Welcome to Brilliance@Work, a series of profiles about stellar collaboration professionals and their best practices at work. This month, we’re continuing to feature Marketing Analytics & Data Science experts.
As a preview to her presentation “Your Data Doesn’t Always Tell the Story,” Nancy shares insights on why the real goal of communication is human connection.
Peggy L. Bieniek, ABC: What is business storytelling? Nancy Kazdan: People tell business stories to communicate and connect with employees, customers, colleagues, partners, suppliers and the media. Business stories differ from regular stories, in that you tell them with an objective, goal or desired outcome in mind, rather than for entertainment.
When you tell a story well, it can create an intense, personal connection between your audience and your message. Effective stories can change our opinions, they can inspire us to achieve goals that we didn’t think were possible, and they can show us how we can change things for the better.
PB: How can big data benefit from storytelling? NK: We are often the bridge between the data and the audience of decision makers we want to encourage to take the desired action. Effective storytelling in business must be focused on tailoring the story to the audience and choosing the right data visualizations to complement the narrative.
PB: How is behavioral marketing and storytelling critical in your work? NK: I’ve seen a lot of products yield mediocre results. Integers do not evoke emotion, but their interpretation into a powerful story can. We wouldn’t be able to get stellar results without stories that touch emotions that create action.
PB: What role does communication and building relationships play in supporting the data story? NK: If you want your data to affect change, then you need to have a relationship with your audience and communicate the way they understand.
PB: What will people gain from attending your conference presentation? NK: Harnessing the power of data doesn’t have to be boring. In this session, I’ve incorporated my knowledge and experience of big data and talent for storytelling. I’ll help you take away your own tools for effective storytelling.
Want to hear more from Nancy? Join us at the Marketing Analytics & Data Science Conference. Learn, network and share best practices with the most influential leaders in data science and analytics. Stay connected at #MADSCONF.
As a preview to his presentation “CSF: The Most Important Metric You’ve Never Used,” Don shares insights on the importance of CSF for business success.
Peggy L. Bieniek, ABC: Why is CSF the most important metric for the success of your business? Don Sexton: CSF (Customer Surplus Factor) is the ratio of a customer’s willingness to pay to the price being charged. The higher that ratio, the more likely the customer is to make the purchase.
CSF therefore summarizes most other marketing metrics such as awareness, knowledge, preference – even likelihood to recommend – and so is a very efficient approach to understanding a company’s position in the marketplace.
In many cases, we have found that CSF results in highly accurate predictions of financial outcomes such as revenue and contribution.
In addition, CSF is a metric that leads to actions. Willingness to pay can be managed with many marketing tools. CSF can indicate which marketing actions will lead to the most attractive financial results.
PB: What are the key differences between CSF and KPI? DS: A KPI is a Key Performance Indicator. Managers use KPIs to monitor their ongoing business situation. KPIs should consist of both lagging indicators (which show where you’ve been) and leading indicators (which show where you’re heading). CSF is a very powerful leading indicator of what financial outcomes you can expect given your strategy. CSF should most certainly be included among the KPIs that a marketing manager regularly reviews.
PB: How is CSF typically used? DS: CSF is used to provide insights for strategy in both short-run and long-run competitive situations. Traditional techniques such as marketing mix modeling primarily focus on resource allocation over the next year. CSF enables managers to not only plan for the short run, but also for the long run. CSF allows managers to understand the position of their product and service in the customer’s mind and suggests what must be done over time to improve their competitive position.
PB: How should CSF be used for successful business outcomes? DS: CSF should be monitored frequently – quarterly or semi-annually – to provide managers with steering control, the ability to set clear objectives and to forecast results of marketing actions before they are implemented.
PB: What will people gain from attending your conference presentation? DS: Managers attending this session will understand an innovative way to analyze their competitive situation, an approach well-grounded in marketing and economics and proven to work by several companies, which clearly links marketing actions to financial outcomes and which is relatively easy to put into practice.
As a preview to her presentation “Deciphering Generations X, Y, and V: How to Understand Next Generations and their Trends for Guaranteed Reach,” Jane shares insights on the importance of understanding generations for business success.
Peggy L. Bieniek, ABC: What are some key strategies for marketing to different generations? Jane Buckingham: There are a few ways to approach this. If your brand is going for a particular age and niche, then you want a generational approach, in which case you want to appeal to the emotional and psychographic needs of that particular generation. Try to understand what sets them apart from the previous generations.
Is it a tone, is it a location, is it inspiring the brand fanatics and hypertailoring (appealing to younger generations), or redefining happiness and success (more Gen X), or helping to inspire and enlighten (for Gen Y)?
On the other side, if you are looking to cut across generations, you may be looking to talk to a mindset over the market. Looking to understand what your particular group of people thinks, what is it about your brand that will be appealing to someone no matter what their age? Are they fitness enthusiasts looking for purpose? Are they looking for comfort? Are they looking for safety? Some of these core values are cross generational and may be appealing no matter what the age.
PB: How do you address the challenge of everyone agreeing on a standard of when generations begin and end? JB: This has gotten a lot trickier since Douglas Coupland wrote Generation X in 1991, and we started segmenting generations by 15 year periods versus 20 year periods. It became much less clear where and when a generation starts and stops.
Technically, generations are really defined by the factors that affect them as they grow up, and the cultural shifts in the world. But, how something will affect someone who is two at the time a generation is coined is going to be much different than how it affects someone who is 18 at the time it is coined, so usually someone who is right in the middle feels most ‘like’ that generation.
So, even if people are off by a year or two, it doesn’t really matter. The bigger challenge is that often marketers are talking about a marketing segment by a “media” age that can be purchased (like 18-34 or 35-54), and that will cut across two generations, but they don’t want to really think about that so they just sort of move the dates to accommodate the media buy.
They will say Millennials are 18-34, when really right now 18-34 would include Millennials and Gen V. In fact, many people seem to think that Millennials are still teenagers because they’ve been the emerging generation for so long, when actually the youngest of them are about 20.
PB: How is data the greatest equalizer in marketing? JB: Data helps to try to “prove” things. The idea is that big data can help quantify what we speculate about and provide greater insight into what we are thinking, doing and how we are behaving.
I LOVE data. And I love that we now have more access to more data than ever. It allows retailers to see how consumers shop, and how price and value works versus brand.
One of the most exciting things about data for marketers and consumers is that advertising is going to start feeling less and less like advertising. Thanks to increasingly sophisticated analytics and predictive modeling, both big and small brands can tap information that will allow them to connect consumers to products and services that are truly relevant and interesting to them.
That said, data isn’t a silver bullet, and can’t and shouldn’t be seen as one. Numbers only tell you part of the story and you need to interpret them carefully. It’s just as important to talk to your consumer to understand the why behind the numbers and any subtleties that the numbers might not reveal.
PB: What will people gain from attending your conference presentation? JB: I’m hoping that attendees will better understand the differences between the generations – as marketers, employers, parents, siblings – so that they can better relate to them, market to them and listen to them.
In addition, I’ll be talking about the macro trends that will be affecting these generations for the next several years as well as some fun trends that are happening now.
Overall, I want the audience to feel like they are better versed in who their next consumers are and who they will be.
As a preview to her presentation “Playing Detective with Data,” Camille shares insights on how the questions you ask while analyzing the data are critical for successful business outcomes.
Peggy L. Bieniek, ABC: How should organizations innovate and improve the way they use data in their business operations?
Camille P. Schuster, Ph.D.: All organizations have a tremendous amount of data available to them including consumer information, heavy users vs. light users, average purchase size vs. large purchase, number of purchases per week or month or quarter, feedback forms, on time payment data, social media data, contact information, and more.
How to use it depends upon the business question. Analyzing data for the sake of analyzing is not particularly helpful. Start with the business question, analyze the data you have, determine whether it is cost effective to obtain other data you would like to have, and move forward.
PB: What is the role of data in data analysis? CS: Data is the raw material that can be used to solve problems. The data itself includes numbers or words (if analyzing text) to be transformed into a standard format for comparison and manipulation purposes. When the comparisons and manipulations are complete, then you have information.
The most valuable skill of all is the ability to interpret what the information means and how it can be used to solve a problem. All three steps are critical for success and require different skill sets.
Preparing and cleaning the data to make it usable for use in specific software requires one set of skills. Knowing which tools are needed to perform what calculations requires a different set of skills. Being able to understand the data and what calculations were formed to be able to interpret the results and make recommendations to solve business problems requires another set of skills. Understanding each of the steps and the interaction between the steps is essential.
PB: What are the attributes of a successful system for reliable data analysis? CS: The attributes of a successful system include:
Knowing the questions that need to be answered
Determining whether you have data that can answer those questions or can get that data
Having people with the skills to perform the necessary steps
Having the appropriate hardware and software for the analyses you need to do
PB: What will people gain from attending your conference presentation? CS: My presentation will demonstrate how the questions you ask influence your results and understanding of the situation which, in turn, will change your business recommendations.
Want to hear more from Camille? Join us at the Marketing Analytics & Data Science Conference. Learn, network and share best practices with the most influential leaders in data science and analytics. Stay connected at #MADSCONF.
As a preview to his presentation “Mo’ Problems, Mo’ Money: Customer Service Matters More than You Think,” Wayne shared insights on how social media is transforming the way consumers interact with brands.
Peggy L. Bieniek, ABC: How does Twitter help to shape the future of online social media? Wayne Huang: As someone with a background in both engineering and in social science, what I find most interesting about Twitter is how it has completely upended the way we communicate.
We’re used to jumping through hoops to talk to a human being at a company. It’s nearly impossible these days to find the phone number of the company you’re trying to reach. But, what strikes me most about Twitter is that brands actually proactively engage in conversations with customers, and not hide behind a maze of automated phone menus.
One of my most memorable Twitter experiences was when I once tweeted a question to Virgin Atlantic, and they responded to my tweet in less than three seconds. That was an incredible interaction that I’ll always remember. It’s a leveling of the playing field between big companies and consumers that wouldn’t have happened without social media.
PB: How does Twitter data help tell a marketing story? WH: Twitter is an incredibly rich source of data. Every day, close to half a billion tweets are sent. Search for any topic, and I guarantee you’ll find someone tweeting about it.
For brands, Twitter is like the biggest permanent focus group in the world, free for you to search to find what your customers really think about you. For example, John Legere, the CEO of T-Mobile, famously spends a ton of time on Twitter searching for what his customers love and hate about T-Mobile. He also responds directly to tweets from users, who were so shocked that he tweeted them that they’re now clamoring to switch to T-Mobile.
PB: How can brands do better on Twitter? WH: Companies should see Twitter as the public, human face of their brands. By human, I mean imagine that your brand is a human being, and imagine your social media conversations as real human conversations you’re having with other human beings.
For example, no one in real life actually wants to be friends with someone who just keeps blabbing on about how he or she is the greatest person in the world. Similarly, your Twitter profile shouldn’t be a one-way conversation where you just post links to corporate press releases or generic product shots.
Instead, engage with your customers. Post advice and tips. Answer their questions and respond to their tweets as quickly as possible. Retweet your users’ content, such as when they post a beautiful photo. Like your users’ content, and thank them when they give you feedback. That gives your users the feeling of a “win.”
It’s basic social reciprocity— just as we need to give and take in our daily relationships, so should brands on Twitter.
PB: What will people gain from attending your conference presentation? WH: Businesses often struggle to understand what their customers are really thinking. In my presentation, I’ll talk about the pitfalls of relying on self-reported surveys when conducting customer research.
I’ll then showcase a novel experiment we ran on Twitter where we tested how a good (or bad) customer service experience from a brand affects the customer’s future decision-making process.
In that experiment, we found thousands of users who had a customer service interaction with an airline on Twitter and how we quantified— in dollar terms— how the customer changed their behavior after those positive interactions. For example, after a good experience, is that customer more willing to fly the airline again? Or will they just default for the cheapest carrier?
We’ll also discuss some interesting findings from recent psychology experiments that businesses should adopt if they want to impress their customers.
Want to hear more from Wayne? Join us at the Marketing Analytics & Data Science Conference. Learn, network and share best practices with the most influential leaders in data science and analytics. Stay connected at #MADSCONF.
Welcome to Brilliance@Work, a series of profiles about stellar collaboration professionals and their best practices at work. Throughout May, we’ll feature Marketing Analytics & Data Science experts.
Danfeng (Dan) Li is a Director at the Alibaba Group, where he oversees the web/mobile analytics team that provides business intelligence and predictive analysis solutions for web pages, mobile apps and smart devices. He’s also a presenter at the Marketing Analytics & Data Science Conference on June 8-10, 2016 in San Francisco, California.
As a preview to his presentation “Get More Whales out of Your Mobile Game Players,” Dan shared some insights on Alibaba’s strategy in the mobile marketplace.
Peggy L. Bieniek, ABC: How did Alibaba help grow the global online and mobile marketplace? Dan Li: In 2003, Alibaba created the Taobao platform to help micro and small businesses sell their products online for free. For the first five years, Taobao did not generate any revenue and did not become profitable until 2010. During this time frame, it grew from 0 to more than 95% of the Chinese online marketplace.
We truly believe that we can only benefit by helping small businesses. We were very patient and applied strategies that are applicable to China businesses, like a customer care system called WangWang, and Alipay to address trust and credit challenges. Of course, Alipay became a very successful story in itself.
A couple of years ago, we noticed the trend that people were moving from shopping online to shopping on mobile. We established many strategies to encourage people shopping on mobile. Mobile sales quickly surpassed online sales. We were just a small step ahead of our customers and our competitors.
Now being the largest marketplace online and on mobile, we continue our innovation with the help of our 10 million merchants and billions of consumers through personalization, streamlining logistics, and experimenting with a C2B business model.
PB: What are key strategies for successful mobile game monetization? DL: The key is to find the potential “whale” players who are willing to pay for your game. We built machine learning models to help identify those players. We target our sales and marketing efforts to them. The key for us to do this successfully is we know a lot about new customers through monitoring user behavior across games and apps.
PB: How will mobile game monetization change in the future? DL: I can only speak for the Chinese market. Right now, venture capital (VC ) money is not that easy to get, so monetization becomes more and more important in the near future. Also, in the Chinese market, the game industry is still very simple. We need a more data-driven approach to make the process more efficient.
PB: What will people gain from attending your presentation at the conference? DL: I’ll share some of our efforts to help the game industry in China and some statistics and trends in the Chinese mobile gaming industry.
As a preview to his presentation “Taking Control of Control Groups,” Eric shared some insights on implementing successful measurement programs.
Peggy L. Bieniek, ABC: What are the critical elements of a successful measurement program? Eric Callahan: It may sound cliché, but the most important thing is support from your executive leaders. There are a ton of moving parts involved in doing measurement correctly (set-up, design, execution, analytics, etc.) so you need a strong mandate that all parties align with the initiative. If even one cog in the wheel is not moving in the same direction as the others, the whole operation is compromised.
PB: Does measurement best practice vary from industry to industry? EC: Different industries may have different marketing tactics and goals, but the fundamental principles of good measurement will not change. However, there may be minor differences in how these principles are put into practice.
PB: What are the consequences of not putting a rigorous measurement strategy in place? EC: The strength of a business’ measurement strategy directly correlates to the quality of insight that it generates from testing. If a company does not institute a solid measurement strategy, it will pay the opportunity cost of insights left on the table. In a worst case scenario, it may come to false conclusions and take harmful strategic actions.
PB: What will people gain from attending your presentation at the conference? EC: Attendees will learn about different types of control groups, what each type is used for, and how to design a program so these are all working together to create a comprehensive measurement suite. We will also cover how to address common implementation challenges.
Want to hear more from Eric Callahan? Join us at the Marketing Analytics & Data Science Conference. Learn, network and share best practices with the most influential leaders in data science and analytics. Stay connected at #MADSCONF.
As a preview to her presentation “Industrial Internet: Building the Digital Industrial World with Data Science,” Beena shared how GE is leading the Industrial Internet.
Peggy L. Bieniek, ABC: How is GE helping to shape the future of the Industrial Internet? Beena Ammanath: The Industrial Internet presents incredible opportunities to connect the world of really important things, and ongoing digital transformation is changing all industries. With 50 billion machines expected to come online by 2020, the Industrial Internet’s impact on GE, our customers and the world is tremendous.
In leading the Industrial Internet, GE has transformed its business by building and deploying Predix, an Industrial IoT platform and opening the platform to non-GE assets. Predix – the world’s first and only cloud platform built exclusively for industry – is the foundation for our Industrial Internet offerings. It offers the strength, scale and security for industrial customers to use with industrial data.
We are actively deploying Predix across our business, IT, and manufacturing operations. We’re also working with many customers to use Predix to secure and monitor the approximately $1 trillion GE industrial assets deployed worldwide.
PB: What were some of the benefits GE realized from the Industrial Internet implementation? BA: Big data and data analytics have brought about a radical change in how our operations do business. We utilize sensors and data processing in our machines to enable predictive maintenance and repair breakdowns even before they occur.
We’ve already seen a reduction of 10-20% in unplanned downtime in our own factories, as well as improved cycle time, improved customer service and reduced costs in the facility.
PB: What makes GE’s Industrial Internet offerings unique? BA: Unlike general-purpose platforms, Predix is optimized to support industrial use cases.
Other platforms and companies lack GE’s deep domain knowledge of assets to enable anything more than general predictive analytics.
Predix was designed specifically to handle the velocity, variety and volume of industrial data from millions of machines, where other platforms remain focused on enterprise data.
Only Predix delivers the end-to-end security that satisfies strict data governance, privacy and integrity requirements – that support over 60 regulatory frameworks worldwide. Other platforms are slow and costly. Predix offers developers the tools to automate the “coding to deployment” cycle, where they may spend up to 80% of their development time integrating with other systems.
PB: What will people gain from attending your keynote at the Data Science conference? BA: Attendees will gain a clear understanding of the Industrial Internet, the characteristics of Industrial Big Data and the kind of business outcomes we can achieve in the Industrial space leveraging Data Science.
Want to hear more from Beena Ammanath? Join us at the Marketing Analytics & Data Science Conference. Learn, network and share best practices with the most influential leaders in data science and analytics. Stay connected at #MADSCONF.
Welcome to Brilliance@Work, a series of profiles about stellar collaboration professionals and their best practices at work. Throughout May, we’ll feature Marketing Analytics & Data Science experts.
Earl Taylor is Chief Marketing Officer for the Marketing Science Institute (MSI) in Cambridge, Massachusetts. Founded in 1961, MSI is a nonprofit, membership-based organization dedicated to bridging the gap between academic marketing theory and business practice.
Read on to learn how Earl and MSI use quantitative and qualitative data to help their members stay on the forefront of marketing thought and practice.
Peggy L. Bieniek, ABC: How does MSI help to shape the future of marketing science? Earl Taylor: MSI’s corporate sponsors represent a cross-section of the U.S. business community. We continuously solicit input from our trustees and others who represent our corporate members about their most pressing marketing challenges.
Every two years, we ask our corporate trustees and leading marketing academics to prioritize these topics to guide our funding of academic research and the focus of our events. Results are summarized in our recently released 2016-2018 Research Priorities, which can be viewed and downloaded free from our website at http://www.msi.org/research/2016-2018-research-priorities//.
PB: How do you use big data to measure brand performance? ET: Marketing academics and MSI corporate sponsors are using big data in a variety of ways to assess marketing effectiveness and brand performance. Increasingly, datasets that link exposure to advertising and other marketing activities with outcomes such as sales and profitability allows managers to determine the exact effects of each element of the overall marketing mix and to allocate resources more efficiently and effectively.
Academic research supported by MSI has demonstrated that properly interpreted and weighted data from social media can be closely correlated with traditional brand health tracking metrics. In fact, social media can yield leading indicators of brand health, allowing managers to anticipate and respond to emerging trends (positive or negative).
MSI helped found and continues to support the Marketing Accountability Standards Board (www.themasb.org), which is dedicated to vetting and promoting metrics agreed to by both marketing and finance that can reliably demonstrate the value created by marketing and branding.
PB: How can data and analytics help tell a marketing story? ET: Most traditional quantitative market research relies on theories of consumer behavior that yield hypotheses that can be tested against empirical findings. The advantage of this approach is that results can be incorporated into a coherent framework for interpretation and application, yielding a cumulative body of knowledge over time.
With the advent of big data analytics using machine learning and other techniques, we can now efficiently discover patterns in data that we might not otherwise have noticed, but which can be interpreted theoretically and applied, thus advancing marketing science and practice.
Regardless of how they are obtained, insights are best shared the way humans have always communicated— through stories, personas and the like that allow managers to understand, assimilate and extrapolate from them as new situations arise.
PB: What will people gain from attending your conference presentation? ET: When data does not readily fit existing quantitative formats and analytics, it is often referred to as “unstructured.” In fact, datasets taken from social media, online review sites and the like are highly structured! Whether in real-time or asynchronously, exchanges in social media are variants of the structures that inform ordinary conversation where sequencing and context largely determine what a given contribution means to others engaged in the dialogue.
While certain insights can be derived from techniques that extract words or phrases and re-assemble them as word clouds, in many cases preserving sequential structure is critical to understanding what consumers are saying and why.
Drawing on sociological research, I will make the case that conversational analysis offers a distinct alternative to purely inductive big data analyses of social media. Importantly, findings on how information is conveyed in stories, jokes and other forms of ordinary conversation can also help us better communicate insights from all forms of quantitative and qualitative analyses.