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!

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!

Inspiring Customer Loyalty in the Service Arena

New customers are considered to be the lifeblood of a business. In recent times, along with efforts towards getting new customers, marketers have been paying a lot of attention to current customers. They have been revamping the concept of customer service. New investments are being made in customer relationship management systems and building of teams for improved communications with customers.

When it’s time for festivities and you’re planning your gift list, there are often some unexpected people who just jump into your mind. They’re not family and they’re not friends. They’re people who make a place in your heart because of the way they treated you.

This is what service is all about. Exceptional service is always appreciated and remembered. It can create long term customer loyalty. The same holds true in the contact center arena.

So what qualities of contact center agents build customer loyalty? We think it boils down to four basic things:

Handling of issues in a customer-friendly way

Such agents make it their business to know the customer base in-depth. They have updated information regarding the latest communication trends and the contact channels that people prefer to use. They are evolving entities who are ready to handle future generations of consumers across various channels.

They are happy to listen to what customers want using channels that customers want. Then they respond in the way customers want them to.

Trying to give a personal touch to all interactions

Agents who manage to add a personal touch without getting too personal always score with customers. They consistently deliver friendly and warm service.

Contact center managers keen to instill such qualities in their agents have distinct systems in place for evaluation. They organize special programs for development of these qualities. Measures are taken to train agents to add a personal touch through multiple-channel communications which include voice interactions, emails, SMS, and chats.

Being naturally gifted

Some contact centers regard new agents as less-skilled members of the corporate workforce. This is certainly not true in all cases – many contact centers require their agents to have a specific level of academic qualification, for example, when a center caters to the health care field.

Contact center managers with a focus on building customer loyalty do not assume that a certificate makes for a good contact center agent. They seek out and recruit superior advocates of customer interaction and take appropriate measures to retain them. They are clear regarding the qualities a “perfect” agent is required to have and look for natural talent during the hiring process.

Going all out to ensure you are satisfied with a product.

Great agents stand behind a company’s product, and do whatever is required to ensure a customer is happy. They want customers to enjoy their experience and keep coming back. Happy customers translate into more business. Instead of simply having the objective of getting a job done, they have the goal of ensuring a remarkable experience for each customer.

In such a setup, various incentives inspire agents to improve their services in a sustained manner.