Tuesday, April 16, 2024

Marketplace Segmentation with Qlik Set Research and Qlik Set Operations

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On this publish, we’ll assessment two elusive strategies inside of Qlik through which key trade questions will also be addressed: Qlik Set Research and Qlik Set Operations.

A commonplace trade purpose is to increase gross sales or resolve strategic effectiveness.  Those issues usually take a kind like probably the most 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 methods?

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 may be expecting alternatives.

Qlik Set Research

Our pattern information 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 be able to start to glean some ancient developments and goal conduct, geographic or different characteristic information from which to spot further gross sales alternatives.

Let’s start by way of figuring out the ones consumers buying bikes.  The use of Qlik Set Research we will be able to establish the ones consumers who’ve bought bikes up to now.  A method to try this is the next:

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

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

Qlik Table

Negating this, we may 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 recognized within the set are the ORDERS relatively 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 more practical means to succeed in this and retain the facility to successfully establish the complimentary set is to make use of the P() and E() purposes equipped by way of 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 consumers have EVER bought bikes’ the place P() signifies Imaginable.

To reach the complimentary set of the ones consumers who’ve NEVER bought bikes [where E() indicates Excluded] we will be able to do probably the most following:

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

– OR –

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

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

Qlik Set Operations

Because it stands, this will also be helpful, on the other hand the strategies’ price is amplified when blended 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 present us the intersection of those two units.  On this case, we’ve the ones consumers who’ve EVER bought each Bikes AND Planes.  We will then temporarily 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)

Then again:

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 a substitute of searching for easy characteristic identifiers, we need to perceive behavioral thresholds, i.e., Gross sales above $175k, we will be able to leverage seek in a extra complex Qlik Set Research.

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

This will also be additional altered and blended 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 beneath)
                <CUSTOMERNAME=E({<CUSTOMERNAME={"=SUM(SALES)>=175000"}>})>
     *
// who've ever bought Planes (see P() conceivable serve as beneath, * 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 ‘Mixed’ column beneath for the gross sales of the required set of shoppers.

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

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