How Big Data Can Help the Real Estate Industry

For decades, real estate agents have gotten to know properties one by one, then presented their selling points to potential buyers. As the number of properties for sale increases and competition in the industry increases, big data can help real estate agents stay ahead of their competition and provide the best possible information to potential buyers. 

Let’s look at several ways that big data can help the real estate industry: 

Informed Real Estate Agents Stand Out in Online Marketing

While it used to be easy enough to stay on top of all the property listings in your area, it’s becoming more difficult for agents to compete with online listing sites that have access to large amounts of data. Informed real estate agents are using big data trends to gain unique insights into the areas that they serve. This allows them to stay ahead of the curve by using this information in their online marketing efforts.

As you build a social media presence, design a website, and market your real estate business, you’ll stand out in the crowd if you have cold, hard facts about the area you work in. This solid information sets you apart from other real estate agents in your area, showing potential buyers that you care about their needs and are willing to go the extra mile to answer their questions. 

Stay on Top of Foreclosure Information

Consumers are more interested in foreclosures now than ever before, as homes that have been foreclosed on are typically for sale for a much lower rate than the going rate in the neighborhood. Big data allows real estate agents to stay on top of trends in the industry, so that they are aware of which neighborhoods are struggling, as well as where and when foreclosures are happening. These informed agents are uniquely prepared to pick up foreclosures and add them to the list of homes that they market. 

Examine Health and Safety Issues

With big data, real estate agents can examine health and safety issues surrounding properties they are attempting to sell. By accessing public records for assessments and inspections in the area, real estate agents can gain information going back for multiple years. This information can be extremely important while trying to sell a property. Whether you’re selling residential or commercial properties, it’s crucial that you have all the information available before making a presentation to a potential buyer. 

 View Industry Wide Trends

Access to big data also allows real estate agents to compile information about an entire neighborhood, street, or school district. This information can be especially useful as you advertise homes in that area to potential buyers. Trends such as average income, school successes and limitations, arrest and drug abuse statistics, and more can all be very interesting to families moving into the area, and can also impact property values. As an agent, staying on top of these local industry-wide trends can be incredibly important. 

When you’re a real estate agent, finding the best possible information for your potential clients is essential. It can be tough to do this on a property by property basis. Instead, use big data and the internet to your advantage, so that you can gather the information you need quickly and effectively, then compile it in an attractive manner. 

The Real Estate Industry and Big Data

Big data has finally been recognized by the real estate industry as one of the best ways to grow business and gain profit. Now more than ever, you can rely on big data and its avenues. The real estate industry has widely used traditional practices for marketing and learning about what buyers want. Now, they can use big data to help their business flourish while keeping cost-effectiveness in mind.

Here are some things to keep in mind when incorporating big data in the real estate business:

Thing big. Instead of setting limits, try looking at big data in a bigger way. Even small sets of data can be useful, but the amount of data that is out there is astounding. And more data means more possibilities when it comes to making business decisions and setting goals.

Don’t be biased. Instead of viewing different types of data as meaningless, look at them for their individual values. If you analyze them, they can be connected and give incredible insight. So, be selective with data without ruling anything out.

Collaborate and share. Data is now being sourced from conventional methods as well as newer ones like Facebook and Twitter. It’s also being shared throughout different types of industries and data teams are focusing on collaboration with their counterparts.

Be focused. Even before you begin to utilize your big data strategy, focus on your approach and your long-term goals for your company. This way, big data can help to answer the most important questions first by focusing on insights. It can also help you to prioritize your business goals.

Don’t rely solely on data. Remember that big data access and analysis do not override expertise and negotiation skills. While big data is extremely helpful in the long run for real estate companies, it certainly won’t solve all of your problems. Big data should be a complement to skills and knowledge in the industry.

Need lead product solutions? iLeads delivers results that fits your needs! We are a lead solutions provider with lead generation covering Insurance, Lending, Real Estate, Direct Marketing, as well as Lead and Data Analytics. Contact iLeads today!

Big Data Strategy

Real estate companies are beginning to realize the importance of big data. However, many of them still do not have a well-thought out big data strategy. There is more to data analytics than discovering how many people visited your site yesterday. In fact, data has no value if it does not provide actionable insights. Builders and realtors need to experiment with volume and variety if they want their big data strategy to work.

Forward thinking organizations are quick to experiment with their products and services. This helps them discover what the customer wants. By making big data analytics an important aspect of your marketing strategy, you can accelerate that discovery.

To make your company more adaptable, you need to experiment with the different aspects of data:

Big data provides a wealth of insights

Big data is available in high volume. When you have access to insane amounts of data, you should be ready to experiment with them. Your experiments must focus on finding that project, which will do well in the existing market conditions.

If you are a builder, you will want to know what kind of projects will sell well in a particular locality.

Execute rapidly

Ask your team to come up with innovative ideas. Let these ideas eventually become your offerings. Here the key is making experiments at a fast pace. You should be able to change ideas into offerings in the shortest possible time. Market conditions can change anytime. You can't take years to launch a project you conceived today after experimenting with big data. You should minimize the time you need to bring that idea into the market.

If all of your offerings are based on one theme, you will land in serious trouble when that theme is off. That is why you need to experiment with ideas.

Think about the kind of services and products you can offer. Make sure that all of them are not of the same kind. You should also think about the potential market these products or services could serve. At any given time, you must have a mix of these products and services. So even if demand for one product decreases, you will still have something else to fall back on.

Don't forget your existing customers.

They are the biggest source of referrals for you. Retaining an existing client is easier than finding a new one, so keep in touch with them.  A real estate company tends to do better when it has a market-driven sales strategy. In this case, it understands a market very well and experiment with projects that tend to do well in that market.

Summary

Organizations that adopt big data tend to have a competitive advantage over others. An organization becomes adaptable only when it is ready to experiment and data analytics is the key to rapid experimentation. It doesn't really matter whether you are a big real estate company or an independent realtor who only serves one market, you can no longer ignore big data. At any given time, you must be experimenting with a large number of ideas. This helps you find that product or service which will ultimately sell well.

Leads Direct provides quality business leads at 40-80% off retail prices so you can grow your business quickly and increase your ROI. Contact Leads Direct today!

Big Data and Its Impact on the Real Estate Industry

 

Big data can help sustainable development. Recent surveys have revealed that buyers are looking for homes with sustainable features. This presents both an opportunity and a challenge for the builder. Builders who incorporate energy efficiency features into their projects will have an edge over others who still rely on the construction standards of the previous century.

But how can a builder understand what sustainable features are popular with buyers? When they analyze big data they will know.

What is big data and why you should care?

Big data is a collection of large data sets that can transform the way buildings are built and managed. It may even affect government policy.

The biggest USP of big data is its speed. Data is being generated at an alarming pace. Every interaction we have with digital technology helps create big data. And to keep pace with the speed with which data is generated, organizations need to invest in competent analytics tools.

Big data provides reliable information in a matter of seconds. A real world analytics firm may take weeks or months to publish a report on sustainable features.

By simply analyzing the information your users leave behind as they navigate through your website, you can understand their priorities and requirements. You can, for example, use this information to identify the most important factors you should consider while choosing a locality to build its project. Data analysis has revealed that most buyers are looking to invest in a crime-free locality with lots of good schools. Buyers also consider commute time. When realtors know what their clients want, they can promote their listings accordingly.

Big data uses current technologies in innovative ways. Data helps investors assess the existing real estate market conditions. The real estate market in the US is recovering. Home prices and sales have gone up. Inventories and foreclosures are going down.

Data helps realtors understand what their customers want. Take for instance the case of a real estate firm that leases space to tenants. Since tenants in real estate are not an organized lot, it is not easy for realtors to make trends. Data helps fill that void. It helps the realtor target specific groups of customers.

Big data makes information accessible to just about everybody. This openness of information will ultimately force government to update archaic laws.

Data and its impact on sustainability

Hurricane Sandy brought issues such as resiliency and efficiency to the forefront. In order to improve a building's resiliency, you should be able to measure it.

A real estate data company recently analyzed data gathered from Local Law 84. This law requires buildings that measure 50,000 square feet or more to submit an energy benchmark report. People often believe that newer buildings are more energy efficient. However, the findings of this data analysis revealed that this was not always true.

Many masonry buildings built during the 1930s have comparatively low energy costs. As data becomes readily accessible, energy managers will be able to take measures necessary to reduce their energy bills. Homeowners can also use data to measure the ROI on their energy efficiency investments. This may even encourage others to invest in green technology.

Need lead product solutions? iLeads delivers results that fits your needs! We are a lead solutions provider with lead generation covering Insurance, Lending, Real Estate, Direct Marketing, as well as Lead and Data Analytics. Contact iLeads today!

 

How to Store Your Data

Forward-thinking real estate companies have already started gathering and analyzing data. The problem with data is that it is mostly unorganized. And this poses many challenges for the data manager who needs an appropriate strategy to store and analyze data.

If you gather data, you will have huge amounts of both structured and unstructured data. How do you organize this data? This is a question that all data managers ask themselves.

One of the biggest data problems that real estate companies face is the large amounts of unorganized data that pile up. Data scientists are aware of the presence of unorganized data. They simply don't know what to do with it. Consequently, they ignore it. But after some time as the stockpile gets bigger and bigger, they will need to decide what to keep and what to throw away.

In some cases, industry regulations may ask real estate companies to keep data for specific periods of time. For example, builders and realtors will have to keep transaction details.

If you are a real estate company that has gathered a large amount of data, you should have a proper plan to sift through data repositories that need not necessarily have any value. You should also have proper strategies to de-duplicate data. You may have the same data in multiple locations. This is a mere wastage of valuable storage space.

Your instinctive drive is to throw away all old data; while at the same time, there is an inherent fear that you might require that data someday. How do organizations deal with the dilemma? The data may exist in internal legacy systems. It may also come from sources such as social media, web sources and machines. To make this information useful, the company will have to invest in long data capabilities.

You will perhaps have to reorganize your data systems over time. This involves sifting through your data repositories, and implementing standards that maintain accuracy. If your company lacks the expertise or resources to do this, you could consider outsourcing this process to data engineers who use cloud-based systems that unify this data into actionable information that will facilitate corporate analytics. And that makes sense because storing your data in the cloud is more economical than keeping it on your data center storage.

In addition, by moving your archival information to the cloud, you save yourself the trouble of having this information on the site. And when this data is finally required, you can retrieve it.

This strategy almost always works. But you may still have difficulty finding out which data you should keep and which data you should throw away. If you decide to keep all data, you will have to buy more storage space and pay more for it.

Data poses many challenges. Besides collecting it and analyzing it, you need to store it appropriately so that you can retrieve it when you need it. If you don't have an onsite system to store data, cloud-based solutions are your best bet. Besides storing your data, they analyze it and turn it into actionable insights.

Need lead product solutions? iLeads delivers results that fits your needs! We are a lead solutions provider with lead generation covering Insurance, Lending, Real Estate, Direct Marketing, as well as Lead and Data Analytics. Contact iLeads today!

Why Builders and Realtors Need to Analyze Big Data

Big data has suddenly become big business. If you are a builder or a realtor, how can you benefit by gathering and analyzing big data? Will data help you sell more homes?

Yes. You might assume that only big organizations need to harness the power of big data. Far from it. Both small and large real estate companies can run their businesses better by analyzing customer data. However, a good understanding of how big data helps your business is essential to develop a suitable business strategy.

Data becomes valuable only when you analyze it. And proper data analysis helps you know your customer better.

Data analysis helps you gain an edge over your competitor

Several builders and agents still don't take the time or trouble to gather data about their target customers. By simply gathering and analyzing data, you can gain an edge over your competition.

By analyzing data, a real estate company can find out what kind of homes sell well in a particular locality. This knowledge will help the builder launch the right project in the right locality.

Data also helps builders and realtors find people who are more likely to buy or sell homes in the immediate future. This will help them create a marketing strategy that targets the right group of buyers.

Data gives you more comprehensive demographic information. It also shows you why certain category of homes sells well in certain localities.

Launch better marketing strategies

After analyzing big data about your customers and the real estate industry, you can use that information to expand your business. One advantage of using big data is that it helps you improve your marketing strategies.

If you run a website, by simply looking at who is visiting your site, you can create a marketing strategy tailor-made for them.

When you have access to data which shows you who your target customer is, you will have no difficulty launching a more personalized marketing strategy.

This kind of analytics is useful for several types of businesses. Data also helps you adapt your marketing strategies for future buyers.

Data helps you decide whether you should implement a marketing strategy or not

By simply analyzing the data gathered by your analytics software, you can decide whether or not you should implement a new marketing strategy. If you market your homes or services to people who are not interested in being a buyer or seller anytime soon, you are simply wasting your time and resources.

When you have real data about your customers and the real estate industry, you can find out whether you should expand your business to other markets.

Realtors sometimes make business decisions with no reason. However, when you work with data analytics, you can make sound business decisions with a reason. A well-thought out business strategy will only benefit you and your company.

How to gather data

It is not difficult for a real estate company to gather data. Most of them already run websites. By simply analyzing the key phrases that attract people to their website, a builder can find valuable information about their potential clients. Both free and paid data analytics software solutions are available.

Struggling to find buyers that qualify? RealTAG gives you the real estate content that drives consumers to your site, and gives you an inside look at their financial situation. Close more real estate deals and generate qualified leads by contacting RealTAG today!

How Experimentation Can Fast Track Big Data Capability

As more businesses explore the vast expanse of big data utilities, it is becoming clear that big data delivers more when managers are ready to be a bit creative. For a real estate business, big data can provide many standard utilities. Standard because soon most of the real estate organizations will gain similar big data advantages—this trend is already underway. So how does a real estate business make itself unique and more competitive?

This is where experimentation comes to the fore. Big data analysis can yield lots of actionable information provided you are ready to take some chances, allow room for creative experimentation, and use data sources that seem irrelevant at first sight. Here we discuss more about how experimentation can drive better ROI for your big data investment.

Big Data is Being Widely Embraced, but Who Will Benefit More?

We understand that big data is still new for most organizations. They are approaching big data with some apprehension along with lots of enthusiasm. It is unfair to expect such businesses to get creative with big data at the outset. This doesn’t make sense, particularly when digitization hasn’t been associated with the real estate industry in a larger perspective. We realize that most businesses in this niche are finding it difficult to let go of their legacy protocols, wanting to hold on to the traditional methods of working. However, the big data voyage has begun, and soon most businesses will be at ease working in a data-driven environment.

There is another aspect to this realization—some organizations have adaptability as a part of their core skills. On the contrary, some are deep rooted in hierarchies and don’t venture beyond the realm of expected outcomes. It seems that the more adaptable businesses have a better chance of extracting better ROI from their big data adoption. The more established businesses will take time as their bigger teams and numerous offices multiply the hierarchy, breeding lesser elasticity.

Big Data can Look Ungainly, But Don’t Fret - Placate Your Team

Big data involves high volumes, mammoth disparities, and real time performance. This means lots of data is collated from various resources, including data that is highly unstructured and processed in real time. During the initial few weeks, you might feel that your big data team is trying to do too much. However, don’t have doubts about this—the typical big data environment is very challenging and to others, it can seem very perplexing.

Data analysts work together, forming strategies on the go, hungry for processing more data. Once you have established a basic level of understanding with your big data team, try to establish an experimentation policy. The idea is to make the team feel that you support a certain degree of experimentation. The team should know that running correlations or comparisons that don’t amount to any conclusive information will not be ridiculed. You can ask the team to allocate certain hours or weekdays for running experimental data studies. This is vital to discover unexplored market service combinations and unearth new consumer zones.

Buy and work leads smarter, contact only the customers you want to engage, and enable your employees to be productive. Connect with your target audience with Live Connect today!

Big Data & Hadoop: What’s Cooking?

If you have been following big data, it is most likely you have come across repeated mention of Hadoop. Before we delve deeper into this discussion, let us take a step back in history and understand how data management has evolved and what led to the current scenario where Hadoop and big data are the favored technologies.

Understand Hadoop: The Early Days

Earlier, computing was limited in volumes and in terms of expectations from outcomes. Applications were created specifically for different data structures to ensure that a problem was comprehensively solved. Since computing volumes were humble, this approach didn’t present too many problems. Multiple data resources were uncommon, almost unheard of during this period. Using organizational databases or network data, larger applications functioned rather well. However, the relationship between data structure and application saw a major upheaval with the creation of relational databases.

Due to this, data access to SQL was standardized. More businesses started using spreadsheets. Together, these two developments created the way for more complex data management systems to emerge.

During the 90s, it became clear that there was more business value in combining data from different applications but this was challenging. Slowly, better and bigger data structures surfaced. However, this picture was soon challenged with the arrival of data-intensive business mammoths in the form of Yahoo and Google. Things changed and data didn’t have a similar, streamlined structure. These organizations were handling massive amounts of data on a daily basis, hoping to create a solution for faster access to retrieve and save data. This paved the way for the creation of Hadoop—a more dexterous, organized, and distributed file system that makes it easy to handle unstructured data.

As Hadoop Progressed, Big Data Arrived

Slowly, the advantages of Hadoop became known to businesses across the world. Managers realized that they could benefit a lot in terms of condensing their operational cycles if data access was made easier without compromising its security. The basics of free data management framework that Hadoop had initiated were then routed towards data-driven businesses that wanted data capabilities of bigger organizations.

The Hadoop-Big Data Connection—Hadoop is the platform that makes it easier for unstructured data to be fed into the big data ecosystem. Conventionally, real estate businesses were somewhat selective about retaining data. They feared massive amounts of data will not be accessible. Big data makes it easier to store everything that seems important. With big data, businesses can feel assured that their organizational and consumer data is secured and easily shareable when needed. With big data analysis, the information can be extracted in an orderly, indexed format and then assessed.

Did Google create Hadoop?

Yes, Google can be credited for bringing Hadoop to the world of IT. This happened with Google’s requirement to index enormous amounts of web data, collating, and categorizing it for its search results. Since the market at that time didn’t have a readymade platform that Google could use, it had to innovate. The platform was first called Nutch. Yahoo played a more integral role in helping Hadoop evolve for enterprise applications.

Need lead product solutions? iLeads delivers results that fits your needs! We are a lead solutions provider with lead generation covering Insurance, Lending, Real Estate, Direct Marketing, as well as Lead and Data Analytics. Contact iLeads today!

Big Data Strategies in Real Estate

Predictive analysis and data mining are yet to become a big deal in real estate, but big data is slowly and surely penetrating this segment. Some analysts might question the relevancy of big data in a business where the consumer/homebuyer typically ventures into the market every 5 to 10 years. We argue that this is precisely the reason big data should be used in the real estate niche.

Why Remaining Connected with Customers is Necessary?

Property purchase decisions are heavily impacted by the goodwill a business can generate and sustain. Here, consumer preferences don’t undergo overnight changes like those seen among fast-moving consumer goods. Real estate industry has a special space for references where past clients are invaluable. Having happy, if not loyal, clients can translate into feeding other lines of business like refinancing. If you can maintain a connection with your past clients, many more serious queries are likely to flow from friends, family members, office colleagues, and even neighbors.

Some folks argue that typical retail businesses, like apparel, have more to benefit from big data rather than real estate. Let us take an example to argue our point here—a young family of four with working parents is expecting another child. Such information is often announced in the form of social media updates. A retail business can use this data to send emailers and SMS with links to newborn clothing range. Now, how can a real estate business use similar data?

When a baby is on the way, it is most likely that the mother has taken temporary leave of absence. There is going to be lesser money to pay the bills. With the newborn retail niche overburdened with branded products, the expenses are also likely to rise. Limited to a single income and facing more expenditure, the family might seriously consider refinancing to save money. With three children, they might reconsider moving to a bigger property.

A real estate business can make relevant interaction by emailing pictures of bigger accommodations with more study rooms and better schools. This strategy is a combination of tracking social media conversations and hyper-targeted marketing. Here, a single Facebook update can lay the basis for a new sale or at least create a very serious home-buying or refinancing query.

There is more to tracking consumers’ social media behavior. Online experts now offer targeted social media analysis. They identify audiences whose recent shopping behavior suggests a definite buying capacity. Home purchases might be offline, but online consumer behaviors can help real estate businesses identify more, promising consumers.

Big Data Strategies Deliver but Be Patient

The strategies we discussed above take time. Signing up with a social data analysis firm doesn’t mean that you will be overwhelmed with a glut of serious queries. This is a gradual process where hurrying into decisions can mean disaster. Secondly, keeping past customers engaged isn’t easy. This is particularly true for real estate where the consumer-connect is soon lost after the transaction. This is where data-sharing practices can solve the problem.

If you are ready to share data about the payment pattern or the mortgaging history of past clients, you can get valuable information in return. For instance, some of your past customers might be avid environmental enthusiasts. You can start a small blog or a newsletter about green building practices. Here, you reason to seek an association is logical since you are in the housing niche. Among consumers, such information is less likely to be spammed because it is relevant to them. The e-mailer/newsletter can carry a small, hyperlinked logo, directing the browser to your parent website.

Buy and work leads smarter, contact only the customers you want to engage, and enable your employees to be productive. Connect with your target audience with Live Connect today!

Why All Businesses Should Embrace Big Data

Some industry analysts have made less-than-enthusiastic forecasts about the widespread adoption of big data. According to them, big data will take time to be adopted by businesses—this can be up to five years.

Is this mere speculation?

There is some truth to this viewpoint. It seems that even among businesses using big data, the zone of maximum productivity remains elusive. This is because some companies have adopted big data without the necessary introspection. It should be understood that big data can deliver only when the resources managing it are well equipped. This means having the technical expertise for data collation, warehousing, sharing, and analyzing. Secondly, some businesses have acquired the expertise but lack the right insight necessary for driving business decisions using big data. Overall, it isn’t the ROI of using big data being questioned — the issue lies with handling it effectively.

The Transformation is Underway

You might think that big data hasn’t really taken off. However, the truth remains that it is much more than just another transformational technology. Despite the slightly-pessimistic perspective, it would be foolish to deny the growing interest in big data. Businesses of all sizes are realizing that big data is a definite, mainstream reality. It is just a matter of time when businesses of all sizes will incorporate the skills necessary to use big data. Big data is here to stay and this realization is underlined by the growing urgency to employ data-centric skills. Some businesses are aggressively hiring manpower that specializes in handling big data. Even better, some companies are looking for trustworthy data solution vendors who can empower them with big data capabilities at much lesser costs.

There is no doubt that big data yields better correlations that drive smarter business decisions. When combined with advanced analysis, big data can help you improve customer service and design better products/services. Data analysis can help you resolve problems that seemed exceptional and unsolvable.

Some Big Data Dynamics You Should Keep in Mind

If you have already begun the big data journey or are on the verge of starting it, please read the following:

Data Protocols

Ensure that you have effective data strategies in place. This includes protocols for collecting, saving, indexing, sharing, and analyzing data. Data should be accessible, but not at the cost of compromising data privacy and security. Whether it is the sales department or CEO’s desk, data protocols should be implemented across the organization.

Expertise to Use Big Data

One huge benefit with big data tools is that they can provide immediate results. This refers to simple data sets. Using this, managers can make decisions that affect the current, internal processes, customer service methods, and marketing strategies. Therefore, ensure that you have the required personnel in place to use big data. You should have data experts or managers who can put actionable data in motion.

Don’t be Apprehensive about being Data-centric

Once you start realizing the benefits of big data, it is likely that you will seek a data-centric approach to regular business processes too. This might upset a few employees and challenge authoritativeness of some managers. Accept this unpleasantness as a part of the transformation that will eventually subside.

Leads Direct provides quality business leads at 40-80% off retail prices so you can grow your business quickly and increase your ROI. Contact Leads Direct today!

 

Have You Realized the Power of Big Data?

In the real estate niche, big data is still finding its way. Organizational ecosystems are still contemplating whether big data will deliver ROI and enable product innovation. Here, we present some aspects about adopting big data that might have escaped your attention:

The Big Data Acknowledgment

Some key elements about becoming data enabled now seem doable to most lending institutions. For instance, collecting relevant data means coordinating with regional or remotely-located business units. Lenders are prepared to collaborate with their partners and even customers to ensure that the maximum volume of usable data is collected.

Secondly, adopting big data presents a change in technological and management infrastructure which is necessary to derive ROI. The big data strategy is now better understood, helping lending businesses analyze their preparedness for this transformation. Many times, studying about the big data's journey of an equal sized, but differently-industry business can also help.

How can big data contribute towards your lending business?

Some people believe that big data is more applicable to the retail segment. However, its utility in the real estate market has been proven beyond doubt. Big data is extremely useful for segmenting or hyper-segmenting the market. This forms the basis for more penetrative customer targeting. By combining various data resources, data analysts can decode customer behavioral trends. These can be further grouped across various parameters. The finer nuances of real estate borrowing, such as the last-minute reasons to cancel a loan application, can be better realized through big data.

Sometimes, untapped financing or refinancing requirements can be deciphered by analyzing social media conversations or by analyzing recent credit transactions conducted online. This can help you create loan products, better tailored for a certain regional, age-based, or profession-related demographic. Analyzing customer risk profiles can help you explore the lending market. For instance, people with mid-range credit scores might have shown incredible consistency in bill payments along with reduced credit in recent months. Such consumers can be further assessed for smaller loans.

Is big data out to replace organizational management?

Not really. Big data can ease the decision making, making it more informed, always substantiated with confirmed numbers derived from data analysis. Big data helps managers see beyond their established market practices. Using data-driven business models, you might come across better alternatives to increase employee performance and reduce internal wastages. However, putting big data into practice involves human intervention. In fact, big data needs to be incorporated, run, and used with a reasonable amount of human management.

By streamlining your business practices, big data can reduce staffing and operational costs, but it cannot replace the need for management. Even if big data yields information about better ways to package loan products, it will take efficient management to drive new product presentation, marketing, and consumer education.

Can big data help you discover and create new business models?

Absolutely! This is among the most fascinating applications of big data. Big data has spawned across industries, from online retailers to banking giants. The reason for its success lies in the fact that our generation is information-driven. This information is invariably present in the digital form, i.e. data.

Upon analysis, it might turn out that newly-engaged couples are among the most aggressive loan seekers during the Christmas-New Year period. Sensing an opportunity, you can create customer engagement models that can aggressively tap into this newly engaged demographic. Similarly, data from past holidays inspired borrowing can help you forecast the kind of numbers feasible during the forthcoming holiday season. Consumer profiles that have been denied a mortgage by bigger financial institutions, despite reasonable credit scores, can be very useful. When combined with advanced analytics, it can help you identify borrowing households with steady incomes, greater propensity to pay bills on time, and lesser credit spending habits.

Buy and work leads smarter, contact only the customers you want to engage, and enable your employees to be productive. Connect with your target audience with Live Connect today!

 

Moving Ahead: Big Data for Credit Lending

Credit lending is a competitive marketplace, relying on everyday transactions where consumer profiles are evaluated with extreme precision. This niche took a drubbing when the entire financial sector suffered due to the economic slowdown. Now progressing towards a complete recovery, credit lending is becoming more accommodating. As a result, customers with poor credit histories able to demonstrate a positive financial performance in the recent past are also being offered short term loans.

Big Data Emerges as the Solution

This doesn’t mean that lending institutions are taking upon a bigger risk pool without worrying for the repayments. It is a matter of using data to identify more households and businesses that aren’t fraudulent and need small to medium value loans. This data is essentially big data that is helping lenders become more competitive. Lending businesses use big data in a very computable way. This usually involves handling massive amounts of legacy and current data. The emphasis is on finding information that can ease credit related decision making.

Big Data Acknowledgment and Deterrents

The credit card segment has quicker data handling at its core. For instance, authorizations are provided instantly when customers swipe their cards at a retail desk. Here, pre-authorization or authorization has to be computed in the blink of an eye. This is just an example. There is much more data that can be used by credit card companies. However, due to server related limitations, only a small amount of data can be retained.

The periodic purging of data means that data retention is maintained at a very basic level. This doesn’t help businesses that are trying to become more data driven. For comprehensive data analytics and business forecasting, more data is needed. The more the data, better is chance to assess risk pools. Businesses vying for sustained growth understand this requirement for more data. This also helps to filter unused data warehouses and retain whatever can be translated into useful information.

Don’t Ignore Utility of Data Automation

Not just data analysis, but automation is also a major consideration. A manual process can be very time consuming. It requires the underwriter to individually analyze every application. Spotting fraud in this way incurs more manpower hours and presents risk of miscalculations. Using automated processes, underwriting becomes more accurate and faster. Here customer data merely needs to be fed to a system. Computing algorithms are put into action using minimal human resources. Within seconds; fraud, credit-worthiness, and overcall credit history are electronically computed.

Don’t Fret if You Don’t Have Big Data Capabilities

Some lending institutions have a monthly schedule for evaluating their current customer base. However, this can be done more regularly. The customer portfolio can be evaluated on a weekly basis too. High-risk customer types can be analyzed on a daily basis. This ensures that current models used for customer categorization are up to date with analytical outputs. Yes, doing this isn’t easy. Speed is the secondary consideration here. Firstly, the business should have the capacity for this kind of data processing. Perhaps, a better solution lies in employing the services of vendors who specialize in collecting, retrieving, and simplifying digitized information.

Buy and work leads smarter, contact only the customers you want to engage, and enable your employees to be productive. Connect with your target audience with Live Connect today!

Is Big Data Scaring Banks?

Borrowing money has always been very challenging for startup businesses. However, the trend is changing these days. Preference among startups to seek loans from smaller lending institutions and not banks is emerging as a nationwide trend.

Alternative Lending

Startups, including those that have just begun, are finding it easy to fund their dream projects. Lending is coming rather easily to this segment, including Subprime and Payday loans. The trend is also being referred to as Alternative Lending. This is essentially small business lending that has confused analysts with its unprecedented growth. It seems that this trend is largely fueled by a changing underwriting approach. Conventionally, underwriting has strictly been a standard-driven niche. Credit scores, like FICO scores, have had unchallenged acceptance across the lending sector. Now big data is challenging this typical landscape.

Why this shift?

We believe that this has something to do with the credit crisis that followed the 2008 slowdown. Banks have suffered a lot at the hands of Subprime mortgages. The pessimism continues with banks being apprehensive about anything that doesn’t guarantee repayment. On the other hand, smaller lending institutions have understood the potential of start-ups. They are ready to offer attractive interest rates. Secondly, smaller lending institutions are increasingly using analytical tools to retrieve actionable information from bigger volumes of data.

This big data shift is not as common in the banking sector. Based upon the big data indicators, new underwriting models have surfaced. Start-ups don’t mind their repayment potential being evaluated on the basis of their social media handles or email communication. These individual pieces of data form a part of a widespread big data structure that thrives on using data derived from unconventional resources. This approach is working for startups that are ready to share even more information, as long as the privacy of their business model is not compromised.

Startup Payday Loans Don’t Stretch for Decades

These loans have another advantage over traditional lending instruments like mortgages. An average mortgage might take 15-30 years to be realized. This is too long a period to derive decision driving data that can be used to curate more home loans. However, a bad loan emerging from the startup sector can be analyzed much sooner. Here, the average length of a loan is a few weeks or months. Credit factors that were under evaluated can be quickly identified and used to improve the current underwriting model.

Startup Payday Loan Underwriting isn’t So Demanding

The documentation and repeated screening by the underwriting team can test the consumers’ patience. However, payday loans aren’t as demanding. With little documentation and proofs to establish the borrower’s profile, a payday loan can be executed much quicker. Using analytics to improve this already simplified loan sanctioning process isn’t very difficult.

Payday Reputations are Not so Vulnerable

There is another risk associated with loans to not-so-worthy, newly launched businesses. In case of a default, the bank’s reputation is at risk. Even a few days of bad press can compromise the faith of customers in the bank. However, smaller lending institutions aren’t so sensitive about this issue.

Big Data Costs can be Challenging but Solutions are Emerging

It would be foolish to assume that creating a functional big data infrastructure is without any casualties. The traditional concept of learning as you proceed applies here as well. Using big data doesn’t guarantee the absolute removal of bad loans. Initially, a lender needs to test its big data parameters and their accuracy to observe patterns that can be used for future loan applications. The default rates at the start of the big data journey can be high.

Yes, a big data led underwriting model can be expensive, but another solution has surfaced recently. This is in the form of different business models that use elaborative data. Now lending institutions have the advantage of comparing their organizational structure or lending processes with similar business environments. They can choose a big data model that seems more appropriate for their business environment.

Can Big Data Rescue People with Bad Debt Cycles?

Big data adoption is sweeping across the financial sector, gaining widespread acceptance among lending institutions as well. While some banks might call themselves pioneers of this movement, it is the medium and small scale lenders who are really driving this trend. The lending marketplace is abuzz with how big data is helping to decode more risk-appropriate lending profiles. Data analytics is being used to increase the quality of customer service and arrest cases of fraud.

Big Data Not Always Associated with Bigger Businesses

The adoption of big data doesn’t have a uniform pattern. Some of the bigger, more established financial institutes might be collecting more data from conventional points of customer interaction only. However, lending agencies handling smaller loans are more likely to analyze poor credit histories to ascertain the overall chances of recovery and the ability to repay another loan. Here, the idea is simple—take a calculated, manageable risk by offering smaller loans to credit-worthy consumers.

Big Data Can be Seemingly Insignificant Data

The indicators used to evaluate borrower profiles are breaking new ground with regularity. Even social media channels that carry employment information or email accounts with a regular history of making payments or receiving funds are being analyzed. The emphasis is on looking beyond the typical buying and payment history of a consumer. Instead, the focus is on the present and near-future ability of the borrower to repay a small loan. Having a stable job over the past few months can be a stronger argument against the non-payment of a small loan years ago. Similarly, small businesses paying taxes on time and interacting with customers on Facebook, Twitter, or LinkedIn are more likely to be approved for a loan.

Yes, demographic information and credit history are still important. However, these conventional parameters took a severe beating in the aftermath of the recession. People with otherwise good credit histories too were caught in this mess. Therefore, credit histories that show positive signs of complete recovery are worthy of being given another chance. When analyzed further with big data analytics, many such deserving borrowers can be identified.

Will big data overturn debt cycles that cripple households?

The answer lies in how you perceive the question:

Do you look at it as technology coming to the aid of people who are conventionally not credit-worthy? (unlikely)

Or,

Do you perceive it as traditional evaluation parameters being improved by using contemporary technologies? (practical and feasible)

The ideal way to look at this argument is accepting that big data provides a better way to identify consumers who can make monthly payments despite a somewhat-flawed credit history. Big data is not the magical cure that some families might expect it to be.

Similarly, big data cannot guarantee loan payments. It is essentially a relief to a market that is still recovering. It cannot alleviate poverty or the healthcare crisis. However, big data will ensure that a larger part of the credit-deserving population is well served. Much of the information collected as a part of the big data strategy is already in the public domain. If the reputation of a neighborhood or money earned during seasonal employment is used to determine credit reliability, is someone being harmed?

Why your Big Data Analysis System Should be Insightful

There is a lot of emphasis on data and data analysis these days. However, data analysis alone won't deliver the desired results; companies should be able to turn the insights acquired by analyzing data into valuable actions.

Using data analysis, a real estate agent should be able to identify potential customers who are likely to move homes in the immediate future. Proper analysis of data involves asking the right questions. This will help realtors detect trends and adjust their business processes.

If you are an agent you could perhaps ask:

Why am I making fewer sales? Or how can I know what my customers want? Remember that the goal of data analysis is to change compelling insights into tangible business actions.

Companies should build a data-first strategy that will help them gather valuable data using their new products. Data analysts attempt this type of exploration to discover critical business insights. They will then present these insights to stakeholders, who after interpreting the insights will use them in making tactical decisions. There is one problem, though. Thanks to its broad nature, open-ended explorations may uncover insights that have little value.

On the other hand, closed loop data explorations are more focused. For example, a real estate company might ask: Are there any real estate investors who bought more than 5 properties last month? This information allows the company to flag that investor as a very important customer eligible for special offers. However, even closed-loop data analysis carries the risk of producing insights of little value.

Why it is important to take quick decisions

One of the biggest challenges of doing business is making great decisions. While professionals in the IT sector are quite comfortable delivering reports, real estate brokers are yet to get into that habit. It is even more difficult to take an action based on available information. And if a company fails to take decisions in spite of having lots of insights, its value will begin to erode.

You may have invested in information management tools and generated all kinds of reports and dashboards, but all of these are useless if you cannot convert your observations into actions. In order to benefit from data analysis, companies should be able to quickly and efficiently transform insights into actions.

This wouldn't be possible without investing in streamlined data collection technologies and decision-making strategies.

The need to adopt a "data first"strategy

When you design or launch a new product, data collection and analysis is probably the last thing you do. However, it needs to be the first. To have a 'data first' strategy is to have the right tools in place to gather insights that will greatly improve user experience.

Partnerships

Many organizations do not have the technical expertise required to gather and analyze data. In this case, they should consider bringing in outside expertise through partnerships. And when you have the right partnerships, you will be able to reach customers with deals, offers, content, and ads across different channels.

Why is data so big?

Big data analysis is a powerful tool that will significantly improve user experience and increase profits. But it’s critical that businesses are aware of the process, have the right tools, and realize the need to move quickly and decisively once they have gathered insights.