iod sales and marketing forum 8oct13
DESCRIPTION
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.TRANSCRIPT
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Marketing and data Analytics – How small and medium enterprises can benefit from Big Data,
Marketing and Social Media analytics and good, old school, statistics
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Introductions
• I am ________ and my company does _________
• With regards to data and analytics1. 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?
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What are we going to talk about?
• Big data• Analytics and analysis gap• Social media analytics (Twitter)
(unstructured data)• Business analytics (structured data)
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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
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What is Data?
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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
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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
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What is Big Data
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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.
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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.
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Big Data
The 3V
Volume
Velocity
Variety
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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
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Big Data
• Employees generating data
• Users generating data
• Machines generating data
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Cloud event
BusinessTransactions
Operationaldata
Complex eventprocessing
Business processing& activity monitoring
Data integration& management
Stream eventanalytics
Process eventanalytics
Datawarehouse
Sub-secondlatency
Minuteslatency
Hours/dayslatency
Dataanalytics
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The analysis gap
Analysisgap
• Algorithms• Database• Parallel processing
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Supercomputing
Parallelcomputing
Server/cloudcomputing
Desktop
Spreadsheet
Statistical packages
Business Analytics packages
Purpose designed packages
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SW # of rows # of columns Memory Other Cost
Google Docs 256 20MB 400,000 cells40,000 formula cells200 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 OS150,000,000 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
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Supercomputing
Parallelcomputing
Server/cloudcomputing
Desktop
Spreadsheet
Statistical packages
Business Analytics packages
Purpose designed packages
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System cost vs System complexity
SystemHardwareSoftwareStructureUtilitiesHR
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Why do we need analytics
Because we need to gain insights and act on complex issues
Analytics allow thinking, trending and
what-iffing
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Smart analytics
• Advanced statistics• Predictive modeling and analytics• Web event analytics• Text and social media analytics• Social networking analysis (influencers,
opinions)
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Social media analytics
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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
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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+)
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Blog
Short term objectives1. 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)
Metrics1. Number of posts2. Number of social shares3. Audience growth4. Conversation rate5. Conversions6. Subscribers7. Inbound links8. Directory listing
(Tecnorati, Alltop)9. SEO improvements
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Short term objectives1. Promote content through
Twitter2. Segment influencers and
create lists3. Utilise promoted Tweets4. Communicate support
issues from Twitter to support team and ensure follow-up
5. Listen to relevant conversations
6. Build reputation
Metrics1. Followers2. Mentions3. Retweets4. Retweets reach5. Replies reach6. Number of lists7. Social capital (influence
of twitter followers)8. Number of potential
prospects sent to sales9. Posts
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Social Marketing Analytics
Business Objectives
KPI
Foster DialogueShare 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
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Foster Dialogue
sMentionsCompetitoronsBrandMentionsBrandMenticeShareOfVoi +=
TotalViewsTrackbacksSharesCommentsgagementAudienceEn ++=
enceExposurTotalAudietingeParticipaTotalPeoplonReachConversati =
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Promote Advocacy
atesTotalAdvocst30days)vocates(laofActiveAd#catesActiveAdvo =
ceateInfluenTotalAdvocsInfluencecate'UniqueAdvofluenceAdvocateIn =
yTrafficeOfAdvocacTotalVolumonenConversivocacyDrivNumberOfAdpactAdvocacyIm =
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Facilitate Support
iceIssuesTotal#ServactorilylvedSatisfIssuesResoTotal#utionRateIssueResol =
esiceInquiriTotal#ServTimeryResponseTotalInquiTimeResolution =
rFeedbackAllCustomen)C,B,utA,edback(inpCustomerFeonScoreSatisfacti =
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Facilitate Support
ntionsAllTopicMeionscTopicMent#OfSpecifisTopicTrend =
ntionsAllBrandMesandMentionNegativeBr:Neutral:PositiveatioSentimentR =
MentionsShares,n,onversatioTotalIdeaCMentionsShares,ons,ConversatiofPositive#IdeaImpact =
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# of followersNatwest_help 23kBrand Republic 150kThe Wall UK 15kTwitter Ads UK 14k
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Daniel has 15,600followers
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The Critical Couplehas 11k followers anda blog
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Marketing Weekhas 90k followers
The Drumhas 72k followers
Michael Vaughanhas 460k followers
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Business analytics
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46
mk
nxmk
0x
kxy
nmxy
−=⇒>
>=
+=
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Variable + Fixed CostsSales Revenues
Breakeven point
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0x
ey
x8y410x
7
>=
=−
−
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Infant Failure RateMaturity Failure RateObserved Failure rate
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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
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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.
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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 Pri1 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 4.402 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 2.903 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 3.254 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 2.405 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 2.456 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 2.957 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 4.008 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 2.559 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 2.4011 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 3.0012 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 3.3013 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 3.0014 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 3.2015 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 4.2516 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 3.3017 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 3.5018 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 3.4019 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 2.9020 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 2.8521 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 1.8622 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 2.4023 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 3.0624 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 3.2525 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
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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
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VariableCoun
tMean 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
Freq
uenc
y
Histogram of PintNormal
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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
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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