Market Segmentation with Qlik Set Evaluation and Qlik Set Operations

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On this submit, we’ll evaluate two elusive strategies inside Qlik by which key enterprise questions could be addressed: Qlik Set Evaluation and Qlik Set Operations.

A typical enterprise goal is to increase product gross sales or decide strategic effectiveness.  These issues typically take a type like one of many following questions and are requested with an eye fixed towards historic efficiency.

  • Which of my present clients bought my product?
  • Which of my present shoppers are benefitting from my packages?

Qlik offers an array of instruments to assist within the solutions to those questions.  We are going to use Qlik Set Evaluation to determine clients with particular traits or behaviors after which mix this with Qlik Set Operations to additional perceive the place we would count on alternatives.

Qlik Set Evaluation

Our pattern information set is a listing of fictitious clients and their orders.  We all know their geographic particulars and their order historical past.  From right here we will start to glean some historic tendencies and goal habits, geographic or different attribute information from which to determine extra gross sales alternatives.

Let’s start by figuring out these clients buying bikes.  Utilizing Qlik Set Evaluation we will determine these clients who’ve bought bikes prior to now.  A method to do that is the next:

COUNT( { $ <PRODUCTLINE={"Bikes"}> } Distinct CUSTOMERNAME)

Within the desk beneath we see the shopper’s identify, a depend of shoppers and a depend of shoppers who’ve bought bikes.

Qlik Table

Negating this, we would then look forward to finding these clients NOT buying bikes.

COUNT({$<PRODUCTLINE-={"Bikes"}>} Distinct CUSTOMERNAME)
Qlik Table Example

We see the twond and threerd measure columns above should not mutually unique.  Why is that this? 

What’s being recognized within the set are the ORDERS moderately than the CUSTOMERS and whereas that is equal for the primary case, it’s clearly not for its negation within the second case. 

A more practical methodology to attain this and retain the power to successfully determine the complimentary set is to make use of the P() and E() features supplied by Qlik for this objective.

As a substitute of:

COUNT( { $ <PRODUCTLINE={"Bikes"}> } Distinct CUSTOMERNAME)

We use:

COUNT({$<CUSTOMERNAME=P({<PRODUCTLINE={"Bikes"}>})>}Distinct CUSTOMERNAME)

That is learn as ‘Which clients have EVER bought bikes’ the place P() signifies Attainable.

To realize the complimentary set of these clients who’ve NEVER bought bikes [where E() indicates Excluded] we will do one of many following:

                COUNT({$<CUSTOMERNAME=E({<PRODUCTLINE={“Bikes”}>})>}Distinct CUSTOMERNAME)

– OR –

COUNT({$<CUSTOMERNAME-=P({<PRODUCTLINE={“Bikes”}>})>}Distinct CUSTOMERNAME)

We will now observe that for each buyer they both HAVE or HAVE NOT bought bikes.  (Observe – as written, the Set Evaluation will retain context of any dimensional choices because of the $ notation).  As affirmation of this reality, we will see that the sum of the 2 teams (49 + 43) sum to the overall (92).

Qlik Set Operations

Because it stands, this may be helpful, nonetheless the strategies’ worth is amplified when mixed with different units through Qlik Set Operations.

COUNT({$
                <CUSTOMERNAME=P({<PRODUCTLINE={"Bikes"}>})>
    *
<CUSTOMERNAME=P({<PRODUCTLINE={"Planes"}>})> 
    } Distinct CUSTOMERNAME)

The Motorbike set aspect is multiplied (*) with the Planes set aspect to present us the intersection of those two units.  On this case, we’ve got these clients who’ve EVER bought each Bikes AND Planes.  We will then shortly manipulate the units to reply which ever questions we’d prefer to pose.

Which clients have EVER bought bikes, however NEVER bought Planes?

COUNT({$
                <CUSTOMERNAME=P({<PRODUCTLINE={"Bikes"}>})>
    *
<CUSTOMERNAME=E({<PRODUCTLINE={"Planes"}>})>    
    } Distinct CUSTOMERNAME)

Alternatively:

COUNT({$
                <CUSTOMERNAME=P({<PRODUCTLINE={"Bikes"}>})>
    -
<CUSTOMERNAME=P({<PRODUCTLINE={"Planes"}>})>    
    } Distinct CUSTOMERNAME)

Qlik Set Operations Abstract

Qlik Set Operations Summary

Combining Qlik Set Evaluation and Qlik Set Operations

If, as a substitute of looking for easy attribute identifiers, we want to perceive behavioral thresholds, i.e., Gross sales above $175k, we will leverage search in a extra superior Qlik Set Evaluation.

SUM({$<CUSTOMERNAME=P({<CUSTOMERNAME={"=SUM(SALES)>=175000"}>})>} SALES)

This may be additional altered and mixed through Qlik Set Evaluation Features P() and E() and Qlik Set Operations (* and -) to determine a really particular subset of shoppers for potential evaluation.

These clients…

SUM( {$
                // by no means having over 175k in gross sales (see E() exclude operate beneath)
                <CUSTOMERNAME=E({<CUSTOMERNAME={"=SUM(SALES)>=175000"}>})>
     *
// who've ever bought Planes (see P() doable operate beneath, * operator above)
    <CUSTOMERNAME=P({<PRODUCTLINE={"Planes"}>})>
     -
//however should not positioned in USA or Australia (see subtraction operator above)
    <CUSTOMERNAME=P({<COUNTRY={"USA","Australia"}>})>
    } SALES)

See the ‘Mixed’ column beneath for the gross sales of the required set of shoppers.

We now have the power to ask and reply questions which might goal subsets of shoppers based mostly on any attribute or habits and which could be simply and reliably manipulated with out prolonged or advanced enhancing.

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