Moving Ahead and Staying Ahead with Prescriptive Decision-Making

Moving Ahead and Staying Ahead with Prescriptive Decision-Making

Hospitality organizations today are striving to stay ahead by making better, data-driven, decisions about every facet of their operation – from marketing, where managers attempt to understand the customer better for more accurate targeting and value calculations; through operations, where demand forecasting can support better staffing and ordering decisions; through finance, where performance analysis drives opportunities for efficiencies and strategic growth. As organizations embrace data, analytics and visualizations, they evolve from “gut-feel” reactive decision-makers to more proactive, forward-looking decision-makers.

Casinos are on an inflection point in data and analytics. Most hospitality organizations today have a business intelligence or data management initiative in place. Many organizations have made investments in predictive analytics solutions, like revenue management or marketing analytics. All organizations are looking to expand the analytic intelligence of the entire organization and provide access to the right information at the right time to the right resources to make the right decisions. If organizations successfully build out their data and analytic infrastructure, they will be part of the way there. If they are able to successfully leverage the analytic results across the organization, they will get ahead and stay ahead.

Analytic solutions are simply decision-support tools. They are designed to be used by intelligent managers who have the experience to interpret the results and take the appropriate actions. Revenue management systems, for example, drive revenue because revenue managers can interpret the price and availability recommendations as part of a broader pricing strategy. The job of the revenue management system and the revenue manager are not the same. A hotel cannot simply hook up the revenue management recommendations to their selling system and walk away. However, a great revenue management system managed by a business-savvy revenue manager is a winning combination.

An executive from a large hotel brand recently said to me that one of the driving factors for their business analytics investments is to get better information into the hands of their senior executives faster. “Imagine how much more effective smart and charismatic leaders would be in an investment negotiation or even an internal meeting if they had instant access to performance metrics, to support whatever direction the conversation goes,” he told me. “We have great, highly experienced, leadership, but I’m sure they could drive much more revenue with better, faster information.” It’s not that the information doesn’t exist, or that there aren’t standard sets of reports available. The difference is in the flexibility of the data structure and speed of access to the information. To be able to access information in the right format at the speed of a business conversation, no matter what is needed at the time, is beyond the technical capabilities of most organizations today.

These pieces of insights are not replacing the experience and ability of a top-performing executive, but rather are providing information to better interpret a situation, reinforce a point, convince an investor or make a key business decision faster. This should be the goal not only at the senior leadership level, but also replicated throughout the organization. It will take the right decision-support tools, backed by credible data and advanced analytics, but it will also take the right person in the role of interpreter and decision-maker.

This is why I argue that we are at an inflection point in hospitality and gaming. We are moving through the chain of analytic maturity, and we are getting to the point where we will need a different type of business analyst and manager to move forward and stay ahead. As the needs of the business change, the skills sets and competencies in these roles will need to change, as will the organizational composition.

Descriptive: At the first stage of analytic evolution, organizations develop and interpret historical reports. This is the descriptive phase. The organization could know that occupancy ran about 80% last month, or that 40% of reservations book in the week before arrival. Past revenue is tracked to identify historical trends. Decisions are based on this historical snapshot, which primarily involves reacting (i.e. putting out fires). Reports come from disparate systems, often are built in Excel, passing through multiple hands before being finalized. Creating these reports is time consuming and prone to mistakes. Still, the business has at least some visibility into operating conditions and can report performance to executives – even if it takes a couple of days (or months) to pull together the information. As organizations evolve through this phase, they start to look at building out enterprise data warehouses and investing in business intelligence tools to improve the speed and accuracy of reporting. As more information gets into the hands of decision-makers, they are able to react faster. Frequently, alerts are set up around key metrics so that managers can be made aware when they drop below, or above, certain critical levels.

Predictive: In the next state of analytical evolution, organizations begin to deploy advanced analytic techniques that allow them to anticipate trends and take advantage of opportunities. They start to apply forecasting, predictive modeling and optimization algorithms to existing data, typically either in marketing with predictive modeling on patron data, or in revenue management using forecasting and optimization to set pricing. These models produce results like: occupancy will be 80% next month; the marketing campaign will result in a 2% lift; or revenue is expected to trend down for the next several months. Organizations then prepare themselves to manage through these expected events. They can be more proactive in their approach, setting up the right staffing levels to meet expected demand, adjusting price to take advantage of peak periods or deploying the marketing campaign at the right time to get the best forecasted response.

Prescriptive: The final stage of analytic evolution is all about “what are we going to do about it.” In this phase, organizations are heavily supported by techniques like optimization, which provides the best possible answer given all business constraints, or simulation, a “what-if” technique in which a complex scenario with multiple moving parts is modeled so that parameters and options can be tested and the impact on the whole problem is determined. For example, marketing optimization might give you the best possible set of contact lists for all of your promotions that will provide the highest response rate, but still respect cost concerns and patron contact preferences. Simulation – what-if analysis – lets you test the impact of a particular pricing strategy on demand and revenue generation, or the lift associated with spending a little more on a marketing campaign.

These analytic techniques are valuable, and support complex and creative decision-making, but the true mark of a prescriptive organization is that analysts and managers have the business acumen to both ask and answer the question “so what are we going to do about it?” It’s fine to know that occupancy was 80% and it will be 90% next month. However, the true prescriptive manager can use that information, with their knowledge of the market and the operations, to build a plan to get to 95%. The skill set associated with this manager is different than in the descriptive or predictive phase, but clearly it is one that can move the organization forward – replicating the instincts, charisma and acumen of the executive I described at the beginning of the article in their own area of the company.

Making it Happen

For many organizations, this evolution will happen first in individual departments, rather than for the organization as a whole. This is fine, as success in a small area frequently becomes a benchmark for greater success elsewhere. The goal would be to move the entire organization towards prescriptive decision-making supported by data and analytics.

To achieve this, organizations may need to move to a structure where the advanced, predictive analytic models are managed by a central team of trained and experienced analysts, who work closely with counterparts in the business. The analyst’s role is to build the model with the guidance of the business, and then the business interprets the results through their experience and business acumen. When there is a shortage of analytical talent, this structure ensures analytic rigor is maintained, but also puts power in the hands of decision-makers to access the right information when they need it to move the business forward. It releases the requirement that managers be highly analytical, but requires them to be smart enough to interpret the numbers, and savvy enough to read market conditions.

The point is that knowing what happened and what will happen is no longer enough. We need to build a culture of “what are we going to do about it?” where the whole team uses the organization’s data and analytics to make fact-based decisions that move the organization forward.

Kelly A. McGuire, PhD leads the Hospitality and Travel Global Practice for SAS. In this role, she is responsible for driving the offering set and setting strategic direction for the practice. McGuire works with product management, sales, alliances and R&D to ensure that SAS solutions meet the needs of the market, and evangelizes the value of advanced analytics to the industries she serves. Before joining SAS, McGuire consulted with Harrah’s Entertainment on restaurant revenue management strategies for their major markets. Prior to that, she was a senior consultant at Radiant Systems. She also worked for RMS (Restaurant Revenue Management Solutions) providing menu item pricing recommendations to major chain restaurants. She is also a frequent contributor to industry publications, speaker at industry conferences and is coauthor of the SAS/CHR blog “The Analytic Hospitality Executive”. You can reach Kelly at Kelly.McGuire@sas.com.