Sales Promotion Effectiveness

8/8/2019 Sales Promotion Effectiveness

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Business Decision Making & Data Mining

Study of Sales Promotion Effectiveness

Faculty: Prof. Nagadevara V

Teaching Asst: Ms. Nalini Guhesh

Group 2

Arpitha Bhat (2008013)

Avnish Kshatriya (2008078)

Nizam Mohideen (2008090)

Savita Ashwath (2008047)

Shashidhar A (2008052)

Sundeep Raj (2008112)

Varun Yagain (2008141)


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Contents

1. Business Domain Details ........................................................................................................ 3

1.1. Product Details ................................................................................................................. 4

1.2. Promotion Details ............................................................................................................. 4

2. Business Problem .................................................................................................................... 5

3. Details of the Data ................................................................................................................... 6

3.1. Source ............................................................................................................................... 6

3.2. Quality .............................................................................................................................. 6

3.3. Description of the variables .............................................................................................. 6

3.4. Costs of Prediction errors ................................................................................................. 8

4. Analysis ................................................................................................................................... 9

4.1. Assumptions on Data ........................................................................................................ 9

4.2. Technique ......................................................................................................................... 9

4.2.1. Stage-1 ..................................................................................................................... 10

4.2.2. Stage-2 ..................................................................................................................... 10

4.3. Data Preparation Process ................................................................................................ 10

4.4. Choice of Data mining technique ................................................................................... 14

5. Conclusion ............................................................................................................................. 15

5.1. Actual Results ................................................................................................................. 15

5.2. Strategic recommendations............................................................................................. 16

5.3. Further direction ............................................................................................................. 16

6. Appendix ............................................................................................................................... 17

7. References ............................................................................................................................. 22


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1.Business Domain DetailsThis data mining project is applied on the data of marketing activities of a FMCG business.

These businesses have several marketing promotions used to boost sales and given the limited

resources in launching/running these promotions, it is of considerable interest to find out the

most effective sales promotion (or the most effective combination of them) in order to maximize

the benefit out of limited spend.

In this study, the following promotion campaigns are analyzed

Shop Displays, Price Discounts, Coupons, and In-store Displays

We use sales data per weeks over more than 300 weeks, the promotions in force in each of these

weeks, and the sales numbers of several related products during these weeks. The explanation of

the data used shall follow in the further chapters. The data can also be connected to the locationof stores using the store identifiers.

There could be several useful actionable strategies like below:

The close correlation between different products or combinations of them, hinting at apossible advantage in

o Bundling them togethero Placing them in close display to remind purchaseo Placing them away from each other to increase exposure to other products

The most effective combination of promotional activities can be found out. The most valued set of features of a product, also in relationship with features of other

products, sales and seasonality.

In this project, we stick to understanding the effectiveness of the promotion mix, what is

popularly referred in the Retail industry asPromotion Analysis.


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Aberdeen Group, a popular Promotion Analytics company defines it as: a technique for

determining optimum promotions at the most appropriate price level that help retailers to

increase profit uplift and reduce sales cannibalization. The components of an optimization

solution include a simulation model that provides the most confident prediction of the outcome of

any promotional discount. This solution uses a scenario/results processor, a demand engine, an

activity-based promotions cost engine, and a price optimization engine. The scenario processor

helps a retailer in the optimization process by selecting the most optimum prices and promotions

quantity using the most appropriate promotion vehicle for the promotion period.

1.1. Product DetailsDue to confidentiality needs of the Data owner, the authors are unable to disclose the product

details. However, it is to be noted that these products belong to the Retail Industry and to the

category identified in the industry as CPG Consumer Packaged Goods. Tooth pastes, soaps,

detergents, etc are example of products in this category.

1.2. Promotion DetailsFor the same reasons of confidentiality as with product details, the authors are unable to disclose

the promotion details either. Hence, the promotion details are identified using inexplicit unique

identifiers. Given the industry, one should consider in-house display, discount coupons, bundled

packaging, etc to be examples of promotions.


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2.Business ProblemThe identified scope of this data mining exercise in Promotion Analysis typically involves the

following Business Impact Study, quoting Cequity Solutionsi:

Incremental sales & lift, volume impact and margin potential of promotion Impact of promotion across different dimensions like geography, season, customer set,

products, category etc

The best channel for promotion delivery Promotion elasticity for price, delivery, & media Forecasts of sales for promoted products

It is known that in the Retail Industry, about 71% of the major players employ some form of

Promotion Analysisii.

This is not hard to guess given that research by organizations involved in trade promotionsindicate that forevery billion dollars of promotions spend, upwards of $50 million is poorly

spentiii

.

From the above listed advantages, the focus of this study is to identify the best individual or

combination of promotions that give a lift to the sales of the selected product-1.


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3.Details of the DataThe following sub-sections give out the details of the source data used.

3.1. SourceThe data is obtained from a market analytics company (name not to be disclosed as a part of

agreement). The company gets the data from a data centre which collects data from the FMCG

(called CPG in US and EU markets) stores across the Europe and American markets. The data

mainly contains the information about the volume and unit sales of different FMCG/CPG

products that the stores sell along with the information on average price, different promotionsetc.

3.2. QualityThe data used is QCED data. The market research company usually gets the raw data from the

data centre which is cleaned for errors and misleading and obviously storage data by a set of QA

persons. Any such anomalies is checked and cleaned with constant calls and emails with the data

centre executives. The cleaned data was used for the analysis.

3.3. Description of the variablesThere are 101 fields in all. The below table describes them in further detail:

Field ame Data Type Use/Remark

WEEK Set (1501.. 1565) The week for which the sales data is

collected.

STORE Set The Unique identifier of the 8 stores whose

sales data is included.

FEAT[1-14] Boolean Denotes if a particular feature is part of the

product or not.

DISP[1-14] Boolean Denotes if a particular promotion was

running or not.

VOL[1-14] Range Normalized sales volume of the product.


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NUPC[1-14] Range Normalized number of UPCs in a product

(UPC = Universal Product Code i.e.

packaging/flavour/etc variant identifier)

PRIU[1-14] Range Denotes price per unit for each of the

products.

TREND Range Denotes change in Sales Data.

LBPR[1-14] Range Log of Base Price per Unit for each n

Product Ids.

LPI[1-14] Range Log of price index where price index is index

between the base price and the promoted

price. This helps in identifying the effect of

change in promotion price.

Table 1: Variables in the Data

Furthermore, the following synthesized variable columns were used for our analysis:

Field ame Data Type Use/Remark

CHG_VOL1_BIN Set {Decrease,

No_change, Increase}

Change in sales volumes of Product-1 during

every week, as compared to the previous

week.

COMP_FEA Range [1-13] Sum of all feature of products 2-14. This

gives a hint on competition to Product-1 with

all other products considered as competition.

COMP_DISP Range [1-13] Sum of number of promotions for products

2-14. This gives a hint on competition to

Product-1 with all other products considered

as competition.

Table 2: User Computed Variables in the Data


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3.4. Costs of Prediction errorsThe costs of the prediction errors entirely depend on the business strategies we need to employ,

as put in the table below:

Strategy-1 (Profit Protection):

Limit your promotion budget and protect your

profit margin even at the cost of a loss in sales.

In this case, the case of predicted INCREASE

and an actual DECREASE would be very high,

and has to be avoided.

Strategy-2 (Market Share Protection):Do not lose on sales even at the risk of

exceeding the promotion budget or decrease in

profit margin.

In this case, the case of predicted DECREASEand an actual INCREASE would be very high,

and has to be avoided.

Strategy-3 (Balance Market Share &

Profitability):

Neither lose sales nor lose profits beyond a

certain measure.

In this case, the overall accuracy bears the

most value.

Table 3: Available Business Strategies


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4.Analysis4.1. Assumptions on Data

Null value for FEAT and DISP can be replaced with Zero Null value for PRIU, VOL, NUPC, LPI, LBPR is a data gathering mistake. A mean of some

previous records can be assumed.

4.2. TechniqueWe refine the problem statement to the following

To detect the exact individual feature or a combination there of, of the product to offer

and the exact individual promotion or a combination there of, to be put on display and the

strategy of increasing or decreasing the price (as given by the change log) to employ; so

as to predict an increase in sales volume of the Product-1.

In other terms, each time the competition formulates a feature-promotion-pricing

strategy, the seller Product-1 can formulate his weekly response using the model that we

arrive at.

The sales volume change only in relationship to the previous week was considered while

neglecting other techniques (trend over last n-weeks, historical effect by means of moving

average over last n-weeks, so on) mainly to avoid additional complexity.

In order to avoid complexity of considering the data of all the competing products individually,

we have summed up the Features and Promotions into a composite value (by summing up the

binary variables) and used as one score against that of Product-1.


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4.2.1.Stage-1The scale increase in sales volume was attempted to be predicted by dividing it into bin values

SIGNIFICANT_DECREASE, DECREASE, NO_CHANGE, INCREASE and

SIGNIFICANT_INCREASE. The DECREASE and INCREASE bins lie on negative and

positive sides of 0% change within a distance of 20% while anything beyond in the respective

direction is considered SIGNIFICANT.

Draw backs:

With this intention of predicting not just the trend but also the extent of change did not yield a

significant accuracy. Any of the C5.0 CART or ANN techniques did not yield an accuracy

beyond 51%. We conclude that this intelligence is either:

a. missing all the fields required to be predictable, orb. is not possible to predict given that even the same set of features, promotions and pricing

strategy cannot guarantee a repeat of the sales increase to the same extent.

4.2.2.Stage-2As a modification, we attempted to predict only the direction of change in sales volume, thereby

using only three bin value DECREASE, NO_CHANGE and INCREASE. After this modification

to the business problem, a respectable predication accuracy of 72.67% was reached using the

CART technique.

4.3. Data Preparation ProcessThe original data file was in Excel format and contained 101 fields and 23,378 records. The

fields have already been described in the Variable Description section. The data cleaning and

preparation was done based on the domain understanding of the data.

Attaching a Quality node to the original data in Clementine revealed all the missing values. A

snippet of the node output is shown here; the full list is available in the project CD.


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Field % Complete Valid Records Null Value Empty String White Space

DISP1 98.97 23138 240 0 0

DISP10 67.02 15669 7709 0 0

DISP11 0.16 37 0 23341 23341

DISP12 0 0 0 23378 23378

DISP13 80 18702 4676 0 0

DISP14 100 23378 0 0 0

DISP2 95.91 22421 957 0 0

DISP3 89.12 20834 2544 0 0

DISP4 96.42 22541 837 0 0

DISP5 0.3 69 0 23309 23309

DISP6 0.02 4 0 23374 23374

DISP7 86.23 20160 3218 0 0

DISP8 0 0 0 23378 23378

DISP9 0 0 0 23378 23378

Figure 1: Quality of original data Snippet 1

PRIU1 98.97 23138 240 0 0

PRIU10 67.02 15669 7709 0 0

PRIU11 0.16 37 0 23341 23341

PRIU12 0 0 0 23378 23378

PRIU13 80 18702 4676 0 0

PRIU14 100 23378 0 0 0

PRIU2 95.91 22421 957 0 0

PRIU3 89.12 20834 2544 0 0

PRIU4 96.42 22541 837 0 0

PRIU5 0.3 69 0 23309 23309


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PRIU6 0.02 4 0 23374 23374

PRIU7 86.23 20160 3218 0 0

PRIU8 0 0 0 23378 23378

PRIU9 0 0 0 23378 23378

Figure 2: Quality of original data - Snippet 2

Significant observations regarding the quality:

There are no values for DISP12 attribute. Similarly for FEAT12, PRIU12, VOL12 etc.

There are only 37 records that have a value for DISP11, FEAT11 etc. Similarly it is seen that DISP5, DISP6, DISP8 and DISP9 (and other related fields) have

no valid records or very negligible number of valid records.

As seen in Figure 2 there were also null values for the PRIU, VOL, NUPC, LPI andLBPR fields.

Strategy for cleaning up the data:

Drop the columns that have zero valid records For columns with negligible valid records, drop the column, as well as the rows where

actual valid records were present

Missing values for DISP and FEAT are replaced with Zero Missing values for PRIU, VOL, NUPC, LPI and LBPR are replaced with the mean of

previous 1000 records

Develop a Clementine stream to prepare the dataOutcome of data preparation:

All attributes for products 5,6,8,9,11,12 were dropped; basically dropping the associatedFEAT, DISP, PRIU, VOL, NUPC, LPI and LBPR attributes

107 records were dropped because they had entries for the dropped columns All missing values were populated The output of the Clementine Quality node on the cleaned data is shown in Figure 3


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Field % Complete Valid Records Null Value Empty String White Space

DISP1 100 23271 0 0 0

DISP10 100 23271 0 0 0

DISP13 100 23271 0 0 0

DISP14 100 23271 0 0 0

DISP2 100 23271 0 0 0

DISP3 100 23271 0 0 0

DISP4 100 23271 0 0 0

DISP7 100 23271 0 0 0

FEAT1 100 23271 0 0 0

FEAT10 100 23271 0 0 0

FEAT13 100 23271 0 0 0

FEAT14 100 23271 0 0 0

FEAT2 100 23271 0 0 0

FEAT3 100 23271 0 0 0

FEAT4 100 23271 0 0 0

FEAT7 100 23271 0 0 0

Figure 3: Quality of cleaned data


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4.4. Choice of Data mining techniqueThe data mining techniques of Classification & Regression Tree (CART), Decision Tree Model

C5.0 and Artificial Neural Networks were attempted. The CART technique has consistently

yielded a higher value of overall prediction accuracy. But the final choice of Model depends

entirely on the Business Strategy employed (as detailed in the Cost of Errors section) :

Technique Profitability

(protect/improve)

Strategy(Use lowest

Predicted-

ICREASE,

actual

DECREASE)

Market Share

(protect/improve)

Strategy(Use lowest

Predicted-

DECREASE,

actual

ICREASE)

Balanced

Strategy

(protect/improveboth Profits &

Shares)

(Use Overall

Accuracy)

C5.0 14.07% 54.41% 70.11%

ANN 37.04% 83.57% 72.43%

CART 24.85% 71.16% 72.67%

Chosen

TechniqueC5.0 C5.0 CART

Table 4: Choice of technique based on Strategy Adopted


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5.ConclusionWe conclude that the use of data mining results largely depends on the Business strategy chosen

to be applied.

5.1. Actual ResultsThe actual accuracy and coincidence matrices from each of the techniques are given below as

results obtained by analysing the data on IBM SPSS Clementine tool.

Coincidence

Matrices Overall Accuracy

C5.0 Decrease Increase Error C5.0

Decrease 2496 409 14.07917 Correct 3987 70.10726

Increase 1249 1491 54.41606 Wrong 1700 29.89274

No

change 39 3 7.142857 Total 5687

CARTS Decrease Increase Error CARTS


Increase 790 1950 71.16788 Wrong 1554 27.32548

No

change 32 10 23.80952 Total 5687

ANN Decrease Increase Error ANN


Increase 450 2290 83.57664 Wrong 1568 27.57165

Table 5: Results of the Data Mining.


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5.2. Strategic recommendationsAs can be noted from the Table 4, the choice of model depends on the actual Business Strategy

chosen. We recommend that the result as predicted by the CARTS model be chosen for adopting

a promotion-feature-pricing strategy as a response to the competitions moves.

5.3. Further directionWe list down the following limitation/handicaps in our analysis so far.

Improvements in the Data Mining exercise:

a. Some of the products sales may not be directly correlated to that of the product ofinterest. It would be of interest to narrow down those products which are actually cutting

the sales of Product-1. Such an analysis would require the model to consider the scores of

all the products individually, the accuracy of which was observed to be of limited use.

Hence, we have chosen to use a composite score. It would be advantageous to analyze the

cause of the low accuracy when sales are considered individually, and then remedy the

same.

b. We have considered the changes in sales volumes only in relationship to the immediatepreceding week. This is not realistic enough when the effects of a promotion can lastmore than a week. In this case, it would be an improvement to consider:

i. Trend over the past N-weeks, orii. Moving average over the past N-weeks.

Improvement in the input data quality:

a. The data set consists of only 23000 records. A larger data set might possibly revealfurther trends.


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6.AppendixThe Data Cleaning stream

The mining stream for approach-1


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The Data Mining Analysis Stream for approach-2

Analysis using C.5


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Gains Chart

Analysis using CART

Gains chart for CART


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Analysis and Gains chart using A


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Gains Chart for A:


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7.Referencesi http://www.cequitysolutions.com/inner.php?p=9&sub=50#pa

ii http://www.aberdeen.com/summary/report/benchmark/RA_PromOpt_SA_3909.asp

iii http://www.managesmarter.com/msg/content_display/marketing/e3i1af498fbe3e69d89356d126fc11c1617

Sales Promotion Effectiveness

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