Online & Offline Media ROI Measurement and Media … & Offline Media ROI Measurement and Media Mix Optimization January 2015 Food brand Google Confidential and Proprietary 2 Contents

Google Confidential and Proprietary 1 Google Confidential and Proprietary 1

Online & Offline Media ROI Measurement and Media Mix Optimization

January 2015

Food brand


Contents

Introduction: Marketing levers and modeling process

Project objectives and methodology.

Sales models Key drivers of sales.

Online models Key drivers of Google Paid Search, CTR and queries.

Media mix optimization Optimization of advertisings contribution to sales.

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Project objectives Unilever wanted to identify the main marketing levers and measure the impact of online and offline media on sales of two products of a food brand.

Their key objective was to optimize media investment in order to maximize sales.

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Sources of information The analysis was carried out by Conento, with the support of BIG, between July and December 2014.

1 *Note: We only considered distributors data and the presence or absence of promotions, because we did not have any more data available.

VARIABLES BY CATEGORY PERIODICITY

Sales by product Monthly (sell-out) - Since 2011 Weekly (sell-in): Since Jul 2012*

Web traffic: Total and MicroSites Weekly - Since July 2013

Media investment (online and offline) Offline: Monthly – Since 2011 Online: Aggregated data since

March 2013

Competitors’ Media investment Monthly – Since 2011

Distribution, Price, Promotions* by product Monthly – Since 2011

Queries Quarterly – Since March 2011 Monthly – Since March 2013

Paid Search: Impressions / Clicks / Position / CTR by word group Daily – Since March 2013

Display: Impressions / Clicks / CTR Daily – Since December 2012

Macro-economic Variables: Unemployment Rate / consumer confidence index / GDP evolution Yearly – Long historic

Socio-demographic data (population, Internet penetration, etc) Yearly – Long historic

Temperature, Holidays etc. Monthly – Since 2011


Methodology

–  We tested both linear and log-linear econometric models in order to find the best methodology. Finally, we chose linear models because, although the results of both analysis were quite similar, the interpretation of the coefficients of the variables is far more complex in the case of log-linear models.

–  Using the linear methodology, we built the sales, Google Paid Search clicks and the brand queries models. In the case of the latter, we also used Loess time series decomposition in order to disaggregate the series into their trend, seasonal and remainder components.

–  Using the learnings from the models and an optimization algorithm, we were able to recommend optimal levels of investment for TV, Paid Search and Display, in order to maximize their contribution to sales.

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Econometric models help to quantify the ROI of offline and online media, measuring the impact of the main marketing and business levers.

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SALES

OFFLINE MEDIA ADVERTISING

OTHER (SEASONALITIES,

ETC) DISTRIBUTION PRICE PROMOTION

ONLINE MEDIA ADVERTISING

* In this study data of awareness are not available .


We identified the main communication drivers for each step of the consumer purchase process.

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CLICKS SEM SALES

3 4

BRAND QUERIES

2 MEDIA

ADVERTISING SPEND

MEDIA

Television

Magazines

Internet

1

BRAND


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Google Confidential and Proprietary 10 Google Confidential and Proprietary 10 2

The final sales models are very robust, with levels of fit to actual sales over 94%.

R2: 99.3%

Sale

s (‘0

00 K

g)

R2: 98.8%

Sale

s (‘0

00 K

g)

Product A

Product A references

Product B

Reference 1 Reference 2 Reference 3 Reference 4

98.3% 94.6% 99.5% 99.6%

R2 Product B references

Reference 1 Reference 2 Reference 3

94.7% 95.9% 94.5%

R2

0

1

2

3

4

5

6

Mar

-13

Apr-

13

May

-13

Jun-

13

Jul-

13

Aug-

13

Sep-

13

Oct

-13

Nov

-13

Dec

-13

Jan-

14

Feb-

14

Mar

-14

Apr-

14

Sales JellyFitProduct 1 salesFitProduct A salesFit

0

2

4

6

8

10

12

14

Oct

-11

Dec

-11

Feb-

12

Apr-

12

Jun-

12

Aug-

12

Oct

-12

Dec

-12

Feb-

13

Apr-

13

Jun-

13

Aug-

13

Oct

-13

Dec

-13

Feb-

14

Apr-

14

Sales Double PouchFitProduct 2 salesFitProduct 2 salesFitProduct B salesFit


-‐40

-‐30

-‐20

-‐10

0

10

20

30

40

Oct-‐11 Dec-‐11 Feb-‐12 Apr-‐12 Jun-‐12 Aug-‐12 Oct-‐12 Dec-‐12 Feb-‐13 Apr-‐13 Jun-‐13 Aug-‐13 Oct-‐13 Dec-‐13 Feb-‐14 Apr-‐14

-‐6

-‐4

-‐2

0

2

4

6

8

Mar-‐13 Apr-‐13 May-‐13 Jun-‐13 Jul-‐13 Aug-‐13 Sep-‐13 Oct-‐13 Nov-‐13 Dec-‐13 Jan-‐14 Feb-‐14 Mar-‐14 Apr-‐14

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The models are capable of isolating the effect of each variable, in order to achieve the optimization of media investment.

Sale

s (‘0

00 K

g)

Weighted distribution creates the baseline of

sales.

Media investment contributes to sales

Sales are price sensitive: The higher the price per unit, the more sales decrease

Promotions make sales grow

Product A sales - Key drivers

Sale

s (‘0

00 K

g)

Growth levels: Possibly advertising + promotions extra

effect

Media investment contribution

Possibly advertising + promotions extra effect

Note: Media investment explains 4.21% of real sales.

Contribution of variables

Note: Media investment explains 8.20% of real sales.

Product B sales - Key drivers

60.9%24.0%

4.12%5.3%

4.8% 0.8%

Weighted SellingDistribution

Price

Media Investment

Promotions

Rest of positivevariables

Rest of negativevariables

55.1%41.9%

0.66%0.4% 1.6% 0.3%


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-‐2,000

-‐1,000

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2,000

3,000

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31/03/13

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-‐5,000

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5,000

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15,000

31/03/13

14/04/13

28/04/13

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26/05/13

09/06/13

23/06/13

07/07/13

21/07/13

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05/01/14

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16/02/14

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30/03/14

13/04/14

27/04/14

11/05/14

25/05/14

08/06/14

22/06/14

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Impressions and Average position are the main drivers of Google Paid Search clicks models.


More than 90% of Product A clicks are explained by 3 main variables: Impressions, Average position and Brand queries .

Goo

gle

Paid

Sea

rch

clic

ks

Goo

gle

Paid

Sea

rch

clic

ks

More than 80% of Product B clicks are explained by 3 main variables: Impressions, Average position and Brand queries.

Brand Queries have a positive effect on clicks.

Brand Queries have a positive effect on clicks.

As average position rises, number of clicks decrease

As average position rises, number of clicks decreases

Product A clicks - Key drivers

Product B clicks - Key drivers

The growing trend may be due to the internet penetration increase over time in Russia

See Search explanation

55.0%

20.0%

17.0%

3.2% 4.8%

66.1%12.4%

6.6%

12.4%

2.5%

Impressions

AveragePosition

Queries Knorr

Trend

Rest ofvariables


0%

5%

10%

15%

-‐5,000 15,000 35,000 55,000 75,000 95,000

Shawarma Ryba Burritos StroganoffCaesar Schnitzel Zhulien

3

Synergy between online and offline media. In general, products with higher offline investment also have higher CTR.

Position increases clicks. Increasing / decreasing average position increases / decreases clicks in a non linear way.

Average CTR vs. Offline Investment

Average

position

% of clicks gained /

lost

1 +35.2%

1.3 * -

2 -29.9%

3 -45.3%

Average

position

% of clicks gained /

lost

1 +58.4%

1.4 * -

2 -23.5%

3 -41.2%

*Note: Paid Search average position.

Product A Product B

Offline investment

CTR

In this case average position for Product A was 1.3. If average position had been constantly 1, total clicks should increase by 35.2%. If average position had been 2 (or constantly 2), total clicks should decrease by -29.9%.

CTR

0%

5%

10%

15%

20%

25%

1 1.5 2 2.5

Burritos Stroganoff Zhulien Ryba Caesar Shawarma SchnitzelAverage Position

CTR vs. Average Position


-‐1,000

-‐500

0

500

1,000

1,500

2,000

2,500

3,000

Jan-‐12

Feb-‐12

Mar-‐12

Apr-‐12

May-‐12

Jun-‐12

Jul-‐1

2Au

g-‐12

Sep-‐12

Oct-‐12

Nov-‐12

Dec-‐12

Jan-‐13

Feb-‐13

Mar-‐13

Apr-‐13

May-‐13

Jun-‐13

Jul-‐1

3Au

g-‐13

Sep-‐13

Oct-‐13

Nov-‐13

Dec-‐13

Jan-‐14

Feb-‐14

Mar-‐14

Apr-‐14

Queries Base level GRPs Total Knorr Extra GRPs Advertising Rest of positive variables Decrease queries levelBrand

80.1%

10.3%

9.6%

3

Offline advertising of the brand helps increase Queries (brand online interest), especially when new products are launched.

Advertising helps queries to rise

Goo

gle

Bran

d qu

erie

s

Trend of queries

New references launch period

New references launch period

In some periods, queries are lower than expected.

Key drivers Brand Queries


10.3% of Brand queries come from offline investment.

See Queries explanation


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Based on their contribution to sales, it is possible to calculate the ROI for each media:

4

% ROI

Media contribution (‘000kg) Product A

Product A

Product B

Product B

TV contribution to sales is 3.63 thousand kilos in its total period (Mar 13 – Apr 14)

TV contribution to sales is 9.33 thousand kilos in its total period (Oct 11 – Apr 14)

For each million Rubles invested on TV, Product A sales rise 0.76% over average.

3.63

0.02

0.14

TV

Search

Display

9.33

0.23

0.49

TV

Search

Display

0.76%

0.57%

0.59%

TV

Search

Display

1.04%

1.69%

0.86%

TV

Search

Display


TV is the media with the highest contribution to sales. However, in the case of lower investments, Search and Display have bigger contributions.

Maximum investment

Product A Product B

Note: As online investment is low and the market is growing, we estimated the sales contribution of Search and Display for higher investments using Conento’s benchmark for these media (see dotted lines)

For small budgets, online investment has a bigger contribution to sales than TV

0

0.5

1

1.5

2

2.5

0 10,000 20,000 30,000

Con

tribu

tion

('000

Kg)

Monthly Investment ('000 Rb)

0

0.1

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0.3

0.4

0.5

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0.7

0.8

0 10,000 20,000 30,000

Con

tribu

tion

('000

Kg)

Monthly Investment ('000 Rb)


0%

20%

40%

60%

80%

100%

11 12 13 14 15 16 17 18

% In

vestmen

t by med

ia

Total investment (M RB)% Search % Display % TV

SC1 SC2 SC3 SC4 SC5 SC6 SC7

0%

20%

40%

60%

80%

100%

17 18 19 20 21 22 23 24

% In

vestmen

t by med

ia

Total investment (M RB)% Search % Display % TV


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Optimizing media investment, Unilever should increase advertisings contribution to Product A sales by 2.3%, and to Product B by 8.1%.

See optimization methodology

89.3%

1.7%9.1%

93.8%

1.0% 5.2%

93.6%

0.4% 5.9%

96.4%

0.3% 3.3%

Product A Product A monthly average investment

Optimized investment distribution by scenario Optimized investment

+2.3% Increased contribution

due to advertising

TV: -2.8 pp Search: +0.1 pp Display: +2.6 pp

TV

Search Display Search Display

TV

Product B Product B monthly average investment

Optimized investment distribution by scenario Optimized investment

+8.1% Increased contribution

due to advertising

TV: -4.5 pp Search: +0.7 pp Display: +3.9 pp

TV

Search Display Search Display

TV

Product A average investment

Product B average investment

Optimized vs. real investment

Optimized vs. real investment


0%

20%

40%

60%

80%

100%

28 30 32 34 36 38 40 42

% In

vestmen

t by med

ia

Total investment (M RB)% Search Jelly % Search DP % Display DP + J% TV Jelly % TV DP


% Search Prod. A

% TV Prod. A

% Search Prod. B

% TV Prod. B

% Display Prod. A + B

If we optimized media investment between the 2 products, advertisings contribution to total sales should rise by 23.7%, as Product B sales response is higher.

4

49.3%46.2%

0.7%0.3% 3.6%

37.4%

60.0%

0.4%0.2% 2.1%

+23.7% Increased

contribution due to advertising

Product A + Product B average investment

Product A + Product B

TV Prod. A: -13.8 pp TV Prod. B: +11.9 pp Search Prod. A: +0.1 pp Search Prod. B: +0.3 pp Display: +1.5 pp

Product A + Product B monthly average

investment Optimized

investment Optimized investment distribution by

scenario

Search Prod. B

Display Search Prod. A

TV Prod. A

TV Prod. B

Search Prod. B

Display Search Prod. A

TV Prod. A TV Prod. B

Optimized vs. real

investment


•  Main drivers of sales are Structural Variables The most important contributions to Product A and Product B sales are generated by structural variables (weighted distribution, price, and promotions) though advertising has a clear extra effect on sales. The total impact of media on short term sales is 8.20% for Product A and 4.21% for Product B.

•  Both TV advertising and a better position in Paid Search increase clicks Products that have TV investment, have a higher CTR than those that don’t.

•  TV Advertising has a positive impact on Brand Queries 10.3% of Brand queries are due to TV advertising. Queries also have a significant impact on clicks: they account for 17.0% of Product A clicks and 6.6% of Product B clicks.

•  Increasing Online investment will increase sales Currently TV is the media that has the highest contribution to sales due to its higher share of investment. There is an opportunity to increase sales through higher investment in Search and Display.

•  There are opportunities to optimize media investment in Product A and Product B and increase sales If we maintain the average levels of investment in Product A and Product B and optimize media mix (decreasing TV and increasing search and display investment), then advertisings contribution to sales could improve by 2.3% and 8.1% respectively (and 23.7% with a global optimization of investment between the 2 products).

Summary of Main Learnings.


Thank you


A web user: Enters in www.google.ru In the Google Query String makes a search: · Brand · Non-brand Clicks the impression related to the brand (in the Paid Search Area - SEM) and goes to its webpage.

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We explain these clicks

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Go back

Brand food


Brand Queries (related to the brand)

keywords:

Brand name Misspelled Brand name

Brand web page Brand + other words

…

Queries

Model

•  When working with Brand queries we added up all keywords with representative volume regarding the brand, as showed above.

•  The main difficulty was that we had quarterly data until March 2013. For the rest of the period we had monthly data.

How do we consider the brand queries?

Go back


Total monthly Budget (in RB)

Monthly optimal investment by

media (in RB)

Effectiveness response curves

Input Output Optimization

Once we have defined the data set, parameters, variables and

initial restrictions in the optimization software, we set

the budget to optimize.

With a non linear algorithm, we try to get the highest

contribution to sales using the calculated curves.

We obtain the budget share by media that guarantees the

highest response for sales for the given budget.

How do we do the optimization to allocate spending by media?

Go back

Documents

Online & Offline Media ROI Measurement and Media … & Offline Media ROI Measurement and Media Mix Optimization January 2015 Food brand Google Confidential and Proprietary 2 Contents