More than 20 years ago, The Economist magazine quoted science fiction author William Gibson as saying, “The future is already here – it’s just not very evenly distributed” (1). Gibson’s comment nailed it then, and nails it now regarding the varying degrees that marketers have adopted data-driven decision-making. Much has been done. Much remains to be done.
And in the realm of “to be done,” CMOs have a tall order. They have inched out CEOs as the growth drivers of their companies (2). CMOs realize the importance of data-driven marketing and analytics to achieve targeted, personalized engagement that helps build and retain relationships and drive growth. But they struggle with embracing this data-driven approach. At the same time, consumers demand messages be tailored to their interests, as they begin tuning out mass messaging.
And without data, the results aren’t great. For example, advertising recall has declined despite an increase in companies’ media spend (3). More broadly, data-poor marketing defaults to the mass-marketing mode with ever-decreasing effectiveness.
Marketers are attempting to leverage data to make better decisions, hyper-target media and campaigns, and gain visibility into marketing performance. But the success of these attempts is generally uneven. So what’s the problem?
The challenge is dealing with the massive volumes of data provided through multiple sources, in different formats, at different frequencies. Data platforms are fragmented, often lack customer data and marketing focus, and are not well connected. Data governance is unclear and often distributed, and the balance between deriving short-term value and operationalizing data-driven marketing is difficult to achieve. Marketing departments often lack the teams and processes to analyze, understand, and leverage data on an ongoing basis, defaulting to broad periodic planning and retro-active reporting.
Not all is dire. There are companies that leverage data well, with effective results and clear business value, especially in retail and financial services. Why do they succeed where others might fail or dread to go?
From my perspective, there are six key steps for marketers to consider as they take action to make data-driven marketing real and create targeted, personalized campaigns that bring value.
The sheer volume of data being collected from multiple sources is well beyond the human brain’s capacity to gather, assimilate, and effectively utilize information. Big data can be transformative with the help of machine learning, predictive and cognitive analytics, and the ability to bring your data together. So how do you manage this?
Start with the basics. To use data, you must have data in a usable form. Marketers need to consider named and anonymous customer and audience data and decide which first, second, and third-party data can be brought to bear.
That implies three distinct actions:
- Define integrated data models and flows.
- Implement a cohesive set of data platforms that operate together.
- Establish centralized marketing and customer data governance and processes.
And remember that you are unlikely to find a single technology platform for all your data needs. Traditional data management platforms (DMPs) are important, but they are not enough. You will likely need to marry online analytics, DMPs, customer relationship management (CRM) and sales platforms, customer data warehouses, and possibly more.
Today big players are retooling their data platforms and many small players are emerging. CIOs should expect to blend multiple platforms together to establish a solid foundation.
How you use the data is as important as what data you have, if not more so. Despite the promise of machine learning, artificial intelligence, and cognitive analytics, humans still need to assess and decide how to apply data. Some applications are fairly direct – for example, DMP-driven segmentation that is used in web and media targeting.
But the most compelling results come from data science. Short-term efforts to analyze sparse datasets that use semi-manual approaches and deep-data science experts are a great way to prove hypotheses. This can create value rapidly before a more industrialized approach and capability are in place. That is not to say algorithmic and industrialized approaches are not needed or important. But today, experience-backed data science is a great starting point.
Experimentation is important, but it should be grounded in the context of where the best opportunities exist. To do that, leverage research to redefine personas, key segments, and the desired customer journeys – not marketing journeys or sales journeys, or service journeys. Then prioritize specific stages to optimize based on conversion data and correlation analysis. This process allows for a gradual, phased, and value-focused evolution of marketing capabilities. In other words, define your aspirations, prioritize your business objectives, address the low-hanging fruit, then rinse and repeat.
Machine learning, artificial intelligence, and cognitive analytics. Important? Yes. Necessary? Absolutely. Ready for primetime? Not quite, although they’re going there rapidly. However, these terms are often used incorrectly or are misunderstood. It is important to gain a clear understanding of what these terms mean, what’s truly possible today, and separate experimentation from scalable capabilities.
Remember that while many players are actively working on data-centric artificial intelligence capabilities, the space is still quite fragmented and nascent.
Focus on those platforms that integrate easily with your existing marketing systems to deal with the basics such as anomaly detection, trend and segment identification, and propensity modeling before moving on to more advanced technologies. And anticipate advances in the marketing platforms you have already invested in.
Your organization’s data should be well integrated with web content, online testing and personalization, marketing automation, social marketing, demand-side platforms, sales, service, and CRM, at the minimum. Managing data flows and ETL manually can quickly become cost and time prohibitive, as well as untenable where personally identifiable information, protected health information, and other sensitive data are involved.
Dealing with this complexity requires dedicated resources and well-defined processes and policies for data governance, optimization, reporting, data application, and an always-on, agile approach. For many organizations this is a significant departure from monthly reporting, quarterly forecasting, and episodic insight generation. But this departure is critical for success in the data-driven marketing world. A world-class marketing organization should seamlessly fuse data, analytics, strategies, people, processes, and capabilities to deliver business results.
Applying data to real-time and near-real-time decision-making in marketing and sales is one of the largest transformative opportunities for marketing in recent years. It is also an area with significant risks and far-reaching implications.
Security and privacy cannot be an afterthought. Aim to protect yourself and your audiences from cyber-threats, use the data you acquire in an ethical manner, and be a good steward of your customers’ data.
As the saying goes, “with great power comes great responsibility.” Moving marketing towards a true data-driven model is a long journey. Plan accordingly, and expect to derive value along the way.
Interested in learning how these industry leading practices can help influence your business? Continue the conversation with us at Adobe Summit 2017! As a Diamond level sponsor with several industry breakout sessions and a 40 x 40-foot booth located in the middle of the expo hall (Booth #302), we will be hard to miss. Come find us at Adobe Summit, March 20 to 23, at the Venetian in Las Vegas. In the meantime, learn more at www.deloittedigital.com/us/alliances/adobe or follow us on Twitter at @DeloitteDIGI_US.
Dennis Startsev is a principal at Deloitte Consulting LLP where he focuses on helping clients solve their toughest digital marketing challenges to strengthen brands and grow revenue. He is a veteran of digital disruption, having worked at the intersection of marketing, technology, and operations for over 25 years.
At Deloitte Digital, our passion is to help our clients imagine, deliver, and run successful direct-to-consumer business models. We’ve created a digital consultancy model that provides end-to-end capabilities, bringing together all the creative and technology capabilities, business acumen, and industry insight needed to help our clients enable their OTT ambitions. As a creative digital consultancy, Deloitte Digital has a clear view of the digital ecosystem and how it can be used to influence customer behavior.
(1) The Economist magazine December 4, 2003
(2) cmo.deloitte.com, The CMO Shift to Gaining Business Lift, December 2016
(3) Brand Index, “Ad Awareness” (9/2016).