Tuesday, January 21, 2014

What are the most effective ways to use "marketing data?"


Back to the Harvard Business Review article on how to assess potential marketing professionals, and they poised the question on how to describe and best use "marketing data."
The first and often foremost “marketing data” points discussed in business are anecdotal in nature. These are based on the experiences of executives in the business, and while valuable they may be unreliable and not be truly representative of a situation, especially in industries undergoing rapid evolution based on the impact of technology or strong competitive threats.
Focus Groups - better done now by Video Chat.
The next type of marketing data is more formal in nature, being qualitative or quantitative research. Focus groups and one-on-one interviews are the most popular types of qualitative research. This type of research is now available from online providers like Video Chat Network, who make them really fast and affordable compared to traveling and sitting behind the one-way mirror at central research facilities. 
Web-based research (like Survey Monkey) or  phone surveys are popular quantitative research methods that can gauge things like customer satisfaction levels or attitude / awareness / usage data. The advantage here is statistically reliable data that can help develop projections and be bench marked over time.
HDTV Purchase Intent Visualization.
These surveys can paint the picture of a target audience from a demographic, geographic or attitudinal / behavioral standpoint. They can also measure product awareness / purchase intent and usage patterns of the marketer's product / service and the competition.
Then there is the emerging area of “big data” an increasingly used term that describes the collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. It can range from a few dozen terabytes to many petabytes worth of data. Gartner Analyst Doug Laney defined data growth challenges and opportunities as being three-dimensional: Increase in volume (amount of data), velocity (speed of data in and out) and variety (range of data types and sources). 
Or simply as the updated Gartner definition states big data is Three-V focused: High Volume / High Velocity / High Variety that now requires new forms of processing to enable enhanced decision making, insight discovery and process optimization.
The increase in the sheer amount of data storage capacity over the last twenty years has been both exponential in growth and simply mind-blowing.
Much of this marketing related “big data’” is being generated by advancements in technology like smart phones and changing consumer behaviors like online shopping. In both cases actions leave “electronic finger prints” or data points that can be collected and analyzed for cause and effect.
Then there is the whole world of social media, where companies like Facebook and Linked In track online conversations for key words and posting subject matter and then serve up relevant ad content based on projected subject matter relevance and interest. 
In the end, no matter where marketing data comes from, it should be analyzed and used to inform both strategic marketing plans and tactical initiatives that can then be tracked / measured to basically see what works and what doesn’t relative to business objectives. 
All this can help optimize marketing spend and turn marketing from an expense into a projectable investment with ROI hurdle rates. It puts marketing on a more objective footing with finance and senior management types, making it much less subjective in nature.

Thursday, January 16, 2014

The best way to approach decision making re: marketing planning and investments?


The Harvard Business Review blog recently asked this question relative to assessing a CMO candidate’s technology and analytical “IQ” in addition to marketing savvy.
Decision making in an ideal world is based on facts / data points and insights aligned with clearly stated and measurable business objectives: Revenue targets, share of market gains, and new product / service launches come to mind.
A blinding-glimpse-of-the-obvious is to avoid marketing initiatives in support of an inferior products or bad idea.  I’ve seen way too many fancy graphs and marketing hypotheses in support of a selling proposition that made no sense at all, but no one wanted to speak up and just say “I really don’t get this.”
"What's fun about a building that turns into a robot?"
Well-defined business and marketing objectives are measurable and quantifiable. Duh. But how many times have we sat in meetings when senior management said “we simply need to grow our market share and top-line revenues…”
Sound objectives should have a time frame, often stated by quarter or by year. They should also be attainable – in the context of your offering, sales / distribution / service organization and considering current and future competition. Globalization and fast emerging technologies have thrown a wrench in this area.
Avoid well intended graphs that make no sense.
Both objectives and investment should be measured on marketing metrics that makes sense: B2B situations might dictate cost-per-inquiry / cost-per-qualified lead / customer acquisition cost (CAC) and even calculations for estimated lifetime value so more profitable segments can be identified and targeted.
Well designed marketing dashboards that  attribute the above metrics to a marketing channel / promotional venue  can inform spending decisions in the context of optimization models – that direct spend to the most efficient and effective marketing initiatives, helping increase return on investment.
I learned an old axiom in the world of direct mail marketing that is more relevant than ever in today’s data-driven world. This is Test -> Learn -> Apply. Take the classic champion-challenger approach to as many marketing variables as possible: challenging convention, testing new offers, trying different targets using different ad media, promotional and engagement channels.
This leads to an environment of accountability, and the willingness and ability to try new things, and learn from them even if they fail. This will help reduce the opportunity cost of trying nothing new at all to avoid looking bad. This behavior is a paralysis that grips many large and small companies alike, and limits their ability to find a path to long-term profitable growth.

Saturday, January 11, 2014

What is the difference between metrics and analytics?


The HBR blog Network recently posed some interesting questions regarding the “technology and analytical IQ” of marketing professionals, among them was defining the difference between metrics and analytics.
It is more than simple semantics these days. Webster gives the following definitions:
Visual of Metrics
Metric: A standard of measure.
Analytics: The method of logical analysis – a careful study of something to learn about its parts, what they do, and how they relate to each other. An explanation of the nature and meaning of something.

Here are some thoughts on the matter:
Metrics are based on historic data points, tangible in nature and can come these days from business transactions and the vast output of geographic location / behavioral data from mobile devices and social media.
Analytic Data at Work: SGI  Heat Map of Angriest Tweeters.
Analytics are intangible; future- focused in an attempt to predict possible outcomes and  designed to provide insight that can support better business decisions and feed business scorecards.
Metrics gather information in reports – often from an accounting perspective.  Analytics use that information to ask relevant questions and feed finance-related decisions.
Metrics are transactional and of low value. Analytics can and should be used to inform strategic directions that have the ability differentiate an organization, driving profitable growth thus having a much higher value.
So the heat map of angriest tweeters (based on negative sentiments expressed) doesn't support any business decisions, but may tell me to steer clear of east Texas, Ohio and New England.
In the end the astute 21st Century marketer should be thinking along the lines of:
Metrics ->  Analytics ->  Insights ->  Better Informed Decisions ->   Optimized Outcomes.

Thursday, January 9, 2014

Making the Argument for Marketing as a Science.

So much data, so little time to make sense of it all.
Been reviewing the debate about marketing being more of an art than a science, sparked to a great degree by a recent Harvard Business Review blog post on "How to find, asses and hire the modern marketer."

I agree with their premise that marketing as a discipline has undergone a significant amount of change, mainly driven by emerging technologies and the analytic data it has created. They argue that modern marketers need to be adept at understanding data and analytic information beyond simple metrics.
The Scientific Method

The long and short of it is that modern marketers need to take a "test-learn-apply" approach to nearly everything they do. It doesn't really matter how much ad agencies, marketing executives and senior management like marketing programs / ad campaigns, but rather how well they engage their target audience and get them to do something aligned with the firm's marketing and business objectives.

And it is more about engaging in a mutually beneficial conversation through marketing rather than shouting at or simply entertaining people with advertising. The HBR blog post has some great "test" criteria for evaluating the skills of a "modern marketer" around knowledge and use of data and analytics to aid business decision making and gain learning from failed efforts. They provide some smart guidelines to help assess a marketing candidate's technology and analytical "IQ" in addition to their marketing savvy.

Ad Age just reported that more and more Chief Marketing Officers now have engineering backgrounds. The story cites the likes of Salesforce CMO Lynn Vojvodich and Jet Blue's SVP Marketing and Commercial Strategy Marty St. George, the latter holding an engineering degree from MIT. 

St. George commented "Today, in the world of big data and trying to find a way to turn this incredible volume of noise into insights and actions, I definitely find myself falling back on my engineering tricks." he went on to say "When an issue comes up, my first thought is: How much data can I gather to try to triangulate around what's happening? I think that's definitely consistent with an engineering mentality."

This trend calls for a significant re-education of "seasoned marketers" and I've found many of the local Meet-Ups in NYC a great place to gain best-practice knowledge around data / analytic thinking, including "Data Driven NYC" / "Hardwired NYC" / "NY Tech Meet Up" and fun groups like "NYC Data Wranglers."  Note than many of these events "sell out" fast - so be sure to plan ahead to get a seat.
The modern marketer - akin to winged Pegasus in power?

These Meet Ups draw an eclectic combination of data geeks, business people and entrepreneurs more than willing to talk about what they're working on and how they are defining this new data-driven world. It's great to then connect with them on Linked In and follow them on Twitter.

The best part of the HBR post is the description of the modern marketer - having a blend of creativity and reasoning talents, inquisitive, inventive and enthused by a culture that is advanced and agile. I am going to work hard on further developing all these skills / professional attributes in 2014.