Moving beyond campaign management

Most organizations have adopted a campaign management solution to help them plan, execute, automate and measure their outbound marketing campaigns. A common goal in the usage of a campaign management solution is to support a profitable data-driven marketing campaign strategy across outbound channels to grow revenue, reduce marketing costs with less reliance on IT. However, a growing need is how marketing can leverage the investment in campaign management to optimally manage the customer experience. Meaning, marketing connecting with the customer at the “right time” to ensure an engaging and optimal experience, which increases loyalty, as well as revenue generating opportunities.

For the marketer, this can mean leveraging campaign management, as well as     technology, to encourage customer loyalty, grow profitable relationships, but also providing a meaningful and impactful customer experiences across all channels.

The challenge for the marketer is how to best implement a marketing technology strategy that leverages current investments (i.e. campaign management), as well as how to evolve to keep up with customer demands.  Depending on the organization, a short-term strategy could be to focus on analytics and data management to compliment campaign management strategies, or it might be time to develop a strategy that incorporates omni-channel marketing, marketing optimization and digital intelligence.

Data and analytics

Before considering how to incorporate interactive channels, digital intelligence or marketing optimization into a marketing strategy, it would be wise to start with a commitment to data management, as well as an assessment of how analytics supports marketing goals.

Meaningful marketing and analytics are not possible without good data. The foundation to any marketing analytics strategy is to consolidate, clean, store and provide access to clean data, which can yield benefits, such as a panoramic view of the customer. Data to consider should include, but not be limited to:

  • Transactional data, as one might expect, is information captured during transactions. It includes financial information, as well as when and where the interaction occurred.
  • Behavioral data encompasses actions that occur once, such as abandoning a cart, as well as activities observed over time that help you establish patterns of behavior. This includes email response data, social and mobile data, online behavioral data and campaign response history.
  • Computed data is created by performing calculations on one or more variables. The resulting factor can be as simple as distance from a retail store or as complex as expected lifetime profit value.
  • Integrated online/offline, is the ability to incorporate online, or digital data with offline to provide a panoramic view of a customer. The benefit is to be able to get a full understanding of who a customer is, where they engage, their potential value, etc.

Additionally, beyond being able to access clean data, is the need to commit to agreed-upon data definitions, data dictionary and key metrics, so decisions can be made consistently across an organization. Once data is clean, organized and governed, an organization can then effectively incorporate analytics into a campaign management strategy

Incorporating analytics into a campaign management strategy can help the marketer not only understand customer behavior, but anticipate behavior, which can enhance the customer experience.  The benefit of doing so can result in increased response rates, higher marketing ROI and reduced churn, as well as marketing efficiency gains and cost reductions.

Marketing analytic strategies should include segmentation that allows you to identify how customer segments are most likely to respond to marketing tactics and how much to invest in these segments. Effective customer segmentation will allow marketers to better understand target populations and deliver the right message at the right time. Also, predictive modeling should be leveraged to identify which customers are most likely to respond to a particular message or offer, which can increase ROI, marketing effectiveness customer engagement.

The key is to be able to seamlessly incorporate analytics into marketing campaigns, which can be cumbersome and time consuming. Marketing solutions that include “dynamic scoring” or “inline scoring” capabilities will greatly reduce the time and resources required to take action upon insight gained in analytics. Incorporating analytics into marketing tactics will enhance the decisions you make as you execute on your strategies and plans so you can be more effective and achieve better results.

Marketing Optimization

After ensuring a commitment to data management and marketing analytics, the next logical consideration is to incorporate marketing optimization techniques into your marketing strategy. Marketing optimization includes capabilities that help you maximize economic outcomes by making the most of each individual customer communication while considering your company’s resource and budget constraints, contact policies, the likelihood that customers will respond.

With a marketing optimization strategy, marketers can target customers to maximize profitability, click-throughs and response rates, while taking into account customer disposition, stated preferences and analytically-driven propensities, and other business goals and objectives relevant to campaigns and communications. The benefits of incorporating marketing optimization into a marketing strategy include increased marketing ROI, higher response rates, reduced opt-outs, enforced customer contact policies, eliminating competing offers and increased customer engagement.

Key considerations for marketing optimization include incorporating offer-level propensity scores, integration with campaign management to streamline marketing operations, the ability to create “what if” scenarios to determine the optimal optimization scenario and post-optimization analytics. Marketing optimization should not be limited to just outbound campaigns, but with inbound marketing tactics as well.

Omni-channel

A campaign management solution should not be marginalized and used as a “list puller”. The key is to create an omni-channel marketing strategy to align outbound and inbound marketing tactics across all channels, inclusive of direct mail, email, mobile, social, web, call center, kiosk, etc., where customers typically engage.

To enable this type of strategy, the marketer must be able to quickly define target segments, assign offers, schedule campaigns and analyze results. Additionally, the marketer must be able to incorporate analytics “inline” with marketing flows to score customers for the next best action when it matters most – when and where they engage. When considering how to expand beyond list pulling and incorporating an omni-channel marketing strategy, your technology should support:

  • Event-triggered campaign tactics to ensure timely, relevant marketing strategies
  • Integrated email, social and mobile on one platform
  • One user-interface for the creation of business rules for inbound and outbound
  • Integrated analytics to allow for the next best action to take for each customer
  • Effectively measure campaign performance and response attribution across all channels

The benefits of including additional channels into a campaign management strategy include the ability to design and deploy more profitable campaigns across all channels, but also the ability to lower marketing costs by consolidating marketing technologies and eliminating technology and data silos.

Digital Intelligence

A November 2013 Forrester report titled “Digital Intelligence Replaces Web Analytics” helps paint a vision of how technology could be leveraged to best manage the proliferation of channels, devices and big data to enhance the customer experience, beyond the use of traditional web analytics or campaign management tools.

The “empowered” customer expects consistent, timely and relative content across all channels, which presents challenges for the marketer. It is not just the web – this includes social, mobile and meaningful email content. To address these challenges, the marketer needs to consider the following resources and capabilities:

  • Eliminate data silos to get a single view of the customer across all channels, which is enabled by the integration of online and offline data.
  • The ability to collect, transform and access online data for analytics, targeting and integration with offline customer profiles.
  • Integrated marketing analytics in both outbound and inbound tactics.
  • Multi-channel decisioning capabilities that must be easy to manage and deploy.
  • Marketing optimization to ensure the right content is being targeted to the right customer at the right time.

In addition to moving beyond campaign management, it will important for any marketing department to assess organizational readiness. Questions to ask would be are marketing functions departmentalized? Do you have visibility into marketing performance across all channels and tactics? Do you have the right skillset(s) to support marketing analytics, omni-channel marketing and/or digital intelligence? Do you need a partner to augment your current staff?

Lastly, technology is only a piece of the equation. Put the customer first. Steve Jobs once said “you have to start with the customer experience and work backwards to the technology”. The key is to align your marketing technology and strategy, with that desired customer experience

In summary, building upon your campaign management capabilities will enable you to expedite the purchase path, enhance the customer experience along the way, but also provide insight into what is working best to drive revenue.

Post a Comment

Capture, comprehend and act on the voice of your guests: Text analytics for Hospitality

In service industries such as hospitality and gaming, the ability to capture, comprehend and act on guest or patron feedback is critical. Previously we learned from Kelly that user generated content from social review sites influences the purchase process for hotel rooms.  Take a moment to think about how many times a hotel or casino interacts with their guests or patrons. With each of those interactions, there is the potential for data to be created. What better way to understand the wants, needs and preferences of guests and patrons than by mining that data?

But when it comes down to it - what percentage of the data generated by these interactions are you actually able to use? With so many channels for interaction, such as social media review sites and forums, guest profile comments, guest survey responses, call center logs and emails, whether internal or external, the amount of data can be overwhelming. One of the biggest challenges with feedback data is that the main insights are contained in unstructured text data, which can be complex and labor-intensive to gain insights from. But what if you could organize and visualize customer feedback data, would that help you quickly gain the insights that you needed?

The answer is yes. You can use a variety of analytic techniques to help you interpret or quantify unstructured text data, whether it is publically available or internally generated. The technique you choose should depend not only on the type of data involved, but also on the business problem you’re trying to solve.

When it comes to social data, descriptive statistics can provides a snapshot of current or historical performance. This method is used to answer questions like: “How many followers? How many reviews have been posted over the last two weeks? What is my average rating across the major review sites? How many times did someone mention the word ‘comfortable’ in a review?” This type of analysis is most commonly found in reputation management applications or other applications that help hospitality and gaming companies monitor and respond to social activity.

Social network analysis identifies connections among users in a social network, as well as the impact of the activity of those users. It also identifies interconnected groups of individuals and shows the influence each participant has within social networks. This technique was developed to identify fraud in the financial services and healthcare industries, but these days it’s also used by marketers in the communications, retail and hospitality industries to identify those that are most influential to the purchase decision.

When you need to analyze and quantify unstructured text data, text analytics is the best analytic option. Most text analytics procedures are based on some form of natural language processing (NLP). NLP is a methodology based on linguistics that uses both predictive analytics and rules-based processing to interpret the context and content of unstructured text documents. Within text analytics there are several types of methods that can be used on unstructured text data, whether it’s internal, online or transcribed from voice. The three main categories of text analytics are:

  • Content categorization.  This identifies key topics and phrases in electronic text and sorts them into categories. It eliminates the manual work of reading and tagging documents, giving you much faster results. Text documents can be organized and tagged for search, making it easier to find, sort or process the content. This approach makes it easier to assign certain issues to specific departments that can resolve the issue. It also makes it easier for internal teams to find specific content stored in the text repositories.
  • Text mining, which is similar to data mining. This method uncovers related concepts in large volumes of conversations. It surfaces key topics that can be used in future analyses, like predicting or understanding guest behavior.
  • Sentiment analysis. This helps you understand guest opinions by applying NLP to the text documents. It identifies how your guests feel about key attributes of your product, brand or service – often in great detail.

Unstructured data can be notoriously complex, but applying text analytics makes it easy to filter, search and cross-reference this data. Hospitality and gaming companies have plenty to gain from a deeper understanding of their customers expressed need and preferences. Without text analytics, however, the time required to read and code all of that information can be highly prohibitive.

Post a Comment

The Analytic Hospitality Executive Blog: Announcing a new format!

We are very excited to announce that we are going to slightly change the format of our Analytic Hospitality Executive blog starting this month!  Based on our analysis of the performance of our blogs (and you better believe we look at this stuff carefully!), understanding the topics and people our readers like best, we’re going to move to a column format starting this month.  This way you will know exactly when to expect the bloggers you like best and the content you want to hear (or you can just keep reading them all!).  With this new format, we will be able to deliver even more great content, and most importantly, keep those analytical conversation going!

Natalie and I will each have a monthly column, and we’ll also feature a hospitality research oriented blog from our partners at Cornell.  We’re also introducing a new blogger, Bruce Swann, who is our Customer Intelligence for hospitality expert here at SAS.  You may remember him from Natalie’s post “What’s the future of email marketing” a few weeks ago.

The first full week of every month, I’ll post a blog about trends in analytics in hospitality.  Expect more from my “Pricing in a Social World” research with Dr. Breffni Noone in the coming months.  I’ll continue talking about building a strategic analytic culture, strategic implications of new revenue management analytics and practices, leveraging analytics to support key business initiatives like personalization and industry best practices.   I’ll provide my perspective on how our analytic hospitality executives can prepare to respond to the latest industry trends, challenges and opportunities.  Expect plenty of real-world perspectives, examples, quotes and interviews from practitioners, as well as a solid grounding in current research.

The second week of every month, we’ll feature a hospitality research blog working with our co-sponsors at the Cornell Center for Hospitality Research (CHR).  For the rest of this year, we’ll be working with the organizers of the Cornell Hospitality Research Summit (of which the CHR is a title sponsor).   Expect to hear highlights from scheduled speakers and sessions in advance of the conference in October, and then we’ll continue the conversation with session recaps and take-aways for the rest of the year.  Longer term, we will feature the latest research, responses to industry trends and industry predictions from the research faculty, as we did earlier this year in our blog post “What will 2014 Bring? Thoughts from the Research Faculty at Cornell’s Hotel School.”

The third week of the month, Natalie will continue to be explore the multiple applications of data management and analytics in the hospitality industry. The fourth week of the month, Bruce Swann will join us to discuss things all things digital marketing! When there’s extra time in the schedule, (or when the mood strikes), we’ll produce special feature blogs on topics like revenue management, interviews with research faculty, recaps of industry events, or even reactions to current news.

We have sincerely appreciated the great response from all of our readers to our efforts with this blog thus far, and we look forward to continuing our conversations with you in the months and years to come!  As always, we welcome your comments, feedback, questions and tweets!

Post a Comment

From the desk of the CHRS: Why the Cornell Hospitality Research Summit Has a Hospitality Exemplary Practice Award

The Cornell University School of Hotel Administration invites representatives of industry and academe again this fall to the 3rd Cornell Hospitality Research Summit (CHRS). A conference unlike any other, the CHRS is designed to create new knowledge through the intentional interaction of industry and academic presenters and participants. Although a tremendous amount of information has come from the presentations in the last two CHRS gatherings, one of the more interesting sources of solid industry ideas has been the Hospitality Exemplary Practice awards. Sponsored by IDeaS Revenue Solutions, the award is designed to highlight industry best practices, based in research which have solid implementation.

“The hospitality industry has a long history of innovation, and the CHRS has a specific focus on innovation and best practices,” said Vivek Bhogaraju, Director, Global Strategic Alliances, of IDeaS. “IDeaS is pleased to work with Cornell on the Hospitality Exemplary Practices award, to recognize the best practices that raise the tide for all industry participants.”

For example, one past winner was Best Western, which used consumer studies, supported by Maritz Research, followed by breakeven analysis to develop a system-wide upgrade. The research first had to determine which changes were important to the guests, and then the subsequent study demonstrated the value of making the upgrade to Best Western’s membership.

McDonald’s also got a nod for their research-intensive development of the McCafé Beverage Program, which has been a consumer hit. This was not an accident, because every part of this program had to pass three tests: customer taste tests, operations testing, and market analysis. With regard to operations, for instance, the products had to meet the company’s legendary time standards for order fulfillment.

The conference also acknowledged Wyndham Worldwide for working with Cintas to create an eco-friendly uniform program, which used fabric made with recycled polyester that has helped divert nearly 70,000 water bottles from landfills. The award judges noted the seven-step improvement program implemented by Viventa by Taj—Holiday Village, in Goa, which included relaxing check-in policies, reorganizing breakfast offerings, and instituting a new photograph-based approach to room preparation. Guest satisfaction scores increased by 20 percent after the program was rolled out, and the resort’s revenue per available room substantially exceeded its market “fair share.

What we’ve learned from these awards is that hospitality industry innovations and managerial practices must be based on rigorous research, and the conference would like to recognize even more of the industry’s best practices as part of CHRS 2014. That’s why the conference has asked people to nominate companies—or for companies to nominate themselves—to be exemplars for continued industry advancement for the Hospitality Exemplary Practice Award.

If you’d like to nominate yourself or someone else, follow this link!

As the leading source for research on and for the hospitality and related service industries, the Cornell University School of Hotel Administration invites representatives of industry and academe to the 3rd Cornell Hospitality Research Summit, on the beautiful campus of Cornell University in Ithaca, New York, October 12-14, 2014.
 A conference unlike any other, the CHRS is designed to create new knowledge through the intentional interaction of industry and academic presenters and participants.

Post a Comment

Responsible Use of Big Data: Evaluating New Data Sources

At the beginning of the year, I released my 14 actions for 2014.  I outlined a list of actions that hotels can and should take right now to ensure they are set up for success in the years to come.  Action #4 cautioned analytic hospitality executives to carefully evaluate new data sources.  I thought this action in particular was worth some additional discussion.

In this “big data” era, new data sources are cropping up every day – from internal sources and third party data re-sellers.  With all of this activity, plus constant messages from big data vendors and technology experts about the value of capturing everything you can get your hands on (I recognize that I am part of this as well, of course), it’s tempting to think that you can just shove all of that new data into a database and you’re good to go.  Regardless of how inexpensive storage space is getting and how fast processing is becoming, capturing, storing and analyzing data still takes resources  - technology and human capital.  Further, the wrong kind of data, used in the wrong way, will simply add overhead and noise to your analysis, rather than providing any additional insight.

There are myriads of detailed technical and analytical methodologies for assessing and transforming data to make it useful for reporting and analysis, which I won’t go into here.  In this post, I will provide some business-oriented suggestions for how to think about a new data source, and discuss potential problems that could arise from throwing too much data at a problem.

I’ve said this many times before, the first important step in evaluating a potential new data source is to determine what business value you will gain from accessing that data.  You should clearly and specifically define not just the insight you expect to be able to gain from that data source, but also who will benefit from that insight and how the company will take action.  Assess how the data could contribute to an existing business analysis, improve a decision making process, or help you gain new insight.  Knowing the “fit” at the level of business value will help you justify the investment in acquisition.

Once you understand the potential business  value, you need to be sure the data can actually deliver.  The second step is to understand the characteristics of the data source.  Ask the following questions:

  • What is the data? Make sure that someone in the organization has a clear understanding of the data fields, how they are calculated, what level of detail is available and what they mean. You will also need to understand how this data relates to other data in the organization. For example, if you are looking at time series data, does the level of detail and the intervals match any related sources? Also determine whether the data is unique, or highly related or correlated to another source.
  • How is the data collected?  Understanding where the data comes from will give you a sense of how reliable it is.  If it is heavily driven by user entry, then you need to assess the business process around the data collection.  User driven data is notoriously unreliable unless it has tight business process around it.
  • How often is the data updated and how?   Your systems will need to be set up to receive and store the data in a timely fashion.   If the data comes too fast, and the ETL process takes too long, it might be useless by the time you are able to access it.  For example, tweets or geo-location data are stale almost as they are created, so if you aren’t able to process them in time to use them, it’s not worth the trouble.  Further, if the data delivery process is unreliable (as in it frequently doesn’t show up, or shows up with missing values etc), and you are counting on it for a critical piece of insight, you may want to look elsewhere.

Finally, determine whether you will need any additional technology or resources to manage the data source.  Unstructured text data can be highly valuable to the organization, but it’s large, and it requires some specialized analytics to interpret.    There are also human capital implications for adding new data sources.  Do you have enough people available to manipulate and analyze the data so that it can be effectively used by decision makers?  Obviously, if you need to make an investment in new technology and new resources, more work is required around my first point – understanding the business vale.

If you are just interested in using the new data source for reporting, or descriptive statistics, the previously outlined steps will keep you out of trouble.  Throwing more data at a predictive modeling or forecasting analysis is trickier.  I am going to introduce some statistical concepts that you should be aware of as you are thinking of incorporating more data into an advanced analytic application.

Some of you may be familiar with Occam’s razor.  It is a principle of mathematics developed in the 14th century which basically states that “simpler explanations are, other things being equal, generally better than more complex ones.”  Many statisticians follow this guidance, believing that you should always select the simplest hypothesis until simplicity can be traded for predictive power.  Occam’s razor cautions us that simply throwing more data at a statistical problem might not necessarily generate a better answer.

In fact, statistical analysis bears this out in some cases.  Note that when I talk about “more data” in the next few paragraphs, I am talking about more “predictor variables” not more observations within the same data set.  Generally speaking, more observations will help to increase the reliability of results, since they will help to detect patterns in the data with greater confidence.

Two different statistical phenomenon can occur in predictive analysis with the addition of predictor variables to a model.  In both cases, the addition of variables decreases the reliability or predictablity of the model.  I’m only going to define them at a very high level here, so that you can verify with your analysts whether there’s a concern.  There has been plenty of research on both of these issues, if you want more information.

The first issue to watch out for is multicolinearity.  This happens in a multiple regression analysis when two or more predictor variables are highly correlated, and thus do not provide any unique or independent information to the model.  Examples of things that tend to be highly correlated could be height and weight, years of education and income, or time spent at work and time spent with family.  The real danger in multicoliniarity is that it makes the estimates of the individual predictor variables less reliable.  So, if all you care about is the model as a whole, it’s not that big of a deal.  However, if you care about things like what variable has the biggest impact on overall guest value, or on likelihood to respond, then you do have to watch out for multicoliniarity.

The second thing to watch out for is overfitting, which happens there are too many parameters relative to the number of observations.  When this happens, the model ends up describes random error, not the real underlying relationships.   Every data sample has some noise, so if you try to drive out too much error, you become very good and modeling the past, but bad at predicting the future.  This is the biggest danger of overfitting a model.   This is particularly problematic in machine learning algorithms, or really any models that learn over time (like revenue management forecasting, for example).

So, what is the bottom line here?  Don’t assume more is better, prove it!

Post a Comment

What’s the future of email in hospitality marketing?

This week I spoke with Bruce Swann, Customer Intelligence Solutions Manager for SAS, about the role that email will play in the future when it comes to hospitality marketing. Bruce is a veteran of marketing, having more than 15 years of experience working with marketing and CRM technologies, including marketing automation, predictive analytics and marketing resource management, as well as interactive marketing disciplines like web analytics, social media, mobile marketing and email marketing. At SAS, Bruce works closely with prospects and customers in the hospitality sports and gaming industries to help design the optimal customer intelligence platform based on current and future business requirements. As you can imagine Bruce is a big proponent of data and analytically driven marketing approaches. This week we chatted about how to get more impact for your investment when it comes to email marketing. And of course that raised the following question…

With so many marketing channels available to a consumer today, is email even still relevant?

Bruce explained that without a doubt – he believes that email is still relevant and a very powerful channel for a marketer to leverage for customer engagement and driving revenue. “In fact, while many think email is fading, quite the contrary; it is growing exponentially.” Bruce referred to a recent eMarketer report that projects there will be nearly 240 million Americans with email accounts by 2017.

“One of the contributing factors to the growth of email is mobile accessibility and the prevalence of smartphones,” he said.  “Also, with the advent of new devices, such as Google Glass and wearable devices like smart watches, access to email will be that much more convenient for the consumer,” Bruce explained, “and this presents new options for the marketer to target and engage customers.”

Bruce feels that the challenge for the hospitality marketer is not whether email will still be relevant, but how to be relevant with email and stay ahead of the competition.

How can a marketer be more relevant with email?

Bruce thinks that the bottom line is that it is very difficult for a marketer to be relevant with email, considering email is not the only inbox that a marketer competes with. “There is also Facebook, Twitter, Google+, Instagram, Pinterest and a multitude of other social channels,” he explained. In addition, it only takes a second to lose a customer to an opt-out. “You need to consider that of the top two reasons for a customer to unsubscribe from an email list, the top one is simply volume, or too many emails, and the second is that they are getting content that is just not relevant,” Bruce elaborated.

For an email to be relevant, hospitality marketers should ask themselves the following before an email campaign is launched - Is the email compelling and timely? “Marketers already know that the best way to engage with a customer is to leverage data-driven marketing to target them with content that is persuasive and compels them to act on the call to action,” Bruce explained, “but often what is missed is the timing of the email.” For example, will the email get buried in an inbox and ignored, or delivered to the inbox at the wrong time or the wrong day? “Analytics can provide insight into the best time of day or day of week to target a customer, but an even more productive approach would be to leverage triggered emails,” he explained.  Triggered emails are sent (or
"triggered") based on an action taken by the recipient.

The “triggered” in triggered emails refers more to the technology used to send the email. “Marketers need to remember to factor in some strategy behind the email as well as content to help boost revenue,” Bruce said. He gave examples of triggered emails that included welcome to the hotel emails, reservation purchase confirmation, website behavioral triggers such as interest in specific property features such as the spa, or even abandoned booking. “A recent Epsilon report states that triggered emails have open rates nearly 60 percent higher than non-triggered messages,” Bruce explained, “and the click-through rate on triggered messages is nearly 130 percent higher than on business-as-usual messages.”

What should a marketer consider with data-driven marketing when it comes to email marketing?

Bruce thinks that to really be relevant, personalization is just not enough. “Marketers needs to leverage all they know about a customer to determine the optimal content target,” he said. “For example, a customer who has viewed videos of hotels on the website might be far more receptive to view a video embedded in an email; a new customer might be more receptive to a cross-sell or upsell offer than a lapsed customer; a re-marketing email might resonate better with a frequent online shopper; one who browses on a mobile device should be targeted with mobile-friendly content; also - consider sending socially targeted emails to subscribers who have mentioned you on social media,” Bruce explained.

“For this to work effectively, however, relevant data needs to be considered, such as preference center data, web behavioral data, social and transactional data,” he said. Data also needs to be brought together to provide a more panoramic view of the customer. “Not having a more thorough view of a customer, or missing key data elements, is a major obstacle to data-driven marketing, which impacts the ability to target the customer with the optimal content,” he said. When you incorporate data-driven marketing into an email strategy, along with a focus on what customer is interested in, what they are telling you and what you know about them, you can better determine what to communicate in a way that is relevant and drives engagement. “This also enables more effective segmentation strategies,” Bruce explained, “which will enhance the ability to target customers with engaging content.”

How can analytics be used in email marketing?

“In general, predictive analytics can be used to predict what content, or offer a customer is likely to respond to,” Bruce said, “but another effective use of analytics in an email strategy is segmentation.” Analytics can help determine email marketing segments where the customers within the segments have common needs and priorities. “This means the marketer can better align the content and timing of an email campaign with customers being targeted,” he explained. One of the benefits of this approach is that it can prevent poorly targeted email campaigns from overwhelming subscribers with seemingly meaningless offers. “Analytically driven segmentation narrows the focus content, based on the segment’s characteristics, which in turn decreases contact fatigue or opt-outs associated with poorly targeted emails,” Bruce said.

When it comes to measuring the success of email marketing, what metrics should a hospitality marketer care about?

“Beyond standard operational metrics such as opens, clicks and conversions, the marketer should also measure engagement,” Bruce said. As he explained it, engagement pertains to email activity that drives not just opens, clicks, time on site, and conversions, but also activity that increases repeat business, increased spending and lifetime value. “I would recommend that you seek out metrics that help answer questions such as how many people clicked through on your email but didn’t convert? Of the people who converted on your website, how many of them came from social media? How many opened your last email campaign? Which results in more leads, PPC or organic search: email or social?” Bruce said. “These are the answers that will result in more targeted, engaging and revenue-driving email marketing.”

In summary, when it comes to email marketing strategy, the hospitality marketer should keep a few key considerations in mind: to be relevant with email, the approach must be compelling and timely. The content of the email will mean nothing if it arrives in the inbox along with a deluge of other ill-timed emails; the timing will mean nothing if content is bland and meaningless to the recipient. By incorporating data-driven email marketing, triggered campaigns and analytics, email strategies are more likely to drive engagement and revenue opportunities.

Post a Comment

The challenges and opportunities of hospitality marketing: An interview with Bill Carroll

This week, I reached out to Bill Carroll, senior lecturer at the School of Hotel Administration, Cornell University. I caught up with Bill earlier this year during the HSMAI Digital Marketing conference, where he was the MC, and more recently during the Center for Hospitality Research round table on customer loyalty. It seemed that the time was right to touch base with Bill on the state of digital marketing in the hospitality industry.

Natalie: Bill - what do you see as the biggest opportunities in digital marketing for the hospitality industry?

Bill: One of the largest opportunities for hotel companies is to enhance engagement, particularly in regards to leisure customer segments. First of all, engagement needs to move beyond the loyalty program and expand to all aspects of the process, from the time the booking is made all the way through to the time that the customer submits some review about the property. Second, reputation management is becoming more and more effective. This provides an enormous opportunity to not only resolve any customer issues in real-time, but also to drive overall operational improvements. The third significant area that I see is the increased use of items that a hotel property can offer. Instead of thinking about improving occupancy, maybe we ought to focus on improving the revenue we are making per square meter of hotel asset. And if we began to think that way, then we would focus on increasing sales in meeting space, additional services, food and beverage, and not just in rooms.

Natalie: What trends or focus areas from other industries do you think that the hospitality industry should be adapting or adopting?

Bill: One of the areas where the hospitality industry has to adopt is in the area self-regulation. I get very concerned about the issue of privacy of customer data versus using information to serve customers better. If, as an industry, we don’t start to self-regulate, then at some point the government will step in and do it for us. I see self-regulation initiatives starting and being executed in other industries but not our own, unfortunately.  We need to start partnering together as an industry to draw up guidelines to help regulate private information so that the industry can collectively preserve the value of information, while at the same time protect the customer’s privacy.

Natalie: Bill – this is really an area where you have to have some internal capabilities to manage private information and make sure that it is protected and secure.

Absolutely. And I would really like to see the industry as a whole drive co-operation around self-regulation, so it doesn’t become a competitive sticking point between hotel companies. It should be that we are all going to agree to do it this way as a service to the consumer. So then, it does not become a cartel issue, or an anti-trust issue, it really becomes a privacy issue – and the hotel industry should be co-operating to protect the privacy of the consumer.

Natalie: What are some of the upcoming challenges and opportunities for hospitality marketing that you see?

Bill: Number one has got to be the ability to evaluate return on investment with regard to attribution. We are in the digital age, so it seems that we should be much better at attributing the value of a search site, an online travel agency site and the interaction that occurs there. We don’t know as much as we really should because that the industry hasn’t stepped up to be willing to do that as yet.

The second is really dealing more effectively with the trade-off of privacy versus efficiency and efficacy of service, as I mentioned earlier. The third challenge is that of changing source markets and demographics. When you look ahead to 2025, the world will go from the European Economic Community being the largest global producer of gross domestic product to China being the number one producer. At the same time - the United States will move to number seven. This is a huge challenge that the industry needs to recognize, both in terms of source markets and in terms of the allocation of capital. The question becomes - where is capital going to flow geographically? It is certainly not going to be towards the U.S. How will this change the nature of our industry? There are plenty of aspects to start to prepare for, and all of the early signs of change are already there. I see this change in demographics realized in my classroom every day.

Lastly, one of the inherent challenges for hotel companies is that of loyalty. I think that travel intermediaries such as OTA’s are going to recognize the value of loyalty programs and customer relationship management sooner rather than later. And because they have bigger budgets and can offer points on everything you buy, they will be a real challenge to the hotel’s more traditional loyalty programs.

In summary, there is plenty for the hotel marketer to do to prepare for the future. No only will some of these challenges will require building strong internal partnerships, when it comes to data privacy and service efficacy, for example, it will require a strong partnership between IT and marketing. These challenges also may require external partnerships between hotel companies to drive towards better industry self-regulation.

Marketers - how are you readying yourself for these challenges?

Post a Comment

Streamlining tough choices: Applying an optimization approach to hospitality marketing

You have spent your time carefully crafting a number of campaigns for distribution to your various customer segments, and it doesn’t take you much time after that to realize that you probably can’t execute on all of these campaigns. Perhaps it is because you realize that the combination of campaigns that you have planned will exceed customer contact policies, or perhaps they won’t meet mail-house minimums or maybe you just don’t have enough time and resources to complete all of the campaigns. How do you identify which campaigns will get you the results that you both want and need? Enter the analytic-centric approach of Marketing Optimization. Marketing optimization can help you contact the right customers with the right offers at the right time, while staying within the constraints of your budget and channel capacities, all without diluting future sales or exceeding customer contact policies. Marketing optimization helps maximize your economic results by making the most of each individual customer communication.

Marketing Optimization allows you to construct a scenario that balances your objectives with your constraints with your organization’s contact policy and then identify the optimal combination of campaigns for execution. The objectives for a marketing optimization problem can be defined in a variety of ways, depending on the goals you have for your campaigns. For example, if your goal is to increase profitability, you could chose profit as the metric to be maximized. You may also consider setting an objective to minimize as a goal, such as risk or costs.

Constraints enable you to specify key marketing limits such as minimums or maximums for spending. Constraints can be set at the customer segment level and include examples such as setting budget constraints for any or all campaigns. Often, campaigns need to be a certain size to be worth executing. You can create constraints that reflect the real nature of the direct marketing world through minimum or maximum cell sizes. Outbound and inbound channels also may have limits, whether in terms of the total hours a call center can handle or the number of pieces a mail house can send out. Lastly, you may want to place an additional constraint that drives toward a threshold so that a certain return on investment (ROI) is targeted across the campaigns.

Contact policies are important for planning the number of allowable touches that the overall campaigns or brand can have on each individual customer. You can set these in a variety of ways. A limit can be placed on the number of touches per customer for the overall cycle. For example, an organization might say that each customer can be contacted only twice per cycle. This can be maintained at the overall level or the individual customer level. Contact policies can also be constructed so that they allow certain types of communication more leeway, such as limit on a certain type of offer. A contact policy can also be constructed that limits the number of offers in any given time period. So, a customer could be restricted to three communications in January and two in February. A rolling time period can limit that same customer to, for example, four communications over any two-month period.

The benefits of applying optimization techniques to marketing are multiple: enhanced ROI, better adherence to customer contact policies, and improved overall operational efficiency.

  • Return on Investment:  Increasing your targeting effectiveness results in higher response rates, improved channel effectiveness, and reduced spending on campaigns. The math-based approach offered by marketing optimization techniques produce results that are superior to rules-based or segmentation approaches when it comes to prioritizing marketing offers.
  • Contact Strategy: Complex customer contact policies are required to avoid over-saturating customers and violating your organizations corporate governance requirements. Marketing optimization techniques can eliminate uncoordinated and conflicting communications while accounting for customer risk, advertising exposure and house-holding to ensure that customers are receiving the best possible set of communications across every channel.
  • Organizational efficiency: Marketing optimization techniques can show where and how changes in channel usage, target customer segments, campaign budget, and other constraints will affect the business, and highlight financial opportunities and unused capacity.

The complexity of direct marketing has expanded rapidly in recent years, particularly with the growth of digital marketing channels. Hospitality companies today have to make difficult decisions about targeting the right customers with the right offers while staying within budget and channel capacities, all without cannibalizing future sales or saturating customers with too many messages. That is a lot to manage, particularly when multiple campaigns from one company might also be competing for customers’ attention. Marketing Optimization can efficiently help marketers determine who to contact with which campaigns in a complex marketing environment where customers could qualify for multiple or competing offers.

Post a Comment

Cornell Hospitality Research Summit Announces Call for Submissions - Analytic Hospitality Executives, This is your event!

We here at the Analytic Hospitality Executive along with our partners at the Center for Hospitality Research at Cornell, would like to strongly encourage all of you to submit your innovative thoughts, projects  or topics for presentations, panel discussions, tutorials or workshops  to the third Cornell Hospitality Research Summit (CHRS), set for October 12 - 14, 2014, at the Cornell School of Hotel Administration.  The theme of the conference is “The Future of Service Innovation: The New Science of People, Organizations, Data and Technology.”

Cathy Enz and Rohit Verma, the conference co-chairs, describe this year’s event as follows: “Innovation is essential to the continued health and well-being of hospitality and related service industries now and in the future. We believe the time has arrived for innovation in services to be discussed and explored rigorously. The goal of this conference is to examine service innovation in a new light, focusing on what new ideas, tools, techniques, technologies, processes and structures hospitality firms need to prosper in a time of accelerated global change.  The focus of the conference is multidimensional, including data analytics, a deeper understanding of customers and approaches to engaging employees, the role of technology, service delivery systems, and new product development, to name but a few areas for possible exploration.”

I have attended both of the previous conferences, and this is probably my favorite conference of all of the ones that I regularly attend.   Bringing together academia and industry in a forum that allows for interaction, discussion and debate is a unique format that inspires creative thinking.  Both groups benefit from discussing cutting edge research, and how that can be applied to the industry’s practical business problems.

SAS has always been a supporter of this event, and I must say I’m particularly excited to participate this year, as Cathy and Rohit have been putting a lot of thought into shaking up the traditional conference format – creating an environment for discussion, debate and inspiration.   The conference sessions types will include:

Big question – in which presenters or moderators propose a “big question” for discussion and the attendees work together with the presenters to answer the question.

Show & Tell – an opportunity for your organization to preview an innovation, demonstrate a new product, or teach a new technique to spark discussion and feedback from the audience.

Presentation Plus – Traditional presentation approach, but with the opportunity to creatively encourage interaction with the audience.

Point/Counterpoint – Propose a topic or issue to debate with panelist and the audience.

More details can be found in the submission guidelines here: http://www.hotelschool.cornell.edu/research/chr/events/chrs/submissions.html, and Summit details are found at: http://www.hotelschool.cornell.edu/research/chr/events/chrs/.

Submissions will be accepted between now and March 31, 2014. Submissions will be reviewed and accepted on a rolling basis so early submission is highly recommended as slots will likely fill up before the deadline.

If you have any questions about the event, contact the conference chairs Cathy Enz: cae4@cornell.edu or Rohit Verma rohit.verma@cornell.edu.

Even if you do not submit a session idea, I would encourage you to attend the conference.  It should be inspirational – and provide that much needed chance to take a step back from day to day and think creatively about your business!

Hope to see you up in Ithaca in October!!!

Post a Comment

Forecasting digital outcomes for hospitality

In a big data panel at the HSMAI Digital Marketing Strategy conference held recently in New York, Peter Kim from start-up MightyHive commented that all of your competitors have access to the same third party data you do, and that your own data is much more valuable. Peter’s comment really struck me as I considered digital behavior data for hotels. Many hotel companies don’t even own their own digital behavior data stream, having outsourced it to web analytics providers. What would it take to justify bringing that data back in house? What new analytic capabilities could emerge from access to this detailed data? For my answers I spoke to Suneel Grover, senior solutions architect for digital intelligence at SAS about how to harness digital behavior data and what kinds of new analytic capabilities are available once that data is captured.

Why aren’t more hospitality organizations using predictive analytics with digital behavior data?

Suneel explained that typical web and digital analytics tools primarily aggregate and report on historical information and do not enable predictive analysis. “While data-driven marketers and analysts have used powerful advanced analytics for many years to perform sophisticated analyses – such as regression, decision trees, or clustering – they have been limited to using offline data, primarily due to restrictions on access rights to online data from third-party technology vendors,” he said. The challenge facing the marketing industry today is progressing beyond the multi-channel analytic limitations of aggregated data collection methods used by traditional web analytics, he explained.

The opportunity is to have a digital data collection framework that enables both business intelligence and predictive analytics, Suneel said. “This methodology requires organizations to collect and OWN the data to allow the analysis of the “who,” “what,” “where,” “when,” and most importantly, the “why” of digital experiences.” This data, if collected and prepared appropriately (e.g. considerations for data integration, quality, and governance), can be merged with your company’s first-party (or company-owned) customer data, and then streamed into your analytics, visualization, and marketing automation systems.

Once you have this data collected, what analytic capabilities can you use?

Suneel told me about a digital analytic capability that focuses on search marketing that leverages onsite behavioral data. He explained that one of the common questions debated in digital marketing is how increasing the paid search ad budget will impact website traffic and hopefully conversions. “When you reflect on this question, it touches on the ability to accurately predict not only what website visitation will look like in the future two week time period, but also have the ability to ask “what if I do this”, or “what if I do that,” Suneel said. “In business and visual analytics, this is known as forecasting, and scenario (or what-if) analysis,” he explained. The unique twist comes when you apply this set of analytic techniques to a digital marketing challenge.

“If your organization owns the digital behavior data stream that captures your website’s traffic behavior, this data can be fed into a forecasting model to first predict what will happen in the next two weeks,” Suneel said. Then, marketers can see how overall site traffic might increase or decrease at varying velocities – by week, day, or even by hour – all wrapped up in a 95% upper and lower confidence interval to highlight the most likely, best case, and worst case scenarios. “Given this information, marketers can determine the impact on predicted traffic patterns of allocating more ad dollars to one channel (such as paid search), versus another (email),” Suneel explained. He told me that by using scenario analysis in conjunction with forecasting, marketing analysts can easily inflate the potential impact of a 10%, 20% or 30% increase in paid search traffic, and then view how this will modify the forecasted prediction of overall site visitation.

“You can end up with takeaways that look like this,” Suneel said:

- A 10% increase in Paid Search visitors would provide a 13% increase in overall site traffic in the forecasted time period (Incremental 3% lift)

- A 20% increase in Paid Search visitors would provide a 28% increase in overall site traffic in the forecasted time period (Incremental 8% lift)

- A 30% increase in Paid Search visitors would provide a 56% increase in overall site traffic in the forecasted time period (Incremental 26% lift)

Access to this data, and the use of predictive analytics can help you understand how adding just a little bit more into one digital channel can have varying, and sometimes, dramatic changes in overall digital traffic.

Every hospitality company could use the results of this type of analysis, but just how difficult is it to perform?

Suneel explained that the emergence of visual analytics in the data visualization technology space makes leveraging these approaches much easier to achieve. “We are living in a time period where analytics cannot be meant for only a select few data scientists,” he said. “The opportunities are too many, and the democratization and accessibility of analytics has begun.”

Post a Comment