Search has become a go-to source of information for moviegoers. Understand how Google search and paid click patterns can predict box office, and what digital engagement can tell us about the moviegoer decision-making process. By examining the timing and category of Google searches and paid clicks, we identify several factors that signal how a movie will perform at the box office.
Thanks to Google.
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Posted by david ricketts
In last week’s post I proposed that the hospitality industry move past debating whether they have a big data problem and move towards admitting that they have a big analytics opportunity. In this post, I want to give you some more specific examples of the benefits of “big analytics” (and “big data analytics”).
In Natalie Osborn’s SAS post from earlier this month, industry experts weighed in on the opportunities and challenges associated with analytics in hospitality in today’s market. The overriding theme was around accessing data and turning it into meaningful information quickly.Big analytics is all about speed to decision making.
For hospitality, big analytics comes into play in two high-level areas:
Big analytics for real-time analytical decisions
Personalization has always been a hot topic (and big challenge) for hospitality companies. Delivering that memorable experience, which cements loyalty and increases return likelihood on a mass scale across huge global enterprises with high line-level turnover, is a seemingly insurmountable challenge.
But, if you can interpret behavior and deliver a relevant recommendation to the employee (or device) that the guest is interacting with while the guest is right there, “mass personalization” becomes achievable. If you get it right, you have a “surprise and delight” moment. If you get it wrong, you have an artificial, seemingly scripted interaction that makes the guest feel like one of the crowd.
Big analytics lets you run predictive models based on current, observed guest behavior compared to their past behavior (or that of similar guests) to determine what you can offer, mention, or provide to encourage the behavior you want, in time to make it happen. You can uncover cross- and up-sell opportunities, fill empty restaurant seats, or just wish someone a happy birthday or a welcome back — all right in the moment.
Big analytics also comes into play when your guest is on your website. Matching what you know about them, or how they compare to previous visitors, with their current click patterns can help the analytics predict what content should be surfaced to maximize their likelihood to convert. Extend that to the mobile strategy with location data (and, by the way, now we’re starting to get into some big data — I’m just saying…).
What about operational managers on the floor who want to make real time, proactive decisions about what gaming tables to open and close, how to deploy staff or which restaurant to send guests to? You don’t have time to wait three hours to calculate an updated forecast, when you are trying to decide whether you can save labor cost by cutting a few servers early. Is gut instinct enough when high revenue impact decisions like raising and lowering minimum table bets are on the line? Big analytics helps you to calculate the advanced predictive models that support your management decisions at the speed of business.
Big analytics for scenario testing
Many hospitality companies may argue that they either aren’t ready for real-time or don’t need it. Whether the scenarios I described above are relevant or achievable for your organization or not, there’s a very strong argument for using big analytics to help your analysts make better decisions faster. After all, good analysts are in high demand, so increasing productivity — and making better decisions — with your existing analysts will have a huge positive impact on your business. Here are some hospitality examples where big analytics moves the needle, even if “real time” isn’t the goal.
As the decisions that marketing departments must make about their campaign plans become more complex, marketing departments are turning to optimization to help. Marketing optimization algorithms output a contact strategy for each campaign that meets a campaign goal (maximize response, minimize costs), considering all relevant campaign constraints (opt-in preferences, channels, contact strategies, guest eligibility, prevailing rate, blackout dates, budgets, etc.).
As the number of available offers increases, along with the size of the customer database and the number of channels, so does the size of the problem. Without big analytics, the answer comes back in hours. This could be fine, if you don’t have any options within the problem. Frequently, however, the parameters of the problem have ranges (guest can be contacted between two and four times a month, channels can be added or removed, eligibility can change). With big analytics, you can run multiple versions of the problem in the same amount of time, and determine whether relaxing or removing a constraint gives you an even better answer.
Revenue management is the hospitality industry’s classic big analytics problem. To come up with a pricing recommendation, detailed forecasts are calculated and fed into a complex optimization algorithm. The detail and complexity requires intensive processing power and many passes at the data to solve. In order for results to be relevant, at a minimum, these forecasts and optimizations need to be able to run overnight so that prices can be updated once a day. If it takes any longer, with new reservations coming in all the time, recommendations will be out of date before they are produced.
Without big analytics, even to only meet this overnight goal, revenue management systems would have to make sacrifices and take shortcuts — summarizing data, restricting forecasting methods, and replacing true optimization with heuristics. With big analytics, harder, more complex problems take minutes or even seconds to run. Revenue management algorithms can utilize more and more detailed data, forecasting options can be expanded, true optimization can be utilized, and the entire process can run more frequently, providing better pricing recommendations faster.
Even better for revenue managers, with big analytics, scenario testing and “what if” analysis is now possible. Revenue managers often see the need to override the system based on their unique market knowledge (or demands from the general manager). Without big analytics, revenue managers make the adjustments and have to wait until the next day to see the impact on price recommendations. With big analytics, revenue management systems can support “what if” analysis. With the push of a button, revenue managers can see the impact of parameter overrides on the highly interrelated pricing problem before they put the overrides into production. Not only can they better support their own decision making (and learn how the system reacts to certain types of changes), but they also have evidence to support their position when they disagree with the GM’s demands!
Clearly, big analytics presents big opportunity for hospitality. These are only a few examples. Can you think of any other areas where better answers, faster, would benefit you? We’d love to hear from you!
Have you seen the “Hospitality research you should be using now” webcast?
This originally appeared on the SAS blog, The Analytical Hospitality Executive.
Thanks to allanalytics.com
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Posted by david ricketts
Companies spend a small fortune continually investing and reinvesting in making their business analysts self-sufficient with thelatest and greatest analytical tools. Most companies have multiple project teams focused on delivering tools to simplify and improve business decision making. There are likely several standard tools deployed to support the various data analysis functions required across the enterprise: canned/batch reports, desktop ad hoc data analysis, and advanced analytics. There’s never a shortage of new and improved tools that guarantee simplified data exploration, quick response time, and greater data visualization options, Projects inevitably include the creation of dozens of prebuilt screens along with a training workshop to ensure that the users understand all of the new whiz bang features associated with the latest analytic tool incarnation. Unfortunately, the biggest challenge within any project isn’t getting users to master the various analytical functions; it’s ensuring the users understand the underlying data they’re analyzing.
If you take a look at the most prevalent issue with the adoption of a new business analysis tool is the users’ knowledge of the underlying data. This issue becomes visible with a number of common problems: the misuse of report data, the misunderstanding of business terminology, and/or the exaggeration of inaccurate data. Once the credibility or usability of the data comes under scrutiny, the project typically goes into “red alert” and requires immediate attention. If ignored, the business tool quickly becomes shelfware because no one is willing to take a chance on making business decisions based on risky information.
All too often the focus on end user training is tool training, not data training. What typically happens is that an analyst is introduced to the company’s standard analytics tool through a “drink from a fire hose” training workshop. All of the examples use generic sales or HR data to illustrate the tool’s strengths in folding, spindling, and manipulating the data. And this is where the problem begins: the vendor’s workshop data is perfect. There’s no missing or inaccurate data and all of the data is clearly labeled and defined; classes run smoothly, but it just isn’t reality Somehow the person with no hands-on data experience is supposed to figure out how to use their own (imperfect) data. It’s like someone taking their first ski lesson on a cleanly groomed beginner hill and then taking them up to the top of an a black diamond (advanced) run with step hills and moguls. The person works hard but isn’t equipped to deal with the challenges of the real world. So, they give up on the tool and tell others that the solution isn’t usable.
All of the advanced tools and manipulation capabilities don’t do any good if the users don’t understand the data. There are lots of approaches to educating users on data. Some prefer to take a bottom-up approach (reviewing individual table and column names, meanings, and values) while others want to take a top-down approach (reviewing subject area details, the associated reports, and then getting into the data details). There are certainly benefits of one approach over the other (depending on your audience); however, it’s important not to lose sight of the ultimate goal: giving the users the fundamental data knowledge they need to make decisions. The fundamentals that most users need to understand their data include a review of
The above details may seem a bit overwhelming if you consider that most companies have mature reporting environments and multi-terabyte data warehouses. However, we’re not talking about training someone to be an expert on 1000 data attributes contained within your data warehouse; we’re talking about ensuring someone’s ability to use an initial set of reports or a new tool without requiring 1-on-1 training. It’s important to realize that the folks with the greatest need for support and data knowledge are the newbies, not the experienced folks.
There are lots of options for imparting data knowledge to business users: a hands-on data workshop, a set of screen videos showing data usage examples, or a simple set of web pages containing definitions, textual descriptions, and screen shots. Don’t get wrapped up in the complexities of creating the perfect solution – keep it simple. I worked with a client that deployed their information using a set of pages constructed with PowerPoint that folks could reference in a the company’s intranet. If your users have nothing – don’t’ worry about the perfect solution – give them something to start with that’s easy to use.
Remember that the goal is to build users’ data knowledge that is sufficient to get them to adopt and use the company’s analysis tools. We’re not attempting to convert everyone into data scientists; we just want them to use the tools without requiring 1-on-1 training to explain every report or data element.
Thanks to http://evanjlevy.wordpress.com/2012/11/12/the-formula-for-analytics-success-data-knowledge/
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Posted by david ricketts
Image via CrunchBase
Microsoft Dynamics™ is a line of integrated, adaptable business management solutions that enables you and your people to make business decisions with greater confidence. Microsoft Dynamics works like and with familiar Microsoft software, automating and streamlining financial , customer relationship and supply chain processes in a way that helps you drive business success.
Familiar to Your People:
What if business management technology could free up you and your employees to focus on what’s truly important? What if technology reflected the ways in which people throughout your company actually work? Microsoft Dynamics is the answer to those questions. Microsoft Dynamics works like other Microsoft products you and your people are familiar with, helping reduce the time required to learn how to use it, and freeing up time to focus on what matters most. Designed with a focus on the roles people play throughout your company, Microsoft Dynamics delivers an individualized, task-based user experience and allows your employees to easily customize and automate based on their own preferences and work style. That means less training and development time and a quicker return on your investment.
Fits with Your Systems:
When a business management solution works the way your current technology works, it fi ts easily and seamlessly into your existing systems and helps you maximize your technology investment. Microsoft Dynamics works the way your current technology works so it fi ts easily into your systems, helping to maximize your investment in Microsoft technology. This in turn allows your employees to use a powerful business management solution within a familiar environment. Take advantage of BizTalk tools for data mapping, partner configuration and improved security. Or, integrate with other Microsoft product innovations, including Microsoft SQL Server and Microsoft Windows. Microsoft Dynamics – built to work with and maximize the potential of other Microsoft technologies.
Fuels Your Business Productivity:
Microsoft Dynamics helps fuel your productivity by automating your business-critical operations and adapting to fi t into your type of business helping ensure the most relevant insight. How? With a user experience modeled around tasks and roles and integrated with familiar productivity tools like Microsoft Office. With integration between Microsoft Dynamics and Microsoft SharePoint Technologies Collaboration is fostered among your employees, vendors and customers. And, by integrating financial, customer relationship and supply chain processes to help maximize both internal and external efficiencies, costs are reduced and performance improved.
Enables Confident Decision-Making:
The business landscape in which you thrive is demanding. You need to be able to respond and have the confidence to make informed decisions that have an impact. Microsoft Dynamics helps you respond rapidly to the changing demands of your business, providing you with more complete insight across your organization so you and your people can make timely and informed decisions with increased confidence. With Microsoft SQL Server, Microsoft Office Excel Analysis tools and Microsoft Dynamics together, you gain to critical data. That data can be easily analyzed and your employees get the information they want out of the system in the way they want and need, using a tool that is already familiar to them
Posted by AIB Consultants
For information about C24 and our professional Microsoft Dynamics Hosting solutions please visit our website
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Posted by david ricketts
The business of measuring digital activity has come a long way since its early development. It is now technologically advanced enough to provide a vivid picture of what is going on at and around a site as well as what is going on with visitors. The motivational force behind the advancement of digital analytics has been because of the growth of online business and stakeholders looking for more reliable metrics and precise feedback they can use to maximize profit. As a result, 2012 will be an eventful year for the digital analytics industry as it continues to catch up with the growing online ecosystem, which holds a variety of businesses. Below are three key trends to look out for in 2012.
1. Real Time Analytics:
More and more marketers have asked for real time data to react more quickly to what is going on with their web strategy. In 2011, Google launched a beta version of real time analytics and Facebook as well in 2012, which clearly shows they both already are seeing the need for real time tracking and not predictive analysis anymore. Real time analytics will be extremely important going forward for brands that are working on time sensitive campaigns or want to see data on who is on their site currently and how fast certain information is spreading. This is why real time analytics will play an increasingly important role in 2012 and service providers will continue to develop solutions to make it more practical for users.
2. Big Data Analytics:
In 2012, the hot new thing is data, data, and data! Decision makers cannot get enough of it and lately companies are digitizing more information than ever. Fueling this data explosion are over 30 billion pieces of content shared on Facebook per month, 20 billion Internet searches per month, and five billion mobile phones. Big Data is the platform used for transforming all of this data into actionable items for business decision making and companies that are able to put this technology to work are likely to find considerable revenue generating that will differentiate them from competition and drive success into the next decade.
3. Multi-channel Integration:
Onsite and offsite are becoming more entangled in business as customers research, compare, and make purchase decisions on different site channels and at different times. Measuring return only based on the last click gives an incomplete picture and potentially misses key insights about how customers are reached. For 2012, we are already seeing integration of onsite metrics with offsite metrics and Google Analytics recently released Multi-channel Funnels as a feature to let users look at interactions across different digital media and show how these channels work together to create sales. Consequently, the best business strategies going forward will be those that can take advantage of this interplay between different site channels, which will allow for better understanding and targeting of customers and potential users.
Thanks
http://dobmarketing.wordpress.com/2012/04/25/digital-analytics-key-trends-on-the-horizon-for-2012-2/
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Posted by david ricketts
Interested in slicing, dicing, measuring, and analyzing data for customer and business insights?
According to a recent survey by Bloomberg, 97% of companies with revenues of more than $100 million are using some form of business analytics, up from 90% just two years ago.
While businesses have embraced the idea of fact-based decision-making, a steep learning curve remains. Only one in four organizations believes its use of business analytics has been “very effective” in helping to make decisions. Data is not just ignored but often discarded in many organizations as the business users can’t figure out how to extract signal from data noise.
This is a far cry from the current hype around analytics and big data, raising the questions:
These are questions that managers must ponder as they rampup investments. Many companies start their analytics journey by executing one or two projects of small scope.
That may be fine at the outset, but in order to address the larger performance improvement issues, companies need to move up the maturity curve from repeatable to defined and then to managed and optimized.
The following are research insights highlighted by the survey sample of 930:
Nothing earth shattering here….Like all innovation, adoption will take time and require significant organizational changes across toolsets, skillsets and mindsets. But make no mistake, companies that don’t embrace analytics in a fast paced competitive environment will be left behind. Take for instance Financial Services industry. The sector continues to undergo massive structural change due to de-risking, ongoing regulatory changes (e.g. Dodd-Frank act, Basel 3), curbs on leverage, competition to cash-cows like credit-cards and a massive shift to online banking. This is driving skyrocketing demand for predictive models and creating an unprecedented need for data agility.
In order to change, you have to baseline first – what is your analytics maturity. The business analytics maturity curve represents the arc of progression every company moves along. Maturity levels are measured by your level of experience, the implementation and support strategies you use, and your degree of sophistication around data.
Analytics maturity can be assigned to one of the following four groups:
Which type of organization do you belong to? Where do you want to be?
Source: Bloomberg Businessweek Research Services study, conducted among 930 businesses across the globe in various industries. Focus of the study is to provide insight into the current state of business analytics in today’s organizations. Also examine the challenges companies face when using analytics, and explore tactics favored by companies who have succeeded in using analytics more effectively than their peers.
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Posted by david ricketts
Are you clear on your objective? What is the most important value proposition that you want to achieve through BI and analytics enabled strategies?
•Reduction in operating expenses
•Increased profitability
•Improve growth, competitiveness and market position
•Customer acquisition, loyalty and retention
•Product development and differentiation
The mis-alignment between what C-suite wants and what IT is capable of delivering is quite extraordinary. Many CFOs, CEOs believe that IT is unable to deliver results where it counts: the top line and bottom line. At the same time, IT organizations spend an incredible amount of time, money and resources simply reporting the obvious data within their business processes and workflows. The data overload is making find the obvious in the increasing tidal wave of structured and unstructured data a full-time job. As organizations emerge from the deep recession of 2008, the competitive pressures are putting even greater demands on the decision-making, KPIs and performance management processes of organizations.
To stay competitive means making better decisions more quickly. It means accelerating the “raw data -> clean data -> information -> insight -> decision cycle.” It dictates widening the scope and scale of the data management domain, the analytic landscape and the technological infrastructure.
Is it fair to associate the reports, dashboards of the obvious with the notion of business intelligence (BI)? The short answer is yes and no. The 21st century Instant Age – with its 24×7 customer interaction, global supply chains, increasingly digital/mobile/social/collaborative business processes – will need a BI overhaul and reconfiguration.
BI is more than basic or sophisticated reporting. It is about actionable insight, support¬ing better decision-making by identifying business opportunity or challenge and adapting to business change. Being able to execute practice BI requires architects (both IT and Business) to go well beyond merely reporting the obvious data about business operations. Simply put, leveraging the sophisticated dashboard technology is a means to an end…not the end itself.
Building BI systems to support better decision making requires business and IT architects to answer two questions:
1. What is the decision-making process? There exists a mature body of knowledge about decision support, Key Performance Indicators and performance management (finance, supply chain, sales). The notion of helping organizations make better decisions and become more efficient is hardly new. To effectively implement systems that support the decision-making process of different user communities requires that you hire the right subject matter experts and understand the decision support subject area.
2. How can the BI implementation make that process better? Once the BI archi¬tect has a grasp of decision-making processes, it is important that he/she answers two subordinate questions:
a) What decision-making patterns are recurring, repeatable and supportable? What levels need what information to effectively monitor and control activities and all related performance?
b) What technologies and architecture are necessary to support those decision-making patterns? Is there need for a “single source of truth” or a federated model possible?
The point we are trying to make is that more sophistication is needed in the business driven front-end of the BI projects and not fall into the trap of being technology led (or vendor platform choice led). This sounds basic but we are seeing this pattern again and again.
Our meetings with clients are showing us a clear trend — Data Management, BI and Analytics are priority topics that executives want to get a handle on. It is clear that there is a huge avalanche of demand building on the business side. But paradoxically, we are also seeing that data management and BI platform budgets are severely constrained. Why?
As we pull out of the economic downturn, the future of BI depends on altering the approach and mindset to address the burning questions CEOs care about most. We have to challenge an entire data management industry to change their role from tactical implementors of traditional BI projects to strategic coordinators of organization-wide, transformational data initiatives aimed at profitably delivering value to sales teams, supply chain leaders, and customers. Different times call for different leadership skills.
by Shirish Netke
Thanks to http://practicalanalytics.wordpress.com/2011/03/14/14/
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Posted by david ricketts
A recent study by the Nucleus Research says that Analytics pays back $10.66 for every dollar spent. The study is based on data from 60 case studies and relates to investments in Business Intelligence, Performance Management and predictive analytics. Not surprising are the areas where they saw ROI increase – revenue, gross margin and expenses.
Enterprises have used various metrics to track the effectiveness of Business Analytics. Cycle Time to Information (CTI) is a metric that measures the elapsed time between the occurrence of a significant event and the time this information is available to a decision maker who has to act on that information. Cycle Time to Action (CTA) is variation of this metric which measures the elapsed time to act on information after an event occurs. These metrics are useful to track the efficiency of a Business Analytics infrastructure and the elimination of manual processes to increase productivity. As the volume of data increases in an enterprise, automation in data management will become more complex in the future.
The primary purpose of Business Analytics is to improve the quality of decision-making. Better decisions directly impact the business. Target, a hundred year old retailer, is using Predictive Analytics to expect shopper behavior (See Target Your Shoppers – Retail Predictive Analytics). Concept One, a manufacturer of apparel and accessories, has used analytics to be more selective about renewing their licensing agreements. Procter & Gamble is increasing their analytics staff fourfold while reducing IT spend in other areas (See Proctor & Gamble – Business Sphere and Decision Cockpits).
Yet, Business Analytics adoption in enterprises has not reached its potential. A IBM/MIT study in 2010 cited that the most common barrier to implementing an analytics solution are lack of understanding of how to use analytics to improve the business. Time spent on analytics competes with other priorities for business users.
An ROI analysis is a very useful tool for business managers who are trying to allocate scarce resources to get the biggest bang for the buck. Now they have something to talk to CFO.
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Posted by david ricketts
