Breakthrough Sales Productivity
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The document discusses the challenges faced by sales executives, including limited customer engagement, reliance on legacy products, and lengthy deal cycles. It emphasizes the role of sales operations in transforming productivity through data-driven selling models and advanced analytics, leading to significant reductions in seller time and costs. Additionally, it highlights the need for companies to enhance their analytics capabilities to better predict customer behavior and drive sales growth.
Breakthrough Sales Productivity
- 1. Last Modified 10/7/2016 4:13 PM Eastern Standard Time Printed 9/28/2016 12:04 PM Pacific Standard Time WORKING DRAFT Breakthrough Sales Productivity Improvement through Advanced Analytics October 2016 San Francisco Dreamforce
- 2. Sales executives face a common set of challenges that should be addressed by Sales Operations leaders Sellers don’t spend enough time with customers The organization keeps selling legacy products, not new products Sellers are giving away too much margin Deals take longer than they should There are no universal metrics to review ?
- 3. Sales Operations can play a significant role in enabling transformation and driving sales productivity improvement Local teams with inconsistent selling models based largely on past experience Globally consistent selling approach with data- driven selling models to optimize productivity Sales Operations ▪ Drives top-line growth – 30% reduction in seller time, 40% reduction in deal cycle time ▪ Reduces opex – 30% decrease in sales operations cost, 2-3% annual productivity growth
- 4. Opportunity for Sales Operations to harness the power of advanced analytics capabilities to drive continuous improvement SALES SUPPORT CAPABILITY Analytics capability Strategy & planning Sales enablement Performance management Bid/Project management Intelligent Automation Data-driven Insights Predictive Models Optimize account coverage and target- setting Personalized seller dashboards Segment deal flow based on deal & customer characteristics Redefine hiring and assessments based on data-driven criteria Identify granular growth opportunities Seller incentives based on deal-level pricing dynamics Optimize deal dis- counting based on his- torical “cluster analysis” Predict accounts at risk of churn Calibrate technical sales performance based on rep meta- data Use A/B testing to predict optimal pricing in digital channels Improve value propositions with “next product to buy”
- 5. Expand the range of data beyond basic customer data… ... and use advanced analytics to identify predictive features in the data Most companies today use analytics to predict customer churn, but are only scratching the surface of their data PREDICT CHURN Basic customer data Product install base Current service contract Contract renewal date Expanded data Service contract history Product purchase history Pricing and payments Web portal usage Service ticket history Call center / IVR records Thresholds Aggregations Deviations Trends
- 6. Machine learning is opening new avenues of granular insight and prediction, greatly improving customer focus PREDICT CHURN 10+% monthly churn 2-10% monthly churn <2% monthly churn Build decision tree using advanced machine learning platform to identify extremely high churn microsegments… … with actionable churn reasons Zip code Ziggy: Made service contact last month, heavy TV viewer, in a competitive market Service issue Susan: Less engaged in TV, declining BB usage, called with a service problem Discount hunting Deidre: Promotional pricing ended, light TV viewer, called to ask for a discount Moving Matthew: Called to make a move but does not have a web account
- 7. 53 Slow growers 37 Fast Growers Most B2B companies do not effectively leverage advanced analytics in sales By growth; N = 1,013 companies Companies rating use of analytics in sales as “extremely effective” or “moderately effective” % of companies 43 Overall SOURCE: McKinsey & Company Sales Growth 2015 Survey
- 8. Why companies fail to capitalize on their analytics efforts Building the analytics talent engine Foundational IT infra- structure Consistent data models Next- generation selling talent Embedding into sales workflows
- 9. Analytics agenda for Sales Operations leaders Rethink your talent profiles both inside Sales Ops and the field Re-imagine sales processes through a digital and analytics lens Simplify your IT roadmaps with a bias for impact “out of the box” Invest ahead in defining your data model Aspire to “just-in-time” insight delivery to sellers
- 10. Stay connected Brian Selby Expert Partner & Leader of McKinsey’s Sales Operations Practice McKinsey on Marketing & Sales Brian_Selby@mckinsey.com Brian Selby