20180919 - Predictive Analytics -driving fuel for sales and … · 2018-09-24 · àKNOWING MORE...
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Rikard Candell, September 2018 Driving fuel for sales and marketing Predictive Analytics
Transcript of 20180919 - Predictive Analytics -driving fuel for sales and … · 2018-09-24 · àKNOWING MORE...
Rikard Candell, September 2018
Driving fuel for sales and marketing
Predictive Analytics
Who lives longer –people or companies?
Who live longer lives – people or companies?
0
20
40
60
1950 1960 1970 1980 1990 2000 2010
Average life of people vs companies (years)
Source: BHI Analyses; UN financial department, World population prospects: The 2012 revision
Who live longer lives – people or companies?
0
20
40
60
1950 1960 1970 1980 1990 2000 2010
Average life of people vs companies (years)
Source: BHI Analyses; UN financial department, World population prospects: The 2012 revision
0
20
40
60
1950 1960 1970 1980 1990 2000 2010
Who live longer lives – people or companies?
Source: BHI Analyses; UN financial department, World population prospects: The 2012 revision
0
20
40
60
1950 1960 1970 1980 1990 2000 2010
Average life of people vs companies (years)
MORE DYNAMIC BUSINESS LANDSCAPE
STAYING COMPETITIVE IS MORE DIFFICULT
à KNOWING MORE THAN COMPETITORS ALLOWS YOU TO WINDATA & ANALYTICS FUEL IS NEEDED TO DO SO
Industries are disrupted by digitization, data & analytics
Impact of disruption by industry
TechMedia
Retail
Life science
Utilities
Health care
TravelFinancial Services
Education
Telecom
Manufacturing
Impact
Industries
ILLUSTRATIVE
Industries are disrupted by digitization, data & analytics
World’s largestTaxi company
Owns NO Taxis
World’s largestAccommodation provider
Owns NO Real estate
World’s largestPhone companies
Own NO Telco infrastucture
World’s mostValuable retailer
Own NO Inventory
Most popularMedia owner
Owns NO Content
World’s fastestGrowing bank
Owns NO Actual money
World’s largestMovie house
Owns NO Cinemas
World’s largestSoftware vendors
Own NO Apps
Now business processes are being disruptedExample of drivers for transformation of sales & marketing
SALES & MARKETING
New buying processes
More stakeholders involved
Researching vendors
Cold calling dying out
New channels
Adding value and insight
New technologies & tools
…
Old ways of working not cutting it
Competitive advantage by knowing more
Bisnode mission = ”provide customers with a data advantage”
”65% of the most innovative companiesleverage Big Data …”
“Poor data can cost companies 20-35% of revenues”
“10% increased data availability leads to 65 MUSD increased revenue of a typical
Fortune 1000 company”
“Data-driven retailers can increase margins of >60%”
Source: Waterford technologies, McKinsey, BCG, Forbes
Who? What? When? How?
Summarizing: Why analytics?
• Business and processes are being disrupted.
• Old ways of working not cutting it.
• Data & Analytics give a competitive edge.
• A data-driven organization can drive innovation, growth and profitability.
• Global trade fair and event organizer.
• Ambition to move from traditional sales process into best-in-class.
• Challenge identifying exhibitors due to poor relevance in datasets.
• Proof of Concept for TechTextile fair.
CASE | Predicting leads for trade fair
Classic prospecting
Customer target: good
leads
Machine Learning on online & offline data to predict buyers
Set of best customers
Prospects
Add online- & offline data
TechTextile contextFair context
Language contextCompetition context
…
Example online triggers
FinancialsCompany Name
Number of EmployeesOwn SubsidiaryIndustry Code
…
Example reference data “Structured base”
“Relevance & timing“
Machine Learning on online & offline data to predict buyers
üSet of best customers
Prospects
Add online- & offline data
Machine Learning
algorithm
Predicted buyers as
sales leads
Solution generated 3600 great leads, from 250 M companies
250Mtotal companies
1.1Mpotential target
market
3600great sales leads
+38% improved
leads quality with online
data
CASE | Automotive purchase prediction
We can only marginally affect the demand for cars in the B2B sector.
…But we can predict it.
Predict automotive purchases by analyzing 1,000 data points
We can only marginally affect the demand for cars in the B2B sector.
…But we can predict it. 1,000variables per
company
>700.000companies
Vehicle holding# Workplaces
IndustryGrowth prediction
Company ageTurnoverResults
# EmployeesEfficiency
Example relevant variables
1,000 variables per company predict automotive purchases
We can only marginally affect the demand for cars in the B2B sector.
…But we can predict it. 1,000variables per
company
>700.000companies
Great potential
Moderate potential
Low potential
No potential
Companies by cluster(Forecast December 2016)
New cars by cluster(Outcome December 2017)
1% of companies will make 66% of car purchases in one yearPredicted purchases in 2016 – and the result in 2017
The 8 452 (1%) companies
predicted most likely to buy…
…turned out buying
94 688 cars (66% of total)
1%3%
79%
17%
13%
66%
8%
12%
MidHigh No potentialLowNo potentialLowHigh Mid
What if… 70% business impact up for grabs in two typical citiesHow to distribute marketing resources and budget
CITY 1
• Inhabitants: 34 000
• Car owning companies: 666
CITY 2
• Inhabitants: 43 000
• Car owning companies: 831
+70 %The companies in City 1 will buy 70 % more cars
than City 2 next year.
With this in mind – how would you distribute
the marketing budget between these two
Swedish cities?
…And if you didn’t know?
Three takeaways to get started
OLD WAYS OF SPREADING RESOURCESor
PREDICTIVE ANALYTICS TO SPREAD RESOURCES SMARTER
DATA & ANALYTICS IS A TECH QUESTIONor
DATA & ANALYTICS IS A BUSINESS QUESTION
USE DATAor
MAKE DATA USABLE
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