Organisational Transformation with Data-Driven Practices
Download as PPTX, PDF0 likes68 views
The document summarizes Barry Magee's presentation on organizational data transformation. The presentation includes exploring data transformation lessons learned through case studies, with the goal of introducing data-driven practices into sales environments. It discusses challenges of transforming sales organizations and creating value through data, and focuses on three case studies examining critical success factors and outcomes of technical, behavioral, and organizational changes over time.
Organisational Transformation with Data-Driven Practices
- 1. Organisational Data Transformation Barry Magee – Client Analytics and Data Transformation Leader – IBM Digital Sales Europe vs.
- 2. Plan for the morning.... exploring data transformation lessons learnt What really happens when you attempt to introduce data- driven practices into a sales environment? What are the critical success factors for technical, behavioural and organisational change consideration? • 3 cycles – we’ll walk through and work in groups • What would YOU do next? • 5 lessons –walk through and work individually • 1 application in you own environment • 1 critique or weakness
- 3. Set the Scene – problem context..... 15 mins Case Study Round 1 (Aggregation & Interpretation) - Set the scene - Propose Next Steps - What did happen - Discuss 5 mins 10 mins 5 mins 10 mins 30 mins Case Study Round 2 (Time, Urgency & Data) - Set the scene - Propose Next Steps - What did happen - Discuss 5 mins 10 mins 5 mins 10 mins 30 mins Case Study Round 3 (Stakeholders, Models & Sprints) - Set the scene - Propose Next Steps - What did happen - Discuss 5 mins 10 mins 5 mins 10 mins 30 mins Agenda What’s the plan?
- 5. Page 5 • 1,000 sellers and support • 80% of volume of IBM Europe • Full Portfolio – all product lines • ‘Long-Tail’ part of business • Mix of sales tasks any given day • Each seller has 500-1,000 clients Context What’s the setting and what is the problem to be solved?
- 6. Computing is entering a new cognitive era. Implementing data at the centre of sales What we’d LIKE to have….
- 7. Organisational environments, however, are designed and run with a lot of inefficiencies. Transforming sales and creating value is harder than it sounds What we ACTUALLY have….
- 8. Page 8 Context What’s the setting and what is the problem to be solved? So, what’s the problem? Who? • Sales Reps attempting to manage their sales territory When? • Deciding who to call next with limited time and multiple choices – 30 mins/day 1000s of clients Why? • Traditional engagement cycle focus on renewal events alone – 12% of customers only 5% clients engaged quarterly
- 9. Page 9 Context – Round 1 What evidence is there of the problem to be solved....the clues What’s activity is happening on floor? What are end users (sellers) saying - feedback? What data evidence is normally available? a f d Renewal Dates Shift Proposal Build Work 8 Teams – 70 Sellers 2 Systems of Record (Contracts) 2 Systems of Record (Inventory) 1 Opportunity Mgt System 3% of Renewals/Qtr People and Tools 80% ‘direct debit’ 20% fixed term Matrix Stakeholders 15k active clients - approx. 500k ‘inactive’ 20% of time ‘selling with customers 24% of time ‘pre-sales admin’ 11% of time ‘post-sales admin’ Customers and Time 9.50
- 10. Page 10 1. Data aggregation creates process value 2. Tolerance for data accuracy is very low 3. Visualization drives ‘discovery’ 4. The right data delivery process is critical 5. Time sensitivity of information is important 6. There is over-confidence in ‘effectiveness’ Findings Round 1 Outcomes Apr 2012 – Feb 2015 What did we do? 1. Aggregated multiple datasets into a central set of views for sellers. 2. Created visual ‘HeatMaps’ to allows sellers see and determine ‘valuable’ clients for engagement. 3. Created client engagement planning and execution management process – who did you call and when? 4. Creation of infographic style 360° view of customer - Client-On-A-Page. 5. Delivery process and integrated with the Opportunity Management system I’m too busy!
- 11. Page 11 Context – Round 2 What evidence is there of the problem to be solved....the clues What’s activity is happening on floor? What are end users (sellers) saying - feedback? What data evidence is normally available? a f d Renewal Cycle (need to have) We like the process but we simply don’t have time You said customer had 12 assets - they were all gone! I don’t need this - I know what my customer needs’ are 26% of active clients 42% of target clients called – 1.5 calls/rep/week 20% lead conversion rate – calls to opportunities 56% win rate – opportunities to wins 52 mins saved per seller per day Customers and Time New Business Cycle (nice to have) Hot & Cold (urgency wanes) How do I say no to alternate lists? 10.20
- 12. Page 12 What did we do? 1. Focus on agile approaches – value- mapping, feature evaluation and iterative artefacts. 2. Re-designed workload shift and created extra 52 mins per rep per day time 3. Developed seller and SME based ‘lead indicator’ ranking model 4. Invested $250k in technology platform to scale up and onto real-time web solution 5. Started work to expand approach to other sales teams 6. Completed ‘list audit’ to determine what alternate business direction was being given to sellers 7. Datafication finds your weaknesses first! 8. The role of analytics is secondary 9. Management layers lack line of sight 10. Sellers aren’t doing what we think they are 11. Stop old practices as well as starting new 12. multiplicity drives irrational behaviour 13. Your sponsors may become impatient Findings Round 2 Outcomes Mar 2015 – Dec 2015 I’m too busy on other stuff
- 13. Page 13 Context – Round 3 What evidence is there of the problem to be solved....the clues What’s activity is happening on floor? What are end users (sellers) saying - feedback? What data evidence is normally available? a f d Renewal Cycle (need to have) Do you have an X list about Y? How do I add a Prospect retrospectively? My sponsor wants me to focus on Z! 26% of active clients 30% of target clients called – 1.5 calls/rep/week 8-10% lead conversion rate – calls to opportunities $42m pipeline created 1 single outbound ‘funnel’ – no alternate ‘lists’ Customers and Time Next Best Customer (nice to have) Direction (urgency wanes) What’s the best way to use LinkedIn? Engagement Cycle (need to have) Ren WinBk Values Customer Name Sales Rep NetNew Oppty? IMT Rank Vol Weight Average of % Direct Average of % Indirect Renewals Power Storage Mob ICS WinBack NoCover psWAXIT 9 to 5 SWMA Drop-Offs HWMA No SWMA ETS HMC DataPower XIV Storwize Dell HP EMC Cisco Juniper Motorola Linux Oracle Sun SPECIALIST DISTRIBUTIO Shane Ronan-Duggan No 1 9,302 100 0 0 0 0 0 0 0 52 9,250 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SIG PLC Brian Royle Yes 2 3,960 0 100 0 22 0 0 21 21 1,151 0 0 1,144 1,102 0 42 0 0 439 0 0 0 0 0 0 0 17 0 ARROW ECS UK LTD Shane Ronan-Duggan No 3 3,575 100 0 0 0 0 0 0 0 0 3,575 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 VR012/PGDS LTD Emma Coyle No 5 3,092 0 100 26 0 0 0 0 0 0 0 22 0 0 0 0 0 0 3,044 0 0 0 0 0 0 0 0 0 NORTHAMBER Shane Ronan-Duggan No 6 2,695 100 0 0 0 0 0 0 0 0 2,695 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PRUDENTIAL Enda Scanlon No 7 2,356 0 100 0 0 0 0 0 0 157 415 0 754 50 0 0 0 0 980 0 0 0 0 0 0 0 0 0 TRAVELERS MANAGMENT LT Enda Scanlon No 8 2,314 100 0 0 0 0 0 0 0 0 0 22 0 0 0 127 75 0 2,089 0 0 0 0 0 0 0 0 0 IMPERIAL COLLEGE Anthony Murphy Yes 9 2,215 0 100 0 0 44 0 0 0 209 104 0 884 200 0 0 0 0 774 0 0 0 0 0 0 0 0 0 VR050/INTELLECTUAL Del Tillyer Yes 10 2,201 0 100 0 0 87 0 0 21 0 0 0 1,040 0 0 0 0 0 1,032 0 21 0 0 0 0 0 0 0 VR695/KINGSTON UNI Anthony Murphy No 11 2,141 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2,141 0 0 0 0 0 0 0 0 0 WILKINSONS Brian Royle No 12 2,117 0 100 0 0 0 0 0 0 209 311 0 806 0 0 42 0 0 748 0 0 0 0 0 0 0 0 0 ADMIRAL Enda Scanlon Yes 13 1,967 0 100 0 22 22 0 0 21 0 52 0 936 0 62 0 0 0 851 0 0 0 0 0 0 0 0 0 LOGICALIS UK Suneel Talikoti No 14 1,850 100 0 0 0 0 0 0 0 0 492 0 572 0 166 0 0 0 619 0 0 0 0 0 0 0 0 0 MCKESSON HBOC Louise Noone No 15 1,780 98 2 0 0 0 0 0 21 235 0 22 208 100 42 403 0 0 748 0 0 0 0 0 0 0 0 0 VR012/ EUI LIMITED Enda Scanlon No 16 1,732 0 100 0 0 0 0 0 0 0 0 22 0 0 0 85 0 0 1,625 0 0 0 0 0 0 0 0 0 VR012/HARGREAVES L Suneel Talikoti No 17 1,677 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1,677 0 0 0 0 0 0 0 0 0 VR695/INTELLECTUAL Del Tillyer No 18 1,647 0 100 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 1,625 0 0 0 0 0 0 0 0 0 VR522/NISA RETAIL Brian Royle No 19 1,627 0 100 0 0 0 0 0 0 0 492 0 0 0 0 0 0 0 1,135 0 0 0 0 0 0 0 0 0 TECH DATA LIMITED Shane Ronan-Duggan No 20 1,555 100 0 0 0 0 0 0 0 0 1,555 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 APACHE NORTH SEA LTD Sarah Knox No 21 1,551 0 100 0 0 0 0 0 0 0 104 0 442 526 416 21 0 0 0 0 42 0 0 0 0 0 0 0 VR522/SAGA SERVICE Louise Noone No 23 1,494 0 100 0 0 0 0 0 0 0 0 22 0 0 0 234 0 0 1,238 0 0 0 0 0 0 0 0 0 VR695/SURREY COUNT Anthony Murphy No 25 1,260 0 100 26 0 0 0 0 0 0 0 22 0 0 0 0 0 0 1,212 0 0 0 0 0 0 0 0 0 RAILWAY PROCUREMENT James Gray Yes 26 1,256 100 0 0 22 22 0 0 21 78 0 0 858 0 0 127 0 0 0 0 63 0 46 0 0 0 17 0 KIER GROUP PLC Marese Clarke No 28 1,236 92 8 0 0 0 0 0 0 131 104 22 442 125 0 0 0 0 413 0 0 0 0 0 0 0 0 0 NHS LANARKSHIRE Sarah Knox No 30 1,206 0 100 0 0 0 0 0 0 0 0 0 520 200 125 0 0 0 361 0 0 0 0 0 0 0 0 0 C & J CLARK Marese Clarke No 31 1,174 0 100 0 0 0 0 0 0 0 0 0 624 50 458 42 0 0 0 0 0 0 0 0 0 0 0 0 VR012/2 SISTERS GR Brian Royle No 32 1,157 0 100 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 1,135 0 0 0 0 0 0 0 0 0 VR012/ECCLESIATICAL IN Louise Noone No 33 1,156 0 100 26 0 0 0 0 0 0 0 0 0 0 0 21 0 0 1,109 0 0 0 0 0 0 0 0 0 HRG C/O ARGOS Brian Royle Yes 34 1,138 0 100 0 0 0 0 0 21 0 52 0 442 125 166 0 0 0 0 0 105 20 0 0 0 0 206 0 VR695/DUMFRIES & G Sarah Knox No 35 1,111 24 76 26 0 0 0 0 0 0 492 0 0 0 0 0 0 0 593 0 0 0 0 0 0 0 0 0 SAGA GROUP LTD Louise Noone No 36 1,104 0 100 0 0 0 0 0 0 0 78 0 494 0 146 0 0 0 387 0 0 0 0 0 0 0 0 0 HMV RETAIL LIMITED Sarah Knox No 37 1,076 9 91 0 0 0 0 0 0 0 0 0 598 0 478 0 0 0 0 0 0 0 0 0 0 0 0 0 VR012/HAIRMYRES HO Sarah Knox No 38 1,058 0 100 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1,032 0 0 0 0 0 0 0 0 0 VR012/ATCORE TECHNOLOG Suneel Talikoti No 39 1,056 0 100 0 0 0 0 0 0 0 0 0 806 0 250 0 0 0 0 0 0 0 0 0 0 0 0 0 SCC Suneel Talikoti No 40 1,035 0 100 0 0 0 0 0 0 0 0 0 520 25 0 0 0 0 490 0 0 0 0 0 0 0 0 0 ECCLESIASTICAL Louise Noone Yes 41 1,022 0 100 0 22 65 0 0 21 0 0 0 468 0 62 21 0 0 361 0 0 0 0 0 0 0 0 0 WILKINSON Brian Royle Yes 42 1,005 0 100 0 0 22 0 0 0 26 492 0 0 0 0 0 0 0 464 0 0 0 0 0 0 0 0 0 HALFORDS LTD Brian Royle No 43 1,004 100 0 0 0 0 0 0 0 340 0 0 338 326 0 0 0 0 0 0 0 0 0 0 0 0 0 0 VR012/PLYMOUTH UNI Anthony Murphy No 44 980 0 100 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 954 0 0 0 0 0 0 0 0 0 VR695/KIER GROUP LTD Marese Clarke No 46 954 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 954 0 0 0 0 0 0 0 0 0 OCADO Marese Clarke Yes 48 934 0 100 0 22 44 0 0 21 0 0 0 416 250 125 21 0 0 0 0 0 0 0 0 0 0 34 0 VR012/ISLE OF WIGH Anthony Murphy No 49 929 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 929 0 0 0 0 0 0 0 0 0 VR012/HAMPSHIRE COUNTY Anthony Murphy No 49 929 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 929 0 0 0 0 0 0 0 0 0 VR012/IMPERIAL COLLEGE Anthony Murphy No 51 925 0 100 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 903 0 0 0 0 0 0 0 0 0 Attach Hardware Software Multi-Vendor Channel A RAIN of lists Average of 100 unique “lists” per Europe mission 10.50
- 14. Page 14 What did we do? 1. Paused work with Renewals team and expanded to other sales teams 2. Integrated all the Lists sellers were being given from matrix of stakeholders 3. Implement Next Best Customer and All Thing Considered Next Best Customer. 4. Altered process to capture client feedback on each engagement 5. Implemented Sales Sprints idea instead of open ended execution runs 6. Started focusing on Market Feedback views to assess ‘best’ strategy. 7. Integrated with more downstream tools to simplify research process 8. Commenced Worldwide deployment 14. You must manage the rain of lists! 15. Identify a cohesive strategy isn’t easy! 16. Issues with lack of cohesive direction 17. Issues with ‘perceived wisdom’ 18. Mission creep happens invisibly over time 19. Separation of direction from execution key Findings Round 3 Outcomes Jan 2016 – present We need more lists!
- 15. Inside Out : Where we think clients and markets are…. Outside In : Where clients actually are… What do we do differently? identify & prioritise We codify expertise into models that match clients with needs and prioritizes engagement driving sellers to the right clients B All things considered, who is best customer to engage next? Ren WinBk Values Customer Name Sales Rep NetNew Oppty? IMT Rank Vol Weight Average of % Direct Average of % Indirect Renewals Power Storage Mob ICS WinBack NoCover psWAXIT 9 to 5 SWMA Drop-Offs HWMA No SWMA ETS HMC DataPower XIV Storwize Dell HP EMC Cisco Juniper Motorola Linux Oracle Sun SPECIALIST DISTRIBUTIO Shane Ronan-Duggan No 1 9,302 100 0 0 0 0 0 0 0 52 9,250 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SIG PLC Brian Royle Yes 2 3,960 0 100 0 22 0 0 21 21 1,151 0 0 1,144 1,102 0 42 0 0 439 0 0 0 0 0 0 0 17 0 ARROW ECS UK LTD Shane Ronan-Duggan No 3 3,575 100 0 0 0 0 0 0 0 0 3,575 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 VR012/PGDS LTD Emma Coyle No 5 3,092 0 100 26 0 0 0 0 0 0 0 22 0 0 0 0 0 0 3,044 0 0 0 0 0 0 0 0 0 NORTHAMBER Shane Ronan-Duggan No 6 2,695 100 0 0 0 0 0 0 0 0 2,695 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PRUDENTIAL Enda Scanlon No 7 2,356 0 100 0 0 0 0 0 0 157 415 0 754 50 0 0 0 0 980 0 0 0 0 0 0 0 0 0 TRAVELERS MANAGMENT LT Enda Scanlon No 8 2,314 100 0 0 0 0 0 0 0 0 0 22 0 0 0 127 75 0 2,089 0 0 0 0 0 0 0 0 0 IMPERIAL COLLEGE Anthony Murphy Yes 9 2,215 0 100 0 0 44 0 0 0 209 104 0 884 200 0 0 0 0 774 0 0 0 0 0 0 0 0 0 VR050/INTELLECTUAL Del Tillyer Yes 10 2,201 0 100 0 0 87 0 0 21 0 0 0 1,040 0 0 0 0 0 1,032 0 21 0 0 0 0 0 0 0 VR695/KINGSTON UNI Anthony Murphy No 11 2,141 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2,141 0 0 0 0 0 0 0 0 0 WILKINSONS Brian Royle No 12 2,117 0 100 0 0 0 0 0 0 209 311 0 806 0 0 42 0 0 748 0 0 0 0 0 0 0 0 0 ADMIRAL Enda Scanlon Yes 13 1,967 0 100 0 22 22 0 0 21 0 52 0 936 0 62 0 0 0 851 0 0 0 0 0 0 0 0 0 LOGICALIS UK Suneel Talikoti No 14 1,850 100 0 0 0 0 0 0 0 0 492 0 572 0 166 0 0 0 619 0 0 0 0 0 0 0 0 0 MCKESSON HBOC Louise Noone No 15 1,780 98 2 0 0 0 0 0 21 235 0 22 208 100 42 403 0 0 748 0 0 0 0 0 0 0 0 0 VR012/ EUI LIMITED Enda Scanlon No 16 1,732 0 100 0 0 0 0 0 0 0 0 22 0 0 0 85 0 0 1,625 0 0 0 0 0 0 0 0 0 VR012/HARGREAVES L Suneel Talikoti No 17 1,677 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1,677 0 0 0 0 0 0 0 0 0 VR695/INTELLECTUAL Del Tillyer No 18 1,647 0 100 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 1,625 0 0 0 0 0 0 0 0 0 VR522/NISA RETAIL Brian Royle No 19 1,627 0 100 0 0 0 0 0 0 0 492 0 0 0 0 0 0 0 1,135 0 0 0 0 0 0 0 0 0 TECH DATA LIMITED Shane Ronan-Duggan No 20 1,555 100 0 0 0 0 0 0 0 0 1,555 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 APACHE NORTH SEA LTD Sarah Knox No 21 1,551 0 100 0 0 0 0 0 0 0 104 0 442 526 416 21 0 0 0 0 42 0 0 0 0 0 0 0 VR522/SAGA SERVICE Louise Noone No 23 1,494 0 100 0 0 0 0 0 0 0 0 22 0 0 0 234 0 0 1,238 0 0 0 0 0 0 0 0 0 VR695/SURREY COUNT Anthony Murphy No 25 1,260 0 100 26 0 0 0 0 0 0 0 22 0 0 0 0 0 0 1,212 0 0 0 0 0 0 0 0 0 RAILWAY PROCUREMENT James Gray Yes 26 1,256 100 0 0 22 22 0 0 21 78 0 0 858 0 0 127 0 0 0 0 63 0 46 0 0 0 17 0 KIER GROUP PLC Marese Clarke No 28 1,236 92 8 0 0 0 0 0 0 131 104 22 442 125 0 0 0 0 413 0 0 0 0 0 0 0 0 0 NHS LANARKSHIRE Sarah Knox No 30 1,206 0 100 0 0 0 0 0 0 0 0 0 520 200 125 0 0 0 361 0 0 0 0 0 0 0 0 0 C & J CLARK Marese Clarke No 31 1,174 0 100 0 0 0 0 0 0 0 0 0 624 50 458 42 0 0 0 0 0 0 0 0 0 0 0 0 VR012/2 SISTERS GR Brian Royle No 32 1,157 0 100 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 1,135 0 0 0 0 0 0 0 0 0 VR012/ECCLESIATICAL IN Louise Noone No 33 1,156 0 100 26 0 0 0 0 0 0 0 0 0 0 0 21 0 0 1,109 0 0 0 0 0 0 0 0 0 HRG C/O ARGOS Brian Royle Yes 34 1,138 0 100 0 0 0 0 0 21 0 52 0 442 125 166 0 0 0 0 0 105 20 0 0 0 0 206 0 VR695/DUMFRIES & G Sarah Knox No 35 1,111 24 76 26 0 0 0 0 0 0 492 0 0 0 0 0 0 0 593 0 0 0 0 0 0 0 0 0 SAGA GROUP LTD Louise Noone No 36 1,104 0 100 0 0 0 0 0 0 0 78 0 494 0 146 0 0 0 387 0 0 0 0 0 0 0 0 0 HMV RETAIL LIMITED Sarah Knox No 37 1,076 9 91 0 0 0 0 0 0 0 0 0 598 0 478 0 0 0 0 0 0 0 0 0 0 0 0 0 VR012/HAIRMYRES HO Sarah Knox No 38 1,058 0 100 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1,032 0 0 0 0 0 0 0 0 0 VR012/ATCORE TECHNOLOG Suneel Talikoti No 39 1,056 0 100 0 0 0 0 0 0 0 0 0 806 0 250 0 0 0 0 0 0 0 0 0 0 0 0 0 SCC Suneel Talikoti No 40 1,035 0 100 0 0 0 0 0 0 0 0 0 520 25 0 0 0 0 490 0 0 0 0 0 0 0 0 0 ECCLESIASTICAL Louise Noone Yes 41 1,022 0 100 0 22 65 0 0 21 0 0 0 468 0 62 21 0 0 361 0 0 0 0 0 0 0 0 0 WILKINSON Brian Royle Yes 42 1,005 0 100 0 0 22 0 0 0 26 492 0 0 0 0 0 0 0 464 0 0 0 0 0 0 0 0 0 HALFORDS LTD Brian Royle No 43 1,004 100 0 0 0 0 0 0 0 340 0 0 338 326 0 0 0 0 0 0 0 0 0 0 0 0 0 0 VR012/PLYMOUTH UNI Anthony Murphy No 44 980 0 100 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 954 0 0 0 0 0 0 0 0 0 VR695/KIER GROUP LTD Marese Clarke No 46 954 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 954 0 0 0 0 0 0 0 0 0 OCADO Marese Clarke Yes 48 934 0 100 0 22 44 0 0 21 0 0 0 416 250 125 21 0 0 0 0 0 0 0 0 0 0 34 0 VR012/ISLE OF WIGH Anthony Murphy No 49 929 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 929 0 0 0 0 0 0 0 0 0 VR012/HAMPSHIRE COUNTY Anthony Murphy No 49 929 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 929 0 0 0 0 0 0 0 0 0 VR012/IMPERIAL COLLEGE Anthony Murphy No 51 925 0 100 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 903 0 0 0 0 0 0 0 0 0 Attach Hardware Software Multi-Vendor Channel integrate & simplify We translate the data into simple, understandable Reason of Call visualizations and integrate into Sales Connect C What is the data-driven reason of call? observe & adjust End-to-end management system on all outcomes allowing continual course correction to maximize performance D Where are the clusters of best opportunity found A aggregate & analyse We aggregate thousands of data points most valuable as lead indicators of need into a central repository. What are the best lead indicators of client need? 10% steady state lead conversion rates compared with 1-2% average for outbound
- 16. Page 16 lessons learnt
- 17. Page 17 Interpretation Build a visual sales narrative 17 ?? There’s a LOT of STG activity on this account at the moment and none of it has any maintenance attached! Is there a tech refresh happening? Seems to be Power + Storage…must investigate. 1 There’s been no TSS NetNew on this customer in last 6 months….. No renewal currently this Quarter and the 178 boxes under cover with us are worth $19k per annum but new kit Oppties suggest scope to expand. 2 Odd. They have most of their assets covered on a Direct contract but some isolated boxes under a contract with a Business Partner. Why? Must investigate….. ? 3 Hmm…a lot of boxes on low levels of only 9 to 5 cover. Seems odd given that they have mission critical applications for Wimbledon. Must ask. ? 4 We lost a TSS Oppty to get more business out of this customer a Year Ago. I should call to see how they’re getting on with the service. 5 No multi-vendor that we can see but WinBack suggests there’s more we could do that haven’t captured yet. Must investigate….. 6 11.30
- 18. Agile Sprints 2% 10% Time Lead Conversion observe & adjust observe & adjust observe & adjust observe & adjust “inside-out” model “outside-in” model The integrated feedback loop allows for Marketing and Sales sprints 1% application to commercial market ‘search’ How is this relevant? How do we know where the clients are if we’ve never had them before? We don’t have enough meaningful ‘historic’ data to tell us where the market potential lies... If anything, this historic data is dangerous – existing commercial clients probably look a lot like our industrial and enterprise clients and very different to where we should be! 11.40
- 19. Capture New Data ?? What would I like to know? Ask the right questions first 11.50
- 20. WHERE – IMTs and Industries WHY - patterns % Clients with Positive Outcomes per Lead Indicator Outcome Status for Prospects 9% Oppty Conversion 17% Nurture^ Conversion 47% Misaligned Value Prop* What Market Feedback Did We Receive? 46% Positive Feedback Did customer respond positively to value proposition? Are we on target with message? Did customer respond negatively to value proposition? Are we off target with message? hit What Market Feedback by IMT? What Market Feedback by Industry? What factors link strongest with Positive Feedback? miss hit WHAT - outcomes Iterate Your Models observe & adjust observe & adjust observe & adjust observe & adjust “inside-out” model “outside-in” model Challenge Bias and Iterate Your Understanding of the Problem 12.00
- 21. Solve for issues that will have impact not just blip No Management System No course correction => suppressed results Multiple Lists Poor Contact Data Little or No Market Feedback Segments not Clusters Weak or No Reason of Call How do I use this info to open a client call? Little Client Insight data - not client insight provided Who versus Why Lists of CMRs with little else Ease of Use Designed visually to help discovery of insight No Prioritization or Ranking high opportunity cost of execution Time to Research vs Engage sellers spend 30 mins avg researching per prospect Integration Results Productivity Confused Strategy Opportunity Cost of Execution Suppressed Lead Conversion Revenue Left on Table Understand Causes Vs. Symptoms 12.05
- 22. • What are the right questions? • What is the best process to capture the answers? • Then implement a data-driven transformation Understand what you need to know & what your data is NOT telling you process data insight + = ?? ? ? ? ? What would I like to know? “Stage” Your Problem 12.10 12.10
- 23. Possible Application Critique or Challenge 1 Interpretation Build a visual narrative to bridge from data to insight 2 Agile Sprints Integrated feedback loop allows for Marketing and Sales sprints 3 Capture New Data Ask the right questions first 4 Iterate Your Models Challenge Bias and Iterate Your Understanding of the Problem 5 Causes Vs. Symptoms Solve for issues that will have impact not just blip 5 “Stage” Your Problem Understand what you need to know & what your data is NOT telling you Apply & Challenge
- 24. Possible Application Critique or Challenge 1 Interpretation Build a visual narrative to bridge from data to insight 2 Agile Sprints Integrated feedback loop allows for Marketing and Sales sprints 3 Capture New Data Ask the right questions first 4 Iterate Your Models Challenge Bias and Iterate Your Understanding of the Problem 5 Causes Vs. Symptoms Solve for issues that will have impact not just blip 5 “Stage” Your Problem Understand what you need to know & what your data is NOT telling you Apply & Challenge
- 25. Which customers are in each campaign? WHY engage? WHO to call? WHAT to offer? WHEN to engage? WHICH resource? HOW to engage? WHERE to adjust? Marketing brand and marketing drive prospects into top of funnel plan execute Lead Dev Either a dedicated bus dev rep or sales rep on bus dev activity plan execute Sales Sellers managing a territory of clients or pipeline of opportunities plan execute Identified Validated Qualified What are the best market segments? What are market pain points or CRAs? What products match market needs? Which digital toolset do I use to engage? What is market feedback and where to pivot? Which campaigns should we run? Which digital toolset do team use? How are teams performing? When do I plan for campaign execution? Which Reps should cover which campaigns? Which clients and Oppties are priorities? Jobs To Be Done Which Reps should cover which accounts? How can I see market pain points? How do I define “best” segment? How do I “fit” product to segment? How do I “fit” product to segment? How should feedback adjust campaigns? How do I “fit” product to prospect? Who do I call next? How do I prioritise all my calls? How do I triage my prospects? How do I know where to adjust my actions? Who do I call next? What are the market trends? What are the market trends? What’s best product for my customer? When is best time to engage my customers? How do I triage my prospects? How do I know where to adjust my actions? How are teams performing?
- 26. reading
- 27. Recommended READING Building Better Business Cases for IT Investments Ward, 2007, Cranfield School of Management What Are Key Performance Questions Bernard Marr, API Institute - www.ap- institute.com Big Data: A Revolution That Will Transform How We Live, Work and Think Viktor Mayer-Schonberger and Kenneth Cukier 2013 The Signal and the Noise: The Art and Science of Prediction Nate Silver, 2013 Marketing and Sales Analytics: Proven Techniques and Powerful Applications from Industry Leaders Cesar Brea, 2014 1 2 3 4 6 5 Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing: Delivering the Promise of Business Intelligence Ken Collier, 2011