CMO: 3 Action Items to Prepare for AI and Machine Learning

As a CMO, you are likely feeling pressure to move your marketing efforts towards artificial intelligence and machine learning, two very complex topics. Underlying these topics are a solid understanding of analytics and data. As a graduate student at Northwestern University in the Medill IMC program with an interest in marketing analytics, I have found two articles that address the basics of building a strong analytical foundation in marketing.

The first article, Marketing Analytics Can Improve the Customer Experience, argues that marketing analytics is a crucial part of understanding the customer experience. Most companies have the data but are held back by silos within the company that prevent the company from integrating the data across channels. And it is the responsibility of the CMO to break down these silos and shift to a more customer-centric approach. The most successful companies are those that use measurement and analytics across all customer-facing functions to gain a complete customer view, therefore allowing them improve the customer experience.  The article was featured on the Harvard Business Review by Google Analytics 360 Suite.

www.tibco.com

The other article, How An Analytic Mindset Changes Marketing Culture, discusses the benefits of combining analytics with marketing efforts to not only measure the effectiveness of past campaigns, but also to focus on actions in the future. This allows marketers to be more proactive than reactive. With the availability of data today, marketers should no longer be relying on their gut to make decisions. The key is to use the data to develop insights and tell a story, resulting in the ability to adjust in real-time and improve results. The article was written by Adele K. Sweetwood and published on the Harvard Business Review.

Based on my review of these two articles and relevant studies I have done in the Northwestern Medill IMC program, I have developed three action items for you to take as soon as possible:

• Establish analytical culture – be open to using analytics to improve your marketing efforts

• Break down silos – marketing and analytics should not be separate, and benefits cannot truly be maximized without the contribution of all customer-facing functions

• Find useful insights – companies have mass amounts of data available at their fingertips, but data is not valuable without the ability to develop useful, actionable insights

With the marketing industry moving rapidly towards artificial intelligence and machine learning, consider these three action items to make sure your marketing department is ready for what’s to come.

Ashley Tomzik is a graduate student at Northwestern University’s Medill IMC program, specializing in Marketing Analytics, who will be graduating in December. Prior to Northwestern, I have gained valuable experience, holding a range of analytical roles. You can contact me on Twitter (@ashley_tomzik) or LinkedIn.

Big Data Scientists: Netflix is not maximizing its recommendation systems, are you?

As a Big Data Scientist, it is imperative today to develop recommendation systems which turn insights about customers and prospects into relevant recommendations and quality leads. As a graduate student in Northwestern University's Medill Integrated Marketing Communications program, I have been researching big data issues and have found two articles which address this challenge - using Netflix as an example.

In the article "Putting Big Data in Context", Scott Gnau(@Scott_Gnau), president of Teradata labs pointed out that best decisions and recommendations are made with a combination of data analytics and human intuition. Two mistakes companies tend to make when dealing with big data are: 1. over-reliance on what data tells us; 2. become too enamored with certain types of new data, or look at data in silos. For example, Netflix's recommendation system relies too much on past behavior data while failing to incorporate context data such as real-time emotion and situation individuals are experiencing.

source: Who knew? Big Data is a Social Beast!

Nestor Bally(@NKBailly)  mentioned the restrictions of current recommendation system in "Build A Better Algorithm(With A Little Help From Your Friends)". He said rudimental recommendation algorithms made largely ineffective predictions based on similarities in items and the premise that people with similar historical preferences are likely to share future preferences. However, this led to a low degree of diverse content and a self-amplifying vicious cycle. For example, Netflix does not recommend films based on film fanatics' watching list or what friends are watching, and thus restricts the spectrum of the recommended content. To build a better algorithm, companies should leverage the power of social influence. 

Based on my analysis of these two articles and my classes on social marketing and big data analytics in the Northwestern Medill IMC program, there are three actions you need to consider when developing your social and contextual recommendation system.  They are:

1. Think in context. Consuming content online is an emotional behavior as well as a rational one. You need to get data about people's current emotions, like what Spotify does, and incorporate the contextual data into the recommendation algorithm.  

2. Leverage social. You can provide more personalized recommendations without entering the self-amplifying vicious cycle by integrating the power of social influencers such as experts and friends.

3. Data is not the problem. As a big data scientist, you should not let what data you have restrict what question you can answer. It is critical for your team to think out of the box and disruptively innovate your recommendation system by integrating more varieties of data. 

A better recommendation algorithm can become the competitive advantages of any companies in the long run. HBO just announced that it will join the digital distribution space along with Hulu, Amazon and Apple, Netflix's future will largely depend on whether they can disruptively innovate the recommendation algorithms to provide a more customized and social network for digital content consumers. As a big data scientist, your company may face similar challenges as Netflix. It's time to reinvent your recommendation algorithms and stand out in the big data competition.

Joyce Liu, a marketing wiz and film fan, is pursuing a Master degree of Integrated Marketing Communications at Medill, IMC. I specialize in digital marketing and social analytics. I have helped financial and CPG companies transform their marketing practice in the digital space. Connect with me on Twitter(@Joyce_xinranLiu) and LinkedIn.

Strategists and Data Analysts: Make the Deliberate Connection and Really Excel as Data-Driven Strategist

As data analysts and strategists, being data driven and strategic is really two sides of the same coin, the key is to make the deliberate connection. Being told you are not strategic or data-driven really stings, but despite thousand hours we spent on drawing up detailed plans and investigating data, all too often thy matter every little to performance. As an IMC graduate student in Northwestern's Medill School, I have been focusing on bridging strategic planning with data-driven analytics and I found two insightful articles from Harvard Business Review and McKinsey Quarterly that will help to tear down the Great Chinese Wall between the art and science of marketing communication. 

Being strategic is fundamentally making deliberate connections. In Strengthen Your Strategic Thinking Muscles, Liane Davey argues that sometimes we are just too busy to be strategic. Under the guise of productivity, we have probably squeezed out thinking time, thus the decision is based more on reflex than reflection, more on what has worked before instead of making meaningful connection. Every one has opportunity to be strategic, simply by being more deliberate in our thoughts and actions. Down to the execution level, being strategic also requires making choices and connect things and domains that is currently separated or segmented.

Big Data is now the buzzword everybody is uttering, but few people realized simply collecting Big Data does not unlock its potential value. In Big Data help wanted (badly): How to win the war for talent, McKinsey on Marketing & Sales argues to true tap into the analytical power of Big Data and form data-driven strategies, strategists and analysts should aim at being “translators” who are capable of connecting different business functions and effectively communicating between them. These strategic connection making process is really the prerequisite of being strategic under the Big Data context.

Image Source: McKinsey on Marketing & Sales

Based upon these two articles, I realized there are three action items that could really help to be strategically data-driven: Strategy and Big Data are the most misused and overused words in our business, but instead of being isolated, they are actually complimentary. As the author of Predictive Analytics Eric Siegel once criticized, "big data often means small math", big data can also means meaningless strategy. Being strategically data-driven is really the benchmark for strategic and analytical talents, and that requires strategists really reflect on the true meaning of being strategic and make the deliberate connection between different business functions. 

1. Make more time to reflect before making decisions. 

2. Be courage to make choices and embrace the uncertainty.

3. Create connections between analytics, technology and business decision making.

To be strategic and data-driven are among the biggest expectation of today marketers, all too often they are referred as the “art and science” and seen as hard to reconcile. However, I believe they are actually complimentary and symbiotic. Being strategic is fundamentally about making deliberate connections, and it is even more so in the Big Data context. Marketers to seek to be more strategic need to actively bridge and communicate with different business functions. They can thus become “navigators” and “translators” and truly empowering strategic decisions. 

Aaron R. An is M.S. candidate of integrated marketing communication at Medill School, Northwestern University, specializing in Marketing Analytics and Brand Strategy tracks. Aaron graduated from Peking University with bachelor degrees of Economics and International Relations. Prior to IMC, Aaron worked in Caterpillar’s China strategic development department, Ogilvy PR’s China Outbound Strategy Practice, and Northhead Consulting, helping Chinese companies to form their outbound marketing strategies and US companies to form the localization strategies. 

Any questions or comments? Contact him on Twitter at @Aaronarpku

Big Data + Prescriptive Analysis = Next Powerful Insights!

There are many conversations about which considerations is the most important in realizing business value through big data.  Undoubtedly, understanding big data has become critical to executives and marketers. As a graduate student in Northwestern's Medill IMC program, I have found two articles which shows the value of Big Data, and the next powerful tool --- Prescriptive Analysis.

In a recent "March Madness tournament" called “The IBM Smart Sixteen Big Data Challenge”, the company invites industry professions to decide which aspect are their top priority when leveraging the power of data. Not surprisingly, “predicting customer behaviors” in marketing activities wins the championship (See Picture 1). According to Gartner, more than 30% of predictive analytics projects by 2015 will provide insights based on enormous structured and unstructured data to company. It is no doubt that digging the insights and forecasting the future through big data are helpful in planning marketing strategies, but what's the best course of action once you get a prediction? 

Picture 1 

(Source：IBM）

An emerging technology called prescriptive analysis might help marketers one step further. It goes beyond descriptive and predictive analytics to a next phase that “recommends specific actions and shows the likely outcome of each decision”, according to Jeff Bertolucci's article Prescriptive Analytics and Big Data: Next Big Thing? This new automated technology is made possible thanks to the increasing speed and memory size of computers. Knowledge from big data, business rules, mathematical models and machine learning are combined to analyze potential decisions, their interactions, the factors and constraints on each decision and the ultimate outcome of each scenario to arrive at an optimal solution. No wonder IBM claims it the next big thing and the last step in marketing analytics.

Think about this, by predictive analytics, we marketers can understand the drivers behind customer buying patterns and anticipate the products that fit customers’ need; and then, prescriptive analytics helps us design scheduling, production, inventory, promotion deals and so on so forth in every marketing step to deliver real time response to customers in the most optimized way. The combination of predictive and prescriptive analytics provides a way to achieve both efficiency and effectiveness for marketers in the ever-changing marketplace.

As a data-driven and consumer-centric marketer, catching up with this big data and prescriptive analysis trend is becoming more and more important. From my analysis of these two articles, combined with the advices from 5 Pillars of Prescriptive Analytics written by Atanu Basu, the CEO of Prescriptive Software Developer Ayata,  there are three action items that you should know to best leverage this next big thing:

1. Change Perception and Explore Actively - With the presence of big data and advanced analytics, marketing can be a science. Take advantage of it and optimize your marketing result. If you’re waiting until you have a complete and fully defined data warehouse in place before you explore advanced analytics, then you might be waiting too long. Take the initiative to explore advanced technology in the industry
2. Integrate Predictions and Prescriptions - Predictions and Prescriptions must work synergistically for prescriptive analytics to deliver on its promise. The symbiotic integration of predictions and prescriptions is the key to widespread adoption and inherent value of prescriptive analytics. 
3. Create Hybrid Data - A transformative prescriptive analytics technology should process hybrid data, which includes structured data such as number and categories, as well as unstructured data such as image and video. While most of the marketers rely on just available structured data, incorporating hybrid data is very important in doing prescriptive analytics.

By applying one or all of these tactics, marketers can best leverage the power from this cutting-edge technology with big data and prescriptive analysis. This in turn, will provide powerful insights that supports an ongoing business success. 


Yini is a graduate student of Integrated Marketing Communications in Northwestern University, focusing on market research and analytics. She is a disciple of the power of communications and also a believer in the magic of data. She can be reached via @ninikitten1

What does Predictive Analytics mean for marketers?

If you are a marketer in the current day and age, then your world has never been more interesting. As a student of Integrated Marketing Communications at Northwestern University, I understand that predictive analytics is not just changing the game, but redefining the spaces around us. We have come a long way from a time when early man tried sacrificing goats to please the rain gods to looking at the weather app to see if we need to carry an umbrella.

With the market place evolving at the speed of sound it is becoming more and more difficult to determine what the next minute is going to look like, and that just means one thing- marketing needs to evolve from backward looking to forward looking. It is not enough that we know what trends have looked like in the past, it is important that analytics is used to understand what is happening NOW, and how that affects what tomorrow would look like.

Mobile phones are the single biggest driving force of this change. Never before have consumers carried a device where you can constantly reach them at any given time. And while most marketers haven’t really figured out how to monetize this trend, mobile is big and is here to stay. Digital, is how the world communicates, and with that comes the opportunity for marketers to look at Big Data.

To quote Joseph Dennis Kelly, who talks about the importance of Predictive Analytics:

“Every day, more people are using mobile devices. And a larger number are relying on digital communications to carry out their daily lives. To help organizations leverage this Big Data to perform better and improve services to customers, technology innovators have developed a new generation of software analytics. These advanced analytics solutions provide leaders with the insight to gauge the patterns driving business success.” 

The answer lies not in just Data, but Big data:

‘In running advanced analytics software, organizations can see more than a simple trend analysis. They are looking beyond the structured data stored in relational and SQL databases. And they are tapping into open source-and-NoSQL data (aka Big Data) to mine exabytes of real-time tweets, status updates and blog posts. It’s from this unstructured data that decision makers can distill the current and evolving patterns of thought and behavior affecting their customers, stakeholders, suppliers, partners, employees, and competitors.’

Marketers, suit up!

So what happens when you understand Big Data? Your favorite online dress store always manages to carry what you love, because it is tracking style trends everywhere. Your corner coffee store has come up with the yummiest new flavors for the season cause it understands what it is that would make them click with their consumers. Using a combination of data on market trends, current news and other factors, retailers can now figure out where the world is heading, as opposed to where the world was. All you have to do is listen to your consumers with their most advanced analytics tools.

So what should you do?

1. Respond to changes: It is important marketers understand that it is as important to respond to these changes in pattern as it is to understand them. A late response to a tweet could mean an angry customer venting out his frustration to 500 other people; an immediate response could mean him praising you to the same 500 people.

2. Look for patters: You will be amazed at the kind of insight that can be gained from looking at the data. The trick is to look for patterns and identify gaps, trends and opportunities.

3. Look at the bigger picture: Decision makers need to look at the bigger picture, to be able to use information proactively. Only then can they turn data patterns into actionable information, and actionable information into strategies, tactics, and processes that lead to disruptive innovation.

You see what is happening here? This new digital space has redefined how the world lives. Twitter and Facebook are the new ‘feedback forms’- and so much more effective than the suggestion box at your counter could ever be. Your customers are talking everywhere- all you have to do is listen.

Anchit Dhawan is a student of Integrated Marketing Communications at Northwestern University. Her concentrations include Direct and Interactive Marketing and Marketing Analytics. She is passionate about brands, ideas, start-ups and the world of technology. You can reach her with questions or comments on Twitter @anchitdhawan