The document discusses using social media data and customer relationship management (CRM) data to perform customer segmentation, targeting, and positioning. Key points:
1. The study combines CRM purchase history and Facebook interaction data to calculate RFM scores and segment customers into four clusters.
2. The clusters represent different customer profiles based on shopping and dissemination behaviors - from high values in both to low values in both.
3. Customers in each cluster are targeted on Facebook pages and CRM categories. Positioning identifies topics of most interest for each cluster.
4. The hybrid analysis of social media and CRM data provides insights to better understand customer segments.
Digital Futures: Getting ROI from Social Media - Georgia HalstonBranded3
Georgia takes a detailed look at why investment in Social Media is a justifiable spend for your business and what return on investment you can achieve from effectively utilising its channels.
Principal in Charge of Assurance Department at Decosimo Tom Eiseman presented "Back to the Future Part I & II - Plans for Private Company Reporting" at the 2013 Decosimo Accounting Forum hosted by the University of North Alabama on July 19.
Digital Futures: Getting ROI from Social Media - Georgia HalstonBranded3
Georgia takes a detailed look at why investment in Social Media is a justifiable spend for your business and what return on investment you can achieve from effectively utilising its channels.
Principal in Charge of Assurance Department at Decosimo Tom Eiseman presented "Back to the Future Part I & II - Plans for Private Company Reporting" at the 2013 Decosimo Accounting Forum hosted by the University of North Alabama on July 19.
Türkiye'de gönüllülük ve hayırseverlik davranışı, gönüllülük kavranımın gençler arasında algılanışı, TEGV gönüllülerinin ve gençlerin değer ve tutumlarındaki farklılıklar
Decosimo's Darcia Wise and Elizabeth Powell presented this PowerPoint at the 2012 Decosimo Tax Seminar held on October 30, 2012 in Chattanooga, Tennessee.
A bankok világa erősen szabályozott, nem csak jogi szempontból, hanem a berögzült szokások szempontjából is. Bár lassan felismerik a bankok, hogy elemi üzleti érdekük széles rétegeket megszólítani szolgáltatásaikkal, de komoly paradigma váltásokat igényel ez a koncepió szinttől, esetenként a legkisebb mezőkig. Egy csokor valós példán keresztül megmutatnám, hogy milyen harcok formálják a jó UI koncepciókat, mire egy működő alkalmazás kerül a felhasználók kezébe.
Türkiye'de gönüllülük ve hayırseverlik davranışı, gönüllülük kavranımın gençler arasında algılanışı, TEGV gönüllülerinin ve gençlerin değer ve tutumlarındaki farklılıklar
Decosimo's Darcia Wise and Elizabeth Powell presented this PowerPoint at the 2012 Decosimo Tax Seminar held on October 30, 2012 in Chattanooga, Tennessee.
A bankok világa erősen szabályozott, nem csak jogi szempontból, hanem a berögzült szokások szempontjából is. Bár lassan felismerik a bankok, hogy elemi üzleti érdekük széles rétegeket megszólítani szolgáltatásaikkal, de komoly paradigma váltásokat igényel ez a koncepió szinttől, esetenként a legkisebb mezőkig. Egy csokor valós példán keresztül megmutatnám, hogy milyen harcok formálják a jó UI koncepciókat, mire egy működő alkalmazás kerül a felhasználók kezébe.
Finding Your Focus in the Social Media Maelstrom with Margaret Dawsonsemrush_webinars
The social media landscape is becoming more complex and crowded every day. It feels like marketers need to be everywhere in order to have a brand presence and gain awareness among the fast-growing social media audience.
But sometimes you need to step back and prioritize your digital marketing initiatives and focus on where you can have the greatest impact. If you are like most marketers, you have incredibly limited resources and bandwidth.
To help you prioritize, we've brought in Rival IQ CMO Margaret Dawson, who brings 20 years marketing leadership experience with startups and Fortune 500 companies, including Amazon.com, Microsoft and HP. In this Webinar, Maragaret will go through clear steps in identifying the right social channels for your organization, whether B2C or B2B. She will also provide best practices for the leading social networks.
This webinar will delve into key questions, such as:
Where is your target customer spending time?
Where are your competitors on Social Media?
What Social Media channels are driving the greatest traffic and conversion?
To help find the keywords that will resonate with your audience the most, attendees of this webinar will have access to a FREE 30-day trial of SEMrush Guru.
Game, set and perfect match. Wimbledon and IBMSusanna Harper
Game, set and perfect match. Wimbledon and IBM.
How IBM solutions help enhance the experience for tennis
fans and increase efficiency during Wimbledon and how
these solutions can be applied to your business
How Can Airlines Prevent A Social Media Strategy Crash Simplify360
This deck is a life saver for all the airline operators who value their customers. It encompasses all the key metrics airlines should track and how Simplify360 can help them in managing their online complaints.
Airline industry is the second most social industry. It is transforming the way Airlines are establishing and adopting customer relation policies.
Customers are proactively engaged and empowered through social CRM. The main objective is to improve customer experience.
Travellers from around the world form communities and connect with others to share their experiences with airlines, giving these brands a huge opportunity for a word of mouth referral system.
How do you explain and/or justify the investment in time and money behind your social media engagement? By metrics and hard and soft business values which you can base on realized results from other companies in your market.
Phil Penton – Social Media for DealershipsSean Bradley
When looking to buy a new or used car, more and more people are going online first, rather than immediately to the dealership. More importantly, car buyers are logging onto Facebook, Twitter, and Google+ to discuss and ask for advice from friends and family on a potential car purchase. After the entire process is over, they are logging back on to Facebook and Twitter to discuss their experience at your dealership. Phil Penton will be discussing the importance of Automotive Social media and why it is essential for your dealership to establish a strong online social media presence.
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Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
3. Increased exposure for companies’ business is the
marketers use social media for sale the things.
Source: http://www.pagetrafficbuzz.com/learn-companies-connecting-social-media/17235/
4. Learning from the markets, recognizing the
group of customer patterns still a critical
job for marketers
Because marketers must have the vision to see the value of customers
5. According to Woodruff (1997)
“a customer perceived preference for and evaluation of those products
attributes, attribute performances, and consequences arising from use that
facilitate (or block) achieving the customer’s goals and purposes in use
situations” by Woodruff
6. Segmentation
Identifying meaningfully
different groups of
customers
Targeting
Selecting which segment to
serve
Positioning
Implementing chosen image
and appeal to chosen
segment
Marketers
Customers
STP is a familiar
strategic approach
in Modern
Marketing.
Segmentation, Targeting and Positioning (STP)
Process
7. Recency
Frequency
Money
When did the customer
last make a purchase?
How often is the customer
making purchases?
What is the customer’s
return rate?
RFM models is a scoring model and do not explicitly provide a dollar number for
customer value. However, RFM are important past purchase variables that should be
good predictors of future purchase behavior of customers. (Gupta et al., 2005)
RFM Model
8. We used practical data
combining RFM model and
the STP strategy with data-
mining in CRM & Social
Media.
CRM Records
FB Interactive
Records
RFM Scores
Calculations
Social interactive Fanspage
CRM shopping category
Customer
Segmentation
Association Rules
&
Jaccard Coefficient
Data
Presentation
Customer
Targeting
Modularity Algorithm
&
Betweenness Centrality
Data
Preprocessing
Customer
Positioning
Research Process
K-means
9. CRM Records
FB Interactive records
Data Format
Facebook_ID Post_time page_id page_name post_id
100000998715544 2015/1/1 152905932107 152905932107_10152635799292108
1786662017 2015/1/1 152905932107 152905932107_10152635799292108
100001469254677 2015/1/1 127628276929 127628276929_10152470414971930
100000802633627 2015/1/1 127628276929 127628276929_10152478522571930
100007917434130 2015/1/1 127628276929 127628276929_10152478522571930
Facebook_ID Money Among Date Product name Product category
100000144441048 840 1 2015/1/1 12
100000199943019 699 1 2015/1/1 6
100000144441048 150 1 2015/1/1
( .159)2015
100000998715544 238 1 2015/1/1
101
100000998715544 227 1 2015/1/1
( ) :
Connections
11. Data Preprocessing
Unit: Month
CRM
Facebook
1st day of next month
- trading date
E.g. : 2/1 - 1/1 = 31
Accumulating a
month’s BUY frequency
E.g. : 1/1 to 1/31
bought 5 books
Accumulating a
month’s spending
E.g. : Accumulating
5 times spending = 1000
1st day of next month
- visiting date
Accumulating a
month’s frequency
of put the like
Accumulating a month’s
interactive CW Pages’
posts / Accumulating a
month’s total visiting
fanspage
E.g. : 2/1 - 1/20 = 11
E.g. : 1/1 to 1/31
push the like button
4 times
E.g. : 10 CW pages posts
/ Accumulating100 posts
= 0.1
12. Facebook ID
CRM Facebook
R F M R F M
1438706957 9.333 9 2783 18 1 0.018
1667846450 27.024 1 587.5 12.333 5.667 0.103
100000294682354 6 5 1553 31 0 0
636098539 3.548 1 1980 31 0 0
100000193167290 18.617 4 774 22 1 0.007
1018548854 5 2 585 4.75 20.25 0.292
The result of Data Preprocessing
near end of
month
Spend
money
number
of books
near middle
of month
visiting
weight
number of
interaction
13. RFM Scores
We based on the 80/20 rules to segment the data into five different levels
Locating the buy times
1st level score = 3.0
2nd level score = 8.0
3rd level score=13.0
4th level score = 20.0
The range of scores
Reader <= 3.0 = 1
3.0 < Reader <= 8.0 = 2
8.0 < Reader <=13.0 = 3
13.0 < Reader <= 20.0 = 4
Reader > 20.0 = 5
The distribution of the RFM scores
Scores
level
CRM Facebook
R F M R F M
1 177 2080 215 1431 2245 1629
2 699 306 737 166 146 347
3 768 64 810 319 37 211
4 554 5 535 438 22 179
5 258 1 159 102 6 90
a lot among readers
don't read CW’s
Facebook
14. The result of RFM Scores
Facebook ID
CRM Facebook CRM_scores Facebook_scores
R F M R F M R F M R F M
1438706957 9.333 9 2783 18 1 0.018 4 3 4 3 1 1
1667846450 27.024 1 587.5 12.333 5.667 0.103 2 1 2 3 1 2
100000294682354 6 5 1553 31 0 0 4 2 4 1 1 1
636098539 3.548 1 1980 31 0 0 5 1 4 1 1 1
100000193167290 18.617 4 774 22 1 0.007 3 2 3 2 1 1
1018548854 5 2 585 4.75 20.25 0.292 4 1 2 4 2 4
They aren’t active user in each CW Fanspage
15. Customer segmentation: K-means
Num. of observations
Clustering
1 428
2 896
3 497
4 635
Clustering result
Clustering
1 2 3 4
CRM
R 2.9 2.71 3.27 3.29
F 1.18 1.06 1.22 1.34
M 2.92 2.16 2.83 3.89
Facebook
R 3.48 1.06 3.74 1.08
F 1.43 1 1.25 1
M 3.84 1.03 1.78 1.05
1. High Disseminating value, normal shopping value
2. Both shopping & disseminating is low
3. High shopping value, normal disseminating value
4. High shopping value, low disseminating value
Loyal readers,
sometimes buy
some books
Not loyal
customers
loyal buyer,
sometimes reader
from Facebook
Loyal Buyers
20. The customers are concerning the
Fanspage’s Posts
519
410
tripass 367
322
voguetaiwan 306
275
_crm 247
245
icook 228
213
goodlife 208
184
183
(
)
182
_crm
164
_crm 162
goodtv 158
157
_crm
145
141
Foundation & Volunteer
Political
Traveling
Magazines
New Tech & Entrepreneur
Customer Positioning
The result of 2nd clustering : 896
Despite both shopping & disseminating is low, they sometimes buy the books from the Internet
21. Customer Positioning
The result of 2nd clustering : 896
Magazine & Health
Family & Parenting
Education
Parenting
Cooking
Cosmetic
22. The customers are concerning the
Fanspage’s Posts
icook 4986
4380
3411
2861
mamaclub 2584
2490
2362
2347
voguetaiwan 2172
2170
sisy'sworldnews
2093
1918
1801
qqmei 1681
1635
( ) 1628
cheers 1606
-
lessonsfrommovies
1505
tripass 1410
1380
Magazine
Parenting & cooking
Customer Positioning
The result of 3rd clustering : 497
Political
Celebrity
Women’s talk
Illustrator
High shopping value, normal
disseminating value
23. Customer Positioning
The result of 3rd clustering : 497
Parenting & Living
Women’s talk
Financial
Health
Parenting
Cooking
Traveling
Celebrity
Investment
Soul
Illustrator
Health
Celebrity
24. Political
New Tech & Entrepreneur
Illustrator
The customers are concerning
the Fanspage’s Posts
_crm 650
gracetw 332
_crm 282
212
_crm 184
vivianhsu 182
_crm 182
icook 175
165
159
158
duncandesign 156
133
133
130
janethsieh 114
yilan 109
pansci 109
2xpeople2 108
Customer Positioning
The result of 4th clustering : 635
High shopping value, low
disseminating value
25. Customer Positioning
The result of 4th clustering : 635
Investment
Stylish
Parenting
Cooking
Cosmetic
Celebrity
Financial & Magazine
Parenting
26. Conclusions
1. We established a hybrid method to combine the CRM
and social media data.
2. We found the difference of each clusters, which the
company focuses on.