market share data, but also first-party data
skepticism from the outset. It’s also effective to find the curious, interested champions and with whom you can partner for ‘quick wins’ before scaling up. You also have to articulate your vision and strategy and where you’re headed very clearly and often, not just to your team and your analytics organization but also to the entire business. This means speaking in plain terms to demystify data and analytics and make it something that
business and the analytics space will continue to evolve. We have to constantly look at what’s happening externally to make sure that we’re still taking the most relevant approach for our business. We need to stay current with emerging approaches, data sources, and technologies. We are most excited about more predictive and prescriptive analytics for the future and using AI to automate decisions that require human effort. However, it is always with the approach of tying it to a business question, then building, showing value, and scaling.
and data partnerships with retailers. Being able to mine that data, draw the relevant insights and spur action is key to real scalable success. Are there specific adoption challenges that companies like yours face? And what practical steps can be taken to overcome them? Change management can be a big challenge – it’s the case in any organization where people are comfortable working in a particular way. Building trust across the organization is incredibly important to solving adoption challenges. That starts with really taking the time to listen and understand your stakeholders’ priorities and their business problems so that we can address the most important areas for them. I invest in building relationships, whether that’s through one-on-one meetings, lunches, or informal chats. By communicating effectively and, most importantly, by listening, you can build trust from the beginning and therefore overcome a lot of
CSC:Miami Content Supply Chains must be forensic in their detail. Television broadcasters have long relied on instinct,
everyone understands. We have our analytics
strategy on a single page at Colgate, including all of our strategic pillars. It acts as a kind of north star that can be referred to constantly to communicate why we’re doing what we’re doing, and to make sure that we’re focusing
market knowledge and spreadsheets to forecast TV viewership - but instinct needs to partner with information; market knowledge is never enough; and spreadsheets are no way to excel. As witness to these challenges, Fractal undertook its own detective work. By combining AI, data engineering and user-centric design, Fractal created an industry-first TV forecasting system for Europe’s leading media and entertainment company. The result? Up to 30% improvement in forecast accuracy. Fractal: perfectly targeted and timed TV, no drama.
and staying on track with what’s most important.
What does the future hold? How do you expect your strategic objectives to evolve in the coming years? We have our strategy, and we know where we’re headed, but we also recognize that the
17
Made with FlippingBook - PDF hosting