Big data is a blanket term for any 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. And for email marketers, data is mission critical.
With the right set of data, you could send better targeted and real-time emails to your subscribers providing them best in class inbox experience.And, if you are up to utilize big data effectively, you ought to understand the characteristics of big data!
The quantity of data that is generated is very important in this context.
The term ‘velocity’ in the context refers to the speed of generation of data or how fast the data is generated and processed.
This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.
The quality of the data being captured can vary greatly. Accuracy of analysis depends on the veracity of the source data.
Data management can become a very complex process, especially when large volumes of data comes from multiple sources. This situation, is therefore, termed as the ‘complexity’ of Big Data.
What happens once you fill hot air in the balloon, it rises of course! And, now that you are through with
Bernard Marr’s 5 aspects of Big Data, it’s time for you to rise, transform data sets and embark on a new journey..!
A heap of data is of no use. Big data is useful only when it is properly collected, assessed and evaluated over the period of time so that it can be transformed into actionable data sets.
Well, you might have a question here!
What about data from multiple sources or channels?
Data comes from a variety of sources like social media, CRM, analytics, point-of-purchase, web interaction, etc. So what should be done to manage the multi-channel data stores in the disparate systems?
Like a hot air balloon, you have the data upright and you are ready to rise, but you need to know how to drive! How if you make the journey blend with some insightful models of big data?
Clustering tells a story about who your consumer is by grouping similar customers together. With clustering you let the algorithms, rather than the marketers, create customer segments. Algorithms are able to segment customers based on many more variables than a human being ever could. Clustering is not the end all and be all of data modelling. Once you have the customers grouped into “like�? behavioral groups, you then can look deeper at the data to determine how good a customer/prospect they truly are, based on numerous signals.
Propensity models make predictions about a customer’s future behavior. With propensity models you can anticipate a customer’s possible future behavior. The certainty of action however is a prediction and you have to gauge the degree of certainty. You could use this to analyze your subscribers’ propensity to engage, propensity to unsubscribe, propensity to buy in a specific product or service category, churn, etc.
These recommendation models were made famous by Amazon with their “customer who liked this product, also liked …�? suggestions. There are different types of recommendations. You could use the same for upselling or cross selling products in your order confirmation or transactional emails which can have a high open and engagement rate well above promotional communication.
For all of us who are email marketers, we need specific email data types for better targeted campaigns!
So, which are those? CTOR, Purchase Histories and Browsing Behavior, may be?
Basic email interaction data shows where the customer has clicked within the email, open rates, opened links, clicks, customer conversions, and related metrics.
Access to a recipients’ web interaction data can help marketers gain an
in-depth understanding of how the customer is browsing a website.
Abandoned shopping carts and completed applications for instance, will show what the consumer is in the market for, thus filling the blanks for what kinds of email campaigns will prove most compelling.
Data from user profiles, such as location, age and gender, may not be as reliable as the users’ most recent email or web interactions but is still valuable in baseline targeting. Also, do consider data obtained from third party sources. That data can give you insight into consumer behavior when they are not engaged with your brand or information that has not been disclosed that can be used for insights.
Past purchase data can be a valuable predictor of a consumer’s next move. By looking at what the subscriber has purchased in the past and what they are using now, email campaigns can be customized to suggest a personalized next step. For example, if the customer just bought a new mobile phone, knowing specific specials for phone cases and screen protectors can help direct the next purchase.
Read up on the latest articles and perspectives on the use of Big Data.
Test the data at each stage of refinement and build a comprehensive database.
Do not set long term goals based on existing data as with time, you will require to update to
new data types based on the third party information and customer preferences.
Test the effectiveness of data on small samples before building an entire program based on the data.
Understand the data backup mechanism and how will you manage removing
or replacing obsolete data with new data and information.
While using data modeling for your email campaigns, integrate separate data sources with conformed dimensions
that hold together separate data sources and allow them to be combined in a single analysis.
Maintain privacy as an important aspect of big data governance. Also, ensure the systems are
having robust mechanisms to prevent data theft.
Qualify existing and required tools, email systems and architecture that will support the big data lifecycle being managed.
Do not overuse the data which is available to you, but not explicitly shared by customers.
It might create permission related issues or even breaching the privacy.
Measure results, operational as well as business value ROI. Use control groups to get the real effectiveness.
A December 2013 survey of digital shoppers conducted by Harris Interactive found that 70% were even willing to disclose personal information in order to get emails that were more relevant to their buying situation.
So when you have more people ready to ride your hot air balloon,
why not utilize the space in your balloon and make the joy ride more accessible?
And, we can't thank Ryan Phelan enough for all the help he has extended to us with the infographic content with his views and thoughts about Big Data.
Ryan Phelan has over 15 years of email and digital marketing experience most recently with Acxiom, BlueHornet, Sears and Responsys. Ryan is a respected thought leader and nationally distinguished speaker on subjects relating to using complex data to drive effective strategies in email marketing, social and mobile. He currently resides in the San Francisco Bay area.
Uplers design and code flawless emails, newsletter templates, landing pages, and digital assets such as banners.In addition, we also offer email automation and campaign management services. To know more, email us at contact@uplers.com or visit email.uplers.com
