerce sites. Delivery and returns have always been one of the pain points of the online shopping experience, but improvements in technology have improved delivery significantly. PureNet predicts same day delivery will continue to rise in 2017, and more ecommerce integration with drop-off point providers such as Doddle.

 

5) Technology

Technology will continue to play a greater role in automating the retail experience. With the help of the latest technology, sales people will be able to focus on building relationships with customers to increase sales. In order to stay competitive, businesses must invest in high-performing ecommerce solutions. Providers such as Charter Global, apply leading technology tools to deliver customized ecommerce solutions to mid-size and Fortune 1000 companies. Experienced providers offer expertise across multiple, proven Cloud-based ecommerce platforms such as SAP HANA, SAP Business One, Magento, PCI DSS, Spring MVC (Framework ), Magnolia CMS/ Blossom module, HTML5, CSS3, JQuery, and Struts.

 

6) Chatbots

Chatbots are AI-led automated messenger services that allow your customers to engage with your brand via instant messenger via machine learning. Over the last few years messaging apps have exploded in popularity, and thanks to ecommerce personalization, artificial intelligence technology has been adopted by many large retailers. This year will see these two trends combine to deliver a new way of shopping and communicating.

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What is Usability?

So what exactly is “usability” with respect to software and application development? Usability means making products, systems, websites and web applications easier to use, matching them more closely to user needs and requirements. A website very often serves as the first impression to your customers – and you need to ensure it is a good one to keep them coming back. Good usability and user experience should capture the best possible human computer interaction.

 

A highly usable system doesn’t just offer benefits to the users, but to your business as well.

 

The primary benefits to users are that they can achieve their tasks easily and efficiently. This sounds simple, but the feeling of achievement that people get when they use a computer system without frustration should not be underestimated.You don’t want your users getting frustrated because they can’t navigate your site. Consider your website as the first impression your customers get, and you want that introduction to leave a pleasant memory, not a bitter one.

 

Components of Usability:

 

• Effective – Effectiveness is the completeness and accuracy with which users achieve specified goals.
• Efficient – Efficiency can be described as the speed in which users can complete the tasks for which they use the product.
• Error – The ultimate goal is a system which has no errors.
• Easy to Learn – How easy is it for users to accomplish basic tasks the first time they encounter the design?
• Satisfaction – How pleasant is it to use the design?

 

How to Improve Usability:

 

There are many methods for usability, but the most basic and useful is user testing.

 

User testing is different from focus groups, which are a poor way of evaluating design usability. Focus groups have a place in market research, but to evaluate interaction designs you must closely observe individual users as they perform tasks with the user interface. Listening to what people say is misleading: you have to watch what they actually do.

 

Usability’s Role in the Design Process:

 

Usability plays a role in each stage of the design process.

 

• Before starting the new design, test the old design to identify the good parts that you should keep or emphasize, and the bad parts that give users trouble.
• Conduct a field study to see how users behave in their natural habitat.
• Make paper prototypes of one or more new design ideas and test them. The less time you invest in these design ideas the better, because you’ll need to change them all based on the test results.
• Refine the design ideas that test best through multiple iterations, gradually moving from low-fidelity prototyping to high-fidelity representations that run on the computer. Test each iteration.
• Once you decide on and implement the final design, test it again. Subtle usability problems always creep in during implementation.

 

Usability Guidelines and Standards:

 

Usability guidelines and standards can be useful to provide ideas about usability issues, but they must be assessed to determine whether they are relevant to the users and context.

 

As an example, a common website guideline is that all content should be available within three clicks. The intent of this guideline is good – it highlights that people should not have to click endlessly for information. However, there are situations where it is not applicable. Large sites or sites with information designed to present increasingly detailed pages may not meet the guideline, but may be very usable for the intended audience.

 

Guidelines and standards are best used to identify the most obvious usability problems and fix them before a usability test is conducted.
 

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DevOps and the Modern Workplace

 
In the DevOps and Agile world, CIOs express four primary concerns concerning governance, risk management, build-versus-buy decisions, and enterprise architecture. Contrary to waterfall development, which is limited to IT control of product delivery, DevOps involves collaboration across the entire business organization. In order to achieve business goals, cross-functional teams need to adopt both business and technology skills simultaneously.

 

The DevOps Perspective

 

Cross-functional teams can take ownership of the strategic goals, working together to accomplish them while maintaining accountability for the outcomes. This is opposed to passing requirements and product back and forth between IT and the rest of the business. Now, it’s possible to explore diverse approaches, test hypotheses, and invent new ways of doing tasks.

With DevOps, teams can experiment on ideas and receive feedback quickly, as well as put together their plans from the results they get. Instead of being given a set of requirements and a deadline, the team can be charged with an outcome.

These are the most common concerns a CIO faces when dealing with DevOps:

 

Enterprise Architecture:

 

Traditionally, enterprise architecture plays a role of standardizing and constraining. However, that is not the case in an agile world. Because architectures evolves through teamwork, enterprise architecture should play a hands-on role, simplifying and guiding the enterprise to a flexible and agile architecture.

 

Build Versus Buy:

 

Conventionally, buying off the shelf seems to be preferred when possible. However, building allows for a more user-centric, incremental delivery process, which ensures quick feedback as well.

 

Risk:

 

Acknowledge and get accustomed to risky processes before eliminating or moderating them, as risk-based returns are often underestimated. When it comes to IT leadership, the consequences of agile and DevOps thinking are far-reaching. It is time for IT leaders to take personal responsibility for business outcomes, rather than being passive recipients of business requirements.

 

Governance:

 

Usually, companies make governance decisions at the granularity of projects or programs. Decision making becomes tedious and time consuming when it is made at a singular level,  aiming to fulfill one requirement at a time.

 

Governance has gone beyond the formal process of approving project proposals. Rather, by generating available resources, true business objectives are more readily visible. In a digital world, IT plays the role of providing requirements rather than accepting requirements from the business.

 

DevOps involves an approach where cross-functional teams engage in a process utilizing fast-feedback loops and constant adjustments to keep concurrent with businss objectives. The IT team is a part of the business teams, working closely together, discovering better ways to meet enterprise needs.
 

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Don’t Make these 3 Social Recruiting Mistakes

 

Social media recruiting efforts involve more than just job posts

 

By Leila Kojouri

 

Social networks have changed everything about the way we interact with one another. The hiring process in particular has seen some major developments in the past decade with regard to sourcing qualified candidates and top talent across every industry.  One such development is the rising popularity of leveraging a social media recruiting strategy.  

 

Formerly, paging through classified ads in the local newspaper was considered a great way to find and connect with employers. Now, the internet serves as a one-stop-shop for hiring managers and job seekers alike.

 

Social media has become a markedly effective recruiting tool, especially with platforms like Monster, Indeed, or better yet, LinkedIn. Virtual resumes are intimate, living documents; with LinkedIn, for example, empowering candidates with real-time endorsements and recommendations previously unavailable in any other medium. The stats don’t lie:

 

• 70% of HR mangers confirmed successful hiring via common social media channels.
• 80% of employing agents affirm the possibility of finding most suitable candidates with social recruiting.
• Talent hunting and hiring via social media is being achieved by 91% of employers.

 

Yet, as with any powerful technology, discretionary caution should be excersiced to avoid making common mistakes. 

 

Here are some identified social recruiting mistakes and approaches to prevent them from happening:

 

#1: Using Social Media Only for Publishing Job Openings

 

Job seekers are interested in much more than just the description inherent in your job postings. As a recruiter, simply posting job openings isn’t enough. Candidates want to know what direction they’re heading in  – what’s the company culture like? What does a day in the life at ABC Corp really look like? Will they be entering a dull, lifeless environment detrimental to professional growth, or can they trust your company to cultivate their best talent and give them room to grow?  With all of the information available on the internet nowadays, without an online presence, even passive candidates will forego job descriptions and take their business (or yours) elsewhere.

 

Bottom line: Don’t limit your posts to job-related openings; rather, build a following by sharing diverse, unique content.  Display employer brand, culture, and personal brand. Showcase a day in the life of an employee on and off the job. The more relatable, the better. Does your company offer childcare services? Let’s see what the daycare facility looks like! Do work perks include a gym membership? Post some footage of the top-notch equipment they’ll have available to them 7 days a week. 

 

Does the employer contribute to any charitable causes? Showcase a fundraiser and display why your company is more than just a workplace. Giving back to the community, for some, is a deal-maker. Create and post contents that highlight your brand’s uniqueness, culture and image. Leave the job posting shop-talk to phone screenings and interviews.

 

#2: Not Prioritizing Interaction

 

You should do more than attract likes and comments. Consistent interaction with potential talents should be your priority as a recruiter. You need to build powerful connections with your target audience.

 

What to do: Build relationships by interacting with other users. Facilitate peer-to-peer discussions on and off the clock. Join job groups and forums – or better yet, become an admin in a group or forum to establish trust with candidates. Job seekers insecuriies quickly dissolve when they see how openly and freely you communicate. 

 

Engaging with other posts in addition to your own is vital. Make it a point to get involved with other users – commenting, cross-posting, and even back-linking your own content in others comment sections is a great way to fortify this engagement and cultivate your online personality. This will quickly and steadily build rapport and grow social relationships with a target audience. However, bear in mind that you have to invest quality time for this purpose.

 

#3: Uncertainty in Preferential Candidates before Posting on Social Media

 

Ah, the proverbial stabbing in the dark. Without first determining who your ideal candidates are, chances are, you won’t get very far if you’re efforts are going unnoticed. More importantly, you’re probably using the same approach when you post a different job opening. In order to source the right talent pool, you need to make certain your casting the right net. 

 

Think of it this way: A skilled fisherman doesn’t simply drop his line into the nearest body of water. Several variables are taken into consideration, like forecasting the weather and the tide – and that’s just part of his routine. He also needs to understand the direction of the current, the type of water he’s fishing in, and most importantly, the particular fish he’s looking to catch. 

 

What to do: Think like the fisherman. Know your target – or candidate – and the essential characteristics and traits (plus goals, employment history, skills and more) you’d like them to possess. 

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What are the Benefits of Machine Learning in IoT?

 

Machine Learning is a valuable player in the realm of the Internet of Things. ML and Internet of Things (IoT) have gained tremendous popularity over the past few years, considered by many as revolutionary, game changing tech. Yet, much confusion exists in terms of understanding the purpose of Machine Learning, along with it’s benefits and suitability for use. 

 

Here’s a breakdown of Machine Learning, benefits of ML in AI and IoT, when it should be used, and it’s real-world applications today.
 

Data Analytics vs. Machine Learning

 

With all the aforementioned hype around machine learning, many organizations are asking if applying machine learning could benefit their business model. In the vast majority of cases, the answer is a resounding no. In the case of big data, however, Ml may prove very useful.

 

Machine learning takes large amounts of collected data and generates useful, real-time insights that help the organization based on it’s inherent learning capabilities. That could mean improving vast amounts of processes, cutting costs, creating a better experience for the customer, or opening up new business models.

 

The thing is, most organizations can get many of these benefits from traditional data analytics, without the need for more complicated machine learning applications.

 

Traditional data analysis are great at explaining data. You can generate reports or models of what happened in the past or of what’s happening today, drawing useful, insightful conclusions about your organization.

 

Data analytics can help quantify and track goals, enable smarter decision making, and then provide the means for measuring success over time.

 

When Is Machine Learning Valuable?

 

In general, machine learning is valuable when you know what you want but you don’t know the important input variables to make that decision. So you give the machine learning algorithm your stated goals, or inputs. Based on learning systems, and then it “learns” from the data which factors are important in achieving that goal.

 

The data models that are typical of traditional data analytics are often static and of limited use in addressing unstructured, fast-changing, sequestered amounts of data. When it comes to IoT, it’s often necessary to identify correlations between dozens of sensor inputs and external factors that are rapidly producing millions of data points.

 

In addition, ML  has the ability to accurately predicting future events. Whereas the data models built using traditional data analytics are static, machine learning algorithms constantly improve over time as more data is captured and assimilated. This means that the machine learning algorithm can make predictions, see what actually happens, compare against its predictions, then adjust to become more accurate.

 

The predictive analytics made possible by machine learning are hugely valuable for many IoT applications. Let’s take a look at a few concrete examples.

 

How are Machine Learning Applications used in IoT?

 

Cost Savings in Industrial Applications:

 

Predictive capabilities are extremely useful in an industrial setting. By drawing data from multiple IoT sensors in or on machines, machine learning algorithms can “learn” what’s typical for the machine and then detect when something abnormal begins to occur.

 

Predicting when a machine needs maintenance via IoT data is incredibly valuable, translating into millions of dollars in saved costs. A great example is Goldcorp, a mining company that uses immense vehicles to haul away materials.

 

When these hauling vehicles break down, it costs Goldcorp $2 million per day in lost productivity. Goldcorp is now using machine learning to predict with over 90% accuracy when machines will need maintenance, meaning huge cost savings.
 

Shaping Experiences to Individuals:

 

We’re actually all familiar with machine learning applications in our everyday lives. Both Amazon and Netflix use machine learning to learn our preferences and provide a better experience for the user. That could mean suggesting products that you might like or providing relevant recommendations for movies and TV shows.

 

Similarly, in IoT machine learning can be extremely valuable in shaping our environment to our personal preferences.

 

The billions of sensors and devices that will continue to power connected devices, smart homes, and IoT devices in the coming years will generate exponentially more data. This huge increase in data will drive great improvements in machine learning, opening countless opportunities for us to reap the benefits.

 

Not only we will be able to predict when machines need maintenance, we’ll be able to predict when we need maintenance too. Machine learning will be applied to the data from our wearables to learn our baseline and determine when our vitals have become abnormal, calling a doctor or ambulance automatically if necessary.

 

Beyond individuals, we’ll be able to use that health data at scale to see trends across entire populations, predicting outbreaks of disease and proactively addressing health problems.

 

Although both machine learning and IoT can be over-hyped, the future of machine learning applications in IoT are worthy of that hype. We’re really just scratching the surface of what’s possible.

 

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