Marketplace Segmentation with Qlik Set Research and Qlik Set Operations

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On this submit, we’ll assessment two elusive strategies inside Qlik in which key industry questions can also be addressed: Qlik Set Research and Qlik Set Operations.

A commonplace industry purpose is to amplify gross sales or resolve strategic effectiveness.  Those issues normally take a kind like one of the crucial following questions and are requested with a watch towards ancient efficiency.

  • Which of my present consumers bought my product?
  • Which of my present purchasers are benefitting from my techniques?

Qlik supplies an array of equipment to assist within the solutions to those questions.  We can use Qlik Set Research to spot consumers with particular traits or behaviors after which mix this with Qlik Set Operations to additional perceive the place we would possibly be expecting alternatives.

Qlik Set Research

Our pattern knowledge set is an inventory of fictitious consumers and their orders.  We all know their geographic main points and their order historical past.  From right here we will start to glean some ancient traits and goal conduct, geographic or different characteristic knowledge from which to spot further gross sales alternatives.

Let’s start via figuring out the ones consumers buying bikes.  The use of Qlik Set Research we will establish the ones consumers who’ve bought bikes previously.  A technique to try this is the next:

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

Within the desk underneath we see the client’s identify, a rely of shoppers and a rely of shoppers who’ve bought bikes.

Qlik Table

Negating this, we would possibly then look forward to finding the ones consumers NOT buying bikes.

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

We see the twond and threerd measure columns above don’t seem to be mutually unique.  Why is that this? 

What’s being known within the set are the ORDERS moderately than the CUSTOMERS and whilst that is an identical for the primary case, it’s obviously now not for its negation in the second one case. 

A simpler means to reach this and retain the facility to successfully establish the complimentary set is to make use of the P() and E() purposes equipped via Qlik for this goal.

As an alternative of:

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

We use:

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

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

To succeed in the complimentary set of the ones consumers who’ve NEVER bought bikes [where E() indicates Excluded] we will do one of the crucial following:

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

– OR –

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

We will now practice that for each and every buyer they both HAVE or HAVE NOT bought bikes.  (Word – as written, the Set Research will retain context of any dimensional picks because of the $ notation).  As affirmation of this reality, we will see that the sum of the 2 teams (49 + 43) sum to the full (92).

Qlik Set Operations

Because it stands, this can also be helpful, then again the strategies’ worth is amplified when mixed with different units by the use of Qlik Set Operations.

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

The Motorbike set component is multiplied (*) with the Planes set component to offer us the intersection of those two units.  On this case, now we have the ones consumers who’ve EVER bought each Bikes AND Planes.  We will then briefly manipulate the units to respond to which ever questions we’d love to pose.

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

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

However:

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

Qlik Set Operations Abstract

Qlik Set Operations Summary

Combining Qlik Set Research and Qlik Set Operations

If, as an alternative of looking for easy characteristic identifiers, we need to perceive behavioral thresholds, i.e., Gross sales above $175k, we will leverage seek in a extra complicated Qlik Set Research.

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

This can also be additional altered and mixed by the use of Qlik Set Research Purposes P() and E() and Qlik Set Operations (* and -) to spot an overly particular subset of shoppers for doable research.

The ones consumers…

SUM( {$
                // by no means having over 175k in gross sales (see E() exclude serve as underneath)
                <CUSTOMERNAME=E({<CUSTOMERNAME={"=SUM(SALES)>=175000"}>})>
     *
// who've ever bought Planes (see P() imaginable serve as underneath, * operator above)
    <CUSTOMERNAME=P({<PRODUCTLINE={"Planes"}>})>
     -
//however don't seem to be situated in USA or Australia (see subtraction operator above)
    <CUSTOMERNAME=P({<COUNTRY={"USA","Australia"}>})>
    } SALES)

See the ‘Blended’ column underneath for the gross sales of the required set of shoppers.

We now be capable of ask and resolution questions which is able to goal subsets of shoppers according to any characteristic or conduct and which can also be simply and reliably manipulated with out long or complicated enhancing.

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