Global companies spent over $2.83 billion on marketing analytics in 2020. This figure certainly increased in light of the pandemic, as digitization accelerated.
Marketing has always been about numbers. Now, those numbers are highly refined, narrowed by algorithms and databases, and processed by people with advanced degrees. Indeed, data and marketing are a match made in heaven, taking much of the guesswork out of a profession that once was as much about luck as it was about creativity.
In this article, we look at how data impacts marketing. We also review what it takes for a business’ marketing division to find real success with their data implementation efforts.
Technique Matters:
Proper data analysis is very method dependent. Businesses that wish to use data in their marketing efforts first need to consider what data analysis techniques are right for them, and how they can use them to improve outcomes.
Some degrees specialize in data-driven marketing. To truly master this art form, consider pursuing an advanced degree in data analysis, or investing in staff with the appropriate background.
Knowing Your Audience
The best thing about data in marketing is that it helps you understand who your audience is. This is critical not only in how you describe your product but also in how it is framed. If your audience consists primarily of middle-aged business people, you’ll probably want to reach for a more formal tone. If your audience is millennials, humor might be more appropriate.
Data reveals the story that marketers need to tell.
Knowing What Features to Emphasize
Your product can probably do a lot more things than you are going to want to fit into a Tweet or a short ad. A successful marketing campaign knows how to emphasize the features that will appeal to the largest number of people.
Of course, not every marketing campaign is about casting the widest possible net. Numbers can also help you narrow the focus of your messaging by zeroing in on what features your best customers respond the most to. Not only does this maximize the impact of your ad efforts, but it also helps attract ideal customers: people who stick around, spend lots of money, and offer referrals.
How to Market Your Product
Data can also make your outreach efforts significantly more impactful. Most social media sites feature their own ad analytic software that helps you see who your demographic is and when they are most likely to be online.
Using this information, you can create targeted ads that only show up during the peak web traffic periods. Not only does this boost ad engagement but it also makes sure you aren’t wasting money.
The Necessity Of Making it A Data-Driven Culture
It’s important to understand that half measures will never produce any of the results listed above. Companies all too often invest heavily in data infrastructure, buying tools and software subscriptions that never get used, or worse yet, get used poorly. Superficial data implementation can lead to:
High rates of turnover: Employees who have little or no tech experience are often very discouraged when they are told they need to master a new software tool. It’s important to allocate a significant amount of time (months) to training. No one should be expected to master the tech overnight. True data implementation is a long-term investment and should be treated as such.Wasted Tech: On the flip side, some people will just ignore new software entirely. The average American worker has a company-provided tech stack filled with tools they don’t understand and never use. Why? Usually, it comes down to company leadership. If management isn’t taking data implementation seriously, the staff won’t either.Half-baked conclusions: Finally, poor data implementation also just produces bad results. Unless the training is significant and the tools are on point, the conclusions generated by a data implementation strategy are not going to produce the results you are looking for.
A true data-driven culture stems from the top down. Management must take the adaptions seriously, work towards understanding and implementing themselves, and check in regularly with the rest of the staff, not to breathe down their necks, but to address concerns and see how they can help smooth the transition along.
Analytics is becoming more important than ever in the world of business. Over 70% of global businesses use some form of analytics. This figure will rise as globalization, supply chain challenges and other factors increase competitiveness.
This is an important year for enterprises keeping in view that most global industries are recovering from the pandemic horror, and the era of web 3.0 is at the doorstep. For both reasons, the role of CIOs has to embrace automation and analytical thinking in strategizing the organization’s initiatives. Until now, they were proactively involved to maximize IT efficiencies and accelerate cost savings in general.
However, the rapid technology change, the increasing demand for user-centric processes and the adoption of blockchain & IoT have all positioned business analytics (BA) as an integral component in an enterprise CoE. They are using analytics to help drive business growth. And since no one except CIOs has the technical nerve of a business; they are seizing the opportunity to increase value to the organization while positioning themselves as thought leaders.
While we are at it, Gartner’s 2022 report on business composability further pushes the need for analytics. It suggests the immediate need for decision-makers including CIOs to embrace automation to absorb the pressures of the rapidly changing business landscape.
Continuous Building of BA practice in your organization
Even though business analytics operates across organizational verticals and all levels of the hierarchy, a CIO owns the responsibility to ensure the building of the practice. It requires a deep-rooted understanding of the organizational workflows and the scope of analytics in making significant improvements. Therefore, building BA is a continuous practice and often involves partners to kick-start.
One such education institution called Adaptive US collaborates with multiple organizations with a focus on building/improving their BA practice. Such platforms provide full-stack training solutions for IIBA certifications & several training programs for professionals of different work experience groups. Various educational groups deliver sessions in different formats such as self-learning, guided learning, live & recorded sessions, exam prep sessions, office hours and others to suit the learning needs of the various kinds of learners.
CIOs, in collaboration with such groups, can categorize their practice building into 3 focus areas:
Team Upskilling: Train business analysts on planning, gap analysis, scoping & blueprinting, cost-benefit calculation of new initiatives, solution architecture, modelling, elicitation, requirement management, performance management, and other improvement initiatives. Process Upgrade: Ensure following industry standards and benchmarking the process against the Business Analysis Body of Knowledge (BABOK) and modernize them.Tools Updating: A business analyst is as good as his/her tools. Having effective tools is vital to the organization’s success. Having cost-effective and high-quality business analytics tools such as Atlassian, MS Visio, Business Process Modeller, Balsamiq, and similar BA tools is essential for org initiative improvement.
Not to miss, Cloud analytics are increasingly dominating their on-premise predecessors. It is growing at a CAGR of 23.0% and shall touch USD 65.4 bn by 2025.
Executive Portfolio Management
Unlike previously when only corporates would consider portfolio management as an important arm of C-level decision making, scenes are different today. Given the on-demand accessibility of data, start-ups and SMEs want their CIOs to manage resource investments through application portfolios, infrastructure portfolios, and project portfolios.
Since there’s vast information at disposal, business analysis has a crucial role for CIOs. Here, the dashboard could include project health elements such as cost, schedule, scope, ROIs, feedback, value to the partner, evaluation of meaningful outcomes, and management hierarchy to name a few. Not to miss, a low-level (detailed) view of such a dashboard would include a problem statement, business case, status, budget, delegation, etc.
The answers captured above further enable a CIO to create a top-level view and evaluate how optimal is the spending, how progressive is the pipeline and ultimately how on track are the committed deliverables.
Extract Value From Customer
Predictive analytics have an unquestionable influence on drawing patterns around consumer behavior and their likelihood to either re-subscribe or discontinue the service. Based on the engagement with the product, the customer experience team, a key part of the CIO’s team has to score customers on these metrics and position them in the growth funnel, also known as the pirate funnel.
This sales/marketing funnel can consume insights from BA to predict the probability of upselling. Subsequently, the outcome of the tool/funnel would prompt the ‘sales’ to offer incentives or other rewards against an upsell or referral to the potential customers. This analytical ability to predict customer churn can assist insetting appropriate goals but also strengthening their financial planning area as discussed previously.
Financial Management
Financial planning is a critical area and involves all stakeholders including CIOs. For most organizations, it sets the narrative for project forecasting, recruiting, scaling, and others. CIOs, along with everyone on the leadership board use finance models to anticipate any hurdles.
For CIOs, financial planning coupled with business forecasting through analysts is a primary role. So if the overall spending trails the expected forecast, it’s a good indicator and an opportunity to discover newer avenues that could work for the organization. At the same time, if the spending exceeds the expected limit, CIOs have to identify weak links and rewrite the quarterly plan.
Financial visualization in key areas can fuel analytical decision-making. These include budgeting, forecasting, actuals & accruals, variance & absolute variance, spending economics, vendors, contracts, and more.
Conclusion
Organizations waking up to the importance of analytics at C-level decision-making need no detailing. However, the market trends in the post-pandemic era ask for rapid adoption and CIOs are at the helm of that transformation. So far, we discussed the confluence of business analytics across verticals and how that changes the role of CIOs. From the discussion, it is inevitably clear that this association will further strengthen in the web 3.0 era. Organizations that have captured the hint will have a lasting stint in the competition.
If you are building a career in business analytics, you are a step closer to building a profession as a future CIO.
Businesses have been using websites to reach customers for nearly 30 years. The first websites predated modern analytics technology. Google Analytics wasn’t launched until 2005. However, advances in analytics over the past decade have made it easier for companies to create quality websites. This has in turn increased the demands of customers using modern websites.
If you are trying to create a great website, then you have to leverage analytics tools to improve the quality of your site by assessing the right metrics. Keep reading to learn more about using analytics to optimize your website.
Analytics is Crucial for Optimizing Websites
Your website is only as brilliant as the amount of effort you put into it. A well-built site will automatically respond to your visitors by tailoring content for their needs. More thoughtful websites make users happier, generate more leads and conversions, and increase the odds that they will return.
A more imaginative visitor experience takes time to accomplish. However, making investments will give you the best return based on your goals and resources is best.
Web optimization positively impacts your revenue, whether you profit from advertising or sales via content distribution. You can use sophisticated digital and behavior analytics technology.
Analytics technology can measure the effectiveness of your website’s elements, reveal weak areas, and show you what needs improvement. You can track every performance component, including network bandwidth usage, user interaction patterns, and system response times.
One of the best ways to use analytics in website optimization is to test different value propositions. This will help you choose the best for your website.
A value proposition is a message that your site sends to visitors. It’s the promise of what you’ll provide, including customer service and products or services.
Your site should include offers highlighting your product or service and highlight what makes it distinguishable from other options available. It’s also vital that the value proposition is tailored to your audience and does not come across as generic or insincere.
You want to use Google Analytics or another website analytics tool to split-test different value propositions. You can create a Conversion Goal in Google Analytics and test the effectiveness of each value proposition to see which ones work the best.
Optimize your visual creatives
You will also want to use analytics technology to test different visual creatives. You can evaluate many different images, banners and buttons by setting up conversion goals in Google Analytics or creating heat maps with analytics tools like Crazy Egg.
Visuals are a super important part of any website. It’s a great way to bring your website to life and help you stand out in the crowd. Consider investing in photography and video production.
Visual content can build awareness, increase engagement and generate sales. It’s also great to share personal stories and experiences with your target audience. Remember that maintaining clear and consistent visual elements, including color, graphics, and contrast, is essential.
Therefore, you should use analytics tools to carefully optimize all of your visual creatives.
Focus on mobile responsiveness
Mobile devices account for a significant portion of all web traffic. To meet the growing needs of your customers, make sure your website is mobile-friendly.
Your site should also be compatible with multiple mobile devices, including smartphones, tablets, and small handhelds. Remember to include instructions and information about any mobile device-specific features.
Analytics technology helps you see how customers respond to different elements, so you can tweak them accordingly. You can also use AI applications that make websites more responsive.
Prioritize accessibility
Analytics technology can also help make your website more accessible to people with disabilities. Tom Widerøe has an entire article on Medium on this topic titled Can we use web analytics to improve accessibility?
Web standards allow people with disabilities to access websites, including those with impaired vision and hearing loss. For example, you can include alternate content such as text descriptions or transcripts for visual media, braille transcripts for audio content, and text-to-speech technologies for video and audio content.
Analytics technology helps identify the areas where people with disabilities will struggle to access your content. However, you will need to know what changes to make.
Alternatively, you can also use screen readers to provide access for blind users to multimedia content. If you aren’t already implementing these principles, it may be an excellent time to review your site’s accessibility and make any necessary updates.
Identify key integrations
You can also use analytics technology to identify outdated plugins or other issues. Then you can take measures to update your site.
One of the quickest ways to improve your site’s functionality is by integrating it with other web and mobile applications. Integrate your website with email marketing solutions, digital dashboards, and lead management services.
Site owners can use plugins that add to their existing customer platforms, such as shopping carts, and eCommerce platforms, such as Shopify. You can also integrate your website with social media platforms like YouTube, Facebook, and Twitter.
Test different calls to action
A clear CTA is vital for creating a memorable experience for your website visitors. Chances are, if your site lacks one, you may be missing the opportunity to keep your website visitors engaged with the content.
Compel users to take action and increase the chances of making a purchase, subscribing to your email list, or downloading your content.
Again, you will want to use analytics tools to test different calls to action. Setting up conversion goals in Google Analytics will help a lot.
Update regularly
Your website must reflect the same quality, functionality, and aesthetic style you display in your marketing materials. If not, then you’re quickly losing traffic to competitors.
You may find it easier to implement updates after the initial launch, so there is less risk of messing up or forgetting about them later. You can also use the timed release of new features as a way to ensure a consistent level of improvement throughout the life cycle of your website and content.
Analytics Technology is Essential for Website Optimization
With a more imaginative visitor experience, you’re more likely to generate better results. With transparent analytics and progress tracking, you can measure the success of your efforts and closely monitor website performance. Using effective web design, you can quickly build a more substantial online presence while retaining the interest of your online customers.
Data science is both a rewarding and challenging profession. One study found that 44% of companies that hire data scientists say the departments are seriously understaffed. Fortunately, data scientists can make due with fewer staff if they use their resources more efficiently, which involves leveraging the right tools.
There are a lot of important queries that you need to run as a data scientist. You need to utilize the best tools to handle these tasks.
One of the most valuable tools available is OLAP. This tool can be great for handing SQL queries and other data queries. Every data scientist needs to understand the benefits that this technology offers.
Using OLAP Tools Properly
Online analytical processing is a computer method that enables users to retrieve and query data rapidly and carefully in order to study it from a variety of angles. Trend analysis, financial reporting, and sales forecasting are frequently aided by OLAP business intelligence queries. (see more).
A user can ask for data to be examined so that they can see a spreadsheet with all of an industry’s beach ball products that are sold in Florida in July, compare revenue statistics with all those for almost the same items in September, and compare other demand for a product in Florida during the same time period.
Several or more cubes are used to separate OLAP databases. The cubes are structured in such a manner that it is simple to create and read reports. Online Analytical Processing (OLAP) is a term that refers to the process of analyzing data online.
Data processing and analysis are usually done with a simple spreadsheet, which has data values organized in a row and column structure. For two-dimensional data, this is ideal. OLAP, on the other hand, comprises multidimensional data, which is typically collected from a separate and unrelated source. Using a spreadsheet isn’t the best solution.
What is the mechanism behind it?
The data is processed and modified after it has been extracted. Data is fed into an Analytical server (or OLAP cube), which calculates information ahead of time for later analysis. A data warehouse extracts data from a variety of sources and formats, including text files, excel sheets, multimedia files, and so on.
Types:
HOLAP stands for Hybrid Online Analytical Processing. The consolidated totals are saved in a data model in the HOLAP technique, while the particular data is maintained in a relational database. This provides both the ROLAP model’s data efficiency and the MOLAP model’s performance.
OLAP on the desktop (DOLAP): In Desktop OLAP, a person receives a dataset from the system and analyzes it locally or on their desktop.
Portable OLAP: Mobile OLAP allows users to use their device to access and evaluate OLAP data.
Web OLAP (WOLAP) is an OLAP system that can be accessed through a web browser. The WOLAP architecture is three-tiered. There are three parts to it: a client, software, and server software.
SOLAP (Spatial OLAP) was developed to make it easier to manage both temporal and non-spatial data in a Gis Mapping (GIS).
ROLAP (Relational OLAP): ROLAP is an extended RDBMS that performs typical relational operations using multidimensional data mapping.
A single OLAP cube cannot have a significant number of dimensions.OLAP necessitates data organization into something like a snowflake schema. These schemas are difficult to set up and maintain.Any change to an OLAP cube necessitates a complete upgrade of the cube.An OLAP system cannot access transactional data.
The Benefits of OLAP
There are also benefits of using OLAP, which include:
OLAP is a business platform that encompasses strategy, planning, monitoring, and analysis for many types of businesses.In an OLAP cube, data and calculations are consistent. This is a significant advantage.Search the OLAP data for broad or particular terms with ease.Create and analyze “What if” scenarios quickly.Corporate simulation models, and performance reporting tools all use OLAP as a foundation.It’s useful for looking at time series.Users can slice up cube data using a variety of metrics, filters, and dimensions.With OLAP, finding clusters and anomalies is simple.
The online analytical processing tool, also known as the OLAP, is a technology which helps the researchers and surveyors to look into their business from the various overviews.
The current world is undergoing a rapid transformation as a direct result of the many scientific breakthroughs and technological advancements enabling the production of an abundance of intelligent gadgets, appliances, and systems.
Such intelligent devices, gadgets, and systems encompass automation, smart sensor networks, communication systems, and various other gadgets. Examples are robotics, smart cars, intelligent transport systems, and home robotization.
The management of parking lots is not a simple job for businesses and other organizations since there are many moving factors, such as the flow of traffic and the number of available places.
It is an activity that needs human effort, takes a lot of time, and is wasteful overall. Utilizing a parking management system may assist a company in lowering the administrative expenses associated with parking and lessening their parking space’s influence on the neighbourhood in which they are located.
Smart Parking is a low-barrier solution because it can be installed quickly and easily, is scalable, and is economical. It is the ideal solution for contemporary cities that wish to leverage the power of the internet of things while providing possible advantages to their residents.
Deep Learning Technology has started being used increasingly in managing parking areas. Learn more here.
What is Deep Learning Technology
Deep learning is a kind of machine learning that involves teaching machines to understand in the same way people do naturally, via observation and imitation of others.
Deep learning is one of the most important technologies underpinning autonomous vehicles since it allows them to detect things like stop signs and tell a human from a signpost.
It is essential to implement voice control in consumer electronics like mobile phones and tablets, televisions, and hands-free speakers. Recently, many emphases have been focused on deep learning, and for a good reason.
It means accomplishing goals that could not have been done in the past. A computer model may learn to execute categorization tasks directly from pictures, text, or voice using a technique known as “deep learning.”
Models trained using deep learning have the potential to attain an accuracy that is comparable to or even superior to that of humans.
And What is Parking Management System
Innovative technologies that answer problems in the parking business are collectively referred to as park management systems.
The fundamental concept that underpins any parking management system is easy to understand. People, businesses, and other organizations may benefit from this approach to better manage parking areas.
Car Parking Problems That the Modern Drivers Are Facing
Nearly two-thirds of U.S. drivers described experiencing worried when searching for parking, as per the research. The research also found that almost 42% of Americans had missed a meeting, 34% had aborted a visit due to parking challenges, and 23% had been the victim of bad driving.
Secured parking has become more sought after owing to two primary trends: development and a rise in automobile ownership. Drivers are having difficulty finding a parking space due to an increase in automobiles on the road.
Drivers in highly crowded locations lose a lot of money, production, and time due to the inefficiency of the existing parking structure. This irritates motorists and adds to the volume of traffic, increasing commuting times by 35 percent.
As a result, typical parking systems aren’t up to the task of making parking easier for drivers while lowering the amount of time they spend searching for a spot.
This demonstrates the logic for using cutting-edge technology in urban transportation to modernize the system and alleviate the difficulties experienced by drivers.
Ways Through Which the Deep Learning Technology Helps Parking Management Systems
Here are some of the most prominent ways through which the Deep Learning Technology helps parking management systems improve:
Reduces Search Traffic on Streets
The hunt for parking spots is responsible for over a third of all of the congestion that can be seen in metropolitan areas. Cities can better manage and reduce the amount of parking search traffic on the roads thanks to parking management solutions.
This software also assures parking safety, but its most significant contribution to reducing traffic congestion is that it makes the parking experience quicker, more pleasant, and less burdensome overall.
There will be fewer automobiles driving in circles around the neighbourhood looking for a parking place due to the use of intelligent parking technology.
This finally makes traffic flow smoother and reduces the amount of search traffic on the streets to the greatest extent feasible.
Eliminate Parking Stress
Most people want to avoid travelling to the more crowded parts of the city because they do not want to find themselves caught up in the parking problem that causes tension and anxiety among drivers.
It is frustrating to know that you will have to spend a lot of time looking for parking spots but that you will ultimately have to park your vehicle in a location that is quite a distance from where you need to be.
In addition, it is a source of frustration to have to drive up and down the same street many times to locate a parking spot, but to no avail.
The goal of developing Deep Learning Technology is to remove the stress associated with parking for motorists.
The users of the smart parking apps are informed about the available parking spots in the region they want to visit.
The uncertainty and stress involved with locating a suitable parking place close to the intended location are reduced as a result.
Minimize the Personal Carbon Footprint
One additional advantage of smart parking is that it helps cut down on the pollution caused by automobiles by reducing traffic and the need for drivers to move about as much as they do now.
When drivers have to go from one location to another for parking, this increases the individual environmental footprint.
In the United States, the average time spent searching for parking by car is about twenty minutes, which wastes fuel and time and contributes to increased congestion in metropolitan areas.
The longer it takes to find a parking spot, the greater the carbon footprint left behind; however, this time may be cut down significantly with the assistance of an intelligent parking solution.
There is a negative effect on the environment caused by the carbon dioxide emissions produced by all types of fuel, including diesel, gasoline, and fossil fuel.
The fact that the emissions from personal carbon footprints caused by automobiles do not immediately affect human existence is another unfavorable aspect of these emissions. Still, they might be a component in the progression of climate change.
When intelligent parking solutions are incorporated into metropolitan areas, there is a subsequent reduction in a person’s environmental footprint, particularly the amount of carbon dioxide released.
A Time and Cost-Efficient Solution
Intelligent parking technology may result in time and financial savings for motorists. When the drivers reach the crowded parking lot, they spend several minutes looking for a location to park their vehicles.
Since of this, eventually, they are wasting their time, which causes them to get upset because the drivers cannot arrive at the required place on time.
Similarly, travelling more kilometres in search of parking causes an increase in wasted gasoline, which in turn causes an increase in the amount of money spent by the drivers of the automobile’s fuel.
A system based on the Internet of Things (IoT), Smart Parking is outfitted with sensors that convey data to apps regarding empty parking places.
Instead of spending time and fuel driving around in circles looking for a parking spot, the drivers may use this application to guide them to the now available places.
Helps Consume Less Fuel
Deep Learning Technology is a consequence of human inventions and technology that allows easy access to parking places and helps save precious resources, including fuel, time, and space.
Deep Learning Technology is an outcome of human innovations and advanced technology. Deep Learning Technology provides easy access to the parking spots. When Deep Learning Technology is implemented in metropolitan areas, cars are directed directly to vacant parking places.
This makes the most efficient use of limited space. Because of this, there is no longer a need to drive additional kilometres to locate available parking places.
Because of this, using the Deep Learning Technology results in less wasted gasoline, which eventually saves the drivers money and makes the parking experience more pleasant for them.
Now Businesses Have More Commercial Parking for Rent!
Now you know how Deep Learning Technology has impacted the parking management system. In the world that we are living today, such innovations have made major advancements to accommodate people and businesses.
One major advantage for businesses is freeing up many spaces for a commercial parking lot for rent. Businesses have used data algorithms to ensure they stay ahead of the curve and generate sustainable profits.
Machine Learning (ML) is part of everything we do at Wayfair to support each of the 30 million active customers on our website. It enables us to make context-aware, real-time and intelligent decisions across every aspect of our business. We use ML models to forecast product demand across the globe, to ensure our customers can quickly access what they’re looking for. Natural language processing (NLP) models are used to analyze chat messages on our website so customers can be redirected to the appropriate customer support team as quickly as possible, without having to wait for a human assistant to become available..
ML is an integral part of our strategy for remaining competitive as a business and supports a wide range of eCommerce engineering processes at Wayfair. As an online furniture and home goods retailer, the steps we take to make the experience of our customers as smooth, convenient, and pleasant as possible determine how successful we are. This vision inspires our approach to technology and we’re proud of our heritage as a tech company, with more than 3,000 in-house engineers and data scientists working on the development and maintenance of our platform.
We’ve been building ML models for years, as well as other homegrown tools and technologies, to help solve the challenges we’ve faced along the way. We began on-prem but decided to migrateto Google Cloud in 2019, utilizing a lift-and-shift strategy to minimize the number of changes we had to make to move multiple workloads into the cloud. Among other things, that meant deploying Apache Airflow clusters on the Google Cloud infrastructure and retrofitting our homegrown technologies to ensure compatibility.
While some of the challenges we faced with our legacy infrastructure were resolved immediately, such as lack of scalability, others remained for our data scientists. For example, we lacked a central feature store and relied on a shared cluster with a shared environment for workflow orchestration, which caused noisy neighbor problems.
As a Google Cloud customer, however, we can easily access new solutions as they become available. So in 2021, when Google Cloud launched Vertex AI, we didn’t hesitate to try it out as an end-to-end ML platform to support the work of our data scientists.
One AI platform with all the ML tools needed
As big fans of open source, platform-agnostic software, we were impressed by Vertex AI Pipelines and how they work on top of open-source frameworks like Kubeflow. This enables us to build software that runs on any infrastructure. We enjoyed how the tool looks, feels, and operates. Within six months, we moved from configuring our infrastructure manually to conducting a POC, to a first production release.
Next on our priority list was to use Vertex AI Feature Store to serve and use AI technologies as ML features in real-time, or in batch with a single line of code. Vertex AI Feature Store fully manages and scales its underlying infrastructure, such as storage and compute resources. That means our data scientists can now focus on feature computation logic, instead of worrying about the challenges of storing features for offline and online usage.
While our data scientists are proficient in building and training models, they are less comfortable setting up the infrastructure and bringing the models to production. So, when we embarked on an MLOps transformation, it was important for us to enable data scientists to leverage a platform as seamlessly as possible without having to know all about its underlying infrastructure. To that end, our goal was to build an abstraction on Vertex AI. Our simple python-based library interacts with the Vertex AI Pipeline and Vertex AI Features Store. And a typical data scientist can leverage this setup without having to know how Vertex AI works in the backend. That’s the vision we’re marching towards–and we’ve already started to notice its benefits.
Reducing hyperparameter tuning from two weeks to under one hour
While we enjoy using open source tools such as Apache Airflow, the way we were using it was creating issues for our data scientists. And we frequently ran into infrastructure challenges, carried over from our legacy technologies, such as support issues and failed jobs. So we built a CI/CD pipeline using Vertex AI Pipelines, based on Kubeflow, to remove the complexity of model maintenance.
Now everything is well arranged, documented, scalable, easy to test, and well organized in terms of best practices. This incentivizes people to adopt a new standardized way of working, which in turn brings its own benefits. One example that illustrates this is hyperparameter tuning, an essential part of controlling the behavior of a machine learning model.
In machine learning, hyperparameter tuning or optimization is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. Every machine learning model will have a different hyperparameter, whose value is set before the learning process begins. And a good choice of hyperparameters can make an algorithm perform optimally.
But while hyperparameter tuning is a very common process in data science, there are no standards in terms of how this should be done. Doing it in Python using a legacy infrastructure would take a data scientist on average two weeks. We have over 100 data scientists at Wayfair, so standardizing this practice and making it more efficient was a priority for us.
With a standardized way of working on Vertex AI, all our data scientists can now leverage our code to access CI/CD, monitoring, and analytics out-of-the-box to conduct hyperparameter tuning in just one day.
Powering great customer experiences with more ML-based functionalities
Next, we’re working on a docker container template that will enable data scientists to deploy a running ‘hello world’ Vertex AI pipeline. It can take a data science team more than two months to get a ML model fully operational on average. With Vertex AI, we expect to cut down that time to two weeks. Like most of the things we do, this will have a direct impact on our customer experience.
It’s important to remember that some ML models are more complex than others. Those that have an output that the customer immediately sees while navigating the website, such as when an item will be delivered to their door, are more complicated. This prediction is made by ML models and automated by Vertex AI. It must be accurate, and it must appear on-screen extremely quickly while customers browse the website. That means these models have the highest requirements and are the most difficult to publish to production.
We’re actively working on building and implementing tools to streamline and enable continuous monitoring of our data and models in production, which we want to integrate with Vertex AI. We believe in the power of AutoML to build models faster, so our goal is to evaluate all these services in GCP and then find a way to leverage them internally.
And it’s already clear that the new ways of working enabled by Vertex AI not only make the lives of our data scientists easier, but also have a ripple effect that directly impacts the experience of millions of shoppers who visit our website daily. They’re all experiencing better technology and more functionalities, faster.
For a more detailed dive on how our data scientists are using Vertex AI, look for part two of this blog coming soon.
Artificial intelligence has become a very important technological development in the life sciences. Michel L. Leite and his colleagues at Universidade Católica de Brasília addressed this phenomenon in their study Artificial intelligence and the future of life sciences.
AI is helping advance the life sciences in many ways, which include improving the outcomes of clinical trials and making certain features more accessible to both researchers and patients. One of the benefits of AI in the life sciences is in the field of cryopreservation.
Cryopreservation is the practice of preserving cells and tissues. It is a complex process where things can easily go wrong, but AI technology can help mitigate some of the challenges.
How Can AI Improve the State of Cryopreservation?
There is a growing need for stem cell storage which makes the idea of starting a business around this practice can be very profitable. Companies like IBM have also been involved in biological research. However, the preservation of the cells can be a very complex challenge to overcome. Getting the cells preserved while also maintaining their structural integrity is complex and is the reason why so many labs will outsource storage.
There are many benefits of using artificial intelligence in these settings. They include improving safety and making sure biological materials are stored optimally. It also helps with bioinformatics.
In order to appreciate the benefits of using AI, it is important to first understand the issues clinicians and researchers will face. Biological material needs to be preserved for long periods of time and it is essential that it be kept in the same exact condition by the end of the process as it was in during the beginning. If you are trying to do this as a medical field business then it is important to understand the process well. In this article, we will go over the essentials to keep in mind when you are trying to use a cryopreservation liquid nitrogen freezer for stem cell preservation.
Here are some ways that AI can help with this process.
1 – Identify the best medium with machine learning
Every system is going to need its own medium to successfully preserve the samples. It is crucial to understand which one is going to give you the right results.
Cryoprotectants are used for a few primary reasons. The first is to slowly freeze the sample while also reducing the salt concentrations. Secondly, they decrease cell shrinkage, and lastly, they minimize intracellular ice formation. With all that in mind, it is usually the case that you’ll need Glycerol, salts, and dimethyl sulfoxide (DMSO).
Some of these protectants are more effective at protecting biological systems that are cooled quickly since they promote extracellular glass formation. It’s important that the water remains liquid during this process which requires a temperature just below the glass formation stage.
For some applications, ready-to-use solutions are the best idea as they are the most effective and also easy to use. They also work with various media for great results. Unfortunately, they may not be best for all storage options.
AI technology is useful for identifying the best mediums to store biological material. Many labs carefully track data on the different storage systems they use and figure out which ones seem to work best under different conditions. An application with AI monitoring tools can recommend the best medium for a given material based on readily available data.
2 – Use the right method
The freezing method is as important as any other part of the process. There are several different methods to choose from but the one that’s best for you is the one that will give you specific results with your specific medium and sample.
The most common method and one that works very well with many different media is liquid nitrogen. It’s a popular choice due to it being chemically inert, non-flammable, and often the most economical.
Another common method is using Direct Temperature Feedback which does also use liquid nitrogen as it’s injected directly into the chamber and automatically adjusts for freezing rates.
When using a small number of straws or vials, the Plunge Freezing method is useful as it controls the cooling temperature by submerging the block of vials which controls the freezing rate.
AI technology can make it easier to automate the use of these materials. You can use a robot that relies on machine learning technology to handle these processes, so you don’t have to expose yourself to harmful liquid nitrogen and can accurately control the use of these materials.
3 – Safety
The lab environment that uses a lot of nitrogen can be quite dangerous if protocols are not adhered to. Since it is odorless and colorless, it can be inhaled without knowing. It is important to make sure that all containers are properly sealed and stored in a way that will prevent them from exploding or becoming contaminated with other chemicals.
This is another area where AI technology can be very helpful. More clinicians and researchers are using AI to improve the safety of their labs.
Consumers today want retailers they do dealings with, to provide them with simplified and personalized services. If you fail to meet their standards for an effective and focused product offering, most of them will quickly take their money elsewhere.
According to many surveys, more than 90% of retail organizations across a wide range of sectors feel location data is crucial to their success.
In fact, the popularity of location data has grown so much that it is expected to touch about $35 billion in value by 2027.
In this article, we will talk about nine ways location data can help you excel in retail.
9 Ways Location Data Can Help You Excel in Retail
Retailers can utilize data analysis to uncover trends in consumer behavior in order to make wiser decisions about product development, price, customer engagement, and so on.
From the most basic to the most complicated concerns, data may assist guide your decision-making strategy.
You may go beyond mere purchase behavior by incorporating social media activity into marketing techniques specific to your key demographics.
As the current business world has changed rapidly owing to technology and other advancements, the most agile merchants have thrived and even prospered, typically by employing data-driven tactics.
Enhance the client experienceBetter Understand Customer DemographicsForecast customer behaviorImprove pricing decisionsHighlights Expansion PotentialImprove product display in-storeReduce customer acquisition expensesImprove the online customer experienceCustomer persona identification
Here it goes.
1. Enhance the client experience
Data analysis is critical for designing experiences that meet today’s consumers’ desire for personalization. Retailers of all sizes may benefit from current analytics systems’ capacity to take enormous volumes of data and provide relevant insights.
This information may be used to match a customer’s interests to the interaction your brand provides.
This application can take the shape of developing items, adapting marketing strategies, or generating recommendations based on the products that a consumer is most interested in.
Accurate client data collection and analysis are critical to maximizing all of these activities.
2. Better Understand Customer Demographics
A marketplace, or the geographic region from which a retailer acquires its clients, is crucial to the longevity of a retail shop establishment. So, your retail personnel must make sure that you open stores in locations where your target consumers reside or visit regularly.
Using retail site selection mapping software to gain better insights into your client base will allow you to give more appropriate services and goods. Such software provides you with real-time data and behavioral insights about the people who live and travel near specific retail locations.
You will learn much more about the demographic trends of your intended market’s clients. Additionally, you can also examine where customers like to purchase or visit in their leisure time.
This will immediately improve your leads. It will also help you to better plan your marketing and spend more in specific age brackets because everybody in that region would pick your store first.
3. Forecast customer behavior
Consumer data gives you a glimpse into future customer behavior by allowing you to examine it for trends. This may include purchase history, customer habits, and other crucial data points.
This method entails examining patterns and connections uncovered when recording client interactions with your company at various touchpoints.
You may anticipate future behavior by analyzing this data to establish the major elements driving client purchase decisions, and then utilize these predictions to steer your branding and marketing development efforts..
4. Improve pricing decisions
Given how easy it is for people to price shop on the internet these days, merchants want as much info as feasible when formulating pricing determinations.
Making these judgments entails doing more than just evaluating your internal cost/profit assessment. It also involves engaging in competitor intelligence gathering to design a pricing strategy that is most likely to succeed given market circumstances.
Pricing information may also assist you in determining when giving discounts or other promotions is most likely to help you sell things.
5. Highlights expansion potential
Retail location data also aids in the finding of ideal sites by evaluating what interests them and their profit potential. As a consequence, it is an essential instrument for doing market research and conducting extensive competition analysis.
It is also vital to discover external elements that influence the success of your present and anticipated enterprises. Intelligent mapping allows corporate executives to see how certain regions are evolving, helping them to have a competitive edge in a variety of sectors.
6. Improve product display in-store
Product placement in-store is critical for increasing retail sales.
Products that are likely to be purchased on impulse, for example, are often positioned closest to the checkout register to optimize their sales potential.
By observing how different techniques function in real-time, data analysis may assist support diverse product placement methods.
7. Reduce customer acquisition expenses
Data analytics allow you to see which of your web marketing initiatives are working and which aren’t. Rather than waiting days or weeks, as with conventional marketing initiatives, digital marketing gives real-time or near-real-time information that may be used to enhance your marketing efforts.
Reduce client acquisition expenses by eliminating low-performing portions of your ads and accentuating top-performing features.
Campaign data analytics also aids in the scalability of paid advertising campaigns, allowing you to increase traffic to the sites and advertisements with the highest conversion rates.
This tailored method outperforms allocating advertising expenditures over a variety of channels without analyzing each channel’s performance in terms of ROI and client acquisition cost.
Analyzing your marketing and website data allows you to avoid wasting money on strategies that don’t provide a favorable ROI. By reducing the speculation associated with marketing, expenditures that might otherwise be squandered may be allocated to channels that have been demonstrated to be fruitful.
Any channel activity that does not eventually lead to sales and revenue should be eliminated in web marketing.
8. Improve the online customer experience
When customers visit your website, they generate data points that may be mined for important insights about what works and what doesn’t on your site.
Statistics such as average time on page, bounce rate, conversion rate, and others may be used to perfect your website and make changes to make it more appealing to visitors.
The longer people stay on your site, the more likely they are to convert, and the higher your conversion rate.
9. Better identify customer persona
Client data may be used to segment your customer base based on demographics and other factors, allowing you to better understand who and where your consumers are.
You may create an ideal customer persona by assembling the attributes of your top customers and using them in product development and marketing efforts.
Given digital marketing’s capacity to accurately target certain groups of individuals, having a good grasp of your model client persona is critical for improving your marketing initiatives.
Location Data is Helping Retailers Improve their Competitive Edge
That’s all for this article. Hopefully, after reading it, you are compelled to use location data for your retail business too. Remember, customers these days are looking for personalization and wouldn’t mind taking their business elsewhere if you fail to meet their needs.
Retailers who want to prosper in the future months and even years can’t risk making choices without using data. Agreed, establishing the correct processes and understanding how to gather intelligence may take some effort, but the information and insights you obtain will make it all rewarding.
How are you utilizing retail insights in your company? Tell us in the comments.
Author – Tuba Sarosh
Tuba Sarosh is a versatile copy and content writer and editor, who helps businesses turn their readers into clients. She writes about trends, tips, how-tos, and other cool stuff that helps businesses serve their customers better. When not writing, she’s either reading a good book or experimenting with recipes.
Artificial intelligence is gaining traction in different sectors, including customer service. Call centers alone spent over $3.4 billion on AI in 2019, but the market for AI in customer service sector as a whole is much greater.
AI technology offers an opportunity for businesses to enhance their services while learning more about their customers. Interestingly, AI can enhance customer service in almost all elements, including improving response time and providing predictable insights.
Some people might be aware of the potential benefits of AI in customer service on a rational level, but don’t appreciate it from a visceral perspective. They are more amenable to the idea after hearing that 91% of top companies use AI to boost their customer service strategies.
Below is how AI supercharging your customer service.
Multichannel Support
Customers prefer different channels when engaging with a business. However, some businesses find it challenging to maintain multiple channels at once. AI offers an intelligent support system that encompasses parallel support channels. Overall, AI in customer support ensures a total balance in the support system. This is actually one of the biggest customer service challenges that can be solved with AI. Besides meeting customer needs, AI also enables agents to fulfill their service commitments and relieve loaded support channels.
Most businesses can be overwhelmed with a high volume of customer queries, which can be impossible for agents to handle. However, with AI, bots provide an effective way to manage a high volume of customer inquiries. AI can also collect actionable insights on each interaction and estimate the time the next customer will wait before interacting with a live agent.
CRM Compatibility
AI can easily complement the role of CRM in your business. CRM assists in using customer data to understand their needs, but AI can step in and unveil means to offer a more personalized experience. AI uses customer data like age, gender, region, and sales history to understand their needs quickly. Additionally, AI integrated with CRM systems can assist in automating tasks in return saving time. You can engage entities like Goodway to understand how your CRM can be integrated with AI.
Improved Response Time
Most customers appreciate businesses that respond to their queries promptly. Relying on human input can result in unnecessary delays. However, leveraging AI to build a customer engagement platform can automatically deploy behavioral data from the customer to determine the right message. Additionally, using AI can monitor how different customer messages perform and then suggest the correct response in real-time.
Data Gathering
With AI, data collection is simplified significantly and unified to create a single customer view. The data is collected based on factors like the customers’ behavioral patterns. With advances, AI can quickly request the correct data without human interference. The data is analyzed and instantly responds to the needs and sentiments of the customer. Most importantly, eliminating human intervention guarantees the accuracy of the data collected.
Prediction and Insights
Insightful predictions on customer behavior can be used for future policies impacting customers based on their choices. Additionally, AI can suggest the best action for agents through understanding the proper responses to the customer-generated ticket.
Chatbot Customer Support
Customer support staff are tasked to handle multiple calls every day. They also face the challenge of reducing the time it takes to resolve customer inquiries per hour. Chatbots play a critical role in answering customer queries promptly and accurately in real-time.
AI is Invaluable to Customer Service Strategies for Most Brands
Artificial intelligence is changing many aspects of modern business. One of the overlooked departments that is evolving in response to advances in AI is customer service.
Customer service is the backbone of any industry. Based on the highlighted benefits, businesses need to leverage emerging technologies like AI to improve the customer experience to sustain growth. It is important to utilize the best AI tools to get the most of your customer service strategy.
Data breaches are becoming increasingly common each year. The costs of data breaches are becoming worse, which can easily cripple many companies. An estimated 60% of companies that fall victim to data breaches have to shut down within six months. As a result, 10% of all companies file for bankruptcy due to data breaches.
Companies are not wasting any resources trying to mitigate the growing risk of data breaches. They are hiring security experts that can fortify their security to stop cybercriminals in their tracks. If you want to do your part to fight data breaches, you should consider getting a network security certification and join the front lines against these cybercriminals.
Getting a Network Security Certification Can Help You Thwart Data Breaches
The security of the network is critical to the overall security of any organization, especially since data breaches have become so common. There were 4,145 publicly recorded data breaches last year, which exposed more than 22 million records. This figure is going to increase as cybercriminals become bolder.
Unfortunately, fighting data breaches is easier said than done. Most organizations have large numbers of devices such as computers, laptops, servers, and other equipment that are all interconnected with one another and this network is always vulnerable to attacks. Communication between devices occurs on a regular basis, and the sensitive data that passes through them is usually shared among them.
It is, therefore, necessary to ensure the security of the network and the devices that are connected to it. This is one of the most important lines of defense against data breaches. To build a better infrastructure where enterprises face fewer threats and can better protect themselves from cybercriminals trying to steal their data, organizations must be constantly on the lookout for qualified individuals to join their teams.
To properly configure and secure a network, firms look for applicants that are experienced in debugging, configuring, troubleshooting, and securing these devices. Candidates that have received training are always in demand, and organizations is always looking to hire them to fight data breaches.
Certification is the first step in this direction because it demonstrates the worth of a candidate. To obtain the certification, a candidate must pass an exam that typically contains a large number of different questions and may also require completion of a practical exam. Several certifications are available that can be beneficial to those who want to advance their careers in the field of network security.
Let us talk about a couple of them.
CATO SASE Expert Level 1
Gartner has developed a new enterprise networking technology category called Secure Access Service Edge (SASE) that brings together network operations and security point solutions to form a unified, global cloud service.
Because of SASE, which advances networking technology, many organizations are shifting their attention to SASE and recruiting individuals who are capable of handling and implementing it in their organization. Given that Cato Cloud was the first SASE platform, Cato Networks’ SASE Expert Level 1 certification course has successfully provided a comprehensive understanding of SASE and its architecture, and it has done so in an exceptionally well-organized manner.
The CATO SASE expert Level 1 certification covers a wide range of topics, including how networks evolved from MPLS to SD-WAN to SASE, as well as the improvements that have been made, their essential distinctions and the advantages they provide, virtual private networks (VPNs), and other related subjects of security such as CASB, FWaaS and DNS. All of these guidelines can help security professionals learn to fight data breaches. In order to obtain the Cato certification, you must first apply to the Cato SASE course and then pass the certification exam with an 85 percent passing score.
GIAC Security Essentials
As the name implies, the GIAC Security Essentials certification is governed by the GIAC. The candidate who holds this certification demonstrates that they are competent in executing security-related actions and that they not only possess theoretical knowledge but also have hands-on experience in the field of data security. Candidates’ understanding of information security and terminology beyond the basic level is verified and validated through this certification.
Practical job experience is quite beneficial for passing this exam, and there is a great deal of live training available for these qualifications. Those with a security experience or network understanding, such as security experts, pentester testers, forensic analysts, and information technology engineers, can pursue this certification.
The topics addressed by this certification include active defense, access control on devices, IT risk management, security policy development, endpoint security, Linux security, incident response, and incident handling, among others. This certification requires candidates to pass a web-based, proctored examination that comprises 106-180 questions and must be completed in 4-5 hours with a minimum score of 73 percent.
CCIE Security Certification
The CCIE Security Certification is governed by CISCO, and it is most commonly chosen by experienced networking professionals who wish to advance their careers to the level of network security professional. Network security professionals are capable of implementing, maintaining, configuring, securing, and troubleshooting the complex enterprise networking infrastructures that exist in organizations today. In addition, candidates who earn this certification are capable of creating a network for a business while keeping security in mind, which is a significant amount of responsibility.
As it demands both experience and knowledge, the candidate will need to dedicate a significant amount of time to passing this certification. They must also pass a practical examination, which is conducted in a lab, to verify their technical ability. This is an expert-level certification that covers a wide range of security topics, including wireless, service provider, routing and switching, data center, and collaborative work environments, among others.
The exam for this certification consists of 100 questions that must be completed in 120 minutes, and the candidate must score 80 percent or above in each part in order to achieve this certification. They must also pass a practical lab to receive this certification.
CompTIA Network+
The CompTIA Network+ certification is apt for those capable of performing basic network troubleshooting and wishing to learn more about the security perspective, for people who wish to learn about networks from a security standpoint,. To design a network infrastructure, applicants must have a fundamental understanding of networks and related technologies, including correct installation or configuration of devices, creating topologies, and comprehension of network protocols.
According to the new guidelines, this certification covers five main areas of network security, including network concept, network infrastructure, network security, network troubleshooting, and network tools, among others.
Although this certification covers the exterior security of infrastructure, it does not cover the internal or operational security of infrastructure. This certification consists of a series of 90 questions, which can be either multiple choice or performance based, and the applicant must finish it in 90 minutes to be eligible for the certification. The passing score for this certification is 80 percent, which is the minimum requirement.
The Right Certifications Can Help Fight Data Breaches
Today, we learned some network security certifications for different levels of experienced candidates. Experts with these credentials will be able to help fight data breaches. Certification helps candidates grow their career, learn something new, make themselves unique and sustainable in the highly demanding market of security. There can be more certifications, but picking the right one depends on individuals choice, interest, and experience.
Before pursuing any certification make sure to carefully consider your interest first. Because, if you have interest, you will put your effort into gaining that certification and organizations always prefer skilled professionals. Lastly, certifications are very helpful in negotiating for competitive compensation.
