It is a challenging time to be a marketer. Consumers demand more personalised experiences than ever, and for marketers if feels like the customer's expectations just keep on rising.
In such a complex business environment a sophisticated approach to data is the key to success.
Increasingly, however, CMOs hear from software vendors, analysts, and the business press that it is not enough to be a great marketer these days. In an era of data-driven marketing, they now need to be a data scientist as well.
It is worth asking, whether that is really true or even practical.
Look around you at your peers in the marketing department. The reality is that your creative director needs more freedom to create. They don’t really need to program in Python or R. Likewise your field marketers do not need a technical specialist’s deep grasp of algorithmic concepts in order to put a qualified lead into the hands of the sales team.
Swings and round-abouts
We have been down this road before.
Think back a few years when marketing technology really broke into mainstream business conversations. Marketers were told then they needed to be technologists who understood IT almost as well as they understood their own core disciplines.
It didn’t happen because marketers and IT specialists started working in a collaborative fashion, each bringing their own unique expertise to the conversation. The IT department provides a robust infrastructure while marketers use the tools to ensure the right messages reach the right customers.
And data scientists? They ask questions of the company’s data and run experiments to help Marketing maximise the effectiveness of their campaigns and understand the responsiveness of their audience.
Marketers have always known that the better they understood their customers, the more tailored the experience and messaging they could provide.
So what is compelling the closer relationship between marketing and data science?
Changes in consumer behavior and the competitive landscape demand and technology have enabled, a shift from a waterfall approach to marketing where one campaign starts as another one ends, to a more agile campaigning.
These always-on campaigns driven by programmatic technologies let brands adjust on the fly as the data informs them what is working and what is not. (These are the kinds of questions real data scientists are great at asking!).
In this new method of marketing, brands look for the data they need to make a difference. That is the only data that should inform their marketing campaigns.
Take an everyday example.
Think about your favourite barista who sells you your coffee each morning. They may have read studies on the conversion rates with men over women, compared who has an IOS or Android phone, and they may even know your geolocation, your sex, and an estimate your age. But all of that is less relevant than the two most salient data points required to satisfy your needs as a customer -“What’s your name and what type of coffee do they drink!!”.
In the rush to prove their data-driven credentials too many CMOs are actually making their own lives harder. Research last year by Capgemini and the MIT Initiative on the Digital Economy called “From UX to CX: Rethinking the Digital User Experience as a Collaborative Exchange” clearly suggests that many brands are still trying to hoover up every piece of information they can gather without any regard for how it is used.
Capgemini’s Senior Vice President & Global Practice Leader Digital Transformation, Didier Bonnet suggests too many brands are missing an essential point - consumers want simple value exchange - a good product (associated with nice, easy experience) in return for their custom.
Marketers today can collect so much data on their audiences but the key is to focus on getting access to data you need. For instance, if you are in retail you don’t need all the transactional data, you may only need the last 5 transactions under 90 days old. Similarly, in financial services you don’t need all the call center data or branch data, you just need the last 3 activities over the past 60 days.
So how might this play out in the real world?
A financial services institution might, for instance, implement a simple approach with both new and returning customers where they inform them of their last five conversations across all channels. That way the customer can see the last thing they did on the website, or a call they made to the call center, or what they did on their most recent visit to a branch.
If the company can deliver that information in near real-time into the campaign platform for next interaction, or back to the call center or branch staff they will be better placed to meet the customer needs.
Likewise, imagine the value to a salesperson in a retail store if they could see the last five interactions the shopper in front of them had with the brand.
Marketing is made better and more effective when great data is sensibly applied to meet the needs of consumers. That doesn’t mean the CMO needs to be a data scientist. It just means they need to understand which data helps them best understand what really matters to the customer.
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