Chapter 10. Sales Discovery

Throughout this book, we have shared the driving forces in the creation of Qlik Sense and key capabilities to aid in helping organizations make better business decisions. This chapter is the first of four that will apply Qlik Sense to the challenges of analyzing sales performance within your organization. This example and many others are available for you to explore live at http://sense-demo.qlik.com. Please bookmark this link as additional demonstrations and examples are constantly being added and updated. Now, let's turn our attention to the following challenge of sales analysis and how Qlik Sense addresses this common business challenge.

In this chapter, we will cover the following topics:

• Common sales analysis problems

• The unique way Qlik Sense addresses these problems

• How the Sales Discovery application was built

The business problem

Analyzing sales information can be a difficult process for any organization, and is critical to meeting sales expectations and understanding customer demand signals. What makes sales analysis so difficult is that many perspectives can be taken on the enormous amount of information that is captured during the sales process.

Some key questions include:

• Who are our top customers?

• Who are our most productive sales representatives?

• How are our high margin products selling and to whom?

The key thing here is that during the analysis process, one answered question always leads to further questions depending on the results; in other words, the analysis process's diagnostics. These paths to discovery cannot be precalculated or anticipated. With this in mind, let's take a look at how the Sales Discovery application seeks to meet these requirements.

Application features

Qlik Sense's associative model allows users to answer the common questions outlined in the preceding section through the selection of elements in filter boxes, but more importantly, to drive follow-up questions. Often, this relies on Qlik Sense's ability to instantly identify the associated and nonassociated data, which is also known as The Power of Gray after the color assigned to nonassociated elements highlighted in Chapter 3, Empowering Next Generation Data Discovery Consumers. The following are two key beginner questions that will drive additional questions as the analysis begins.

Key questions include:

• Who are our top customers?

  • What are these customers buying?

  • Where are these customers buying from?

  • Are the products getting there?

  • Who are our bottom five customers?

• Can we cross-promote products?

  • Who are our most productive sales representatives?

  • What products are the most productive sales representatives selling?

  • Whom are they selling to?

  • Which regions are they being sold...

Summary

In summary, Qlik Sense provides unique capabilities to meet the challenging task of analyzing sales data. Without the capabilities offered by Qlik, this task can be difficult due to the size of the data and the many perspectives that can be taken in trying to understand customer buying habits, sales representative productivity, and the responsive nature of the organization in meeting customer needs. Qlik's associative indexing engine powers this exploration. This means meeting these requirements is no longer challenging at all.

In the next chapter, we will explore how Qlik Sense will meet the needs of Human Resource Discovery.