This is the presentation given at the IoD Sales and Marketing Forum held on the 8/10/2013. It is aimed at Company Directors of SME with a view to providing an introduction to the value of collecting and analysing data. The focus is on big data, social media data and business data analytics.

1. 1
Marketing and data Analytics – How small and
medium enterprises can benefit from Big Data,
Marketing and Social Media analytics and good,
old school, statistics

2. 2
Introductions
• I am ________ and my company does
_________
• With regards to data and analytics
1. The board of Directors is sensible to their
value and we have a strategy in place.
2. We have data in our company.
3. What data?

3. 3
What are we going to talk about?
• Big data
• Analytics and analysis gap
• Social media analytics (Twitter)
(unstructured data)
• Business analytics (structured data)

4. 4
A Google search…
Keywords # of Hits
Big data 2bn
Big data analytics 98m
Social media 2.8bn
Social media analytics 132m
Marketing 1.67bn
Marketing data 1bn
Marketing data analytics 60m

5. 5
What is Data?

6. 6
Data …
• The result of observations (how many road accidents on the
M27, how many people report to A&E in a day, how tall are the members of IoD, how
many positive tweets are received in one day)
• Primary first hand experience
• Secondary data collected by somebody else
• Structured they can be organised in structures (databases) based on specific
methodology (numbers, words)
• Unstructured text, images, sounds, videos
• Data become information after they are
analysed and provided with sense

7. 7
Why do we need data?
• Every single business objective is
measurable
• Nobody has unlimited resources, hence
everybody needs strategies
• How do we select strategies and measure
progress? By comparing models based on
numbers and by collecting and analysing
data

8. 8
What is Big Data

9. 9
Big Data
Big data describes a massive volume of both
structured and unstructured data that is so
large that it's difficult to process using
traditional database and software
techniques.

10. 10
Big Data
The term may seem to reference the volume of
data, but it may refer to the technology
(which includes tools and processes) that an
organization requires to handle the large
amounts of data and storage facilities.

11. 11
Big Data
The 3V
Volume
Velocity
Variety

12. 12
How are Big Data generated?
Nectar Cards 19,000,000 24 swipes every second everyday
Tesco Clubcard 16,000,000
Credit Cards 63,000,000
Debit cards 88,000,000
10.3 bn yearly transactions, £502 bn
Mobile
subscriptions
82,700,000
Home broadband 21,700,000 55% of adults have a social media
profile

13. 13
Big Data
• Employees generating data
• Users generating data
• Machines generating data

14. 14
Cloud event
Business
Transactions
Operational
data
Complex event
processing
Business processing
& activity monitoring
Data integration
& management
Stream event
analytics
Process event
analytics
Data
warehouse
Sub-second
latency
Minutes
latency
Hours/days
latency
Data
analytics

15. 15

16. 16
The analysis gap
Analysis
gap
• Algorithms
• Database
• Parallel processing

17. 17
Supercomputing
Parallel
computing
Server/cloud
computing
Desktop
Spreadsheet
Statistical packages
Business Analytics packages
Purpose designed packages

18. 18
SW # of rows # of columns Memory Other Cost
Google Docs 256 20MB 400,000 cells
40,000 formula cells
200 sheets per workbook
Free
Excel 2003 (32-bit) 65,536 256 2.1 GB 16,777,216 cells £150+
Minitab 10,000,000 4,000 System Hardware and OS
150,000,000 cells
£1,200/y
STATA System System System 2bn cells £4,000/y
R System System System 3bn+ cells (128TB on a Linux 64-bit system) Free
SPSS System System System 100,000 rows on display £2,000+/y
SAS System System System BI solution £4,000+/y
Fortran System System System System (Mainframes and supercomputers) A few £k

19. 19
Supercomputing
Parallel
computing
Server/cloud
computing
Desktop
Spreadsheet
Statistical packages
Business Analytics packages
Purpose designed packages

20. 20
System cost vs System complexity
System
Hardware
Software
Structure
Utilities
HR

21. 21
Why do we need analytics
Because we need to gain insights and act on
complex issues
Analytics allow thinking, trending and
what-iffing

22. 22
Smart analytics
• Advanced statistics
• Predictive modeling and analytics
• Web event analytics
• Text and social media analytics
• Social networking analysis (influencers,
opinions)

23. 23
Social media analytics

24. 24
Social media sample goals
1. Increase inbound leads at a low cost
2. Expand reach of thought content
3. Engage and excite influencers
4. Better understand, identify and engage
potential buyers
5. Improve customer service and satisfaction
6. Enhance outbound campaign program
effectiveness

25. 25
Social media tactical plan
1. Blog (Wordpress, Blogger, Tumblr, Typepad, Blog.com)
2. Social networks (Facebook, Linkedin, Pinterest, Google+)
3. Microblogging (Twitter/Vine)
4. Social PR (Bloggers)
5. Widgets
6. Bookmarking / Tagging (Reddit, Digg, Stumbleupon; Zite, Flipboard)
7. Blog commenting / Q&A sites (TripAdvisor)
8. Online video (YouTube, Vimeo, 70+)
9. Photo sharing (Flickr, Photobucket, Pinterest, 30+)
10. Podcasting
11. Presentation sharing (SlideShare, Scribd, 30+)
12. Other: Wikipedia, RSS (Feedly), Wikis (80+)

26. 26
Blog
Short term objectives
1. Increase recognition
– X number of posts
– Blog publication schedule
– RSS/Social share button
2. Increase engagement
– Encourage comments
– Interact with active
readers (they are all
potential advocates)
Metrics
1. Number of posts
2. Number of social shares
3. Audience growth
4. Conversation rate
5. Conversions
6. Subscribers
7. Inbound links
8. Directory listing
(Tecnorati, Alltop)
9. SEO improvements

27. 27
Twitter
Short term objectives
1. Promote content through
Twitter
2. Segment influencers and
create lists
3. Utilise promoted Tweets
4. Communicate support
issues from Twitter to
support team and ensure
follow-up
5. Listen to relevant
conversations
6. Build reputation
Metrics
1. Followers
2. Mentions
3. Retweets
4. Retweets reach
5. Replies reach
6. Number of lists
7. Social capital (influence
of twitter followers)
8. Number of potential
prospects sent to sales
9. Posts

28. 28
Social Marketing Analytics
Business
Objectives
KPI
Foster Dialogue
Share of Voice, Audience engagement, Conversation
Reach
Promote Advocacy Active Advocates, Advocate Influence, Advocacy Impact
Facilitate Support Resolution Rate, Resolution Time, Satisfaction Score
Spur Innovation Topic Trends, Sentiment Ratio, Idea Impact

29. 29
Foster Dialogue
sMentionsCompetitoronsBrandMenti
onsBrandMenticeShareOfVoi
+
=
TotalViews
TrackbacksSharesCommentsgagementAudienceEn ++=
enceExposurTotalAudie
tingeParticipaTotalPeoplonReachConversati =

30. 30
Promote Advocacy
atesTotalAdvoc
st30days)vocates(laofActiveAd#catesActiveAdvo =
ceateInfluenTotalAdvoc
sInfluencecate'UniqueAdvofluenceAdvocateIn =
yTrafficeOfAdvocacTotalVolum
onenConversivocacyDrivNumberOfAdpactAdvocacyIm =

31. 31
Facilitate Support
iceIssuesTotal#Serv
actorilylvedSatisfIssuesResoTotal#utionRateIssueResol =
esiceInquiriTotal#Serv
TimeryResponseTotalInquiTimeResolution =
rFeedbackAllCustome
n)C,B,utA,edback(inpCustomerFeonScoreSatisfacti =

32. 32
Facilitate Support
ntionsAllTopicMe
ionscTopicMent#OfSpecifisTopicTrend =
ntionsAllBrandMe
sandMentionNegativeBr:Neutral:PositiveatioSentimentR =
MentionsShares,n,onversatioTotalIdeaC
MentionsShares,ons,ConversatiofPositive#IdeaImpact=

33. 33

34. 34
# of followers
Natwest_help 23k
Brand Republic 150k
The Wall UK 15k
Twitter Ads UK 14k

35. 35
Daniel has 15,600
followers

36. 36

37. 37
The Critical Couple
has 11k followers and
a blog

38. 38

39. 39
Marketing Week
has 90k followers
The Drum
has 72k followers
Michael Vaughan
has 460k followers

40. 40

41. 41

42. 42

43. 43

44. 44
Business analytics

45. 45

46. 46
mk
n
xmk
0x
kxy
nmxy
−
=⇒>
>
=
+=

47. 47
Variable + Fixed Costs
Sales Revenues
Breakeven point

48. 48
0x
ey
x8y
4
10x
7
>
=
=
−
−

49. 49
Infant Failure Rate
Maturity Failure Rate
Observed Failure rate

50. 50
Year Sales CAGR YoY Av sell
2011 1,000,000 1,000
2012 1,249,000 25% 1,249
2013 1,499,000 14.5% 20% 1,498
2014 1,707,151 14%
Sales 2011/13 and forecast 2014
£0
£200,000
£400,000
£600,000
£800,000
£1,000,000
£1,200,000
£1,400,000
£1,600,000
£1,800,000
2011 2012 2013 2014
Years
GBP
Sales

51. 51
Sales analytics
Year # of customers Sales Mean sale Median sale
2011 1000 1,000,000 1,000 1,000
2012 1000 1,249,000 1,249 1,000
2013 1000 1,499,000 1,498 1,000
The above results are achievable by having, in 2012,
999 customers spending £1,000 and 1 customer spending 250,000 and, in 2013,
998 customers spending 1,000 and 2 customers spending 250,000 each.

52. 52
Average price of a pint of Lager in
290 locations
Loc Pri Loc Pri Loc Pri Loc Pri Loc Pri Loc Pri Loc Pri Loc Pri Loc Pri Loc Pri Loc Pri Loc Pri
1 3.00 51 3.35 101 3.00 151 2.34 201 4.00 251 2.94 26 3.90 76 2.85 126 2.80 176 3.90 226 2.50 276 4.40
2 4.20 52 3.05 102 3.00 152 2.55 202 3.10 252 2.90 27 2.60 77 3.00 127 2.40 177 2.00 227 3.20 277 2.90
3 2.30 53 3.50 103 2.85 153 2.40 203 3.00 253 3.10 28 3.00 78 2.50 128 3.30 178 1.59 228 2.70 278 3.25
4 3.00 54 3.20 104 3.55 154 3.05 204 3.25 254 3.00 29 3.00 79 2.50 129 2.60 179 2.75 229 2.90 279 2.40
5 1.62 55 2.20 105 3.10 155 2.89 205 0.25 255 2.85 30 3.00 80 4.20 130 3.95 180 3.25 230 2.65 280 2.45
6 2.75 56 2.50 106 2.00 156 2.70 206 4.00 256 3.50 31 2.80 81 3.85 131 3.25 181 3.10 231 2.20 281 2.95
7 3.50 57 0.99 107 3.00 157 2.58 207 3.00 257 2.50 32 3.20 82 2.70 132 4.50 182 4.50 232 2.90 282 4.00
8 2.92 58 4.50 108 3.20 158 4.30 208 3.00 258 3.25 33 2.85 83 3.90 133 3.80 183 2.60 233 2.02 283 2.55
9 2.78 59 2.60 109 3.20 159 2.90 209 4.20 259 2.80 34 2.77 84 2.10 134 3.20 184 2.80 234 2.90 284 2.65
10 2.85 60 2.40 110 3.00 160 3.60 210 2.10 260 1.60 35 2.85 85 2.95 135 3.50 185 2.00 235 2.30 285 2.40
11 2.70 61 3.50 111 3.85 161 2.95 211 2.22 261 1.76 36 1.95 86 3.10 136 2.37 186 3.15 236 2.50 286 3.00
12 3.00 62 3.50 112 4.40 162 3.00 212 3.30 262 1.95 37 2.58 87 2.87 137 1.99 187 3.00 237 3.30 287 3.30
13 3.60 63 3.00 113 2.50 163 2.50 213 2.30 263 2.60 38 3.08 88 2.50 138 3.30 188 2.95 238 2.60 288 3.00
14 3.64 64 3.40 114 2.90 164 2.30 214 2.72 264 2.00 39 2.36 89 3.60 139 3.55 189 3.00 239 1.46 289 3.20
15 3.20 65 3.22 115 1.80 165 3.00 215 2.90 265 3.40 40 3.70 90 3.50 140 2.52 190 2.65 240 4.80 290 4.25
16 3.30 66 3.80 116 2.78 166 3.80 216 3.20 266 3.00 41 2.60 91 2.52 141 2.30 191 2.50 241 3.30
17 1.75 67 2.75 117 2.35 167 2.10 217 3.20 267 3.20 42 2.85 92 2.60 142 2.20 192 3.00 242 3.50
18 2.45 68 2.50 118 3.00 168 1.95 218 4.30 268 2.50 43 2.90 93 3.14 143 2.60 193 2.50 243 3.40
19 3.25 69 3.10 119 2.90 169 3.15 219 3.60 269 1.90 44 2.72 94 2.45 144 3.50 194 3.00 244 2.90
20 2.34 70 3.00 120 3.00 170 2.50 220 2.40 270 2.88 45 3.60 95 3.20 145 2.80 195 2.01 245 2.85
21 3.50 71 2.85 121 2.60 171 3.05 221 3.00 271 2.60 46 1.00 96 3.38 146 4.10 196 4.50 246 1.86
22 2.96 72 3.40 122 3.25 172 2.95 222 2.90 272 3.50 47 1.99 97 2.40 147 4.20 197 2.75 247 2.40
23 3.30 73 2.85 123 2.30 173 2.90 223 2.90 273 3.30 48 2.80 98 2.55 148 3.00 198 2.60 248 3.06
24 3.10 74 4.90 124 6.00 174 2.50 224 2.60 274 5.90 49 3.40 99 2.80 149 3.33 199 2.35 249 3.25
25 2.00 75 2.50 125 4.00 175 1.75 225 2.95 275 2.90 50 2.90 100 3.40 150 3.30 200 1.90 250 2.50

53. 53

54. 54
Average Price of a Pint of Lager in 290 locations
0
1
2
3
4
5
6
7
1 14 27 40 53 66 79 92 105 118 131 144 157 170 183 196 209 222 235 248 261 274 287
price

55. 55

56. 56

57. 57
Variable
Coun
t
Mean StDev Minimum Median Maximum Mode
N for
mode
Skeweness Kurtosis
Pint 290 2.94 0.69 0.25 2.91 6.0 3 30 0.51 2.89
6.005.254.503.753.002.251.500.75
70
60
50
40
30
20
10
0
Pint
Frequency
Histogram of Pint
Normal

58. 58
Conclusions
It is possible to observe everything
It is possible to collect and analyse data in high
volumes, variety and velocity
Companies must equip themselves with adequate
resources and tools (people and systems)
Do not underestimate the weaknesses of Excel
Do not underestimate the value of employing a Data
Analyst/Scientist alongside your Marketing
Manager

59. 59
Conclusions
The most valuable intangible asset of a company is
the insight of their target market
Given the technology and the knowledge available,
there is no excuse for not having insight of a target
market