The most common Qlik application involves sales data analysis. Period.
Well, I don’t have enough information to back that up, and since data analysis is my life, I can’t make unsupported claims without some major nervous facial twitching (or so my wife says). However, I would bet based on personal experience that it is one of the most common applications, and moreover, I’d go as far to say that it is often the first analytical application that businesses develop after buying Qlik.
One the most obvious reasons that this could be true is that sales data is huge, low-hanging fruit. Sales is what drives most businesses and the data trail it leaves is usually the most readily available data to analyze. When a company purchases Qlik, it is often after continuous investments in an ERP (Enterprise Resource Planning) system, a CRM (Customer Relationship Management) systems, a customer portal and/or numerous Excel reports – all of which make sales data ripe for harvest.
Many of you are probably familiar with the following sales metrics.
|Gross Sales Revenue||Sales before discounts measured in monetary units
|Net Sales Revenue||Sales after discounts measured in monetary units
|Sales Volume||Sales measured in non-monetary units such as an individual item, boxes, pallets, kilograms, tons, etc.
|Unit Sales||Also referred to as Average Price, it can be defined as Net Sales Revenue divided by Sales Volume
|Hits||Number of sales transactions or invoices
|Gross Profit||Net Sales Revenue minus the Cost of Goods Sold (COGS)
|Gross Profit Margin||Gross Profit divided by Net Sales Revenue
If you are familiar with these metrics, you’ll also be well acquainted with the series of dimensions that often slice and dice them. Catalogs that describe customers, sales representatives, products, dates, branches, stores, promotion codes, and channels answer the questions of who, what, when, where, why, and how that surround the sales numbers. You’ll also recognize the importance of using some type of reference data that comes in the form of a budget, a forecast, or at the very least, historical data.
Almost immediately after businesses start to take advantage of this basic, yet powerful, sales data analysis, they start to adjust their business questions. We can answer some new questions by adding a new metric, a new dimension, or a new visualization, but some new questions involve more advanced analysis techniques. One of the most popular questions that I encounter and that requires a more sophisticated approach is the evaluation of customer retention and the detection of customers that the business is endanger of losing. For here on, we’ll refer to this type of analysis as customer churn.
Continue reading “Sales Analytics in Qlik: From the Basics to Statistical Modeling”