We have frequently talked about the merits of using big data for B2C businesses. One of the reasons that we focus on these sectors is that there is so much data on consumers, which makes it easier to create a solid business model with big data.
However, data analytics technology can be just as useful with regards to creating a successful B2B business. Business-to-business companies need to understand their customers and market to them just as effectively .
One of the benefits of data analytics in B2B marketing is with using digital signage. Digital signage has always been effective for improving marketing, but it can be even more useful when used in conjunction with data analytics tools.
Data Analytics Helps Companies Create More Effective Digital Signage Marketing Strategies
For B2B companies, digital signage may not be the first thing that comes to mind when developing a marketing strategy. However, many businesses in this sector can benefit from this form of marketing, which has been steadily growing in popularity. Businesses spent almost $21.5 billion on digital signage in 2020 alone. It can be even more useful if you use it with big data.
The first step to getting started with digital signage is to develop your content strategy. Data analytics technology can help immensely at this and all subsequent stages.
Set Goals and Develop a Strategy with Data Mining
For digital signage to be successful, you must set goals and develop a strategy. This is one of the most important ways that big data can help. What is the purpose of your signage?
Do you want to improve corporate communicationAre you looking to show business intelligence tools?Do you want to internal market your brand?Do you want to advertise products and services?Are you looking to provide wayfinding?
For most B2B companies, the primary purpose of digital signage will be to attract new customers and engage your own employees. Whatever your goals may be, make sure that you write them down so that you have a way to measure your progress.
You may not need to use data mining to outline your goals, but you will probably need this technology to conceptualize them. You can use data mining technology to learn more about the challenges and obstacles that you will encounter, which is going to be important for the next steps.
Placement and content will be two vital things to consider when investing in digital signage. For example, shopping malls may not be the best location for B2B digital signage, but metro stations and convention centers may be a good fit.
Data mining technology can help you learn more about the different places your digital signage can be used. You can learn more about the foot traffic, regional demographics and other relevant factors about various venues, so you can use it appropriately.
As part of your strategy development, you’ll need to better understand your target customers and their behavior. Demographic data can be especially useful in this regard. Your message needs to be seen by decision-makers, and getting to know them will help you find the best place to install your signs.
So, in order to make your digital signage strategy a reality, here are the steps you should follow:
Leverage Digital Tools and Machine Learning to Create Engaging Content
In order to bring in new customers or achieve your goals, your digital signs must display content that will appeal to your target audience. This is one of the ways that big data technology can help.
First, it’s crucial to find digital signage networks that will allow you to display your content the way you envision. The right platform will make it easy for you to set up your signage and display images, text, video – whatever content you’re investing in.
As for the content itself, it’s important to understand your audience. You should use data analytics technology to learn as much about them as possible, so you can craft content that will resonate with them. Invest in content that will appeal to them. Machine learning technology can help immensely with the content generation process. In the B2B sector, this typically means creating content that focuses on their pain points and how your products/services can solve them.
Your content can come in the form of:
Graphics that are eye-catching. They can include behind-the-scenes photos, images that showcase your business or even before and after photos. We recommend leveraging tools like Canva to create graphics that impress. Videos. People love video content, and even in the B2B sector, they can be appealing to target audiences. Videos that demonstrate your product/service and its results can be very effective.Customer testimonials. Want to win over the trust of your target customers? Incorporate customer testimonials or reviews in your content for social proof and credibility. Find a digital signage provider that offers a quote app to create content in minutes.
Regardless of the type of content that you create, big data and AI can help you create it more effectively. Platforms like Canva use sophisticated AI and data-driven design algorithms to create the best possible content for marketers. No matter what type of content you create, it’s vital to ensure that:
All digital images are of high qualityVideos are engaging and also of high quality
As a general rule of thumb, you want to avoid looping the same videos and images repeatedly.
The Proof Is In the Reports
Digital signage is a powerful communication tool and it’s important to show management teams the impact of digital signage. There are two methods we suggest:
Proof of Play Reports. Detailed proof of play reports and campaign reporting will show a detailed view of what is working, and what content changes should be made. Gain insight into the screen location and name, the impressions, timestamps, and number of plays. Business Intelligence Tools. Find a digital signage provider that offers a secure BI tool to display that visualization data. This is a powerful app and can help your business visualize performance across all your marketing campaigns.
Create Playlists and Schedules
Digital signage software allows you to create playlists. These are the order by which you want content to appear on your digital signage displays. Playlists and schedules work together to ensure that the content you want is played in the order and at the time that you want.
For example, your images, videos or messages can change depending on the time of day, season or even the weather. Having fresh content throughout the day can be a great way to keep your audience engaged and prevent decision-makers from becoming “blind” to your ads or content.
When using digital signage for your B2B company, ensure that you’re taking advantage of playlists and scheduling features. AI tools have made this much easier than ever.
Engage Your Audience with Calls to Action
To make your digital signage more effective, make sure that you include calls to action (CTAs) in your content. CTAs tell your target audience what to do next – call, text, email, walk in to learn more.
Because you will only have the viewer’s attention for a brief second or two, it’s essential to make sure that your message is short and memorable. The desired action should also be as easy as possible for customers to do. For example, you can encourage decision-makers to follow your B2B company on LinkedIn, Twitter or Facebook. From here, they can learn more about your business and what you offer.
Data analytics can help with the calls to action. You can use automated split-testing tools to see how various CTAs perform and optimize accordingly.
Assess and Adjust
Once you have your digital signage campaign up and running, it’s vital to assess the results by looking at data analytics tools and adjust as necessary.
Over time, you may need to change your playlist or even move your signage to a new location. Adjusting and assessing as necessary will help you maximize the results of your campaign.
Big data technology has been one of the biggest forces driving change in the financial sector over the past few years. Financial institutions servicing small businesses have been among those most affected by developments in big data.
There are a number of data-driven trends shaping the future of small business financial management. This can be most easily observed in the context of small business lending. Many institutions that lend capital to small businesses are relying more heavily on data analytics, AI and other data-driven technology than ever before.
Big Data is the Future of Small Business Lending
We are in the age of data. Every month something amazing, and life changing, comes available on the market. This not only changes the way consumers approach companies, but it has a significant impact on how businesses run. For businesses to run many of them will have to rely on banks, and other lending agencies, to give them loans. Big data has changed the way that many of these lenders handle the actuarial and loan processing.
As companies need bigger loans to cover the costs of technologically improved equipment, banks will have to find ways to include tech advancements when accessing a business for a loan. There are four main data-driven trends that will impact small business lending the most, with many others on the way.
IOT (Internet of Things)-For those of you that already have their heads wrapped around technology, and the lingo that goes along with it, you already know what IOT means. In simple words it is the internet as we know it today. The IOT is one of the biggest breakthroughs in big data technology. Anything that you can ever want, or need, can be found on the internet in numerous areas. If you cannot find the exact article or blog that you are looking for, you will be able to find something close.Ecommerce-Along with the increase of laptops and mobile devices it has been found that the demand for online products and services has grown. As more people work, shop, and even learn online, companies that want to stay ahead of the game must have a plan to dominate in their fields on the web. Looking good in store is now only a small part of owning a profitable business. Your impact on social media sites and internet searches will improve, or break, your cash flow potentials. The ecommerce sector is also relying more extensively on data than ever.Virtual Events-Improved speed, and reliability, in computers and mobile devices makes it possible to have virtual events and meeting. It saves time, and a substantial amount of money, when you compare it to the costs of traveling. Attending a virtual class for school allows you to stay relaxed within your own home, but it will require a device that can keep up with the site giving the class.AI (Artificial Intelligence)-This is a concept that many people think of when they hear about a science fiction story based in the far future. If you look around, though, AI is already present in some businesses and homes. It can speed up production lines, allow for automated contracts to be completed online, and can even help create new products and services by using information that it collects over a small amount of time.
You may be wondering how the growing reliance on big data pertains to loans, especially unsecured business loans. The more tech that you have for your business, the more capital that you have in the eyes of a lender. This will increase your odds in getting a basic loan that has to be secured. You will need to understand the new data-driven algorithms that lenders use and know how to present your business in the best light possible. If your devices and equipment have been up and running for a while, you will have noticed that your cash flow has increased because of the ease at which technology can make your business run.
If the lender does require you to have some type of collateral, your tech equipment will have a decently high value. This will allow you to get a bigger loan than the company down the street that still uses a typewriter and orders products by hand.
Automation will be the way of the future, for lenders as well as businesses, and the public. It will allow you to file for a loan online. Get your response through an email or text. And within hours your money from the loan can be deposited straight into your bank account. This process will become simpler as technology continues to expand. Loans will be easier to qualify for and bigger amounts will be available.
Big data has helped companies identify promising cost-saving measures, recruit the best talent, optimize their marketing strategies and realize many other benefits. However, there are a lot of other benefits of big data that have not gotten as much attention. Over overlooked advantage of big data is that it can help improve outsourcing strategies.
Outsourcing is becoming a lot more important than ever. Global companies spent over $92.5 billion outsourcing tasks in 2019. Smart companies realize that the right outsourcing strategy needs to be carefully executed, which requires using technology the right way.
Data-Driven Businesses Are Shifting More Towards Outsourcing Models
The thought of outsourcing some of your business’s responsibilities and functions may have crossed your mind for several reasons. Volume is picking up, an instrumental employee is about to leave, or you’d like to expand but lack expertise. Partnering with independent contractors and outsourcing companies is an effective solution to these scenarios.
Placing everything on the shoulders of an in-house team is sometimes an unrealistic expectation. Outsourcing can give your business access to new knowledge and skills and make your staff’s workloads more manageable. Plus, you might gain access to additional markets and be able to serve your customers better. Small to large companies often benefit from outsourcing in these ways and more.
You will get even more benefits from outsourcing if you incorporate big data technology into it. Here’s why.
Access to Extensive Talent Pipelines with Data Mining
Finding qualified candidates is a challenge for many businesses, especially if the local job market is highly competitive. Nationwide labor shortages can also compound the problem, making it difficult for smaller companies to match larger firms’ higher salaries. Yet every organization needs exceptional talent to fill the shoes of essential roles.
The good news is that big data technology has helped companies deal with the process of identifying talented employees. Employee networking sites like LinkedIn have massive databases of talented employees that can make it easy for companies to find the professionals they need. These databases are usually used to find full-time employees, but can useful for outsourcing projects to qualified experts as well.
Having those positions remain empty for months on end puts strain on existing staff. That extra work could turn into additional resignations, leaving a business in jeopardy. By using data mining to assist with outsourcing and hiring in different countries, your company can access a broader range of talent. You won’t limit your pipeline to local labor and will gain the staff you need to keep everything running smoothly.
While hiring outside your business’s home country does carry some added risk, you can minimize this through research of international hiring guides and investing in partnerships. For example, companies that hire international employees sometimes work with a vendor that’s established legal entities in other countries. The hiring business doesn’t have to incur the expenses of doing this. The vendor takes care of the paperwork according to international labor laws, letting you focus on finding skilled workers.
Ability to Concentrate on Core Business Functions
Over a third of small companies outsource at least one business process. Accounting, IT, and digital marketing are the top processes that small businesses outsource. While these are all necessary functions, they may not be the reason your company exists. Perhaps your core function isn’t to troubleshoot computer hardware and software or design ad campaigns. Instead, your area of expertise could be selling books, providing insurance, or creating jewelry.
Outsourcing departments and functions your company needs to run but doesn’t have proficiency in can be more efficient. There won’t be a need to find and hire staff with the know-how. Expensive training won’t be necessary, and you’ll save the time it will take to find qualified staff. In-house employees can direct full attention to sharpening the skills that sparked your business idea and contributed to your growth.
Farming out non-core business functions also gives in-house staff added flexibility. If your company has a few employees wearing multiple hats, they can refocus on core responsibilities. Employees can develop specialties rather than shift between unrelated tasks and attempt to be “jacks of all trades.” Your business will benefit from further skill enhancement and knowledge that aligns with its purpose.
Scale Operations According to Cyclical Activity
One of the other benefits of data analytics is that it can help forecast future business activity. You can use predictive analytics tools to anticipate future sales volume, regulatory issues and much more.
Sales and work volumes are impacted by seasonal demands, economic activities, and product or service life cycles. Some businesses respond to fluctuating staffing needs by hiring temporary workers. But relying on an internal HR department to constantly scale up and down can eventually hurt a company. Your sources of talent could dry up as you become known as a revolving door of unstable employment opportunities.
Outsourcing some of your business’s hiring needs can remove some of this stigma. A vendor supplies the supplemental manpower you need during temporary increases in volume. You could then end your contract with the outsourcer once the volume decreases. An alternative is to keep an active contract going, allowing a vendor to take on added work when it’s available. The outsourcer can redistribute its employees according to changes in your activity.
For example, business outsourcing companies hire the same staff to move between different accounts. In call centers, for instance, an employee might work on two accounts simultaneously. For part of the week, they handle customer service calls for one account. The remainder of the week, they take tech support calls for another account. Both companies get the extra labor they need without worrying about hiring, payroll, onboarding, and retention costs.
You want to use data analytics to both make better outsourcing decisions and understand the future needs of your business. This technology-centric approach will lead to higher quality decision making and help you ensure resources are used appropriately.
Control Operational Costs
Increasing cost efficiency is a primary reason why large organizations decide to outsource. But smaller businesses can also realize cost savings by using data analytics solutions to work with outsourcing companies. This is because some of your fixed expenses can become variable, allowing you to reinvest into your company’s growth.
For instance, you might need an assistant to handle incoming calls, emails, and scheduling. However, that need may not be high enough to justify the expenses of a full-time employee with benefits. You can outsource tasks an employee would normally handle and only pay for the time a virtual assistant works. Your overall overhead will be lower, and if volume drops, you won’t have to lay someone off. If you use a data mining tool to find the right talent to outsource to, then you will get the most bang for your buck.
Short-term or one-off projects are another way outsourcing helps small businesses remain cost-efficient. You’ll save the expense of bringing another employee on board to handle the overflow and stay on budget. You won’t have to divert employees’ time away from other responsibilities, pay overtime, or allocate extra comp days. And you’ll keep your projects and tasks on schedule instead of putting them at risk by stretching internal resources too thin.
The Benefits of Data-Driven Outsourcing Are Clear
Big data is invaluable for many aspects of a company’s operations, including outsourcing various functions. Outsourcing one or more of your company’s departments provides benefits, such as flexibility and cost savings. Using data-driven approaches to business to collaborate with outsourcers adds to internal skills and expertise while expanding whom you can hire to support your operations. Relying on independent contractors and outsourcing companies will help you better manage fluctuating business cycles. Through outsourcing, you can discover added value and develop a longstanding competitive advantage.
Much has changed since the time when organizations only knew of antiviruses and simple firewalls as the tools, they need to protect their computers. To address newer challenges, security providers have developed new technologies and strategies to combat evolving threats.
Stephanie Benoit-Kurtz, Lead Area Faculty Chair for the University of Phoenix’s Cybersecurity Programs, offers a good summary of the changes security organizations should anticipate, especially in the time of the pandemic. “The threat landscape over the past 18 months has significantly changed in complexity and frequency of attacks. Long gone are the days when a lone wolf attacker was manually knocking at the door.”
To get acquainted with the ways security firms are handling the new breed of threats in cyberspace, here’s a rundown of the notable strategies the leading cybersecurity platforms and security firms are offering.
Breach and attack simulation
One of the headline features of modern cybersecurity platforms is breach and attack simulation or BAS. Designed to test the efficacy of existing security controls and improve them, BAS spots vulnerabilities in security environments by mimicking the possible attack paths and methods that will be employed by hackers and other bad actors. Gartner says that “breach and attack simulation tools help make security postures more consistent and automated.”
BAS is one of the top features in security posture management platforms for enterprises. It is not only able to check whether or not security controls are working the way they should; it also maximizes the ROI on these controls. Many organizations may not pay that much attention to this, but they are getting the return on their cybersecurity investment every time they elude disruptions and other forms of damage from cyber-attacks. BAS is easily one of the highly effective new ways of examining and improving cybersecurity efficacy.
Breach and attack simulation is designed to catch the most recent attack techniques employed by advanced persistent threats. Together with the MITRE ATT&CK framework, it achieves what some security firms describe as “threat-informed defense” by taking advantage of the latest threat intelligence and the knowledge of the tactics and techniques cybercriminals use. It effectively simulates the way malicious software and cyber-attacks impact endpoints, commit data exfiltration, and move around a network laterally.
Continuous automated red teaming
Red teaming is the strategy of using a group of ethical hackers to simulate a cyberattack on an organization. It is a form of security testing that relies on white hats or security professionals who will attempt to break through cyber defenses in whatever way they can think of.
Red teaming is a labor-intensive endeavor. To adequately cover all of the security controls and related aspects of an organization in a timely manner, several team members will have to work together. The problem is that this kind of approach is no longer compatible with the current cyber threat landscape, given how aggressive, frequent, and sophisticated the attacks are nowadays.
To keep up with the rapidly evolving threats, organizations need a continuous approach in security testing. Security vulnerabilities can emerge anytime, and defects in the protective measures put up by an organization will not wait for when the next red team evaluation would take place. There should be no gap in the integrity of an organization’s cybersecurity to ably deal with new attacks.
For these, the elements of continuity and automation are necessary, continuous automated red teaming or CART is an appropriate solution. Serial cybersecurity entrepreneur Bikash Barai, who has spoken at the RSA Conference and TEDx, calls CART the future of security testing.
While BAS tools usually require both hardware or software agents within an organization to simulate the way real cyber-attacks work to penetrate an internal system, CART takes on a different approach. It does not supplant BAS, but something that complements it. “CART on the other hand works using an outside-in approach and conducts real attacks without the need for any hardware, software, or integration,” Barai explains.
CART has a pronounced edge over traditional red teaming because of its consciousness. Because it is automated, it can replace people and reduce the cost of conducting red teaming while making sure that the security testing is not only periodic. Continuous automated red teaming is even designed to discover risks and attack surfaces on its own, not necessitating any human-initiated launching and inputs to undertake multi-stage attack simulations that evaluate networks, apps, policies, and even human behavior.
Advanced purple teaming
Another notable new approach used by leading cybersecurity platforms is advanced purple teaming. For those who have some background with red (attack) and blue (defense) teaming, the first thing that comes to mind upon hearing about this strategy is that it is a combination of the red and blue teams.
This preconception is not completely wrong, but it is also not exactly right. Yes, it combines the elements of the attack and defense cybersecurity teams, but it does not result in the creation of a new team with red and blue members. Rather, it is the adoption of a new mindset in conducting security evaluations.
Instead of keeping the two teams totally separate and independent, purple teaming enables some degree of collaboration to enhance each other’s abilities in achieving their respective goals. The blue team gets to see things in the perspective of the attack simulators for them to develop threat-aware defenses that anticipate lateral attacks and tweaks they would otherwise miss if they only focus on their defensive mentality. Similarly, the red team benefits from the collaboration by obtaining insights on how the blue team would likely plug vulnerabilities and respond to new attack tactics.
Purple teaming removes the problem of siloing that holds back the optimization of cyber defenses. It maximizes the scale of adversarial expertise, which leads to the crafting of new ways to scrutinize and bolster security controls that suit the unique cybersecurity environment of an organization.
As veteran international management expert who specializes in cybersecurity strategies and communication Tanya Candia explains, “Purple teaming is a proven way to provide stronger, deeper assurance — with more certainty — that the agency is being protected.” Through this approach in security testing, cybersecurity teams with opposing perspectives operate under unified overall goals. “The functions of both red and blue teams are taken on simultaneously, with members working together to enhance information sharing,” Candia adds.
Advanced purple teaming is a significantly improved way of undertaking purple teaming that employs automation. It is designed to make it possible to simulate attack scenarios that are automatically correlated to security control finding in examining breach detection functions as well as the capabilities of an organization to respond to security incidents promptly and effectively.
New but proven strategies
Many of the world’s top cybersecurity platforms and security solution providers have already embraced breach and attack simulation, continuous automated red teaming, and advanced purple teaming. These strategies in securing organizations may be relatively new, but cybersecurity professionals can vouch for their effectiveness in view of the new kinds of problems presented by cunning malicious actors in cyberspace.
They are not perfect silver bullet solutions that guarantee foolproof protection against attacks. However, they represent the advancement the cybersecurity industry has to offer to better handle the evolution of threats in the digital online world.
Big data technology has been a huge gamechanger in the insurance sector. More insurance are using big data to assist with the underwriting process. They have discovered that data analytics has made the underwriting process a lot easier. They are getting a better understanding of risk and choosing rates for their policyholders.
However, insurance companies aren’t the only ones affected by big data. Businesses are also using data analytics to make sound decisions when choosing a policy.
The Role of Using Data Analytics in Choosing Business Insurance
We’ve almost witnessed the world falling apart over the last few years. It proves things like insurance are crucial if you want to stay safe, but it doesn’t mean you shouldn’t try to save money on your policy whenever possible.
One of the most important changes you can make is using data analytics technology to choose the best possible policy. This can help you get the coverage you need and save a lot on premiums. It really has become a huge gamechanger in the insurance industry. I bet you don’t need to spend as much money on insurance as you think. There are lots of ways to lower your costs if you’re smart.
It might not make a massive difference to your bottom line, but let’s look at some things you can do to save money on insurance with data analytics.
Use data to understand your needs and make sure they are covered
An insurance company can use data analytics technology to better assess the risks associated with an insurance policy. However, you are going to need to need to use data analytics yourself. An insurance agent can help you pick the best policy for your needs, but they might not know what your needs are because they don’t understand the nuances of your business.
This is where your own data becomes important. You need to anticipate the likelihood of various disasters that can impact your company. You will want to use predicative analytics tools to project these issues based on your own company’s experiences, industry data and third-party risk analyses.
A builder might need hefty contractors professional liability insurance if they work on old churches. If a church burned to the ground, it would cost millions. If someone only worked on garden walls they would need much less. Big data can help them make these determinations.
You only need a good enough insurance policy to cover your individual needs. Maybe a little more if you want to stay on the safe side. It’s a bad idea to spend money on a policy you’ll never need no matter what happens.
Always Read the Fine Print
You can’t assume you’ll be covered for everything once you find good online commercial property insurance. Before you sign anything, you must make sure you read the fine print, even if everything’s been explained to you.
If anything does go wrong and you’re not covered you’ll kick yourself. Don’t have a claim rejected only to find out it was preventable. When you need to spend money out of your own pocket it’s going to destroy your business.
You can use data analytics tools to help with this process. Sophisticated data mining tools can help you search your document for key phrases that could indicate any potential pitfalls with your policy.
Bundling Up Your Policies
There are a large number of insurance policies you can get for your business. The exact amount will depend on things like what industry you’re in. Try to use the same company for all your insurance needs if possible.
When you bundle them up, you’ll be able to save lots of money. It’s sometimes different when you need a complex insurance policy. A specialist provider might be able to offer you better protection in case you end up in trouble.
Altering Your Policy Details
Once you sign up with an insurance provider no rule says you can’t alter your policy. What do you do when trying to save on business expenses? You’ll usually write everything down to see what can be changed.
You must do the same with your insurance policies once per year. Find out if you can do anything to reduce your costs. You might even be able to ask for a discount if you’ve been with the same company for years.
Insurance analytics tools can help you figure out what changes you can make with your policy to make sure it will better meet your needs.
Speak To the Insurance Company
I bet you know lots of ways to save money in your industry because you’re an expert. The provider you use will be an expert when it comes to insurance. Phone them up and ask if they know any tricks to bring down your costs.
Make sure you speak to someone with lots of experience for the best results. Maybe it won’t change much, but it’s worth asking at the very least. If they don’t want to help, you can always look for a more helpful company.
Data Analytics is Crucial for Choosing the Right Policy
Data analytics technology has been a gamechanger for the insurance industry. Insurers aren’t the only ones depending on it. Businesses must also use data analytics to better understand their issues and get the best policy. Try to bring your costs down when your company is still small. Even if the savings aren’t huge, they’ll grow as your business increases in size.
According to the 2021 CMO Spend Survey by Gartner, budget allocation for marketing analytics failed to make the top 3 in priority falling behind digital commerce, marketing operations and brand strategy.
While I understand that selling products, cutting costs and delivering brand strategy is important for long term business results, the lack of priority in using data troubles me. It’s more difficult to reach consumers and technology buyers today than it ever has been in the history of marketing and advertising. Data is really the only way to get a 360-degree view of your customer and how they behave online.
Here are two ways that marketers can use data to drive better marketing performance.
Data-informed buyer persona
Building a buyer persona is more than just downloading a template online, filling in the blanks, and giving a fancy name to your customer. Today, there are ways to build audiences based on a variety of different variables and create personas that deliver actionable insights. A few of these variables include bio descriptions, self-identifiable interests, or followers of a specific keyword, hashtag or brand.
Sophisticated, data-informed buyer personas can provide insights uncovering which brands, media outlets, and influencers that audiences have a high affinity for. This is particularly helpful for PR teams who are trying to determine which media outlets they need to prioritize and pitch for certain stories.
For example, let’s assume that one of your buyer personas is an IT Decision Maker and the data tells you that they have a higher affinity towards the Wall Street Journal versus the New York Times. While it might seem like minor insight, this little nugget could help define the difference between reaching an audience are wasting time and effort in pursuing something else.
Topical-based social conversations
Once you build your audience and complete your buyer persona, you can then track the conversations that they’re having online on platforms like Twitter, Reddit, and YouTube.
For example, let’s assume that one of your buyer personas is male, between the ages of 30 and 55, interested in health and fitness, and needs to have the latest technology gadgets no matter what they cost.
A topical conversation analysis might uncover that this audience cares more about battery life then they do anything else when they decide to make technology purchases. They might even be vocal about which mobile carriers they use, which ones they have used before, and which ones that they would never use again.
Using the same approach, you can also track conversations specifically at the purchase funnel level. In other words, what is the audience saying when they are in the market for your product? Are they asking their community feedback and recommendations? Perhaps they narrow down your product against one of your main competitors. It’s not uncommon for people to ask others publicly for their thoughts when comparing two competitive products.
This type of conversational data and insight can only be extracted when clustering social media mentions and conversations amongst a target group of individuals.
A topical analysis can also uncover the top keywords and hashtags that the audience uses within their social conversations. This data would be priceless for marketers who are using that data to inform which keywords to bid on in paid search, which web pages to optimize for keywords from an SEO perspective, what keywords and phrases to use in blog headlines and press releases, and which hashtags to use in social content.
In summary, data driven marketing is critical to the success of marketing and communications teams globally. It’s not only takes out the guesswork in predicting success, it ensures that you are reaching your customers with relevant content and in the channels where they spend the most time.
One of the best things about digital marketing is that it’s often at the forefront of the latest online technologies. It doesn’t get any more cutting-edge at the moment than machine learning, and it’s not only large companies that have already started to take advantage. As far back as 2018, a veritable eternity in the world of online marketing, over 80% of marketing organizations reported the deployment or growth of their AI and machine learning efforts.
With machine learning tools becoming more affordable and easy to use, machine learning appears set to be the next step in harnessing data and taking marketing efforts to a whole new level. Here are five ways in which this technology can make any marketing plan more effective.
1. Signpost the Purchasing Journey for Individual Customers
Personalization is considered a crucial component of virtually every aspect of marketing. There’s always speculation that it’s old hat, but that speculation often involves the most basic efforts, such as including someone’s name in a promotional email. However, machine learning enables much more.
Perhaps the most significant advantage machine learning can provide is personalizing the entire sales funnel. From emails to website visitors and those that see your ads to anyone that fills in a form, technology ensures you can display content that matters to them.
2. Insight Into What Products to Promote Next
Artificial intelligence can help with product marketing as it provides valuable information about what people want to buy from you based on their activities. This is the same kind of data that can take hours or work or a lot of luck to uncover manually. From monitoring chatbots to tracking ads and links, this technology can provide genuine reasons to promote specific products while informing marketers on the best way to do so.
3. Greater Opportunities from Split Testing
Split testing has formed a cornerstone of digital marketing since the beginning. From merely trying two different images in online ads to sending audiences to two vastly different landing pages, it has proven a relatively slow but effective means of determining exactly what an audience wants.
The key here is speed. With machine learning, marketers can quickly deploy the same split testing campaigns but understand the results immediately. Furthermore, when configured correctly, the adjustment phase ends up being completely hands-off. Your AI setup will tweak copy, ads, and everything else in the marketing process based on performance and continue to report in on what’s working best.
4. Remove Guesswork from Marketing Campaigns
As the likes of split testing and cold outreach would suggest, marketing is rarely an exact science. Many new campaigns involve educated guesses based on past experience, other profiles, and other factors that are not always precise in their usefulness.
A robust machine learning mechanism can take care of everything from the best advertising channel for a specific audience to deciding on how much ad inventory is required to meet specific sales targets.
Your own machine learning efforts will also interact favorably with other aspects of the campaign too. For example, Google’s Smart Bidding system relies on machine learning, as do many prominent AI content creation tools currently available to marketing teams.
5. Lead Scoring to Better Understand Audiences
Whether the primary function of a given marketing campaign involves raising awareness, building a brand, or any other form of audience engagement, it often all comes down to the return on investment. Lead scoring is all about working out how likely a potential lead is to turn into a customer, and it can quickly become one of the most resource-intensive parts of the marketing plan.
Machine learning helps immeasurably, as it not only reduces the labor requirements but often assigns the most accurate possible scores to each lead in the database. That greater accuracy means less wasted effort and greatly improved chances of conversion.
Artificial intelligence and machine learning will change many industries in the coming years, and marketing is no exception. Fortunately, it’s not an industry in danger of being taken over by computers any time soon. Instead, the perks of the new technologies mean less time spent on sieving through data and guessing at what your audience wants and more spent on achieving data-driven results.
The average consumer is unaware of the phenomenal benefits that big data provides. One of the biggest benefits of big data is that it can help improve driver safety.
Data analytics technology is becoming more useful when it comes to stopping traffic accidents. A lot of companies are sharing data to help make roads and vehicles safer, as well as helping drivers make better driving decisions on the road. They can help minimize the threat of distracted driving by leveraging data to better assess driving behaviors and deal with it when it arises.
Big Data is the Key to Addressing Driver Safety Risks
As cars and computer software become more entangled, both automotive companies and cellular carriers are working together to reduce the incidence and subsequent damages caused by distracted driving. They have accumulated a lot of data on their drivers and are using it to help address these kids of risks.
What is Distracted Driving?
According to the National Highway Traffic Safety Administration (NHTSA), the term distracted driving can include anything from talking or texting on a telephone to adjusting one’s radio or navigation systems or even eating or drinking. Anything that impairs the driver’s ability to safely operate a vehicle is considered distracted driving, but personal cell phone use is considered the most dangerous and prevalent practice by both the NHTSA and major auto insurance carriers such as GEICO.
While the driving world may be hopeful that the future will include cars capable of using AI and road data to provide autonomous driving capabilities, tech and car companies must account for immediate problems before such an option might be realized. “Drivers should always be cautious of distracted drivers as there can be grave potential and legal consequences” says Attorney Stewart J. Guss of SJG Injury Accident Lawyers, a Houston-based car accident lawyer who has been handling these types of claims since they first began.
Fortunately, both mobile carriers and automotive manufacturers are using big data solutions to reduce a driver’s temptation and ability to send text messages while driving. According to the National Safety Council (NSC), over 300 drivers were killed in America by distracted driving involving a cell phone in 2019, and there were tens of thousands of non-fatal crashes involving cell phone usage in the same year. The ubiquitous nature of not only cell phones but access to social media are both often cited as major obstacles by both law enforcement and safety officials.
Fortunately, the same era of big data technology that brought the devices creating these risks could also bring the solutions to them.
How are Cell Phone Carriers Helping to Reduce Distracted Driving With Big Data?
Fortunately, cell phone carriers have started to adapt to the blue-tooth capabilities and navigational systems found in many modern cars with digital layout systems. They have used this technology to collect a lot of data on their customers, which gives them better insights into their behavior. When used correctly, this data could be the solution to dealing with crises like distracted driving accidents.
Forbes talked about a new data-driven app developed by CMT that does just this. They said that the app collects data on driving behaviors and reports issues of distracted driving. It is analyzing this data to better address these situations.
Many modern phone manufacturers have responded to this issue by using an individual’s GPS capabilities to enter “driving mode” where the phone can sense that a user is traveling at a speed that would necessitate a car and can have the text messaging system automatically respond to calls/text messages that the user is unable to respond at the moment.
Samsung and Apple both host applications that allow parents to not only keep track of their children as they drive but can also limit the amount of access their teenage children can have on their phones while they are driving. Apps such as AT&T’s “Drive Mode” and Sprint’s “Drive First” are leading applications in this particular field.
How Are Auto Companies Discouraging Distracted Driving?
Auto companies have also answered the call by streamlining the digital menus in their cars to reduce the amount of time that drivers will have to interact with the system, thus maximizing the amount of time drivers can spend on their driving.
Older systems of driver monitoring used to use steering data to suggest when a driver should stop to rest, but these are currently being phased out in favor of hand sensors on steering wheels to ensure that the driver always has proper control of the vehicle.
Volvo (historically praised for spearheading movements in automotive safety) has adopted tracking systems in certain models to monitor a driver’s attention. Volvo claims that such measures can eventually signal an alarm to the driver and even reduce speed if the vehicle detects that the driver’s gaze has wandered from the windshield. The system can be considered a sort of reverse dash-cam, making sure that the driver is keeping a consistent watch on the road.
While exercising one’s due diligence while driving will always ultimately fall on the responsibility of the operator, potential car buyers can rest assured that both cell phone carriers and auto manufacturers are implementing changes with big data to reduce the urge and access to one’s cell phone to combat distracted driving. One should never underestimate how little time is required to turn a seemingly benign drive into an unnecessary tragedy.
Big Data is Helping Address Distracted Driving Concerns
Big data has helped address many safety issues in recent years. One of the biggest benefits of big data is that it is resolving issues associated with distracted driving. This is one of the most important ways that big data is helping improve the driving experience.
There is no denying the reality that artificial intelligence is setting new standards in the financial sector. In fact, AI is the basis for the sudden boom in Fintech. We have talked extensively about the role of AI in investment management and insurance.
However, there are other segments of the financial industry that also rely on AI technology. The banking industry is among them. Banks have been slower to adapt AI technology than some other institutions. They currently spend just under $4 billion in 2020. However, the market for AI in banking is expected to grow over 30% a year and will be worth over $64 billion by 2030.
New software uses AI to manage bank loans. Banks are finding ever more creative applications to AI technology to conduct actuarial analyses.
AI Makes Bank Lending Software Far More Reliable
The upcoming year is going to be transformational for banks, while the pandemic times sufficiently changed the customer needs related to payments, deposits, and lending. In this article, we decided to cover the tendencies in banking loan software in 2022 and give a brief market outlook of AI-driven lending software as a whole.
Digital banking market
The Deloitte report says that in the second quarter of 2020 the largest 100 banks in the USA reported $103.4 billion in net loan losses due to the pandemic burst. However, it didn’t stop the growth of the lending segment in banking. On the contrary, COVID-related challenges became opportunities and had a major shaping impact on the banking tech forcing incumbents to invest in digital channels. From blockchain adoption to the use of AI scoring and digital underwriting – a lot of banking processes became paperless and online. They have found that AI has truly changed the lending process.
2020 became the year when a lot of customers first experienced their remote interaction with banks and enjoyed it. Growing trust in online and mobile banking due to meeting customers’ resource-saving expectations was the driving force behind digital lending growth in 2020-2021.
This is why banks are working on the quality of their digital lending platforms and expanding their functionality with AI technology. Here is what they include now:
1. End-to-end loan management with AI
According to Accenture, half of the banking routine is still performed manually and can be streamlined. It refers to underwriting, customer onboarding, document management, analysis, and statistics. Using up-to-date lending software, banks solely in the North American market have an opportunity to save over $70 billion by 2025. Automation assists employees and allows them to serve a larger number of borrowers. Decision-making and loan servicing take less time, while customer satisfaction grows. The relationship managers get access to relevant information about borrowers, too.
Online loan applications and third-party services integration for processing them can be game-changers as they use AI to provide a convenient way to apply for a loan and to consider the application using relevant data.
Loan approval, as one of the biggest bottlenecks due to inconsistency of information between teams, may increase business risks. However, the process of decision-making can also be automated by lending business software that has sophisticated AI features. Such an approach speeds up the process and ensures everyone is on the same page.
Due to interactive dashboards available on digital lending platforms, banks can monitor customer interactions, keep track of their risks and financial results, access document databases, and get relevant analytics.
2. Integrated lending module
Many banks are not ready to replace their core banking systems only to improve their lending segment.
A great way out is to use cloud-based microservices software that can be used as a middleware integrated into the existing digital banking solutions. This software typically has very sophisticated AI algorithms that help improve its functionality. Banks can add such services as modules, and remain flexible without extending the budget.
3. Exploring partnerships and business opportunities
Digital lending can exist in a number of ways, like POS (Point of Sale) transaction model or embedded lending, for example, Buy Now, Pay Later. All these business opportunities require top-notch AI technology solutions.
As for partnerships, lenders can collaborate with data providers, 3rd party processors, collectors, and AI technology companies to expand their customer base and automate more routine transactions. A popular direction is partnering with digital compliance providers for online ID verification, KYC/AML, and so on.
4. Automated credit scoring
Conventional scoring takes time and can miss significant factors leading to false-negative and false-positive decisions. In addition, traditional scoring implies extensive documentation, high-interest rates, increased decision-making times. Implementing alternative data-driven scoring methods in addition to conventional ones may improve the processes in digital banking. Alternative data includes personal data, business data (like cash flows, POS transactions, bank account statements, and financial statements), and behavioral data (like spending habits or psychometric tests).
5. Customer-centered banking and omnichannel capabilities
Digital lending allows AI-driven self-service with a high level of support and personalized offerings. Customers can apply from home and receive money on their bank accounts in hours, not weeks. All that is tightly connected with omnichannel interaction, where customers’ preferences are prioritized. Digital lending platforms in banks can be integrated with chatbots, SMS, and email services for notifications and assistance to provide borrowers with the needed balance of in-person and digital communications.
Loan Software in Banking in 2022: The Bottom Line
Dominated by technology, data, and a customer-centered approach, the lending ecosystem in banks is going to embrace the change. Banks still need to deal with compliance and regulations issues, but it’s not going to stop the digitization of the loan lifecycle.
There are a lot of different ways that big data can help companies streamline certain processes and resolve various challenges that they face. The advent of data visualization has made it easier than ever. It just one of the many ways that data analytics is helping optimize organizational processes.
The global market for data visualization services is expected to be worth over $5 billion by 2026. This figure is going to keep growing as more companies discover the benefits of various data visualization tools.
One of the ways that companies can use data visualization is by integrating it with Kanban boards. This is a creative way to help navigate various issues your company will face.
Using Kanban Boards with Data Analytics Technology
Examples of kanban boards are a great source of inspiration for building and improving your boards. By examining how other companies use the Kanban boards to outline workflows, you can improve your board design, perform more excellent modes such as WIP restrictions and process policies, and acquire more power from your Kanban boards. You can often integrate data visualization technology to get a more interactive and communicative interface.
You’ll find similar points about how other teams are building boards and tracking work with data analytics tools. However, just as your team has distinctions that differ from other groups, your Kanban board also has nuances that differ from these examples.
Development Team Kanban Board Example
The software development team has adopted the Kanban system from the earliest. Here are three standards of how the advancement team practices the Kanban board with data analytics tools to maintain parallel processes, improve pull systems applying the “Ready” queue, and implement WIP restrictions.
Data visualization of concurrent processes
Some teams may have several processes in parallel that are closely related but are so different that they require their workflows. For example, an advancement team may need to track feature development and testing.
Rather than simplify the process steps to meet the needs of both processes in a single workflow, teams can use horizontal swim lanes to design boards to track both approaches in parallel.
Enhance pull systems with ready queues
Examples of kanban boards are a great source of inspiration for building and improving your boards. Examining how other organizations leverage Kanban boards to plan their workflows will benefit you by enhancing your board layout, performing more excellent modes such as WIP restrictions and process management, and acquiring more power from your Kanban boards.
Like this Kanban board example, queuing data and “ready” matrices can be incorporated into the board design, allowing the team to understand their work in the process better. By adding “In Progress” and “Completed” sub lanes within the Development mark, the team can recognize which work details are actively being managed, which work items have been developed but not yet started testing, and which work things are actually in the testing stage.
Instead of pushing work items into a “test” lane, developers can put them in a “ready” queue and then pull them into the appropriate route when testers are ready to start testing.
Implementing WIP Limits
In-Process (WIP) limitations are another important cancer concept that helps all teams, including development teams and data scientists, actively manage the flow of work within the system.
The development team uses WIP limits to limit the number of tasks in a particular lane expressly. Not all routes have WIP limits (indicated by numbers in the upper right of the lane title). It is a step that the team has determined is likely to put a strain on the system. It is whether the team’s ability to complete that step is limited or the task itself is intrinsically complex (or a combination of these two factors).
Passive visualization of WIP limits is helpful, but only if the limits are respected. With online Kanban boards that enforce limitations, like this example of a kanban board, your team is more likely to notice when WIP limits are met or exceeded.
Data Visualization is Fundamental to Using Kanban Boards for Organizational Management
By using data visualization with your Kanban boards, the team can communicate priority conflicts, identify solutions, and reclaim the flow of work. Examples of Kanban Boards for Different Operations Teams show how teams can manage different types of demand, visualize changing priorities, and bring everyone together while reflecting the expertise of team members.