Sunday, November 27, 2022
HomeBusiness AnalyticsMarket Segmentation with Qlik Set Evaluation and Qlik Set Operations

Market Segmentation with Qlik Set Evaluation and Qlik Set Operations


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

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

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

Qlik supplies an array of instruments to help within the solutions to those questions.  We’ll 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 anticipate alternatives.

Qlik Set Evaluation

Our pattern information set is an inventory of fictitious clients and their orders.  We all know their geographic particulars and their order historical past.  From right here we are able to start to glean some historic developments and goal conduct, 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 are able to 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 under we see the shopper’s identify, a rely of shoppers and a rely of shoppers who’ve bought bikes.

Negating this, we would then anticipate finding these clients NOT buying bikes.

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

We see the twond and threerd measure columns above aren’t mutually unique.  Why is that this? 

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

A simpler technique to attain this and retain the power to successfully determine the complimentary set is to make use of the P() and E() capabilities supplied by 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 clients have EVER bought bikes’ the place P() signifies Doable.

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

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

– OR –

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

We are able to 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 as a result of $ notation).  As affirmation of this reality, we are able to see that the sum of the 2 teams (49 + 43) sum to the entire (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 provide us the intersection of those two units.  On this case, now we have these clients who’ve EVER bought each Bikes AND Planes.  We are able to 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 searching for easy attribute identifiers, we want to perceive behavioral thresholds, i.e., Gross sales above $175k, we are able to 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 under)
                <CUSTOMERNAME=E({<CUSTOMERNAME={"=SUM(SALES)>=175000"}>})>
     *
// who've ever bought Planes (see P() attainable operate under, * operator above)
    <CUSTOMERNAME=P({<PRODUCTLINE={"Planes"}>})>
     -
//however aren't positioned in USA or Australia (see subtraction operator above)
    <CUSTOMERNAME=P({<COUNTRY={"USA","Australia"}>})>
    } SALES)

See the ‘Mixed’ column under 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 conduct and which might be simply and reliably manipulated with out prolonged or advanced enhancing.

RELATED ARTICLES

LEAVE A REPLY

Please enter your comment!
Please enter your name here

Most Popular

Recent Comments