Recently, Connor Consulting CEO Viresh Chana, attended the IACC 2019 Conference to join a panel of esteemed experts to discuss the impact and opportunity for data analytics and AI in the world of brand protection. Below is his retrospective on the event and the panel discussion for those of you who weren’t able to attend.

Having the opportunity to meet with Chanterelle Sung, Director of Strategic Planning and Operations at Pfizer and Morris Wilder, Senior Account Executive at Yellow Brand Protection to discuss how each of our organizations are leveraging data to drive results in the Brand Protection space was truly remarkable.  The idea by the IACC team to have a panel that covered a brand perspective with service providers across different parts of the spectrum was a great way to approach it in my view.  Having preached the importance of analytics, machine learning (ML) and AI for the past two years, it was educational and extremely insightful to learn about the best practices and approach Pfizer and Yellow have taken to manage their brand protection programs.

What was clear from follow-on discussions with several brands is that there is tremendous opportunity to leverage meaningful analytics to move the needle forward without immediately having to jump into ML and AI.  The first step can be accomplished in a relatively short period of time, with the right team and technology.  Connor’s white-glove service is the easiest way to start.

As for companies looking to take on the task internally, here is a summary of what we discussed:

• Deeper Analytics – A simple Excel analysis or the use of a business intelligence (BI) and visualization tool like PowerBI with quarterly trends is not analytics from our point of view. That is simply the use of data to reactively make decisions. Our approach to analytics is to leverage ML to make predictions to help companies be more proactive in making smarter decisions.
• Data Cleansing – Data is the backbone of any successful analytics initiative. However, one of the biggest challenges to operationalizing analytics is the inability to make sense of disparate and dirty data. Implementing data normalization and data validation processes are key to addressing this initial challenge for downstream analytics and requires thought and planning to do it well.
• Establish Goals – Know what you are trying to achieve with analytics, ML or AI because the end goal is different for each company.
• Get More Data – Ask your online monitoring providers to give you the underlying data of the listings taken down and not just a dashboard.
• Plan Long Term – A true ML & AI implementation will take years, not months, and will be heavily dependent on the data you have. Remember, data is the fuel to any ML and AI machine.
• Expect Change and Document – Data capture and data consumption processes will change. Employee training is important.
• Get Expert Support – Brand protection teams hold the knowledge but they need to be supplemented with Data Scientists and experts who have the advanced techniques to fully implement ML.

Thanks again to everyone that attended and to those who have already reached out to learn more. If you are interested in having further discussions and to share war stories and learnings, feel free to contact us at any time.