Client Spotlight: Kern Health Systems

Kern Health Manages Rapid Growth with HPA’s RPA-as-a-Service

Robotic Process Automation (RPA) is an ideal solution for the unique challenges facing many payers in the U.S. For Kern Health Systems (KHS), RPA was just what the health plan needed to keep up with rapid growth, both in claims volume and member services. KHS serves primarily low-income Medicaid members in California, and its broad provider network includes hospitals, facilities, primary care doctors, and specialists. KHS is dedicated to improving healthcare for its members through an integrated managed healthcare delivery system.

In 2015, KHS migrated from a legacy system to TriZetto’s QNXT™ Enterprise Core Administration System. As the KHS team began evaluating processes for automation, they focused on specific pain points in the claims lifecycle that were time-consuming and resource-intensive. The health plan was also experiencing rapid growth in membership and utilization, which prompted the need for a solution that would reduce overhead while allowing it to maintain a superior level of care for members. HPA’s healthcare automation experience and close relationship with TriZetto made the automation provider an ideal choice for KHS.

“Any business process that has a definitive path can be automated with HPA to gain operational efficiency,” said D’Ln Brown, KHS Configuration Analyst. “HPA’s automation has decreased processing times, allowing for faster payments to providers, denials, and consistent processing, removing the risk for human error.”

Improving Client Outcomes with RPA-as-a-Service

RPA is a perfect fit for health plans due to its efficacy in cutting costs, preventing errors, and scaling effortlessly to work volume. However, the RPA-as-a-Service model provides the added benefit of ensuring clients are paying for positive results, not licenses and additional support resources. For payers looking to reap all the benefits of RPA while avoiding the risk of building and maintaining a program in-house, RPA-as-a-Service (RPAaaS) can be a real game-changer.

For KHS, the RPAaaS offering made the experience as seamless as possible, from evaluation to go live. HPA’s automation specialists guided the health plan in evaluating process candidates and created an automation strategy that could scale to its growing business needs. “HPA’s automation specialist took the time to understand our internal processes and was able to improve our automation outcomes,” said Brown. “The timeline from initial discussion to implementation was minimal, and we were able to improve our automation outcome.” With HPA’s unique RPAaaS approach, automation specialists are very hands-on with the client team to make recommendations that improve current processes and success rates. According to Brown, this attention and dedication made a critical difference. As a result, KHS experienced a seamless implementation and was able to get its new robots up and running in the anticipated timeframe.

HPA’s services have revolutionized the way we work, from accuracy and speed to employee allocation. We are now able to handle processes optimally instead of straining our resources. HPA’s robots help us achieve our business goals as we continue providing top-notch service for members and providers, which is huge for us.Victoria Hurtado, MBA, PMP - Director, Information Technology Operations

Authorization Matching Robot  - Claims that require authorization prior to payment may be pended in the QNXT system until they can be processed. Health plans are challenged by processing claims like these because different authorization matching rules and exceptions apply to certain services, members, and contracts. This process is time-consuming and can lead to a backlog of claims that examiners must manually service. However, HPA’s robots are able to search for authorization matches for members in the system based on data like provider name, service location, dates of service, and codes. If a match is found anywhere in the system, the robots will attach it to the claim.

With an average monthly volume around 7,000 per month and an average manual handling time of 5 minutes, prior authorizations created a considerable strain on KHS’s resources. Immediately after deployment, KHS quickly decreased its overall pended claims inventory. To date, KHS has saved more than 9,000 manual hours and enjoyed an average cost savings of 74%.

Read the case study to learn how Kern achieved 82% auto-adjudication and 55% cost savings with automation.

What is Hyperautomation?

Hyperautomation brings together robotic process automation (RPA), machine learning (ML), and AI to drive end-to-end process automation, as well as AI-based decision making. The term “hyperautomation” was coined by Gartner in its October 2019 article, Gartner’s Top 10 Strategic Technology Trends for 2020. However, industry analysts may use different terms for the same concept: Forrester refers to it as “digital process automation,” and IDC (International Data Corporation) uses the all-encompassing term “intelligent process automation.”

How is hyperautomation different from RPA?

RPA can be used to tackle a wide range of processes, but the software has limitations. RPA robots cannot be programmed to make independent decisions, as their human counterparts would. While many organizations consider this a strength, as they have assurance that their robots are taking the correct steps outlined to complete the task, business needs have expanded beyond rote process execution. Hyperautomation is merely the catch-all phrase for the addition of intelligent capabilities, like optical character recognition (OCR), natural language processing (NLP), and machine learning. These advanced capabilities can assist where a robot would typically hand off to a human to make a decision. Robots cannot read the text in images, but OCR engines can. Robots cannot understand the sentiment or context of an email, but NLP engines can. Essentially, a combined set of advanced tools can be applied to mimic, and even aide, human decision-making so that processes can be fully-automated and the business can maximize its efficiency.

How does hyperautomation benefit an organization?

Unstructured data accounts for about 80% of the data that companies process every day, like images, audio clips, PDFs with images, paper forms, customer service e-mails, or unstructured text files. Cognitive capabilities like OCR and NLP can be applied to unstructured data to extract and structure it for use with RPA robots. NLP can help extract data from free-form content, like customer voicemails and e-mails, while OCR can be used to identify data from scanned images. The symbiosis between AI capabilities and RPA robots allows organizations to automate a wider variety of business processes while their human workforce focuses on more complex cognitive tasks.

Is hyperautomation necessary for my automation plan?

In the realm of automation capabilities, you should walk before you run. Master RPA tools, processes, and company-wide alignment of the automation strategy before progressing to hyperautomation. In other words, explore automation of lower-level processes across the organization that rely on structured data and don’t require thought or decision-making. The lessons learned through RPA carry forward into hyperautomation and will make it much easier to implement, maintain, and scale these more advanced capabilities.

For companies who have conquered RPA and are looking for full-cycle efficiency and operational improvement, hyperautomation is the next logical step. A successful hyperautomation integration relies on the synergy created by the proper system-wide application of advanced process automation tools. This is why it is wise to weave your hyperautomation strategy in with the digital transformation initiative. Plan for before, during, and after states, as your hyperautomation strategy must account for old transforming to new and everything in between. Pre-existing automation strategies will need to be re-evaluated and re-tooled to meet the needs of the changing technology landscape. As new and improved business processes are created and new technologies are adopted, opportunities for automation will reveal themselves, both in the RPA and hyperautomation arenas. Post-transformation, effectively harnessing these capabilities will aide in solving the more complex business issues that were once much more difficult to tackle with a disconnected tech stack.

Applying RPA in Digital Transformation Initiatives

In today’s digitally-focused age of business, tech buyers are more motivated than ever to invest in software to help them achieve digital transformation. Many companies were prompted to begin or accelerate digital transformation initiatives this year as the impact of COVID-19 amplified the inefficiencies within their business. It became harder to ignore the negative impact of legacy or disconnected business systems that echo throughout an organization in the form of dysfunctional processes, disparate data silos, and lengthy cycle times. Robotic process automation, or RPA, can act as a functional layer across business systems old and new to facilitate cross-platform communication. However, companies must take care to thoroughly integrate RPA and digital transformation strategies to reduce rework and ensure both projects progress forward together.

What is Digital Transformation?

Digital Transformation is the integration of digital technologies throughout a business to transform how it operates and meets the needs of consumers. Such an initiative requires a radical mind shift in how the organization will use its’ technology and people to achieve desired goals. To begin, companies will need to take a hard look at the business to determine where the inefficiencies lie and what is driving those inefficiencies. Thorough evaluation of the current tech stack and underlying business processes is performed to determine where investments and improvements should be made. Some companies will have the resources to upgrade the tech stack en masse, while others may have to make difficult decisions about which technologies should be prioritized. In either case, there will be a massive shift in how the business operates as the old way of doing things is rapidly expiring.

Applying RPA in Digital Transformation Initiatives

The adoption of modern, interconnected systems paves the way for sunsetting clunky business processes that were formerly dictated by legacy systems. However, there is a common misconception that RPA is only useful for helping manage  the issues that arise from disparate or legacy systems, which is simply not true. RPA is centered on automating human processes. The application in which the process is performed is insignificant. Modern applications offer interconnectivity and may facilitate more efficient automation methods, like the use of APIs. Still, employees will build processes around newly-adopted technologies, just as they did with legacy systems (albeit more efficiently, fingers crossed.) Those newly-minted processes will still be excellent candidates for automation because humans still perform them.

As new technologies are adopted, the business will find itself having to account for the old and new way of doing things. The RPA layer over systems and processes old and new will also have to account for that dynamic as well. This is where proper planning and project management will be crucial to keeping the project from going off the rails. To start, complete documentation of current business processes is highly recommended to establish a clear frame of reference for both the current and planned states of the business. Business analysts and process owners will need to work together on this documentation so the business has a complete view of the process and its dependencies. The automation Center of Excellence (COE) will also need to drive cross-departmental collaboration as new systems come on line to develop the RPA strategy around them. New business processes will also require thorough documentation to accelerate the development of robots that will interact with these new systems.

Enterprise-wide coordination and close alignment across the COE, Operations, and IT is crucial to keep both projects on track. DevOps, or Development Operations, is a methodology that combines principles of software development and IT Operations. Many companies are utilizing DevOps practices to accelerate development cycles without sacrificing quality. This methodology can be applied in both RPA and digital transformation initiatives so that the organization can deliver applications and services in tandem at a higher velocity. Under a DevOps model, development and operations teams are no longer siloed so that crucial operations information can be incorporated earlier in the product or robot development cycles. This kind of collaboration ensures each project is fed crucial, relevant information as it progresses to reduce rework, keep momentum going, and raise your chances of success.

Integrating Machine Learning with Robotic Process Automation

In June 2018, a report from McKinsey & Company predicted the transformational power of artificial intelligence (AI) and automation combined. AI and its subsets have since become intelligent additions in the world of automation to attempt to solve the issues companies are facing beyond problematic repetitive processes.

Machine learning (ML) is one of many forms of artificial intelligence (AI) being applied in the RPA industry today. Machine learning is the application of AI to provide systems the ability to learn and make decisions without being explicitly programmed to do so. In RPA, ML can advance robots beyond rote process execution and allow them to take on tasks that traditionally required human decision-making. Artificial intelligence capabilities can also be applied in a variety of ways to improve data integrity, add structure to unstructured and semi-structured data sets, enhance business insights, or improve automation execution.

Broadly categorized, there are three different ways that machines learn:

• Supervised, in which the algorithm is trained on a labeled data set and given desired output values. The goal is to find specific relationships or structure in the input data that can produce the correct output. A structured data set would show the ML model information (photos, words, numbers) and the model would remember this information for later use. When presented with new data, the model compares it to examples it learned while being trained. Supervised learning is helpful for projects like classification problems, which ask the algorithm to identify input data as a member of a specific category or group.

• Unsupervised, in which the algorithm is fed unstructured data and is not given the desired output values. Instead, these algorithms independently detect patterns and rules within data. The most common algorithms within unsupervised learning are clustering, representation learning, and density estimation.

• Reinforcement, in which the algorithm is continuously learning and reiterating based on feedback. The best example of reinforcement learning in RPA is remote desktop automation robots, also referred to as attended robots, which are trained by human inputs as they work alongside them.

How Machine Learning is Applied in RPA

Let’s explore the various ways machine learning is being applied in automation.

Data structure and quality

There are 2.5 quintillion bytes of data created each day. Over the last two years alone 90 percent of the world’s data was generated. On average, companies with less than 1,000 employees have an average of 22 applications deployed. For enterprise companies, the average number of applications deployed jumps to 788. Given these factors, it is no surprise that the majority of companies have more data than they know what to do with. Bringing data together in logical ways to glean meaningful consumer and business insights is an ongoing challenge that is slowly being eliminated with advancements in machine learning.

Unstructured data accounts for roughly 80% of the data that companies process every day. Examples of unstructured or semi-structured data include images, audio, image-based PDFs, paper forms, text files, or customer service emails. Machine learning, and other cognitive capabilities like optical character recognition (OCR) and natural language processing (NLP), can be applied to these data to extract and structure it for use in automation. OCR engines can be used to identify, extract, and categorize data from scanned images. NLP can be trained to understand sentiment in free-form text, like customer service emails, chats, and voice inputs.

Improving Automation Execution

Machine learning algorithms can also improve the delivery of automation services. For example, algorithms can be used in computer vision to train robots how to recognize and interact with onscreen fields and components. Recursion is often used to reduce code complexity and optimize robot runtime, and machine learning models are also used for exception handling.

Task mining is another emerging application of machine learning in automation. In this instance, robots are trained to analyze daily task information gathered from employees to produce process maps and suggest processes for automation based on the highest return on investment (ROI.) This application can be a mixed bag as it requires a great deal of training to strike the appropriate balance between ROI, level of effort, and general fit for automation.

Attended Automation

Attended automation, sometimes referred to as remote desktop automation (RDA), is where robots work alongside humans to supplement their work or aide in better decision-making. Machine learning can be used to ingest data from various sources in real-time, which allows robots to help the human determine the next best step in their workflow. Machine learning can also be combined with other cognitive capabilities, like NLP, to allow robots to replicate the simpler decision-making within a human’s workflow to move even closer to achieving end-to-end automation.

Industry-specific Applications

Each industry has its own unique challenges that are being solved, in part, by applying machine learning.

• Healthcare - The industry that is perhaps most overwhelmed by the volume of data, and the potential insights to be found within it, is healthcare. Healthcare data is a wealth of information that can enable providers and payers to proactively provide individualized care to the people they serve. However, to achieve a proactive, and even preventative, care model requires a complete understanding of the patient through data. Non-healthcare factors such as demographics, environmental, lifestyle, diet and exercise are equally important in understanding risk factors, personalizing treatment options, or aiding in disease identification and prevention. Gaining actionable insights from the myriad data required to complete the patient profile would be impossible without machine learning.

  Within the context of RPA, machine learning can aide health plans in cleaning and structuring the data they receive from providers to be used in automation. ML is also ideal for detecting anomalies in claims and identifying opportunities for process improvement within claims. For example, robots can be trained to observe factors that cause a claim to be manually processed and determine if those factors could be resolved.

• Insurance - Insurers have already seen success by deploying machine learning to massive data sets to forecast loss and calculate risk. With regards to RPA, ML is primarily applied in two key areas: data intake, through the extraction and structuring of semi-structured or unstructured data for use in automation, and advanced analytics, through the analysis of historical data to drive better real-time decisions, eliminate leakage, and aide in forecasting.
  Primary examples include:

  • Claims intake, which can happen online, or via email, fax, or phone. ML, OCR, and NLP can be deployed to extract information from FNOL submissions, which allows robots to determine intent and route the claim accordingly.

  • Subrogation, where claim notes, diaries, or police reports can be examined to highlight or eliminate subrogation candidates. NLP can be used to analyze the text and identify related phrases that could be used to determine potential fault. For example, “a rock hit my windshield” (not a candidate), or “other driver ran stoplight” (candidate.) This “intelligent” first pass on claims would surface subrogation opportunities to ensure they won’t be missed by humans, a problem that costs the insurance industry millions every year.

  • Determining things like Cause of Loss or Bodily Injury, where robots are trained on what to look for in claims information to enable more timely and accurate registration of claims.

• Banking and Financial Services - AI capabilities are being deployed in the banking and financial services sector in a variety of ways, from detecting fraud to chat bots to processing credit agreements en masse. Anti-Money Laundering (AML) and Know-Your-Client (KYC) are two compliance processes that are popular candidates for machine learning. Forensic accounting to detect anomalies within transactions is time-consuming, prone to error, and incredibly costly for banks. However, robots can ingest data from a variety of sources and be trained to identify the markers that indicate risk and potential fraud.

  Banks have also adopted cognitive capabilities to improve service to their customers. For example, JPMorgan Chase has implemented a virtual chat assistant to provide personalized customer support 24/7, and several competing banks have followed suit. Banks are also using ML to examine consumer behavior online to predict growth opportunities. Robots can be inserted throughout the customer service process to supplement for humans where possible, or deliver issues to humans for resolution.

Client Spotlight: CareOregon

CareOregon Implements Automation to Reduce Manual Intervention in Claims Processing

Founded in 1994, CareOregon is a nonprofit organization providing health insurance services to more than 375,000 low-income individuals in Oregon. CareOregon is a managed care company with a broad goal: to make world-class healthcare available to all Oregon residents, regardless of income. The plan has built a healthcare delivery system that assures member access to physicians and healthcare professionals who understand special needs and provide quality care. True to their mission, it also supports local programs that offer housing, food, and job training to all Oregonians.

As a Safety Net Health Plan, it operates as a 501(c)(3) that derives nearly all of its revenue from public programs like Medicaid (the Oregon Health Plan), Medicare and the State Children’s Insurance Program. CareOregon currently manages health plan services of three coordinated care organizations (CCOs) in Oregon, and operates a Medicare Advantage plan and a dental care organization. Its health plan services received a 3.5 STAR rating from CMS in 2019, and its drug plan services received 4 stars.

Applying Automation in Claims Management

Reducing administrative cost is a top concern for health plans. Human intervention can expose claims to human error and lengthen the claims lifecycle, both of which contribute to an increase in overhead cost. Lengthy cycle times are also a key contributor to claims backlog. CareOregon was experiencing similar struggles when they reached out to HPA in 2015 to begin automating claims processes. The plan currently has five automated processes in production with HPA and administers claims on the QNXT Enterprise Core Administration System by TriZetto. HPA is fully-interlocked with TriZetto on product upgrades, hosting needs, and enhancing overall quality of service delivery to clients.

Controlling costs, improving operational efficiencies, and reducing waste in the health care system is better for everyone, and a top priority for CareOregon. Our partnership with HPA enables us to apply more resources and focus towards fulfilling our mission to promote equitable healthcare for individuals and our communitiesAmy Dowd, Chief Operating Officer

Coordination of Benefits (COB) claims were selected as the initial target for automation at the beginning of CareOregon’s engagement with HPA. COB allows health plans to determine who is responsible for the payment when a member is covered by two different plans. A member’s benefits and reimbursement rate should not exceed 100 percent of allowed medical expenses, and examiners must ensure there are no duplicates in the system. COB claims are also paid or denied based on the Medicare plan, benefit plan, type of claim, and location of services rendered.

When manually processed by examiners, each COB took an average of 3 minutes. With an average monthly volume of around 5,600 COBs, CareOregon would have to spend more than $130,000 in overhead annually just to process this one claim type.

Since the automated process went live in November of 2016, the plan has saved over $300,000, as well as 3,700 manual hours annually, the equivalent of 3 full-time employees. Additionally, when CareOregon experienced a spike in volume due to increased Non-Emergency Medical Transportation submissions, HPA’s robots seamlessly scaled to the new volume, allowing the client to avoid backlog
and overtime.

Read the case study to learn more about CareOregon's RPA initiative and the incredible ROI results it has achieved through automation.

Unlocking the Potential of Healthcare Automation

In recent years, the healthcare industry has increased its adoption of automation as means of achieving operational efficiency without sacrificing quality of care to their members. While RPA is a natural fit for the inefficiencies payers experience across their organizations, many have struggled to develop and implement an automation strategy that truly captures the return they had hoped to achieve.

In a Cognizant study surveying 200 respondents across 10 unique payer organizations with 38 plan subsidiaries, it is clear that payers recognize automation as a valuable tool for improving processes and member care. Over 75% of respondents think automation is either important or critical to their organization and the future of the industry. However, many payers are taking a conservative approach to implementing automation, which is causing them to miss opportunities for savings and efficiencies. Less than half of the payers in the study have a defined automation strategy or fully deployed automation program. Additionally, close to 60% cited “evaluating cost-benefit of use cases” as a barrier to adopting automation.

So, how can payers develop a winning RPA strategy?

• Establish a Center of Excellence (CoE). The CoE is the engine of the automation program. This group consists of stakeholders, analysts, and IT resources who manage all aspects of the initiative, driving enterprise-wide adoption and guiding the program to maturity. Incorporate automation expertise in the CoE to ensure the initiative is designed and built with a strategic vision, which we discuss here.

• Understand the business case and ROI. What factors are driving RPA adoption within your organization? This seems pretty straight-forward, but we frequently see payers struggle to articulate why they targeted certain processes and how they will define and track success. We often hear “reducing manual intervention”, which is a solid reason for automating, yet difficult to quantify. Payers must work to define process-level metrics that they can easily track to see how their business is benefitting from automation over time.

  First, define the problem statement for each process. For example: We targeted this process because it is cumbersome and takes most examiners around 6 minutes per claim. Next, put some numbers around it based on what you already know. A typical claims examiner earns $50,000 per year. At an annual volume of 250,000 claims, the manual cost for this process is $540,000. At 6 minutes per claim, that’s 25,000 hours per year, the equivalent of 17 full-time employees (assuming the average FTE works 6 full hours per day, accounting for lunch, breaks, and switching between work items.) If 70% of these claims could be processed successfully with automation, labor savings alone would be $250,000 annually. Total cost savings is dependent on the RPA provider and the total cost of ownership, a necessary factor in calculating overall ROI.

• Select the right processes. It is tempting to target the most complex processes first. The problem with this approach is that complex processes take longer to automate and require frequent change management, which equates to greater cost to the business. If cost savings is the primary driver, knock out the “easy wins” first: processes with high volumes and low-to-medium complexity that your people do every day or that are causing you to incur penalties. If operational efficiency is the primary driver, explore the work that is creating bottlenecks, work-arounds, or backlog. However, for work-arounds, do consider how those processes will evolve in the near future. If you’re upgrading your claims management system soon, those work-arounds could be eliminated or could change significantly, resulting in additional change management and potential downtime.

• Avoid implementation silos. Stakeholders across the organization need to be aligned with the initiative from the start, which is why the CoE is so critical. Without this enterprise-wide cooperation, automation opportunities will be missed. Partial RPA adoption only serves to limit the program’s ability to reach maturity, which is where maximum ROI is realized. A mature deployment is generally defined as having 10 or more automated processes in production.

Applying automation to payor processes

In our experience building RPA programs for more than 80 health plans, the departments with the greatest ROI potential are Claims, Enrollment, and Provider Maintenance. Examples of these processes include authorizations, member/provider updates, COB, timely filing, and adjustments to pricing or copays, OOP, diagnostic codes, or fee schedules.

Below are a few examples of how health plans are achieving operational efficiency and cost savings with automation:

For one client, who serves over 400,000 Medicaid members, taking on a new line of business without the aid of automation meant a 172% increase in staffing levels, an added cost of roughly $1 million annually. In addition to the staff increase, this new line of business created unique operational challenges. The plan is required to adapt to new requirements as they roll out, which can be problematic when modifications to their claims management system are required in order to comply. Additionally, processing accuracy of 99% has to be maintained or they risk financial penalties and a poor performance record, which puts their contract in jeopardy. With automation in place, the client continues to meet all requirements and has retained their contract for this business. They currently have 16 automated processes in production, generating 39% cost savings and over 30,000 hours in time savings every year, the equivalent of 24 full-time employees.

A Blue Cross Blue Shield plan with 3.5 million members initially approached HPA to handle a large-scale claims adjustment with a tight deadline. It took five weeks for 25 examiners to clear 40,000 of the more than 100,000 claims. However, with the deadline approaching they knew they couldn’t clear the remaining claims in time. With automation, the remaining 64,000 claims were processed in less than 8 days with a 96% success rate. In total, we have automated six claims adjustments for this client, processing more than 340,000 items and saving the client 13,570 manual hours. Overall, this plan has 36 processes in production, which has saved them nearly $3 million and over 250,000 hours of manual work, the equivalent of 119 full-time employees.

Automation wins aren’t limited to time and cost savings. For a plan processing Texas Medicaid claims, they were required to adhere to program-specific formularies for Medicaid and Children’s Health Insurance Program (CHIP). This required examiners to manually validate against a data crosswalk between National Drug Codes and Healthcare Common Procedure Coding System, as well as a fee schedule issued by Texas Medicaid and Healthcare Partnership. This error-prone process yielded an average processing accuracy of 57% for four years. Within three months of automating, processing accuracy hit 99% and has remained there. Another client contracted to handle Texas Medicaid claims saw a dramatic reduction in complaints and an increase in its STAR Rating as a result of automation. Today, this client’s auto-adjudication rate rests at 84%. HPA automates an additional 12% of the clients’ claims, bringing its total auto-adjudication rate to 96%, a rate that is rare for a plan of its size.

Simplifying Texas Medicaid Claims with RPA

Learn how HPA helped eight managed care organizations implement RPA to comply with evolving regulations and secure their Medicaid contracts with the State of Texas. Read the case study.

The Medicaid program in the United States is administered through the U.S. Department of Health and Human Services through Centers for Medicare and Medicaid Services (CMS). CMS establishes the program requirements and monitors each state’s program to ensure their compliance. States are required to follow the CMS protocol for service quality and eligibility standards, a responsibility that most states contract to health insurance companies.

A provision of the Affordable Care Act called for expansion of Medicaid eligibility in order to cover more low-income Americans. With this expansion, the federal government would cover 90% of the cost for the state. As part of a Supreme Court ruling in 2012, states cannot be forced to expand their programs; 14 states have opted against expansion.

Texas, one of those 14 states, has the largest Medicaid coverage gap in the country, with roughly 1.1 million residents falling outside of the state’s eligibility requirements. In 2011, the state was able to negotiate a Medicaid 1115 waiver, a five-year agreement that secured $25 billion in federal funding, which is set to expire in 2021. The funding has allowed the state to expand Medicaid managed care while preserving hospital funding, provide incentive payments for healthcare improvements, and direct more funding to hospitals that serve large numbers of uninsured patients.

As part of this waiver, the state must adhere to federally-mandated terms and conditions to prove they are working to reform their healthcare delivery system while also maintaining quality of care. Adherence to these guidelines has proven challenging for the managed care organizations (MCOs) which have contracted with the State of Texas for Medicaid business. To add to the complexity, the sheer population size of Texas Medicaid members results in a large volume of claims to be processed, often retroactively. This creates a burdensome operational constraint for MCOs, which need to staff up quickly to process claims within a specific timeframe, or reconfigure their claim management systems to meet these unique requirements as they roll out.

Eliminate the operational challenges created by Medicaid claims

The nature of Texas Medicaid claims has caused many health plans to seek out robotic process automation to comply with the ever-evolving state and federal requirements while also ensuring their Medicaid business is secure.

Today, HPA is automating Texas Medicaid claims for eight MCOs contracted with the State of Texas. Our automation specialists researched Texas Medicaid requirements and worked with the clients’ subject matter experts to build out the process requirements, as well as address the configuration limitations within their claims management system.

Long Term Services & Support (LTSS)

LTSS claims are submitted in both high-volume and frequency, due to the nature of services being rendered. A state-mandated change in billing requirements shifted each unit of service from one-hour increments to 15-minute increments. On top of this, the contracted pricing of these claims is dictated by the service modifiers and many platforms still do not allow for complete customization on all modifier combinations.

One client, in particular, faced a tough decision without automation: hire 19 employees to manually process the claims, or pursue a three-year custom IT solution. Due to the timely filing and minimum processing accuracy requirements, the client couldn’t afford the increased cycle time and inaccuracy that comes with manual claims processing. HPA’s automation experts built out custom pricing tables so that claims could easily be processed according to the new billing requirements, without customization to its system.

HPA was also able to quickly process the client’s inventory of 50,000 pended claims, well ahead of the 30-day deadline. Over the last three years, HPA has processed more than 500,000 LTSS claims, offsetting more than 30,000 hours of manual processing tasks.

LTSS Electronic Visit Verification (EVV)

EVV is a computer-based system that verifies the occurrence of authorized personal attendant service visits by electronically documenting the precise time a service delivery visit begins and ends. As part of the 21st Century Cures Act, CMS requires EVV for all Medicaid personal care and home health services, a responsibility that falls to the MCOs. HHSC negotiated delays to the EVV start date for new programs, services, and service delivery options affected by the Cures Act, meaning more operational changes for MCOs. In order for these payers to process claims for Medicaid services currently included in EVV criteria, additional fields on the claim file were required that did not exist within the claim management system, a change that would require custom configuration.

HPA’s robots utilize the claim file from the EVV portal to supplement the missing fields and process the claims, allowing clients to comply with new requirements without operational impact or system configuration.

Calculating RPA's Total Cost of Ownership

Making the initial case for RPA is easy—every modern business can see the value of shifting mundane, repetitive tasks from humans to robots. RPA, or Robotic Process Automation, offers the promise of reductions in labor costs and cycle time, greater operational efficiency, and elimination of human error. This all sounds great, right?

RPA software providers have carefully crafted messaging to promote all that is bright and shiny about RPA, but stop short at explaining how difficult and expensive it will be to build it yourself. And why wouldn’t they gloss over this? Would companies purchase their multi-thousand-dollar licenses to conduct proofs of concept if they knew that 30-50% of RPA initiatives fail? Or, worse, that less than one percent of RPA initiatives reach full maturity with multiple bot deployments across all lines of business? They're in the business of selling software, not helping you make your processes work.

To be clear, when building your own program, the magical ROI of RPA being touted across the internet will only be realized when your program reaches maturity. Sure, you will see incremental lift as you start knocking out those high-touch, high-volume processes, but the savings realized early on will not surpass automation’s total cost to the business. Let’s unpack the why behind that.

HfS Research estimates that licensing costs represent only 25-30% of RPA’s total cost of ownership (TCO). The remaining 70-75% represents the part that no one is really talking about: the ongoing cost of support personnel.

To simplify the concept of TCO, we’ll divide it into two categories—cost of technology and cost of support. Within each category are one-time, upfront costs and ongoing, annually-recurring costs.

Breakdown of Technology Costs

One-time:

• RPA vendor licensing

• RPA vendor training

• Infrastructure setup

• Third-party integrations

• Proof of Concept

Ongoing:

• Annual RPA license(s) renewal

• Annual maintenance renewal

• Annual third-party integration license renewal

• Infrastructure maintenance and/or expansion

• Vendor management (where applicable)

• Management of program SLAs

Let's zoom in on vendor licensing costs. As HfS Research points out, RPA software pricing and licensing models are notoriously confusing. The technology set is typically broken down into separately-licensed modules dedicated to a specific use—bot builders, bot orchestrators, bot dashboards, and advanced analytics hubs. License models can be annual, perpetual, consumption-based, or SaaS. Licensing costs can also vary between unattended and attended automation, number of users, number of bots, number of machines, and run-time. Some vendors even carry bot or license minimums. As if you needed additional challenges in building your own RPA program, pricing across all vendors remains unnecessarily complex thereby making it difficult for your COE to keep tight control of total technology costs as your program expands.

Breakdown of Support Costs

One-time: Establishing the COE

• Hire or reallocate analysts and developers to plan, design, build, and maintain the program

• Secure IT support for infrastructure and deployment

Ongoing:

• Annual overhead for support personnel

• Ongoing training

• Continuous process optimization

• Continuous expansion of the program

• Continuous bot monitoring

• Continuous bot maintenance (Note: The average life cycle of a bot is 18-24 months)

The RPA Center of Excellence (COE) is essentially a dedicated steering committee responsible for guiding, managing, and building your RPA initiative. This COE is typically comprised of 5% stakeholders, 25% business process experts/analysts, and 65% IT personnel. So, let's say you start small with a COE of 15 people. Depending on the provider selected and the processes being automated, your COE will require between three-to-nine developers. With an average salary range between $100K-$160K for RPA developers in the U.S., you're looking at a minimum annual cost between $300K-$480K just for IT personnel, on top of annual licensing costs. It's a significant expense that must be factored into the overall ROI of the program.

But, what about pre-built bot libraries? Won't that help reduce the need for experienced developers? Not exactly. Every major RPA vendor provides user-generated, pre-built bot libraries to "accelerate" RPA deployments. So, one would think it's a safe assumption that subject matter experts (SMEs) at any experience level could throw together an automated process in an afternoon, right? Great in theory, problematic in execution. Here's why. Most applications available on the market today are completely customizable, and few companies use applications "straight out of the box", thus edits to pre-built bots are practically guaranteed. Using proprietary or highly-specialized applications? You’re likely building from scratch every time. Automating Citrix-based applications? Prepare for extra maintenance as automation that utilizes computer vision is significantly less stable. Bot libraries can be a great start, but they in no way remove the necessity for qualified personnel.

There are myriad factors that can lengthen time-to-implementation, require more expensive or expert personnel, or increase change management (and downtime). These unforeseen factors, while difficult to calculate, can have a significant impact on the overall cost and success of your program. Understanding this from the outset is imperative to building a smarter, more scalable program.

Tips for More Effective Hotel Pricing in Today's High-tech World

In a previous post, we focused on the intertwined connection between pricing rooms and forecasting rate thresholds. How they’ve become linked in our new digitally driven era. It’s an important realization needing to be made before it becomes possible to fully capitalize on revenue optimization opportunity.

In this post, we’re discussing how to leverage this influential insight to seize pricing power.

Here are several pricing improvement tips:

Think Beyond BAR Pricing

We’re all familiar with BAR, ‘Best Available Rate’, but is it really the right benchmark from which to base all pricing? While it is still a very valuable component, focusing solely on pricing based on leveraging BAR rate fails to maximize revenue potential.

Typically, hoteliers use BAR with specific amendments suiting different business mixes, including:
BAR + $20 for deluxe room
BAR + $100 for a suite
BAR – 10% for promotional rate
BAR + $10 for breakfast rate
BAR – 5% for advance purchase rate

For example, a property with a BAR rate of $100/night, will amend the rate to:
Deluxe room=$120/night
Suite=$200/night
Promotional rate=$90/night
Breakfast rate=$110/night
Advance purchase rate=$95/night

But this approach limits pricing that might be more upwardly flexible in certain areas, but not others. This methodology doesn’t mathematically allow for a surging demand segment, for example, therefore missing the chance to capture extra revenue.

BAR pricing means the revenue manager only does a single calculation (the BAR price) every day, rather than a set of calculations based on the best rate for each individual segment. A more fluid approach to rate setting is required, such as with a machine learning based revenue management system that calculates wide swaths of data, breaks it down, then provides actionable insight for each scenario.

Use Channel-specific Strategies

It’s also typical to have multiple rate categories for various booking channels such as direct, OTAs, in app-branded reservations, last-minute apps and more. So, revenue managers must know room rates for each rate category, how the rate was calculated, and through which channel(s) that rate is being offered.

Revenue managers must have a separate strategy for each customer type. But we don’t mean simple family versus business travel. Partner with marketing to create an effective strategy that plays into the specific hotel’s key target customers.

For example, say the hotel has a wellness focus, and marketers would like to boost business to yoga enthusiasts as a key target market. When marketing and revenue managers work in concert, rather than separately in silos, interesting opportunities can be created.

Marketing can devise a campaign targeting this specific traveler, while the revenue side crafts a rate appealing to that group. By bundling into the stay morning yoga classes with complimentary smoothies, for example, the property can achieve premium pricing. It also drives the guest to book through a specific channel, usually a higher profit yielding direct channel. Plus, guests responding to marketing messages are less price sensitive than those guests staying at the hotel for another purpose.

Be sure to review what opportunities marketing is thinking about and respond accordingly.

Offer Multiple Rate Options (but not too many!)

Different consumers want different rooms at different prices. They also have varying reasons they’re coming to your town, each one bringing with it a new rate threshold. So, offer a few rate options. Not too many. The more choices people have, the less likely they’re going to make the right decision for your business. In “The Paradox of Choice”, a book written by American psychologist, Barry Schwartz, he writes: “Eliminating consumer choices can greatly reduce anxiety for shoppers.” In general, we recommend hotels have no more than six different rate categories. If you do, that potential guest is more likely to book with a competitor that had fewer options.

When shopping for an RMS, make sure to select one that makes it easy to offer these options to potential guests. Otherwise, having multiple rate options will create a great deal of unnecessary extra work for a revenue management team.

Final tips…

Don’t ignore the importance of forecasting (for a refresher on why forecasting is so integral for pricing, read the previous post. Remember, we’re just at the beginning of a data deluge. It’s already more than we can handle manually at any moment. It’s critical your RMS continuously collects and sorts through the data on your behalf, determining which sets are relevant on a given day. Choose a highly-sophisticated RMS to collect and analyze all online data possibilities, because it’ll help optimize pricing and channel management strategies. After all, the best technology suggests the highly-optimized rates that bring the most bookings while earning the highest possible revenue.

Improved Pricing with Machine Learning

In our blog on big data, we learned what it is, how it is sourced and how hoteliers can utilize its insights to improve their revenue strategy.  Those important, revenue-improving insights wouldn’t be possible without the predictive capabilities of machine learning.

What is machine learning?

In its simplest form, machine learning gives computers the ability to learn from data sets without being programmed to do so. Data scientists build algorithms which are applied to sample data sets to train the computer on what to look for within live data sets. These algorithms learn from historical relationships and trends within the data to produce reliable, predictive analytics.

How is it applied to the hospitality industry?

The process of buying and selling rooms is entirely fluid today. The myriad data sources being used to dynamically determine rates is greater than even the most-seasoned revenue manager could compute on their own. Revenue management systems utilizing machine learning evaluate high-volume, disparate data sets in real time. As more data is ingested, the system instantly evaluates it, tailoring pricing predictions with surprising accuracy and detail.

In addition to dynamic pricing, sentiment algorithms analyze signals from user reviews and social media interactions to improve guest experiences. Demand algorithms strengthen your comp set with insights into weather, events, and nearest neighbors, with longer lead times than traditional methods. Machine learning also evaluates consumer buying behaviors and booking patterns to create fluid segmentation. Right rate, rate room, right customer, right channel, in glorious, granular detail.

Will machine learning replace humans?

It is understandable that some revenue managers would feel a bit threatened. For all the benefits we’ve experienced from technological advancement, one can’t help but recognize the ways it has replaced certain functions of our jobs. Despite what Hollywood wants us believe, machines cannot learn on their own. Revenue managers should embrace machine learning as an enhancement to their knowledge, a powerful resource to bolster their revenue strategy. At the very least, see it as a tool to gain back some valuable free time.

Big Data's Influence on Hotel Pricing

Big data can be a powerful resource for driving effective rate strategy. By harnessing the predictive capabilities of big data analysis we can power smarter revenue decisions for hotel owners. You may be thinking that big data is overkill for your hotel, perhaps too complicated or pricey to even consider. If there is variation in a customer’s willingness to buy within your market, a data-driven revenue management strategy will have a positive impact on your bottom-line.

Origins of big data

Big data comes from traditional sources like call centers, point-of-sale and financial transactions as well as digital sources like social channels and web applications. Unstructured data refers to data that is not easily interpreted by data models, like tweets and Facebook posts, or metadata. There is also multi-structured data that is derived from human interaction with online applications people typically use every day. Facebook data is a great example of multi-structured data. A user can follow groups, search for destinations, like, comment, and even make purchases. This is a unique blend of structured data, like financial transactions, and more contextual info like user search and like history.

Improved revenue with intelligent insights

At its core, revenue management attempts to strike a balance between a customer’s willingness to pay and the margin the hotel would like to make. Today, this is more of a moving target than ever before. Hotels often revise rates multiple times a day, working toward the concept of different rates on different days to different consumers, all for the same product. Big data analysis provides the insight to further streamline this process. Factors like weather, events, flights, and surrounding vacation rental inventory can sometimes affect demand and price elasticity. By analyzing these data sources, we can predict factors that impact your demand well in advance, not after the fact, as most hotels are still doing.

Consider another important factor, the overall value of a customer, which can vary greatly by segment. Market segmentation allows hotels to target and personalize rates to different customer segments, like corporate, groups, and leisure. Typically, revenue managers review length of stay, revenue per room and per client, no show ratios, cancellation percentages, etc. to predict trends in customer segments. Through the analysis of big data, patterns in consumer behavior reveal those more likely to be your optimal, high-value customers. You also gain insight into the most profitable channels to reach them.

What lies ahead

Speed and accessibility are two key areas for improvement in big data analysis. Due to the sheer volume of hotel data, tech providers will have to continue to make serious investments in data infrastructure as well as advances in machine learning algorithms. As for accessibility, consumers have come to expect a plug-and-play experience with everyday technology and revenue management systems should be no exception. Our industry is seeing a dramatic shift away from B2B-oriented platforms in favor of more user-friendly B2C experiences. It is now possible to manage nearly every aspect of your life via your mobile phone, why not manage your hotel’s revenue too. It’s 2016, afterall.

What are your thoughts about big data’s impact on our industry? Are you currently utilizing it to improve your rate strategy? Revenue managers, we want to hear from you in the comments.

The Science Behind User Experience

Think about the technology that you enjoy using most. What are some of the key features that help make your experience great? Do you get a quick response when you click something? Is it laid out in a way that is intuitive to you? You’re likely unable to pinpoint any specific aspect, you just know that it works. Successful user experience feels like this.

The science behind user experience

A surprising amount of intelligence is baked into a great user experience (commonly referred to as ‘UX’). The science behind it ensures the engineering is both intuitive and strategic. Every aspect of the user interface (‘UI’) is also evaluated from the perspective of a potential user. Does it function as expected? Do elements interact appropriately and are they laid out logical? This evaluation is often achieved through a process called ‘user acceptance testing’ where the product is tested by everyday people prior to being launched.

User experience encompasses these basic principles:

• Discoverability – the ease with which a user can navigate the system for the first time

• Efficiency – optimization of repetitive tasks and minimization of workflow distractions and interruptions

• Performance – sound interface responsiveness and behavior when a user performs an action

• Familiarity – utilization of familiar framework, patterns, and symbols to aid in quick user adoption

• Delight – simplification of complex information or actions to streamline the user’s workflow and make their life easier

These basic principles of user experience have largely become universal in the technology you see today. Like highway systems and signage, users can easily adapt to new interfaces which are built using these fundamental elements.

Learn more about LodgIQ’s approach to user experience

Why companies should invest in UX

Apple’s business philosophy was built around empathy for the consumer. Every new concept revolved around providing solutions where none existed. And by truly understanding the end user, they transcended being creators of products or solutions. They dictate the needs of users in the marketplace and consumers eagerly anticipate (and perhaps even celebrate) new product releases.

Naturally, competitors have been clamoring for years to produce similar results, albeit with less investment than Apple. The problem is that technology providers have lost of sight of the human condition. The common driving factors behind product creation are competitiveness and profit. By sacrificing the user experience, you’ll unfortunately won’t attain either. There are three options for product development: be the fastest to market, have the cheapest product, or provide the best quality (hint: you may only choose two). Technology providers that truly invest in and understand the science behind user experience stand the best chance of winning over the modern consumer.

Revenue Gains with a Positive Booking Experience

The average consumer has more purchasing intelligence available to them than ever before. You’ve probably heard this approximately 856 times in the last six months. And while the statement is true, it is just vague enough to not have any real impact. According to an Expedia report, consumers visit 38 sites before booking. That kind of endurance takes dedication. Dedication to maximizing their experience and their budget. With the right marketing, you can influence potential guests to book directly with your hotel.

Improving the booking experience

Your hotel website – This is your hotel’s face to the entire world so think about it in those terms. Is it easy to navigate? Does it accommodate language translation? Do you offer full descriptions and images of all room types? Do you offer information about your location, like history, nearby activities and events? Have you included positive guest feedback and FAQs? Do you run promotions on your site? Is it mobile friendly?

Once these user experience elements are solid, establish a clear direct booking path on the front page of your website.

Booking engine – Your booking engine should be intuitive, visually aligned with your website, and mobile friendly. Offer multiple languages and currency conversion, full descriptions and images for all room types, transparent policies, and a confirmation email. The confirmation email is a huge opportunity. It should be branded, offer contact info for guest questions, a way to sign up for hotel promotions as well as links to social media and maps.

Guest email capture and communication – OTA reservations may exclude guest information but that shouldn’t deter you from capturing their contact info at check-in or check-out, through a guest registration card or guestbook, or as a requirement to log-in to wi-fi. Do use this information responsibly. No one enjoys being spammed, including potential repeat guests.

Loyalty programs – The obvious benefits are to reward your guests and build return customers. You may have also read recently that hotels are using loyalty programs to offer lower direct booking rates without violating the terms of their OTA contracts.

Social media – Social channels are great for both paid promotions and reminders to book direct with your hotel. Facebook is the top channel for travel research and it is most popular among baby boomers and women. Twitter is more popular with men, urbanites, people under 50 years of age, and people earning over $50k annually. It’s no surprise that Instagram is most popular with millennials and women. Social stats via Pew Research. It’s important to tailor your message to your target demographic to drive the best engagement.

Search Engine Marketing (SEM) – With Google dominating the market with over 60% of web traffic, let’s review their advertising product Google AdWords. SEM, also known as PPC or pay-per-click advertising, allows brands to bid on keywords which, when searched, will display a link to their website in prominent areas of the Google search window. You may find that many high-traffic keywords are dominated by OTAs and larger chains. Just as you tailor room rates to demand factors, you can also follow this same process for your target keyword list. For example, try a campaign that attracts those seeking hotels near a major sporting event, concert venue, or conference happening near your hotel. Long-tail keywords are another option. They take advantage of people’s tendency to use voice search. While these keywords are typically longer, they are much less saturated and easier to bid on.

Hotel Distribution Online: A Game of Skill

Chains and independent hotels have at least one thing in common: to sell hotel rooms you must be visible within online booking channels. To achieve this visibility, hotels have to meticulously curate their own direct booking channel, as well as participate in the same third party channels as their competitors. As new booking channels emerge, hoteliers have to decide whether to jump on the bandwagon or “get left behind”. This is the game of hotel distribution but with the right data insights it’s less like crossing your fingers hoping for the best and more like gaming the system.

The Major Distribution Players

It amazes me how quickly the online distribution landscape can change in a matter of months. A new channel threatens to siphon all the marketshare from other channels and you absolutely have to get your hotel listed STAT. Unfortunately, that isn’t always true and I’ll explain why in a bit.

First, let’s do a quick review of the key players and how they impact your hotel:

GDS – Ah yes, the GDS. The giver of life and travel data to OTAs and travel agents. By far your most expensive reservation but some OTAs offer direct connectivity to bypass the GDS fees (or they just pass that fee directly to you.)

OTAs – OTAs are a sore subject for a lot of hotels because it feels like they retain all of the control. And for the most part, they absolutely do. Mandatory inventory allocation, rate parity, guest anonymity, retail versus merchant agreements. If you are lucky enough to negotiate lower fees, they still take between 10-15%. You pay a premium for the “visibility” yet in some markets I believe that term is being used loosely.

Metasearches – Metasearch emerged from the need to better refine search for travelers. Super great in theory; like Google search for hotel data. When metasearch first became popular everyone flocked to it like it was a revenue holy grail. While engines like Trivago, Kayak, and TripAdvisor do provide a great search experience, most hotels still have to pay for primo visibility in addition to a booking fee (because they are now all owned by OTAs….)

Your hotel’s website – Direct reservations are wonderful for two reasons: 1) it’s typically the most affordable reservation for your hotel and your guest and 2) it gives you access to all sorts of data insights you won’t get from other channels.

It’s All About the Channel Mix

None of these booking channels are a singular magic bullet to revenue. A hotel’s online distribution is a strategic mix of channels and is unique to every property. This is where skill comes into play and it all boils down to calculating your CAC, or Customer Acquisition Cost, per channel. CAC can be found by dividing your total marketing expenses by customers acquired in a given time-frame (note that you wouldn’t want to track short-term testing as that would skew your metrics.) Perhaps that newly-introduced channel would be great for your hotel. With these insights you can test and refine your mix to deliver truly profitable reservations.

Vacation Rentals’ Impact on Hotel Revenue

Vacation rentals have the hospitality industry in an uproar lately and for good reason. Recent reports indicate that vacation rentals, like Airbnb, are taking their toll on the average daily rates of hotels in larger metropolitan areas. According to STR data, NYC hotel rates dropped by 1.7% in 2015, the first drop recorded since 2009. Early 2016 reports indicate this trend will continue through the year.

How Vacation Rentals Got Their Start

The concept of vacation rentals has been around since the early 2000s. Initially, management companies provided a way for unique and seasonal properties to be bookable. As internet search became more refined, the need for a more organized approach was apparent. An efficient system of organizing and uploading search-friendly descriptions and quality photos simply didn’t exist.

HomeAway launched their vacation rental product in 2006, providing a platform for a wide variety of properties to market themselves online. Property owners paid a monthly subscription fee to list their property and HomeAway took their cut. Acquired by Expedia in 2015, it still remains an outlet for families and groups to find the right space for their needs.

Airbnb: Filling in the Vacation Rental Gaps

Like many hospitality technology providers have learned, if your platform doesn’t grow and evolve with the needs of the marketplace, you can quickly get left behind (or at least give competitors a great chance at stealing your market share.) While HomeAway provided a VR booking channel, it lacked the authenticity and search experience that users craved.

Airbnb was created out of the need for two dudes to make rent. They rented out air mattresses in their own home for a small fee (like Couchsurfer but with profit – genius!) As they continued to suss out market demand, they stumbled upon an unanswered need: people looking for authentic experiences and instant connectivity. They learned that the buying decisions and motivations of modern travel shoppers are more complicated than they used to be. Guests seek personalized experiences and an inclusive booking process that mimics the flow of technology they already know and love. As such, Airbnb built a platform entirely around the user. They’re so tuned in to being responsive to the human element and it is entirely the key to their success.

How Revenue Managers Can Prepare

Part of a revenue manager’s analysis is price-value position against similar competitors. Vacation rentals are prominent in every major tourist destination thereby competing for the potential guests of your hotel as well as your competitors. To stay ahead of that, it is important to look at a broad set of data for your marketplace that includes competitor data and vacation rentals. This is your key advantage – having access to data that vacation rental owners often don’t have. Use data insights to know your ideal guest and intelligently respond to their buying signals through personalized messaging and a targeted channel mix.

The Future of Revenue Management Systems

The current state of revenue management systems is stationary. Maybe even a bit stale. Offering up the same data sets and relying on the very manual process of traditional revenue management to fill in the data gaps. The problem with this process is that every property is unique and buying behaviors are driven by factors that are largely under-represented in typical data sets.

Parsing hotel data into something meaningful is a monumental task. From a technology standpoint, the processing power required to source the necessary data, clean it, structure it, and make it accessible in real time is an investment that few companies want to take on. Don’t get me wrong, all revenue management systems deliver data to the end user, they simply stop short of delivering the entire data picture. You get the standard sources, data from which general assumptions could be made, and the rest of the work falls on the RM and their trusty notebook.

What we are realizing in the ever-evolving landscape of hospitality technology is that we, as an industry, are doing a bad job of making data work for us. Because the development is ridiculously complicated. Because such a project would be too expensive. Because our time would be better spent building upon the product we already have. There are a variety of excuses out there and even more money being left on the table.

The future of revenue management technologies must present a holistic view of hotel data, comparing every buying factor against the hotel’s marketplace and competitors in real time. Technology that elevates the knowledge of the Revenue Manager, conforming to their workflow, compiling data relative to the property to provide market-specific insights a human could never logically compute with perfection. The result is a pricing structure as dynamic as your property, marketplace, and guest. Maximized profits with minimal effort. To some this is a pipe dream, but for us, it’s just another reason we come to work every day.

Writing for Hotel Social Media

Upcoming InnLink Blog Events and Features

• Friday, October 11th, 2013: The Basics of Hotel Website Content, Part 5: Writing for OTAs and BBEs

• Tuesday, October 15th, 2013: The Basics of Hotel Website Content, Part 6: Maps, Landmarks, and Area Attractions

It's Hotel Marketing Tuesday once again and today I'm discussing what social media is and the benefits of engaging your potential hotel guests online. This blog is Part 4 of our latest series on The Basics of Hotel Websites.

What is Hotel Social Media Content?

When people hear the term 'social media' they instantly think of Facebook and Twitter. Social media is actually a lot broader than that, including any channel a business can use to market themselves that depends on word-of-mouth marketing. In other words, you stay active marketing your business in the space and people like you or share your posts thereby growing your audience. This includes blogging for your hotel, Facebook, Twitter, TripAdvisor, Foursquare, Pinterest, Google +, and the like.

Why is it important to my hotel's success?

By being involved in social media channels you are more accessible to the public, more approachable to potential hotel guests, and best of all more likely to be seen. With Google's new Hummingbird algorithm, websites with the most relevant content will be rewarded with higher placement in Google search engine results. You're also more likely to engage guests who love your hotel and are advocates in attracting new hotel guests through good reviews, sharing your posts with their friends, and telling every friend they know.

Where should I begin with social media?

My best recommendation is to be involved with TripAdvisor, Facebook, Twitter and Google + at the very least. You can easily use these channels to run promotions, communicate information about your hotel, respond to guests reviews, and provide additional booking channels.  Google + has the added benefit of improving your hotel's placement in search engine results pages (here's a free guide to get you started).

What should I write or post for my hotel's social media channels?

Be responsive to reviews and interactions via these channels and follow hotel industry news sources to share content through Facebook and Twitter to build your following. I follow news outlets like Hotel News Now, Tnooz, and Hotelmarketing.com on Facebook and Twitter to find news worth sharing with others in the hotel industry. To start out, find people to follow and like, and start sharing news articles you find interesting. Perhaps invite former guests to share a photo from their trip or tell you their favorite thing about your destination. Your goal is to gain likes or follows before you can properly use these channels to successfully market your hotel. People have to like you before they'll buy from you.

Ok, I've gained a decent following, now what?

Once you have reached roughly 100 Twitter followers, 50 Facebook likes, or consistent Google +s, you can slowly work in hotel specials. Once a week at most, while still keeping up with the posting activities we just discussed.

A few things to keep in mind when writing hotel social media content:

• Make a plan. Plan out your posts for the next 6 months and when you plan on posting them. For more ideas, check out our previous post on promotions.

• Keep a positive tone. Try to avoid words with negative connotations.

• Stress the benefits. Remember, you have to tell the guest what's in it for them.

• Generate interest. Share a fun, interesting factoid or humorous quip. It doesn't even have to be true. Did you know that 46% of facts are made up?

• Don't make overstatements or use overly-flowery language. It seems spammy and fake. And no one likes that.

• Be accurate, be specific, be organized. Stay away from false advertising claims. Include specific details about promotions or hotel descriptions. Organize the information with a logical, smooth flow.

• Appeal to emotions rather than intellect. The majority of consumers make purchases based on emotion rather than rationale. Booking a hotel room is no different. They don't need to know about the low-flow toilets you installed, but they would love to hear they will be greeted by your frontdesk with a warm cookie at check-in.

Hotel Website Images: The Good Versus The Bad

The Basics of Hotel Website Content. Check back with us every Tuesday for more fun hotel marketing tips and tricks, and watch on Fridays as we unload new info about how to build website content for your hotel.

Hotel website images: why do they matter?

Good content may get people to your site, but great images sell your property. Images with proper lighting that showcase the lovely features of your hotel make potential guests want to go there. Blurry images, dimly-lit photos, and images of toilets aren’t inviting and have no place on your website.

What makes an image good or bad?

Generally speaking, great hotel website images follow a few major principles:

• Warm, bright lighting – the type of lighting that makes a room inviting.
• Relevant content in the image – otherwise known as “framing the image”.
• Sharp, crisp image detail - no blurry images or camera phone pics.

Here’s a few examples of the bad versus the good:

Examples of bad hotel images.

Examples of good hotel images.

Decent hotel website images don’t require a professional photographer or a fancy camera (check out this article on shooting better photography). What it does require is good lighting and image framing. Professional photographers often refer to the “golden hour“ of photo-taking. This is known as the hour after sunrise and the hour before sunset where everything is bathed in golden light (roughly an hour, sometimes longer depending on your location. Use this Golden Hour Calculator to calculate the best time to photograph your hotel). This lighting is ideal for taking picturesque photos of your hotel.

Equipment for photographing your hotel

You can achieve great results on your own with a basic digital camera. Choose a digital camera capable of shooting 10 megapixels and above for the best results. If your camera gives you the option for White Balance adjustment, you’ll need to change it depending on the type of lighting that exists when you’re shooting, i.e. incandescent versus fluorescent versus outdoor lighting. You’ll also want to shoot on the highest quality setting available on the camera. Remember, it is easy to take high-quality photos and make them smaller; it is impossible to take low-quality photos and make them higher-quality. Also, never use a camera phone to photograph your hotel. They don’t have the image quality you need to take effective photos.

Shooting the outside of your hotel

Schedule a photo shoot when weather is good and during the golden hour I previously discussed. Generally you want to shoot your hotel in the springtime when landscaping is vibrant, unless you’re located in regions known for fall colors or ski weather. Instead of snapping a shot of your hotel’s entrance straight-on, try a few shots at a 45 degree angle to the front door. Most importantly, include any signage in the shot (if you can) and always keep your signage in good shape.

Shooting the inside of your hotel

The interior of your hotel can have different types of lighting so adjust your White Balance settings according to the type of light. Capture all areas of interest to a potential guest: front desk area, breakfast area, hotel gym, laundry area, pool, all room types, etc. Framing the subject of your image is very important here. As you’re photographing different room types, stand back far enough to show the room’s interior. As you’re photographing the front desk area, don’t stand so far back that you can’t really tell what it is. I feel it’s also important to mention that you don’t have to photograph the toilets in your rooms. It is a pretty safe assumption that your rooms are equipped with bathrooms, so unless your bathrooms are stellar they aren’t worth including on your website or your hotel’s profile on the OTAs.

Room lighting tip: In rooms where you have dim lighting, try changing out the lamp bulbs to 100-watt soft white lighting. If there is still not enough light, try setting up extra lamps without the lampshades around the room, out of the frame of the picture.

That’s it for this blog. If you have any questions regarding hotel marketing you can email our Marketing department at InnLinkMarketing@gmail.com.

Online Reputation Management for Hotels

Why is Hotel Online Reputation Management Important?

Hotel Online Reputation Management is the most important element of preserving your hotel’s image. Knowing what is being said about your business, and where it is being said is crucial to your hotel’s success. It takes dedication and appropriate response to show the market that you are a proactive business owner who cares about the guests’ experience.

According to the latest TripBarometer study by TripAdvisor,  9 out of 10 travelers said that reviews were important in their purchasing decision. With the majority of reservations still occurring on OTAs and TripAdvisor reviews feeding directly into many OTAs, negative hotel reviews can literally be broadcast across the world within a matter of days. Mismanaged negative reviews have the potential to ruin the reputation of your hotel and discourage future guests from booking with you, leading to lost revenue.

Responding to Positive and Negative Reviews

In the realm of hotel reviews, a little acknowledgement goes a long way, which means that hotel managers and owners should take the time to respond to both positive and negative reviews. Let your guests know they are heard and that their opinion matters.

It is best to be proactive with feedback from sources like Facebook, TripAdvisor and Twitter; respond in a timely manner using a consistent, positive tone. Negative reviews on your website or TripAdvisor, as well as negative media coverage can be particularly damaging due to their sheer visibility. While your gut response as a hotel owner may be to remove the negative reviews on your website, this often has unintended backlash and makes it appear as if you have something to hide.

In deciding when to remove reviews, ask yourself these things:

• Does it contain inappropriate language?

• Does it contain misinformation?

• Is it a direct attack on the organization’s reputation?

If the answer to any of these questions is ‘yes’ it is customary to remove the comment. Otherwise, simply respond directly to the comment on your website in a positive manner, apologizing for the mistake and stating your plan of action to correct the mistake. Never get in a battle of words in the comments section with an angry guest.

Steps to responding to those tricky negative reviews:

1. Graciously accept the feedback,

2. Focus on whatever positive aspects there are within the review,

3. Make it known that this is not a typical experience at your hotel,

4. Apologize and address legitimate complaints,

5. Communicate your plan of action/ correction,

6. Take the conversation offline to your personal email address or phone number.

Tools for Managing Your Reputation

The good news is that there are a number of tools that make it easy to monitor your online presence. Google Alerts is a free tool that reports back on search results and mentions across the web (this does require a Google email account which is free and simple to set up).  SocialMention and Who’s Talkin report back from social media and blogging platforms on your hotel name as well. If you’re looking for a centralized service with greater functionality, HootSuite is a reasonably-priced Online Reputation Manager suite including campaign functionality, geo-targeting and customized reporting.

Steps for Maintaining Your Positive Reputation

If your hotel has a good reputation, congratulations, you’ve mastered reputation management! A good hotel reputation leads to better reviews, guest referrals, returning guests, and best of all, attracting new guests.

I suggest following these simple steps to maintain your reputation:

• Be proactive. Use the above tools to monitor the conversations happening about your hotel.

• Be active in the online community and share positive information.

• Ask for reviews from happy guests and give them a forum to do so (perhaps on your website, or also encourage them to review you on TripAdvisor).

• Conduct an online search for your business on Google and Bing every month or so to make sure you’re not missing anything.

Paid Search versus Organic Search for Hotels

This is the fourth blog in the Hotel Marketing 101 series where we will discuss Pay-Per-Click (PPC), Search Engine Optimization (SEO) and Search Engine Marketing (SEM).

Hotel Marketing 101: Paid Search versus Organic Search

SEO and SEM: What’s the Difference?

SEO refers to organic search, or the keyword-rich content on your website that improves your website’s placement in a Google search return. SEM refers to paid search, or paying for improved Google search placement on certain keywords that are beneficial to you business. Every article you read tells business owners how important both are but you should understand how each can impact your business. Side note: while Google isn’t the only search engine, it is the most widely-used and provides the best tools for managing campaigns on your own.

The Idea Behind Keywords

Imagine that you are searching online for lawn mowers. Depending on where you’re at in the buying process you could search a variety of terms. To start, you just want to research various lawn mowers and compare features so you simply type ‘lawnmowers’ into Google. Next, you’ve chosen a specific feature so you search ‘self-propelled mowers’. Now you’re ready to buy and need to find a local store that carries that particular brand. Potential guests will search for hotels in the same manner and keywords help Google determine which website is most relevant to those keywords. 

What is Pay-Per-Click (PPC)?

PPC is a form of paid search in which you purchase a specific keyword or phrase andcorresponding advertising space within prominent areas of Google search results. When your ad is clicked, you will be charged for that click regardless of conversion. On average, a small independent hotel could spend anywhere from $100 to $10,000 a month with PPC. However, depending on the search term you choose, your budget could be anywhere from $0.10 per click to $100 per click. That’s why choosing the right term for your hotel, and your budget, is so important.

Follow these guidelines when determining keywords:

• Your hotel’s name
• Any keywords your competitors would also use - such as ‘hotels Nashville TN’. You want to appear where your competitors do.
• Long-tail keywords – such as ‘hotels with free breakfast Nashville TN’. These terms are cheaper and tend to generate less traffic but work great as a long-term strategy for hotels with small online budgets.
• Geo-qualifiers - like your city and zipcode, also nearby landmarks.
• Negative keywords – perhaps equally as important is not appearing for certain keywords, such as ‘hotels with pools’ if your hotel doesn’t have a pool.

Generate a list of keywords and create a Google Adwords account. You’ll want to run your list through the Keyword Planner tool to determine which words are within budget. If necessary, add more geo-qualifiers or be more specific with long-tail keywords to bring down the cost. Once you have a solid list, set your monthly budget within your Adwords account and bid on those terms. Develop a business ad that contains descriptive, catchy copy to lure people into clicking. Also take note of what competitors are doing, especially if they place higher in Google search. It’s ok to be aggressive!

What if I Don’t Have a Budget for Online Marketing?

Meet your new friend SEO. Optimizing your website for Google improves your placement and is also a necessity for any business website. You can use a Content Management System to edit your own website or use the criteria below to evaluate an outside company that you hire. Be sure to monitor your site with your Google Analytics account.

Focus on 4 key things when optimizing your website:

1. Content: Most important for laying the optimization groundwork is Evergreen content. Do you have an interesting story about your hotel? Is your city rich in history? Telling a story allows the potential guest to engage with your hotel and picture themselves there.
2. Domain name and page names: Your domain name should be short (no longer than 25 characters) and easy to remember. Likewise, your page names should be simple yet specific to what the viewer will find on that page, such as www.hotelessex.com/roomtypes.
3. Metadata: Each page of your website has a title, header, description, and focus keywords. This is metadata and it tells search engines like Google or Bing that your site is relevant to the keyword being searched. Thinking back to the process of choosing keywords for paid search, pick a relevant focus keyword for each page of your site and work it into the content of that page. Don’t force it or repeat the word over and over. Keep it relevant to that page’s content. Since search engines can’t read pictures, you also need to attached relevant keywords to your images (called alt tags).
4. SEO monitoring tools: There are many SEO management tools out there like Moz.com, which can help you monitor your site’s success and make adjustments.

Beware, there are a lot of self-proclaimed SEO experts out there but with the right research, great website content, and a little bit of patience you can achieve success with organic search without going broke.