Big Data Lessons for Global Brands
We are truly in the middle of a significant transformation. A transformation that will profoundly change marketing as a discipline—the advent of Digitization has enabled better accountability and transparency across many processes, industries and functions. Measuring things has become the norm than the exception. This in turn has galvanized C-suite players to invest in technologies and people with the explicit mandate of improving efficiency. Global brands are using Data in a variety of ways to help make themselves more nimble and reactive, in some cases, even predictive. What is clear is that these are not things that will go away—there is enough evidence and results to prove that the trend will become mainstream and value will be created in meaningful ways in the future, if not already.
Some of the clear and straight forward application of Data and Social/ Digital Marketing concepts that are becoming mainstream include concepts like Segmentation and Predictive selling, Behavioral Marketing, Social Monitoring for large scale trends and pulling out specific messages and insights, Leveraging machine level data to do a better job of streamlining quality and manufacturing issues, Optimizing Marketing spend in real time to ensure better efficiency and efficacy for Brand campaigns, establishing and working through concepts like Life Time Value of Customers, Real time analysis of 4P execution(Place, Promotion, Price and Product) in a dynamic environment, Brand Benchmarking across Social platforms to name a few.
“Data and Social/ Digital Marketing concepts are becoming mainstream that include concepts like Segmentation and Predictive selling, Behavioral Marketing, Social Monitoring for large scale trends and pulling out specific messages and insights and leveraging machine level data to do a better job of streamlining quality and manufacturing issues”
In my personal experience working these problems over the last several years, I have come away with a few straight forward thoughts and conclusions. My road to these has been riddled with failures, they have been time consuming and organizationally challenging at times, but may help those that are looking at similar problems in their own organizations.
• There is a lot of Hype—don’t buy into it for the wrong reasons. Just because everyone is talking about it doesn’t make it relevant for you. It is important to understand what ‘Value’ means for you and your organization as it relates to Data (Big, Small and everything in between)
• Lot of this new stuff works well only when you have strong internal advocates—individuals or leadership, that are passionate and have knowledge to demonstrate ‘Value’. In the wrong hands, it will die a painful death and worse may not get a second chance until it is too late
• Don’t rearrange the chairs on the deck. These new disciplines require expertise and skills that are unique and scarce, so make sure you get a few ‘strong’ people who understand this well. Have them establish organizational maturity around these concepts and help draft an organizational construct and a governance process to ensure alignment at the top
• Start small, preferably with a well-defined problem. Make sure to articulate what you want as an end result before you start so there is no ambiguity about what constitutes success. Big Data is riddled with small data problems. You will discover that you have to take care of small data problems before you get to those BIG insights you were promised. Don’t take short cuts!
• Technology isn’t the biggest issue, so maintain flexibility in the framework. You may end up with several technologies and that is fine. Resist the urge to consolidate prematurely. The whole field is so fluid that it will be a long while before any clear winners emerge. A im for user f riendliness. T he more the users of the system, the better the chances of long term success
• Establish depth for insights—most times, I notice that the depth of insights improve significantly over time. You need functional leaders spending quality time listening to the work and mentoring the team to dig deeper and helping them with their thinking. The move from casual to causal will take time, so patience is a virtue in this regard
• Establish a governance process—a lot of functional organizations have a lot of data—from Manufacturing to Marketing, Technology to Product Management. It is ok for people to work across a bunch of problems in their functional silos, but ensure there is a good governance model to surface best practices and ensure exchange of ideas and people. This is tricky and can get political—nip it in the bud, ensure strong leaders help manage this by putting the organizational interests ahead of building fiefdoms
• Lastly, don’t be afraid to fail and do over, it will require many tries before you settle on something close to a ‘final’ solution. The key is to ensure you don’t lose a lot of money to get there. Establish metrics like ‘Time to value’ so you can evaluate objectively how much effort is required to generate value.