How Do Marketers Maximize the Value of Customer Data?
Marketing is shifting from an era of data maximalism to one of minimalism. Where once marketers would have collected the maximum amount of information from and about consumers, they are now collecting the minimum that they need to do their jobs effectively. They are also focusing on maximizing the value of the more limited data available to them.
Street Fight connected with Arun Kumar, EVP, data and insights at Hero Digital, to learn more about how marketers can maximize the value of data at a time when third-party data is disappearing from the market.
Businesses have often lamented not having enough data, and privacy changes have underscored the problem. But others say it’s not primarily the quantity but rather the quality of data and what you’re able to do with it that matters. What do you think?
I agree with the latter. Businesses have often been fixated on getting as much data as they can but quickly realize that more data does not mean more insight. Part of it has to do with the hype around “big data.” My position has always been that if you formulate pinpointed hypotheses, you can get more insight out of a 100-row excel spreadsheet than you can from terabytes of data in a data warehouse.
This is not to diminish the importance of the volume of data but to underscore that extracting the right value from data is about asking the right questions. Additionally, with privacy constraints, businesses are going to have to start dealing with a restricted data flow as previously available data becomes unavailable, which further emphasizes the need for the right analytical approach over huge volumes of data.
What tools do marketers need to make the most of the data available to them? Is it easier said than done?
While I could list out a whole set of tools from cloud-based data warehousing solutions to ETL tools to data visualization capabilities, you actually could have all that and still not be able to get the best out of your data. In my experience, a team of analysts that is able to separate the business insight from the data and work with business teams to translate it into action is what really helps to make the most of your data. Your team should be focused on the ‘so what?’ of the data versus the trends. This is also why you should hire people with inquisitive minds.
What holds marketers back from maximizing data’s value?
Not acting on the insights that the data presents is among the key barriers. Another one is the lack of focus on end-customer value, i.e. how will this improve my customer’s experience and their interaction with my service or product? Marketers often end up focusing on metrics that are visible and controllable in the short term (CTR, engagement rate, etc.) versus looking at metrics that are a bit further downstream but more impactful to the business (conversion rates, repeat purchase rates, referrals, loyalty, lifetime value, etc.) and more indicative of how customers are feeling about the entire experience.
How do privacy changes and other current market trends affect this issue? How will the issue evolve over the next few years?
Privacy is always one side of a two-sided coin, the other side being value. The conversation needs to shift from ‘privacy’ to ‘winning consumer trust.’ If we pause and think about how much data some of the large companies such as Uber, Amazon, AirBnB have on consumers — it can be mind-boggling. However, those companies have earned the right to that data by building trust with customers through 1. ensuring the data is well protected and 2. used in a way that maximizes value to the end customer. When customers gain value from their data and know that data is stored in a safe and secure environment, they are quite happy to hand it over.
Broadly speaking, how could businesses be doing a better job of using data to drive business decisions?
A culture of data needs to permeate the organization. Data cannot be just the business of the ‘analytics’ team. A data-driven culture is key — which implies crystallizing business metrics, clearly defining success, and rigorously testing hypotheses before getting them to market. A
Another often overlooked aspect is testing and learning. Being able to use data to understand success and failure quickly, i.e failing fast and adjusting direction, is another important facet of being able to use data to drive sound business decisions.