Predictive Analytics Driving fuel for sales and marketing

Transcription

1 Predictive Analytics Driving fuel for sales and marketing Rikard Candell, September 2018

2 Who lives longer people or companies?

3 Who live longer lives people or companies? 60 Average life of people vs companies (years) Source: BHI Analyses; UN financial department, World population prospects: The 2012 revision

4 Who live longer lives people or companies? 60 Average life of people vs companies (years) Source: BHI Analyses; UN financial department, World population prospects: The 2012 revision

5 Who live longer lives people or companies? 60 Average life of people vs companies (years) MORE DYNAMIC BUSINESS LANDSCAPE STAYING COMPETITIVE IS MORE DIFFICULT à KNOWING MORE THAN COMPETITORS ALLOWS YOU TO WIN DATA & ANALYTICS FUEL IS NEEDED TO DO SO Source: BHI Analyses; UN financial department, World population prospects: The 2012 revision

6 ILLUSTRATIVE Industries are disrupted by digitization, data & analytics Impact of disruption by industry Travel Financial Services Media Tech Impact Telecom Retail Life science Education Health care Manufacturing Utilities Industries

7 Industries are disrupted by digitization, data & analytics World s largest Taxi company Owns NO Taxis World s largest Accommodation provider Owns NO Real estate World s largest Phone companies Own NO Telco infrastucture World s most Valuable retailer Own NO Inventory Most popular Media owner Owns NO Content World s fastest Growing bank Owns NO Actual money World s largest Movie house Owns NO Cinemas World s largest Software vendors Own NO Apps

8 Now business processes are being disrupted Example of drivers for transformation of sales & marketing New buying processes Morestakeholders involved Researching vendors New channels Cold calling dying out New technologies & tools Adding value and insight SALES & MARKETING Old ways of working not cutting it

9 Bisnode mission = provide customers with a data advantage Poor data can cost companies 20-35% of revenues 65% of the most innovative companies leverage Big Data Data-driven retailers can increase margins of >60% 10% increased data availability leads to 65 MUSD increased revenue of a typical Fortune 1000 company Competitive advantage by knowing more Who? What? When? How? Source: Waterford technologies, McKinsey, BCG, Forbes

10 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.

11 CASE Predicting leads for trade fair Classic prospecting Customer target: good leads 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.

12 Machine Learning on online & offline data to predict buyers Example reference data Financials Company Name Structured base Number of Employees Prospects Own Subsidiary Industry Code Set of best customers Add online- & offline data Example online triggers TechTextile context Fair context Language context Relevance & timing Competition context

13 Machine Learning on online & offline data to predict buyers Prospects ü Set of best customers Add online- & offline data Machine Learning algorithm Predicted buyers as sales leads

14 Solution generated 3600 great leads, from 250 M companies +38% improved leads quality with online data 250M total companies 1.1M potential target market 3600 great sales leads

15 CASE Automotive purchase prediction We can only marginally affect the demand for cars in the B2B sector. But we can predict it.

16 Predict automotive purchases by analyzing 1,000 data points We can only marginally affect the demand for cars in the B2B sector. > companies But we can predict it. 1,000 variables per company Example relevant variables Vehicle holding # Workplaces Industry Growth prediction Company age Turnover Results # Employees Efficiency

17 1,000 variables per company predict automotive purchases We can only marginally affect the demand for cars in the B2B sector. > companies Great potential Moderate potential Low potential No potential But we can predict it. 1,000 variables per company

18 1% of companies will make 66% of car purchases in one year Predicted purchases in 2016 and the result in 2017 Companies by cluster (Forecast December 2016) New cars by cluster (Outcome December 2017) 3% 1% 17% The (1%) companies predicted most likely to buy turned out buying cars (66% of total) 66% 79% 8% 13% 12% High Mid Low No potential High Mid Low No potential

19 What if 70% business impact up for grabs in two typical cities How to distribute marketing resources and budget CITY 1 Inhabitants: Car owning companies: % CITY 2 Inhabitants: Car owning companies: 831 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?

20 Three takeaways to get started 1 OLD WAYS OF SPREADING RESOURCES or PREDICTIVE ANALYTICS TO SPREAD RESOURCES SMARTER 2 DATA & ANALYTICS IS A TECH QUESTION or DATA & ANALYTICS IS A BUSINESS QUESTION 3 USE DATA or MAKE DATA USABLE

21 Rikard Candell Group Analytics Director Bisnode

22