Looking Ahead with Sales Performance Analytics

Behind the Scenes Insights from #IBMVision 2013

Vision 2013

Do the current analytics on your Sales Performance data help drive your future path? Do you find yourself re-hashing the results you already had? Do you wonder if you have the “right” analytics?

Today HR Times goes on location at Vision 2013. Each day we’ll feature behind the scenes insights from what’s happening live at the conference. Today we highlight Sales Performance Analytics – A Deloitte and IBM Perspective. To learn more about the conference, to download reports and to read daily blogs click back to the Deloitte Vision 2013 microsite.

As Anshul Gupta of Deloitte Consulting LLP and Brad Burnaman of IBM pointed out today at Vision 2013, businesses, and as a result, their sales functions, are increasingly data driven. While there is alignment on the overall strategy, a different “North Star” guides each stakeholder’s desire for information. Sales operations has to figure out who and how much to pay, set territories and quotas, roll out plans, and then analyze the effectiveness and results of those decisions. HR makes sure all regulations are met, answers payee questions, and analyzes for efficiencies. IT has to manage the architecture and provide information for Business KPIs. With so many moving pieces and complexity, each stakeholder needs to sift through a lot of data to evaluate the eventual key metrics (such as sales margin and revenue growth).

Sales Performance Analytics not only helps make sense of the vast amount of information being made available, but also puts it to work. Instead of just reporting on what happened in the past, sales performance analytics can help you build the capacity to look forward and be predictive, going far beyond typical measurements (who’s earning what from where, top, middle, bottom performers, etc.). You can gain new insights into things like: Who creates the most profitable customer and deals? What are the most profitable deals/bundles/products? Who is making the right investments to drive business and what are they? Which reps capture the most pricing value in the market?

Noteworthy takeaways:

  • Start off right by making sure the analytics project aligns with business strategy, clearly defining project goals, getting the right people involved (sales leaders, sales ops, finance, IT, HR), and choosing the right technology tools for what you want to accomplish.
  • Tackle the biggest problem you have. Resist the temptation to start small. Going big to solve a widely known problem gets you more visibility, more buy-in, and less resistance to change.
  • Don’t focus on just the data or you risk focusing on IT instead of addressing the business problem. Instead, start with the insights you need and then see if/how the data you have can support them. Fill data gaps as you go.
  • Data will keep getting bigger. Set an organization-wide “knowledge agenda” and focus on information consistency to avoid data silos and ambiguity/inconsistency in metrics definition and business rules.
  • Analytics can pay off. There are three levels of analytics capability: Aspirational, Experienced, and Transformational. “Transformed” organizations were 3X more likely than Aspirational ones to substantially outperform their industry peers.1

HR Times ran into as Rob Dicks as he was leaving today’s session. He leads Deloitte’s Sales Force Effectiveness team. His comment: “The companies we work with are consistently looking to gain a competitive advantage through their sales organizations. Companies are just starting to realize the potential locked in the data they already have. Using tools to organize, access, and visualize data and put it to work creates the possibility for huge gains in revenue and profits.”

1 Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute for Business Value study.

2 thoughts on “Looking Ahead with Sales Performance Analytics

  1. Nice article, I see analytics from a slightly different angle- anchor with intuition and experience first then use data to adapt the plan by trying to disconfirm the assumptions

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