For most companies, it's the marketing department's job to generate leads and the sales department's job to turn those leads into customers. However, for best results, these two departments need to work together, taking shared responsibility for increasing their return on investment (ROI).
"When we think about sales and marketing alignment, and how those two need to operate as one, anything that a marketer does should be to increase revenue," says Fergal Glynn of Docurated. At a time when the term "information overload" is so widely - and so accurately - used, the underutilization of information is emerging as a major inhibitor of business growth.
Fortunately, the analytics tools to overcome obstacles of this nature already exist; it is simply a matter of recognizing them and deploying them effectively. Artificial intelligence (AI) is a critical element here. It can uncover patterns in the nature, volume, and timing of sales and marketing techniques.
A common scenario where AI can be applied is the sales funnel, where the departure from the brick-and-mortar model can be a double-edged sword. The non-linear buying process that dominates today poses no shortage of advertising and sales opportunities, but makes it almost impossible to identify critical points in the buyer's decision journey.
A client may begin with a personal visit or an online search and choose just one or both. They may request information from salespersons or through a website. The back and forth may continue over days, weeks, or months and the buyer may continue their queries even after making the decision to purchase. Here, a business needs to have a filter for the analysis of the data from the client. This can be in the form of a skilled data and analytics specialist or simply an AI engine. Amazon is the ideal example of the latter.
Amazon shoppers receive personalized recommendations for related products after making a purchase. For example, someone who buys a smartphone will get recommendations of compatible phone cases and screen protectors. Another Amazon user who leaves without completing the purchase of an item in their cart will get reminders via email or through a social media platform. These can be timed to coincide with a period when the user has made purchases in the past.
One intriguing feature that analytics is adept at finding is the "data double." This term refers to unrelated clients who fit a very specific purchasing profile. By identifying data doubles, a business can use a comparison of their purchase history to "fill in the blanks" of unpurchased products. It is a tactic that delivers especially exceptional results with under-performing accounts.
Both data doubles and Amazon's user purchase tracking are simple but effective examples of business data used in a simple but very effective manner. They do require an investment in data analytics at the beginning, which can be intimidating for a business. However, combined with a good plan for implementation, these investments quickly pay for themselves and then some.