Big Data for Sales Taking Analytics from Insights to Action. Copyright 2015 The Sales Management Association. All rights reserved.

Transcription

1 Big Data for Sales Taking Analytics from Insights to Action Copyright 2015 The Sales Management Association. All rights reserved.

2 SPEAKERS Manu Kumar Head of Data Science Vodafone Global Enterprise LinkedIn: slsm.gt/manu Tony Yeung Principal ZS Associates LinkedIn: slsm.gt/yeung Copyright 2015 The Sales Management Association. All rights reserved.

3 Topics Why B2B opportunities are hard to find and traditional approaches don t work Taking a big data view of the account universe Turning analytic insights into sales actions Monitoring impact 3

4 In B2B sometimes you don t see the full picture B2B Insights What you see: Data, Systems, Intuition, Experience etc. Everything else 4

5 B2B Insights So you could ask an expert 5

6 B2B Insights Or use Analyst Reports 6

7 B2B Insights Or ask an Operations team but they have stuff to do 7

8 B2B Insights But Sales cycles are loooooonnnngggg 8

9 B2B Insights Buying Centers are complex 9

10 B2B Insights So we built TARGET using (big) data 10

11 Machine Learning 3 steps to identifying opportunities with TARGET 11

12 Complexity Machine Learning Step 1: N= (almost) All Inputs Analytical Process Output Finance (revenues, connections, trends, mix) CRM Pipeline Prescriptive (What should I do?) Enterprise Services Catalogue TARGET k-nearest Neighbour Predictive (What's next?) Field Intelligence Wikis Market intelligence (secondary data) Descriptive (What happened?) Applies Amazon / Netflix propensity to buy principles 12

13 Machine Learning Step 2: Ask Questions For every customer, for every product, and for every geography ask four questions! Are we Selling? Unable to sell? Trying to sell? Missing out? 13

14 customers Machine Learning High propensity: Cross Sell High propensity whitespace: Primary Sell High penetration: Upsell Step 3: Build the opportunity universe High potential: Invest Underpenetration: Deep Sell products 14

15 The insights provide a clear view of growth opportunity across different types of customers Machine Learning TARGET Recommendations Realizable Revenue by Industry Illustration Financial Services Manufacturing Technology Professional Services Automotive & Manufacturing Energy & Utilities Current Accounts Greenfield Healthcare 15

16 We also have a view into the concentration of opportunity Account Size (Addressable Potential) 3% Very High 460 k Machine Learning Number of Accounts 8% 220 k High 14% 125 k Medium Realizable Upside Opportunity (per account) 74% Low 25 k 16

17 The secret sauce is the Algorithms 0.0 Data integration Descriptive analytics 1.0 Addressable potential estimates Propensity to buy analytics 2.0 Machine-learning approaches Real-time, cloud-based analytics Machine Learning 17

18 Driving adoption through to the sales force requires thoughtful change management Driving Action We adopt a new idea or behavior when: 1. We understand and believe it 2. We like it 3. We can act 4. We want to act (now!) Successful Change Management wins the hearts as well as the minds, and creates the opportunity as well as the will to act. 18

19 Coordinated focus is a prerequisite for successful adoption Driving Action Strategy 1 Clearly Defined Customer Engagement Strategy People Process 2 Change management Insightful Analytics Right Conditions for Adoption Competencies Data-Driven Planning 1 st line management & coaching Coordinated Engagement Agile Monitoring Tool 7 Enabling Technology 19

20 Data-driven, delivered in actionable way, have the potential to transform the sales process Driving Action Right customers and prospects, right opportunities Targeting Selling Case studies, proof points Account Management Cross-sell focus, expansion opportunities 20

21 The most common customer engagement standards typically fall into one of five categories Measurement Customer Engagement Standards Activity (customer interactions) Pipeline (targets at stage x) Voice of Customer (external satisfaction score) Stakeholder Rating (internal satisfaction score) Results (sales, profit, share, etc.) 21

22 Lessons learned Start small data integration, descriptive analytics, pilots Enlist support from senior executive team Think about impact build the business case Don t underestimate change management Be willing to experiment, and make some mistakes along the way Take an operations view to drive quality and efficiency 22

23 Thank You! Copyright 2015 The Sales Management Association. All rights reserved.

24 QUESTIONS Please remember to speak into the microphone - we're recording! Manu Kumar Head of Data Science Vodafone Global Enterprise Manu.Kumar02@Vodafone.com LinkedIn: slsm.gt/manu Tony Yeung Principal ZS Associates tony.yeung@zsassociates.com LinkedIn: slsm.gt/yeung Copyright 2015 The Sales Management Association. All rights reserved.