Here the Ebriks SEO services company share a most effective presentation on the basis of the collected data that is pointing out the investment in the media.Ebriks are also involved in this field to provide their best services.if you know more about this please visit<a>SEO services</a>,<a>Best SEO Company</a>
" To rise to withstand hard core competition one needs to alter policies Six Sigma was my seminar topic in my college It helped me in my academics so standards upgraded that to achievable in any institute any field you are it serves its role.
" To rise to withstand hard core competition one needs to alter policies Six Sigma was my seminar topic in my college It helped me in my academics so standards upgraded that to achievable in any institute any field you are it serves its role.
How important are the rules used to create smart beta portfoliosRalph Goldsticker
Most Smart Beta presentations are about: “What and Why?”
This presentation addresses: “Do the rules used to construct a Smart Beta portfolio matter?”
Our approach was to use alternative portfolio construction rules to simulate multiple 25-year return histories for Low Volatility, Fundamental Indexing and Momentum strategies, and then compare their average returns, risks, drawdowns and factor exposures.
How important are the rules used to create smart beta portfoliosRalph Goldsticker
Most Smart Beta presentations are about: “What and Why?”
This presentation addresses: “Do the rules used to construct a Smart Beta portfolio matter?”
Our approach was to use alternative portfolio construction rules to simulate multiple 25-year return histories for Low Volatility, Fundamental Indexing and Momentum strategies, and then compare their average returns, risks, drawdowns and factor exposures.
[DSC Europe 22] Using AI to solve complex business problems: optimizing markd...DataScienceConferenc1
Lockdowns during the Covid-19 pandemic created huge inventory surpluses across the fashion industry. Seasonal markdowns are a standard strategy to deal with dead inventory, but established markdown practices provided diminishing returns amidst the compounding surplus. Fashion companies needed to make markdowns more effective, but developing new processes wasn’t easy: reticketing costs, branding requirements, and logistical constraints traditionally require complex business rules that constrain impact. A prescriptive approach that automatically determined intelligent discount strategies by combining elasticity demand and forecast modeling with linear programming made optimal markdowns feasible— and delivered critical turnover without sacrificing margin.
Walmart Sales Prediction Using Rapidminer Prepared by Naga.docxcelenarouzie
Walmart Sales Prediction Using Rapidminer
Prepared by : Nagarjun Singharavelu
I. Introduction:
Wal-Mart Stores, Inc is an American Multinational retail corporation that
operates a chain of discount department stores and Warehouse Stores. Headquartered in
Bentonville, Arkansas, United States, the company was founded by Sam Walton in 1962 and
incorporated on October 31, 1969. It has over 11,000 stores in 27 countries, under a total 71
banners. Walmart is the world's largest company by revenue, according to the Fortune Global
500 list in 2014, as well as the biggest private employer in the world with 2.2 million employees.
Walmart is a family-owned business, as the company is controlled by the Walton family. Sam
Walton's heirs own over 50 percent of Walmart through their holding company, Walton
Enterprises, and through their individual holdings. The company was listed on the New York
Stock Exchange in 1972. In the late 1980s and early 1990s, the company rose from a regional to
a national giant. By 1988, Walmart was the most profitable retailer in the U.S. Walmart helps
individuals round the world economize and live better.
The main aim of our project is to identify the impact on sales throughout
numerous strategic selections taken by the corporate. The analysis is performed on historical
sales data across 45 Walmart stores located in different regions. The foremost necessary is
Walmart runs many promotional markdown events throughout the year and we have to check
the impact it creates on sales during that particular period. The markdowns precede prominent
holidays, the four largest of which are the Labor Day, Thanksgiving and Christmas. During these
weeks it is noted that there is a tremendous amount of change in the day-to-day sales. Hence
we tend to apply different algorithms which we learnt in class over this dataset to identify the
effect of markdowns on these holiday weeks.
II. Information about dataset:
We had taken four different datasets of Walmart from Kaggle.com
containing the information about the stores, departments, average temperature in that
particular region, CPI, day of the week, sales and mainly indicating if that week was a
holiday. Let us explain each dataset in detail.
Stores:
The no. of attributes in this dataset is 3.
They are store number, type of store and the size of store.
Output attribute is the size of store.
There are 45 stores whose information is collected.
Stores are categorized into three such as A, B and C, which we assume it to be
superstores containing different types of products.
The store size would be calculated by the no. of products available in the particular
store ranging from 34,000 to 210,000.
Train:
This is the historical training data, which covers to 2010-02-05 to 2012-11-01.
It consists of the store and department number.
Date of the week.
Weekl.
How important are the rules used to create smart beta portfoliosRalph Goldsticker
Most Smart Beta presentations are about: “What and Why?”
This presentation addresses: “Do the rules used to construct a Smart Beta portfolio matter?”
Our approach was to use alternative portfolio construction rules to simulate multiple 25-year return histories for Low Volatility, Fundamental Indexing and Momentum strategies, and then compare their average returns, risks, drawdowns and factor exposures.
How important are the rules used to create smart beta portfoliosRalph Goldsticker
Most Smart Beta presentations are about: “What and Why?”
This presentation addresses: “Do the rules used to construct a Smart Beta portfolio matter?”
Our approach was to use alternative portfolio construction rules to simulate multiple 25-year return histories for Low Volatility, Fundamental Indexing and Momentum strategies, and then compare their average returns, risks, drawdowns and factor exposures.
[DSC Europe 22] Using AI to solve complex business problems: optimizing markd...DataScienceConferenc1
Lockdowns during the Covid-19 pandemic created huge inventory surpluses across the fashion industry. Seasonal markdowns are a standard strategy to deal with dead inventory, but established markdown practices provided diminishing returns amidst the compounding surplus. Fashion companies needed to make markdowns more effective, but developing new processes wasn’t easy: reticketing costs, branding requirements, and logistical constraints traditionally require complex business rules that constrain impact. A prescriptive approach that automatically determined intelligent discount strategies by combining elasticity demand and forecast modeling with linear programming made optimal markdowns feasible— and delivered critical turnover without sacrificing margin.
Walmart Sales Prediction Using Rapidminer Prepared by Naga.docxcelenarouzie
Walmart Sales Prediction Using Rapidminer
Prepared by : Nagarjun Singharavelu
I. Introduction:
Wal-Mart Stores, Inc is an American Multinational retail corporation that
operates a chain of discount department stores and Warehouse Stores. Headquartered in
Bentonville, Arkansas, United States, the company was founded by Sam Walton in 1962 and
incorporated on October 31, 1969. It has over 11,000 stores in 27 countries, under a total 71
banners. Walmart is the world's largest company by revenue, according to the Fortune Global
500 list in 2014, as well as the biggest private employer in the world with 2.2 million employees.
Walmart is a family-owned business, as the company is controlled by the Walton family. Sam
Walton's heirs own over 50 percent of Walmart through their holding company, Walton
Enterprises, and through their individual holdings. The company was listed on the New York
Stock Exchange in 1972. In the late 1980s and early 1990s, the company rose from a regional to
a national giant. By 1988, Walmart was the most profitable retailer in the U.S. Walmart helps
individuals round the world economize and live better.
The main aim of our project is to identify the impact on sales throughout
numerous strategic selections taken by the corporate. The analysis is performed on historical
sales data across 45 Walmart stores located in different regions. The foremost necessary is
Walmart runs many promotional markdown events throughout the year and we have to check
the impact it creates on sales during that particular period. The markdowns precede prominent
holidays, the four largest of which are the Labor Day, Thanksgiving and Christmas. During these
weeks it is noted that there is a tremendous amount of change in the day-to-day sales. Hence
we tend to apply different algorithms which we learnt in class over this dataset to identify the
effect of markdowns on these holiday weeks.
II. Information about dataset:
We had taken four different datasets of Walmart from Kaggle.com
containing the information about the stores, departments, average temperature in that
particular region, CPI, day of the week, sales and mainly indicating if that week was a
holiday. Let us explain each dataset in detail.
Stores:
The no. of attributes in this dataset is 3.
They are store number, type of store and the size of store.
Output attribute is the size of store.
There are 45 stores whose information is collected.
Stores are categorized into three such as A, B and C, which we assume it to be
superstores containing different types of products.
The store size would be calculated by the no. of products available in the particular
store ranging from 34,000 to 210,000.
Train:
This is the historical training data, which covers to 2010-02-05 to 2012-11-01.
It consists of the store and department number.
Date of the week.
Weekl.
Ebriks-Social media to achieve SEO success ebriksinfotech
Here we share a presentation on the social media to achieve SEO scuccess.Social media activities leads to the better search results and hence good page rank.Ebriks seo company provides the best SEO services in the social media.if you know more about this please visit @www.ebriks.com
Ebriks-An idea to change your bussiness growthebriksinfotech
For the success of every bussiuness the ideas should be imlemented properly and successfully.Ebriks are helping you to imlements the ideas in such a way so that your bussiness will definately grow by providing their best seo services.if you know more about this please visit<a>SEO services</a>,<a>Best SEO Company</a>
Ebriks are provides the best SEO services which best utilize the content marketing stategy. The cotent plays important role because it is valueable part in of the website.if you know more about this please visit www.ebriks.com
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
7. Agenda
• Business application of Marketing Mix
modelling
• A case study
• Strengths and weaknesses
• Brief introduction to more advanced
approaches: pooled regressions and structural
equations
8. Making BP’s media dollars work harder
• “Mindshare helped BP to make the most of their media
investments across the many states of the USA.”
• “BP engaged Mindshare to develop enhanced media
investment strategies to maximise sales and boost revenue
performance.”
• “Drivers of performance were quantified (e.g. media,
promotions, distribution, competitor effects) in seven USA
states, over three years”
• “Return on investment figures were calculated - both short
and long term - for 40 campaigns.”
9. Marketing Mix modelling
• Statistical methods applied to measure the impact of
media investments, promotional activities and price
tactics on sales or brand awareness
• Used to assist and implement a marketing strategy by
measuring:
– Effectiveness: contribution of marketing activities to sales
– Efficiency: short term and long term Return-On-
Investment of marketing spend
– Price elasticity
– Impact of competitors
10. MMM How does it work?
• A statistical model is estimated on historical data with sales as
a dependent variable and list of explanatory variables as
marketing activities, price, seasonality and macro factors
• The simplest and broadly used model is linear regression:
Salest 1 var 1t 2 var 2t ... t
• The output of the model is then used to carry out further
analysis like media effectiveness, ROI and price elasticity and
to simulate what-if scenarios
11. Factors that could drive sales
Advertising Promotions
Competition
TV Sponsorships
Seasonality
Radio Events
Weather
Print Price
Economic
Outdoor Adv quality
Demographic
Internet Distribution
Industry data
Merchandising
Salest 1 var 2 var ... t
1
t t
2
Sales
12. MMM project process
Set out objectives Data preparation
-Define scope •Collect data
-Discuss data •Validate, harmonize
availability and consolidate data
-Design data-warehouse •Present exploratory
analysis to client
Presentation Model development
•Interpretation of •Estimation
results •Diagnostics
•Learning and •Calculate ROIs, Price
recommendations elasticity and response
curves
13. Case study
• An energy company SPetrol wants to evaluate the advertising
investments of its retail business in the US from 2001 until
2004.
• Client’s questions:
• How much have we made through advertising?
• What is the return on investments of our media activities?
• Which marketing drivers have had the greatest effect?
• What’s the influence of price on our sales?
• Are we optimally allocating our budget across products ?
15. Advertising data
• The performance of TV and radio advertising is expressed in
terms of Gross Rating Points (GRPs) . A rating point is a
percentage of the potential audience and GRPs measure the
total of all rating points during and advertising campaign.
– GRPs (%) = Reach * Frequency
– Example: Let’s assume a commercial is broadcasted two
times on TV
1st time on air 2st time on air GRPs
25% of target 32% of target
57%
televisions are tuned in televisions are tuned in
16. Advertising data
• Spetrol has deployed 5 TV campaigns over the
sample with a total expenditure of 300 million $
• Each campaign lasted from 4 to 8 weeks
• Is there any relationship between sales and TV
advertising?
18. Carry over effect of TV
• The exposure to TV advertising builds awareness,
resulting in sales.
• ADStock allows the inclusion of lagged and non
linear effects
ADStockt ( ) GRPt ADStockt 1
0 1
• Alpha is estimated iteratively using least squares.
The estimate is then validated by media planners
20. Below the line promotions
• It may include
– sponsorship
– product placement
– sales promotion
– merchandising
– trade shows
• Usually represented by dummies (variables
equal to 1 when a promotion takes place and
0 otherwise)
21. Below the line promotions
Sponsorship
World Rally
Championship
Sale promotion
Sale promotion
5% Discountt
27. Model diagnostics
• Model:
– Significant F-stat and high R-squared
• Variables:
– Significant T-stats
– Coefficients must make sense
– Variance inflation factor low
• Residuals:
– Normality (Jarque-Bera)
– Absence of serial correlation ( Durbin Watson,
correlogram)
29. Estimated factors contribution to sales
Fitted Salest = estimated Intercept = 167,412
Can be interpreted as Brand Equity:
•Volume generated in absence of any marketing
activity
•Indicator of the strength of the brand and users’
loyalty
31. Estimated factors contribution to sales
in August
Peaks every year
Peacks every year
in August
Fitted Salest = 167,412 + 168* TVt + 161*Radiot +
166* OOHt Equity = Promotiont + 6507* Seasonailityt
+ 580* estimated Intercept = 167,412
Can be interpreted as Brand Equity
38. Does it really make sense?
TheDiminishing in
more I invest
media, returns I sell
the more
39. Response curves
NegExp a (1 exp(b GRPs ))
S a (1/(1 exp(b (GRPs mean(GRPs ))))
Taking into account
diminishing returns
40. Price elasticity
• Assumption: constant elasticity across the sample which
implies a linear relation between volume and price
• By using the coefficient of the regression, it is possible to
derive an estimate for price elasticity:
– Price coefficient = -12631
– Average price = 1.51 $
– Average volume sales = 154,000 Gallons
Avg Pr ice A 10% drop in price
Elasticity * coeff 0.12 increases sales by 1.2%
AvgSales
41. Dynamic price elasticity Elasticity changes with price
200,000
Weekly Volume and $ Sales vis-à-vis price of 1.75L
180,000
Volume (9L Cases)
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
Price (750 ml)
9
20.0
11
13
16
18
25
27
29
10
12
14
15
17
19
21
22
23
24
26
28
30
Volume
Elastic (>1): Demand is sensitive to price changes.
Estimated through non
Inelastic (<1): Demand is not sensitive to price changes linear regressions
42. Client’s questions
How much have we made through advertising?
• 1 billion $ driven by TV
• 500 million $ due to radio
• 200 million $ generated by Outdoor and
promotional activities
Investments in media generated 1.7
billion $ in revenue
43. Client’s questions
What is the return on investments of our media
activities?
For each dollar invested in TV you get 3.5 dollars
back
45. Are we optimally allocating our
budget across products ?
Maximum Optimal
GRPs
Marginal
Return Over Optimal GRPs
Point of
Saturation
Sub –Optimal GRPs
Maximum
Average Return
Invest more in Radio
and less in OOH
46. Marketing Mix – Sample Output
Marketing mix (sample output)
45
Carry Over Effect
5000 Diminishing Returns 40
4500 35
4000 30
Promo TV Saturation
3500
Weekly GRPs
Weekly Sales
25
3000
20
2500
Current Optimal 15
2000
1500 10
1000
5
500
0
0
0 20 40 60 80 100 120 140 160 180 Week1 Week2 Week3 Week4 Week5
Avg. Weekly GRPs
Diminishing Returns is the point were spending
additional GRPs does not results in additional
Simultaneous Effect sales.
Carry Over Effect (Ad Stock) relates to the
Volume
residual effect of an ad.
When all the components are layered on Base
Base/Seasonal TV/Radio/Print Direct Marketing Rates/Promotions
sales, it is clear what drivers contribute to sales
Time and when and their Simultaneous Effect.
47. Pros and cons
• Simple and intuitive • Correlation doesn’t imply
• The outcome is backed by causality
qualitative expertise and in • Risk of spurious regressions
field research especially when modelling
• Constructive way of running in levels
different scenarios and • Model highly depends on
evaluating past variables chosen
performance • Poor in forecasting
• Better with granular data
• Very successful method –
high turnover
48. Spurious statistics
• A high correlation
between sales and TV
could mean:
Sales Media – Either media causes
sales
– or sales causes media
– or a third variable causes
Income both sales and TV
What is the truth?
49. Non sense correlations
• Some spurious • On the other hand, a
correlations: low correlation doesn’t
– death rate and rule out the possibility
proportion of marriages of a strong relation:
Corr = 0.95
Corr = 0.0
– National income and
sunspots Corr = 0.91
– Inflation rate and
accumulation of annual
rainfall
•Correlations must support a theory
•Calculate correlations both in levels and differences
•Always look at scatter plots
51. New media
• Digital Marketing
– Display Marketing
– Search Engine Marketing (SEO & PPC)
– Affiliate Marketing
– Mobile Marketing
– Social Media
52. New media
• Data availability
– Impressions
– Clicks
– Post event activity
– Bespoke engagement metrics
• Example of a tracking centre:
– Double-click
54. Pooled regressions
Sales Local media Nat media Local Price
California California USA California + ... + error
sa
Nevada Nevada USA Nevada + ... + error
Oregon Oregon USA Oregon + ... + error
55. Pooled regressions example
1. SalesCalifornia = c11*TVCalifornia +
c12*TVOregon+c13*RadioCalifornia +c14*RadioOregon +
ErrorColifornia
2. SalesOregon = c21*TVCalifornia +
c22*TVOregon+c23*RadioCalifornia +c24*RadioOregon +
ErrorOregon TVC
SalesC c11 c12 c13 c14 TVO C
Sales c Radio
O 21 c22 c23 c24 C O
RadioO
Media effect is also tested across regions
56. How advertising effects consumers?
Understanding:
– the process by which advertising affects
consumers
– How the effects of advertising are spread over
time
– The role of different media
– The role of competitors
57. The purchase funnel
• A basic process that
leads to the purchase of Awareness
a product consists in:
– Awareness – costumer is
aware of the existence of
a product Consideration
– Consideration – actively
expressing an interest in
the company
– Purchase
Purchase
58. Working on survey data
• A sample of the target
audience is interviewed
about brand awareness,
consideration and choice
• Research agencies provide
awareness, consideration
and purchase time series in
% terms
– i.e. A purchase of 10% means
that 10 out of 100 interviewed
people purchased the product
59. Testing the purchase funnel
Awareness Consideration Purchase
Advertising first exercise its
influence on awareness. Via
awareness there is an effect on
Media consideration which drives the
consumer to purchase
61. Agenda
• Business application of Marketing Mix
modelling
• A case study
• Strengths and weaknesses
• Brief introduction to more advanced
approaches: pooled regressions and structural
equations