In marketing, sometimes it helps to look at the past in order to glean insight about future strategies. Take 2016, for example. This was a year when Google and Econsultancy released two research findings that spoke volumes about the state of marketing then, and where it would be today:
- 61% of marketing decision makers reported data-related challenges in their analytics initiatives
- Successful marketers who exceeded their top goals the previous year were more likely to attribute this to having a clear data and analytics strategy
With those two simple yet glaring insights, the writing was clearly on the wall: a comprehensive data-driven marketing strategy is crucial to winning each moment of the customer journey. This is still true in 2019, and it’s particularly true for financial firms, who must appeal to customers in real time across a wide variety of digital channels.
Below we look at some ways embracing data-driven marketing can save your financial firm in 2019, and how not doing so can break it in the same instant.
Data-driven marketing offers a 360° view of the customer
Every touchpoint and channel with which a customer interacts with your brand produces a wealth of information. Most legacy data systems have only been able to process this info from single channels or interactions, thus offering a limited view of the customer profile. What a big-data platform does is harvest info across all channels and touchpoint at all times and provide real-time analysis.
The end result is that a big-data platform gives you a fuller and more holistic understanding of your target audience. This allows you to better identify valuable customer segments and market directly to them. It’s the difference between analog and digital, between the way things were done before and how they’re done now.
Data-driven marketing enhances customer service
The typical customer wants a seamless experience across multiple channels. They want know that if they have questions about a product or service, there are answers right at the touch of a screen, whether that’s a mobile device, laptop, or tablet. Brands without a data-driven marketing system in place may find that some of their customer-service channels are suffering from neglect, which negatively impacts the customer experience.
What data-driven marketing does, particularly when powered by machine learning, is identify which channels are in most need of an overhaul. Maybe the data reveals your social media channels are being neglected, in which case implementing chatbots to help answer customer queries can remedy the issue. Or perhaps your email marketing is no longer successful, in which case careful analysis of data can tell you how to revamp your email strategy.
Data-driven marketing eases customer pain points
Take TransUnion as a case study. As one of the top credit-reporting/risk-information agencies in the world, their business model hinges on being able to provide customers with the multi-dimensional info they need to make informed decisions. What happens, then, when a company like this comes to the abrupt realization their legacy data platforms would be incapable of processing ever-increasing masses of digital information?
The solution was to integrate a converged data platform that could process larger amounts of data from a wider array of sources more efficiently. This allowed for many things, including greater accessibility of data and, crucially, greater ability to discover customer insights. They could then more effectively market to their customers by enabling their B2B bank customers to make more informed decisions and develop improved risk strategies based on being better able to forecast loan-delinquency rates.
Data-driven marketing allows for personalized ad strategies
Say you’re a multi-national financial services company with x amount of cardholders and merchant customers. By taking the TransUnion example and integrating a converged data platform into your system, you can now break down data silos and organize and process all your data more efficiently. There’s another benefit, though: the ability to make hyper-personalized offers to your customers.
Particularly by incorporating machine learning with your big-data infrastructure, your financial-services company can use customer data to customize product and service recommendations. You can analyze customer behavior and better pair cardholders with merchants by making the most relevant offers possible. And because machine-learning data platforms are constantly learning and evolving, the platform will continue to boost and hone your marketing efforts for as long as you’ve integrated AI into the system.
Also back in 2016, The Economist published a marketing report in which they surveyed 499 CMOs. A whopping 87% of them were so confident they believed they’d own the end-to-end customer experience by 2020. Well, we’re almost there now, and if the above examples serve to clarify anything, it’s that this path to customer ownership in 2020 is simply not possible without embracing a comprehensive data strategy right now, in 2019.