Data Driven: 2 of the Latest ‘Innovations’ Driving Data
Big Data arrived a few years ago and represented an upheaval within something established, namely the scale of data available on any given marketing event. The sheer volume of data has been a shift demanding innovation on the process side of analytics. More cheap storage, faster speeds and a new generation of pivot table Jedi have arrived to manage the data metamorphosis. Much has been written and discussed on these innovations. A more recent and less noticed transformation in the data analytics world has to do with the actual content analyzed.
Customer transactions and marketing data are giving way to a new universe of data around 1) human behavior and 2) a convergence of marketing and financial data. Let’s explore these two latest examples of data-driven “innovations.”
Behavioral Data and Neuroscience
Surveys have been around since well before the first pivot table. While the most common perception of surveys is around customer service content, what is innovative today are surveys asking questions around human behavior. This type of data is being used to define customers’ path to purchase in a very complex omni-channel world. All brands struggle to solve the attribution challenge, and surveys are powerful tools to use in tandem with more scientific rules and reporting.
When we tally up the orders that each marketing media claims as driven by their budget, it usually adds up to more orders than a given campaign generated in total. A customer may have received an email, clicked through a search term and maybe even received a direct mail piece in the same 30-day period before making a purchase. Rules have to be put in place when reporting on marketing data in order to account for the overlap in media attached to a given order.
In addition to the usual order of data driven analysis, asking customers in a survey about what shopping channel influenced their purchases can validate the rules in place for attributing orders to marketing media.
Using probabilistic samples with margins of error of at least 95 percent confidence level is still the standard for actionable survey results. What is new and innovative today is that surveys and the resulting analytics are now being used to interpret and validate data-driven results and helping to make allocation decisions for marketing investments.
We’re also using technology to image the brain, and that has proved what marketers have always believed but could not scientifically validate … until now. Purchasing behavior is based on unconscious feelings. By crafting survey questions properly, we can now have more confidence in how people respond to questions about what is most important.
Through basic segmentation set up by analysts, we can learn that the one group is much more likely to respond to a message about “reliability” or perhaps “selection” than another group. Using well-crafted survey questions, data analysis can now gauge how a group of customers responds to emotional concepts like “trust” (reliability) and “choice” (selection). By informing the design team, these feelings can be acted on.
Convergence of Marketing and Financial Data
There may be only two things that matter when it comes to a successful business: How much does it cost to acquire a new customer and how much value is that customer worth over the time they remain a customer. To the extent that making money is the objective, customer records representing the people/businesses that send their money to you are the fundamental source of value. Analysts are now using a combination of marketing and financial data to establish a cost to acquire a customer record.
What is innovative is how marketing data is being connected to financial data when evaluating customer transactions. The marketing data exists on a campaign level in most cases. We can know how much we spent on a marketing campaign to a certain segment and how many new customers that campaign yielded. As an example, we have visibility that an AdWords campaign with 27,372 clicks produced 655 orders resulting in a cost per order of $43.42.
The only financial data involved so far is how much the check to Google was for. The marketing cost of acquiring this order was $43.42. However, there is a host of other costs involved to get this order out the door and into the hands of the customer. These include cost of goods sold, credit card fees, pick/pack/ship costs and outbound freight margins, to mention a few.
Is it only after ALL the variable costs to acquire and deliver the order are taken into account that we really know if there is any money left to keep the lights on. This is where more financial data must be linked to the 655 orders shown in the campaign that were acquired. What is new in today’s world is leveraging data taken from a general ledger accounting platform to gain visibility of the total cost of an order.
Data from accounting platforms is usually organized into general ledger accounts and historically has been analyzed to meet the needs of minimizing tax risks and reporting to stakeholders of the business. This same data is now being organized in a second way to meet the needs of marketers and handed off to analysts. It is by connecting this set of financial data to the marketing data of orders where innovation in the data-driven analytics world is happening.
New methods, ideas and processes are arriving every year in the world of analytics that meet the definition of innovation.