The coming year will likely bring even more data-driven initiatives and action to your organization. Get prepared for another tsunami of data with these top terms for 2014.
Last click, or giving all the credit to the last touch point before purchase, was all the rage in the early days of online marketing, when branding and traditional marketing were all but declared dead. In recent years, however, it turns out that marketing across the full funnel and path to purchase does indeed still matter, and this is a challenge worth tackling.
With many tools and techniques reaching early levels of maturity, 2014 will be a year in which identifying and valuing all the touch points along the path to purchase will drive how you spend media dollars and what messages are presented to which audience segments at what time.
Learn how Google Analytics Multichannel Funnels and attribution modelling will allow you to reclaim control over your marketing spending: Attribution Modeling & Multi-Channel Funnels With Google Analytics
You’re likely about as sick of hearing this one as being told “this is the (now at least fourth consecutive) year of mobile.” One of the best definitions out there comes from Cardinal Path’s director of strategic intelligence, Stéphane Hamel: it’s just pretty much anything you can’t shove into Excel.
For the marketer of 2014, what we really mean is collecting and using all kinds of data – structured, unstructured, social, clickstream, customer, sales, etc. – to apply technologies and techniques that help us make better decisions at massive scale.
Learn more about Big Data and which areas you should focus on from Cardinal Path’s Stephane Hamel: Big Data – What It Means For The Digital Analyst.
Marketers have been collecting and studying customer data to understand value tiers, assign and predict lifetime value, and provide personalized and behavior or attribute-based messaging for decades.
What makes this so exciting now is that customers are leaving behind an unprecedented and ever-expanding data trail that gets us closer to the elusive 360-degree view of your customers — your best and worst, your most frequent, and even those you want to avoid. This is a very unique tranche of data and one you’ll want to cultivate and mine deeply.
Learn more about the new generation of digital analysis tools where speed is king and customer data is united: The New Generation Of Digital Analysis Tools
Data science is the difference between report-reading and actionable insight. Today’s data scientists are applying mathematical models, statistical techniques and leveraging the power of tools like R and SAS to analyze data in a way that will tell you a meaningful story about the subject you’re studying — whether that’s operations methodology, message testing or sales forecasting.
Good data scientists are and will continue to be in high demand, executing on marketing initiatives that include data mining and analysis to predictive analytics that define and refine recommendations and even programmatic actions in near real time.
Here are a few tips for you to check if you are in the report-reading or insight generation category: Why Your KPIs Are Meaningless & What To Do About It
If you’ve been on the receiving end of a 200 page PDF download of everything your analytics tool collects, then you’ll understand just how useless most reporting has become these days; receiving this kind of report is essentially the same as being told “here, you figure it out.”
Data visualization simply means the selection and presentation of data in an understandable way that provides meaningful insights that drive decisions. On the most basic scale, a pie chart or line graph is a form of data visualization, but today we’ve got everything from interactive motion charts and network trees to geographic overlays and heat maps right at our fingertips. That return on your data investment ultimately comes in the form of inviting and accessible visual representations of complex data that transform information into knowledge.
Learn more about how to create effective visualizations and apply them to your data: Creating Effective Data Visualization
Odds are good you’re already being asked to create an omni-channel plan, and with good reason. Today’s path to purchase crosses devices, media, locations and time. This evolution of multi-channel marketing means it’s not just good enough to make sure you’re there when prospects are looking for you on mass media, searches, social, mobile and more.
Now, IT and marketing coordination is vital to stitch the data together to understand the complex combinations of interactions customers and prospects are creating. Think of the mobile app a user inside a physical store might use to find what they’re looking for and to search for a coupon accessible only to customers that have socially checked in — something they learned about from a video ad. And that’s a simple example!
Personally Identifiable Information (PII)
PII comes down to any data point that can be tied back to a single person. Think email address, credit card number, address, etc.
There are legal, ethical, and practical aspects playing out in this debate, and it’s important to remember that some of our best analysis and actions are based upon aggregated, anonymized data sets. For example, if I can identify a highly profitable user segment that I want to target, I can target anonymous cookies of these and others like them without ever needing to know their name or e-mail address. Examples of making a connection with PII are sending direct mail, doing some telemarketing, or even blasting a personalized email.
Hopefully these things are on your mind as you end 2013, and if you’re going to the kinds of holiday parties I am this season, you’ll be well prepared for the discussion!