A few years back, Big Data came into play and brought with it an enormous scaling of data to be made available on any given event. The incredible volume of data has proven quite a shift – demanding innovation on the process aspect of data analytics. Cheaper storage, increased speed and high level data engineers have been brought together to manage the data transformation. These data innovations have been written and discussed ad nauseam. A more recent but less noticed evolution in the realm of data analytics regards the actual content which is analyzed.
Transactional and marketing data sets are expanding toward a new multiverse of data which includes the dynamics of human behavior and also the coming together of marketing and financial data. Let’s have a look at these latest examples of these recent data driven trends.
Behavioral Data via Neuroscience
The survey, for example, has been around for eons. Commonly, surveys are centered around customer service and satisfaction, but for marketing-to-conversion purposes, surveys with questions about human behavior are on the rise – and rightly so. This type of data is being used to track customer paths to purchases in a very sophisticated multi-channel world. Surveys are very powerful tools to use together with more scientific rules and reporting.
When we sift through and add up the orders that each marketing set claims as driven by their budget, it usually adds up to more orders than any given campaign generated alone. A customer may have received an email, clicked through a search term and maybe even received a direct mail piece all in the same 30-60 day period before making a purchase. Rules have to be decided when reporting on marketing data so that overlap can be accounted for when collecting data for media attached to one or more campaigns.
In addition to the usual order of data driven analysis, asking customers a survey question about what marketing channel influenced their purchase decisions can serve to validate the rules for attributing orders to specific 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’s new nowadays is that analytics from survey results are now being used for interpreting and validating data driven results and can help to make decisions for marketing investment allocation.
State of the art technology is being used to image the brain these days, and that imaging has proven what marketers have always believed yet could not validate – until today. Consumer purchasing behavior is based on feelings whether they’re conscious emotions or not. The ability to properly craft survey questions allows us to have more confidence in how consumers respond to questions about what is most important to them.
Through basic segmentation, we learn that the one group is far more likely to respond to a message about ‘reliability’ or maybe ‘variety’ than another group. Using well crafted survey questions, analysis of data can now determine how a group of customers will respond to emotional criteria such as ‘trust’ (reliability) and ‘choice’ (variety). By informing the design team, these feelings can be drawn on to design the feel of the marketing materials.
Where Marketing and Financial Data Meld
Two main building blocks which matter when it comes to growing a successful business: 1) Cost to acquire a new customer 2) How much is that customer worth for the time period they remain an active customer. In all for-profit business, making money is the objective. Customer records representing the people and/or businesses that spend with you are your A#1 source of value. Analysts today are using a combination of marketing and financial data for establishing the cost to acquire customer records.
The means in which marketing data and financial data are being connected when evaluating customer transactions is much more advanced now than it’s ever been. In most cases, marketing data exists on a campaign level. We are able to pinpoint how much is spent on a marketing campaign for a certain segment, how many new customers were created from that campaign and the average cost to acquire each new customer over a given time period. For example, we can see below that a Google AdWords campaign with 24,480 clicks produced 1041 orders resulting in a cost per order of $24.45.
Adwords Campaign Example:
Month Starting: 9/1/2016 Clicks: 24,480 Average CPC: 1.04 Spend: 25,459 Orders: 1041 Revenue: 41,640 Cost/Order: 24.45
The only financial data involved so far is how much was paid to Google for the campaign vs. a starting point for revenue created. The marketing cost of acquiring this order was $24.45 per customer. However, there are a number of other costs involved to get this order out the door. These include acquisition cost of goods sold, credit card fees, shipping costs payroll and tax, just to name a few.
Only after every one of the variable costs to acquire and deliver the order are taken into account, do we really know if there is any money left to keep the business running. This is where more financial data must be linked to the 1041 orders from this campaign. What is new in today’s world is the ability to leverage data acquired from an accounting platform to gain all of the details to accurately assess the total cost of an order.
Standard accounting platforms organized data into general ledger accounts. Historically, this 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 an additional way to meet the needs of marketers and is thus handed off to analysts. By connecting this set of financial data to marketing data and analysis of orders, innovation in the data driven analytics world is taking shape.
New ideas, methods and innovative processes are arriving each year in the world of data analytics, and keeping up with these can push your business to the top.