Today is Episode 6 of the Interview series on Expert-talks @MAAVRUS, with Leaders in the Analytics, AI and Transformation space. For this episode, our CEO Mahadevann Iyerr (Mahaa) is in conversation with Gary Cokins, Founder & CEO of Analytics-based Performance Management LLC, a company that provides financial insights and analytics. Gary has previously worked with KPMG, Deloitte, EDS and SAS Institute. Early on in his career, Gary worked with Dr Robert Kaplan and Dr David Norton, the creators of the balanced scorecard. Gary completed his BSc degree in industrial engineering and operations research from Cornell University, and MBA from Northwestern University’s Kellogg School of Management. He has also authored many books on activity-based costing, and co-authored a book “Predictive Business Analytics” I am listing below, some key points from the interview : Analytics in enterprise and corporate performance management is gaining traction, for a few reasons like executives frustration with strategy failure, increasing scrutiny of their performance, need for rapid decision making & the accompanying risks, and mistrust of management accounting systems. For Business Unit leaders, overhead cost allocations not being transparent or being too simplistic is a challenge, and organisations are adopting activity based costing to be able to better relate investment/ costs to outcome. Activity based costing is also important to understand Customer Lifetime Value (CLTV), which is a predicted measure of a customer’s profit contribution over the estaimted period, that she / he is likely to be shopping with the brand. Its classical definition is, discounted cash flow net present value prediction for a customer. After accounting for individual expenses like distribution cost, channel expenses, marketing spend, discounts , cost-to-serve etc, organisations most often realise that their largest customer(s) by sales, may not be the most profitable – because they could adding cost elements like non-standard asks, changes in delivery schedules, higher frequency of product returns etc. Hence Business and Marketing teams need to better understand which customer segments are most attractive, whom they need to acquire, retain, win-back and grow. However, most organisations prefer to stay with the out-of-date annual budgeting process, since doing activity based costing requires investment in process, data and time. Also looking beyond the enterprise, organisations need to adopt a collaborative approach with suppliers ( as opposed to being adversarial ). That is because in the changing world, supply chains are competing against other supply chains for end consumer share of wallet. You have to embed analytics in each of the methods / processes, that can help deliver effective measurable and consistent performance. Analytics and Change are like gears and machine, and they have mesh seamlessly. Good Data scientists have to “Search for Surprises”, and build a strong data story, to challenge ingrained ways of leadership working. So it is combination of business understanding, collaborative hypothesis building, exploratory approach and ability to convince with great narration. If there is discomfort with current situation (D), a well-articulated vision of the end state /future (V), and if the First steps to get there are practical (F), then their combined might, will help to overcome Resistance to change (R). So D * V * F should be greater than R. Change leaders will do well to build and validate narratives along the above lines. Explained differently, Transformation and Analytics teams should answer not only the “WHAT”, but also the “SO WHAT” and “THEN WHAT” The future of AI will disrupt the way work is delivered today, and so anything that is repetitive and can be automated will be done by machines, and humans to need focus and build higher order business decision making and people skills.