Business Analytics Capstone Framework for Strategy

Philipp Oser

05/10/2016

Introduction – Yahoo Company Overview Executive Summary

Sources: Yahoo (2016), ycharts.com (2016)

Yahoo is in a complicated business situation as indicated by falling profits and revenues

Key Information

Founded: 1995

Headquarters: Sunnyvale, California, U.S.

CEO: Marissa Mayer

Industry: Internet, Computer Software

Main products: Yahoo News, Yahoo Mail,

Yahoo Finance, Yahoo Sports, Yahoo Search,

Yahoo Messenger, Tumblr, Flickr

Revenue: US$4.96 billion (2015)

Operating income: US$-4.74 billion (2015)

Net income: US$-4.35 billion (2015)

Total assets: US$45.20 billion (2015)

Total equity: US$29.04 billion (2015)

Employees: 9,400 (2016)

Yahoo Stock Price (1995-2016), Quarterly Revenue (2012-2016)

Introduction – Adblocker usage Executive Summary

Sources: PageFair (2016), Yahoo (2016), eMarketer (2016), Fortune (2015)

Adblock usage is on the rise globally, with high growth expected in mobile sector

Global monthly active ad blocking software users (desktop), 2010-2015

Problem Statement

What problems does ad-blocking

software present to a firm like Yahoo?

Problem Statement– The relationship between Yahoo’s advertising revenue model and the adblocker industry

General revenue model: 1. User opens a

website, 2. Website connects to ad servers

and ads are displayed, 3. Advertisers pay for

users clicking on the ads/viewing content

Yahoo’s advertising solutions:

Search: Ads are placed within search results

Premium: Ad placement within special

environments, e.g. major events, magazines

Native: Ads are integrated in the content

format of website, e.g. Tumblr, Hearst

Video: Ads are included in video platforms

Audience: Targeting of users within and out

of the Yahoo network in a granular way

Current performance:

Yahoo net digital ad revenues are projected

to fall 13.9% year-on-year in 2016 to $2.83

billion

Yahoo’s share of the overall digital ad

market projected to decline from 2.1% in

2015 to 1.5% in 2016

General mode of function: Adblocker puts

a wall between ad server and websites,

blocking a) the display of ads completely, or

b) of all ads that are not allowed (“white-

listed” by the adblocker provider)

User motivation for using adblockers:

Avoid obnoxious ads

Protect privacy

Maximize speed and save bandwidth

Save battery life

Minimize distraction

Adblocker impact on digital ad market:

On desktop, ca. 200 million people use some

kind of adblocker

Adblocking estimated to cost advertisers

$22 billion in 2014

Mobile adblocker could reduce total ad

revenue between 3% and 11% in future

Impact projected to rise due to iOS 9

support and new mobile adblocking apps

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Sources: Yahoo (2016), eMarketer (2016), Fortune (2015), Guardian (2015), TIME (2015), Wharton School (2015),

Wired (2015), The New York Times (2007)

By blocking the display of ads, adblockers directly affect Yahoo‘s ad revenue model

Problem Statement– Implications of adblockers on Yahoo’s customers, revenue, and internal organization

Sources: Yahoo (2016), eMarketer (2016), Business Insider (2015), PageFair (2015), Wharton School (2015)

Adblockers impact Yahoo in all three areas. The loss of customer data for analytics is at the

core of the problem. A comprehensive analytics strategy to address the impacts is needed.

1. Customers 2. Revenue & Operations

Yahoo relies on collecting, analyzing,

and leveraging customer data for

selling advertisements. Adblockers

block data tracking. Consequences:

1. Market research: Available data for

research (explanatory, descriptive,

causal) shrinks, affecting robustness.

2. Some users (e.g. affluent) might use

adblockers, data declines a lot. How

to sell ads targeted to such groups?

3. Decision-making gets harder, as less

data means less information on the

validity and reliability of data.

4. Marketing strategies for Yahoo and

its customers are based on

measuring ROI of ads. Incomplete

data prevents a holistic view and

evaluating the “big picture”.

3. Internal Organization

Projected impact: Adblocker lead to

falling ad revenues, as blocking ads

prevents consumer interaction.

1. Yahoo needs to predict the effect of

adblockers for both desktop and

mobile on revenue, in order to

implement a strategy. Simple time-

series short-term forecasting of ad

sales must be conducted.

2. Loss caused by adblockers must be

included in forecasting financial

statements using forecasted sales.

3. Non-financial factors: Customer

satisfaction likely to decline when

ads are blocked. The impact on

future revenue growth must be

assessed for strategy development,

measures developed.

1. Staff turnover and brain-drain

possible, as adblockers impact

sales and revenue.

2. Hiring: Yahoo must determine,

what are the fundamental drivers

of ad sales are. As sales structure

changes, new selection criteria are

needed to ensure right selections.

3. Performance evaluations: The

rising use of adblockers lowers

control over exact outcomes for

employees in the ad-based

departments. New evaluation

criteria are needed.

4. Attrition: Perception of future

opportunities/ trajectory in Yahoo

fall, therefore retention problems

and loss of knowledge occur.

Strategy

Strategy – “Yahoo AdXperience” Baseline Assumptions for Strategy based on empirical evidence

As evidence indicates, creating an enhanced user experience is the key for addressing

adblocker use among the online audience, while “punishing” users is not effective.

Blocking users is not a viable strategy.

Approach is turning potential customers away (PageFair 2016)

Paywalls do not work: only 11% of customers willing to pay (SourcePoint 2016)

Yahoo’s attempt to block adblock-users from Yahoo Mail in 2015 unsuccessful

1

Users are not fundamentally opposed to advertising.

GroupM’s “Interaction Report 2016”: surveyed users not against advertising in

general, but want concepts that enhance user experience instead of distorting it 4

Legal action against adblockers is not effective, innovation could be.

Film and music industries’ legal action against filesharing is an example that

criminalizing users does not work (Wharton 2015)

Business model innovations proved effective (e.g. iTunes, Spotify, Netflix).

3

Asking the user to turn off adblockers is not viable, too.

Less than one visitor in 300 actually turns off the adblocker after viewing such

messages (PageFair 2016) 2

Consequences for

proposed strategy

“Yahoo AdXperience”

1. Poor quality

advertising root of

adblocker use

2. Avoid concepts

that have proven to

be ineffective

3. Focus on the

consumer and his

preferences

Sources: Yahoo (2016), GroupM (2016), PageFair (2016), Wharton School (2015)

Strategy – “Yahoo AdXperience” Part I: Customers

Quality offensive and technology innovation address adblocker problems.

Current

customer

strategy

1. Streamline advertising technology: Yahoo should establish an in-

house-project team consisting of software engineers and advertising

staff to review possibilities for faster advertising that consumes less

bandwidth and battery life. As Yahoo’s reliance on mobile grows,

consumers require this, and Google has been launching its Accelerated

Mobile Pages project, the company must act on this.

2. Re-shift the product portfolio: As Yahoo has begun with Gemini, it

need to even more focus on the areas native advertisements and

mobile. Native advertising is less targeted by adblockers and through

the integration into site contents, not intrusive. These ads must be

based on optimized algorithms that are not only based on an

individual’s page view history, but also on his/her ad impression history

(Dreze&Hussherr 2003). This is relevant for both PC and mobile.

3. Define “experience ads” standard: Yahoo should incorporate a

framework for allowable ads, based on IAB’s LEAN (light, encrypted, ad

choices supported, non-invasive) standard. This must be

communicated to advertisers. It ensures that the contents follow the

experience that user demand within the main growth areas mobile and

native.

New strategy

centers on

quality and

customer

experience

Strategy – “Yahoo AdXperience” Part II: Revenue & Operations

Both financial and non-financial factors are adressed to predict strategic impact of

adblockers.

Adblocker

impact revenues

and operations

1. Yahoo needs to predict the effect of adblockers for both desktop

and mobile on revenue, in order to implement a strategy. In

accounting, this can be done by using sales forecasts.

2. Loss caused by adblockers must be included in operational

forecasts. Simple time-series short-term forecasting of ad sales

must be conducted. Three approaches must be considered: a) make

the forecasting by relying on external data provided by these

specialized start-ups or do the analytics internally, in this case either b)

subjective forecasting (Delphi Method) or c) quantitative. Simple time-

series short-term forecasting based on moving averages performs well

and can be implemented easily. Yahoo should not focus on long-term

forecasting, as technology changes are too frequent in the ad market

and we have limited data for adblocker usage behavior.

3. Non-financial factors: Customer satisfaction likely to decline when

ads are blocked. The impact on future revenue growth must be

assessed for strategy development, measures developed. An

enhanced cooperation with advertisers and new strategy for winning

adblocker-users back addresses these points. Yahoo should conduct

regular workshops with its main customers to review this point.

Clear picture

of impact,

both

financially

and non-

financial.

Strategy – “Yahoo AdXperience” Part III: Internal Organization

Complete reframing of organizational structure: Analytics approach combined with change

of hiring practices and evaluations addresses increasingly volatile business environment.

Organization

must be

prepared for

adblocker

effects

1. New organizational structure for digital advertising team. Teams

are now structured based on the current product areas: 1. Search, 2.

Premium, 3. Native, 4. Video, 5. Audience. Managers of each team get

enhanced responsibility to implement new strategies and try different

approaches (e.g. adaptive, visionary, shaping). Each team includes one

data scientist from the analytics unit. This structure on the one hand

gives the team the flexibility they need to test innovative concept in a

volatile and uncertain environment.

2. Hiring: For each team, key performance indicators and skill profiles are

developed based on a best-practice analytics approach. These are

then incorporated into the staffing processes. The product-based

structure allows for more flexibility in hiring different profiles.

3. Performance evaluations: As teams compete and cooperate

internally, the evaluation can be adjusted and employees can be

assessed against four team performances. As uncertainty in the ad

market environment is high for all teams, performance evaluations

emphasize process. Each Data Scientist in the product teams works

closely with HR to understand and focus on which processes drive

values and lead to desired outcomes.

Organization

is prepared

for change

caused by

adblockers.

Effects and Measurement

“Yahoo AdXperience”: Effects & Measurement Part I: Customers

Anticipated Effects Strategic Measures Measurement

Yahoo should establish an in-house-project

team consisting of software engineers and

advertising staff to review possibilities for faster

advertising that consumes less bandwidth and

battery life. As Yahoo’s reliance on mobile

grows, consumers require this, and Google has

been launching its Accelerated Mobile Pages

project, the company must act on this. `

1. Advertising traffic decreases and website

speed increases as ads consume less

bandwidth, therefore increasing

acceptance especially within mobile users.

2. Rise of adblockers on mobile devices

grows significantly lower or can be halted,

so the mobile consumer segments grows

in percentage of ad viewers.

1. Bit-rate of the transmission capacity over

network communication system compared

with old ad performance data. Website

download speed in kb per second.

2. Percentage of mobile users regarding the

Click Through Rate (CTR) of ad and

market data concerning adblocker use on

mobile devices

1. Streamline advertising technology

Focus on the areas native advertisements

and mobile. Native advertising is less targeted

by adblockers and through the integration into

site contents, not intrusive. These ads must be

based on optimized algorithms that are not only

based on an individual’s page view history, but

also on his/her ad impression history

(Dreze&Hussherr 2003).

1. Higher percentage of ads watched on

mobile devices.

2. Higher percentage of native ads instead of

the other Yahoo products, e.g. premium.

3. More user data available (especially in

native segment) for analytics, as ads

not/less targeted by adblockers.

1. Percentage of mobile users regarding the

Click Through Rate (CTR) of ad (see 1.2)

2. Percentage of native ads in advertising

revenue and Click Through Rate (CTR) of

native advertisements.

3. Total number of ad views and clicks. Total

ad revenue per monthly average user.

2. Re-shift the product portfolio

Yahoo should incorporate a framework for

allowable ads, based on IAB’s LEAN (light,

encrypted, ad choices supported, non-invasive)

standard to ensure user acceptance of content.

1. Higher acceptance of digital ads by users,

espcially user segments that have shown

highest use of adblockers (young, tech-

savvy, affluent).

1. Reach, frequency and gross rating points

(GRPs) by age, gender, demographic

market for measuring segments. Total ad

revenue for measuring macro impact.

3. Define advertisement quality standard

“Yahoo AdXperience”: Effects & Measurement Part II: Revenue & Operations

Anticipated Effects Strategic Measures Measurement

Yahoo needs to predict the effect of adblockers

for both desktop and mobile on revenue, in

order to implement a strategy. This can be

done using sales forecasts.

1. Total advertising decreases. Yahoo digital

ads revenue projected to fall 13.9% year-

on-year in 2016 to $2.83 billion. Adblocker

impact plus competition as main factors.

2. Mobile advertising revenue increases, as

adblockers are fairly new and less applied

here, plus user growth. In the long-run,

adblocker impact could cause 3-11% loss.

1. Revenue from digital advertising business

unit in US$ billion/quarter.

2. Operational forecasting: 1. Forecast future sales (all

other line items function of future sales forecasts),

2. Use forecasted sales to construct pro forma

income statements, 3. Use forecasted sales to

construct pro forma balance sheet, 4. Use pro

forma income statements and balance sheets to

construct the pro forma statement of cash flows.

1. Revenue impact

Loss caused by adblockers must be included in

operational forecasts. Three approaches : a)

relying on external data provided by specialized

start-ups, internally use of b) subjective

forecasting (Delphi Method) and c) quantitative

methods.

1. Stable demand for advertising products,

but significant adblocker effects negatively

impact insights concerning user behavior.

Highly volatile business environment

makes forecasts difficult. Therefore,

combining subjective and quantiative

apporaches while collectiing more data is

promising.

1. Simple time-series short-term forecasting

based on moving averages performs well

and can be implemented easily. Data bias

must be measured. Yahoo should not

focus on long-term forecasting, as

technology changes are too frequent in

the ad market and we have limited data for

adblocker usage behavior.

2. Operational forecast

Customer satisfaction likely to decline when

ads are blocked. Enhanced cooperation with

advertisers addresses this issue. Regular

workshops with main customers to review ad

effectiveness performance to be conducted.

1. Customer satisfaction decreasing in short-

term, as adblockers impact revenue and

available data for Yahoo analytics which

customers rely on. High uncertainty.

1. Build regression model with customer

metrics (e.g. overall satisfaction, likelihood

of renewal), operational metric, and

financial outcomes of interest, to analyse

impact on future revenue growth. .

3. Non-financial factors: Customer Satisfaction

“Yahoo AdXperience”: Effects & Measurement Part III: Internal Organization

Anticipated Effects Strategic Measures Measurement

Teams are now structured based on the current

product areas: 1. Search, 2. Premium, 3.

Native, 4. Video, 5. Audience. Managers of

each team get enhanced responsibility to

implement new strategies and try different

approaches (e.g. adaptive, visionary, shaping).

Each team includes at least one data scientist

from the analytics unit to ensure capabilities.

Collaboration (the action of working with others

to create something) between employees

inside the organization will intensify, both

within and between teams. Through the team

structure, cooperation and friendly competition

among functions (e.g. Data Scientists, mobile

experts, ad experts) will increase and best

practices can be implemented faster.

Organizational Network Analysis (ONA).

Collaboration networks are 1. described, 2.

mapped, 3. evaluated, and 5. interventions based

on these insights implemented. Sampled are all

departments directly related to generating digital

ad revenues. A survey is then created for data

collection. UCINET is used for visualization. The

ONA results are linked to ad revenues and

interventions realized (e.g. avoid work overload).

1. New organizational structure for digital advertising team

For each team, key performance indicators and

skill profiles are developed based on a best-

practice analytics approach. These are then

incorporated into the staffing processes. The

product-based structure allows for more

flexibility in hiring different profiles. Data

Scientists in each team ensure analytics

approach for selecting hires.

1. Diversity of teams grows as there will be

more tailored hiring staffing criteria for

each team, e.g. journalists working in the

ad team for native advertising.

2. Higher performance due to analytics-

based selection process.

3. Higher attrition due to better job fit.

1. HR data on staff composition.

2. Predicting hire performance through

multivariate regression, apply selection

correction to account for who was hired

and attrition from sample.

3. Use of survival/hazard rate models to test

which factors accelerate risk of exit.

2. Hiring Strategy

As uncertainty in the ad market environment is

high, performance evaluations emphasize

process. Each Data Scientist in the teams

works closely with HR to understand which

processes drive values and maximize outcome.

Fundamental drivers of value in digital

advertising teams identified. New performance

evaluation includes adblocker effects by

including 50% what is achieved and 50% how

it is achieved, therefore minimizes bias.

Use analytics to identify drivers, e.g. if the

number of advertising customer contacts

influences the number of sales. A broad set of

objectives and variables is to be considered for

this. Key performance indicators are derived.

3. Performance Evaluation

“Yahoo AdXperience”: Effects & Measurement Some comments on measuring adblock usage impact on Yahoo revenue

Comments

I ran a linear regression with Yahoo annual revenues

as dependent variable (Source: Yahoo), and a set of

independent variables including e.g. internet users

global and Adblock users (Source adblock users:

Adobe/PageFair).

1. I have removed all independent variables with

exception of adblock users due to multicollinearity.

2. The time series data is limited, with only 2010-2015

available for adblocker use. This is clearly a very

small sample and inferences can hardly be drawn.

3. Based on our regression, we see that based on our

small sample, adblocker users total does not come

out as significant with p>0.3. R squared is very bad

as well, even worse than the baseline model.

4. However, the coefficient estimate for adblock users

is negative and at least points to a negative impact

on Yahoo revenue. Plot points to lower Yahoo

revenue when adblocker user number rise.

This is to be seen more of an illustrative approach

than a valid statistical analysis due to very limited

data availability. Based on the strategy I outlined, I

estimate that the negative effect should be smaller,

but that cannot be included due to data limitations.

“Yahoo AdXperience”: Effects & Measurement Some comments on possible implementation

1

2

3

Data Collection and Cleaning

All measurements, as this is an analytics-based strategy, require data, which can either be

collected internally (e.g. HR, user analytics), or externally (e.g. adblocker use, forecasting)

Data can be collected via a variety of methods, e.g. using surveys (e.g. for Organizational

Network Analysis) or by reviewing the existing databases, also via purchasing

Data must be cleaned and organized accordingly

Model Simulation and Interpretation

The clean data must be analyzed and modelled with appropriate tools and models (including

e.g. as mentioned multivariate regression or survival/hazard rate models)

Software e.g. R, Python, Hadoop/Spark (for big data), Tableau (for visualization), UCINET

Results must be traced back to the initial question and validity and reliability ensured

CRISP-DM can be used here as a standard process model, see also Peng (2016) for this

Implementation

First, top management support must be ensured, so make sure all presentation materials can

be understand by leadership without prior analytics knowledge

Second, run workshops with all affected teams and departments and implement their

feedback from early on (include Kotter’s change management principles)

Track and monitor results and performance and adjust when necessary

I cannot detail the implementation of measurements here, due to two reasons: 1. most measures require internal

data that is not available, 2. I could not include the raw data as I can only upload one file. Critical review would not

be possible without making the raw data available for recalculation though. I therefore outline the next steps here.

Thank you for reading.

Thank you for your time and feedback

for this presentation. I wish you all the

best and success for your career.