The power of AI and CX, together
A Forrester study showed that the companies that lead the pack in Customer Experience (CX) are able to grow revenue six times faster than CX laggards. In light of such studies, it should come as no surprise that CX is shaping up to become the most important battleground in marketing over the next two to five years. But to win this battle, marketers will need to be armed with better data to gain a deeper understanding of consumer needs and wants. This statement is corroborated by a study from eMarketer, showing that for most consumer/retail executives, the no.1 marketing use case for AI is gaining deeper consumer insights.
In a world where customer needs and preferences are highly dynamic and constantly evolving, the only way for brands to remain responsive is by improving what is called the time-to-insight metric. The faster you can optimize the customer experience, the more competitive you can be. By extension, the best way to gain insights faster in the complex pool of Big Data that is now digital marketing, is by leveraging data science and AI. In an article by Aaron Burciaga published on Datasciencecentral.com, he lays out the way data scientists and CX leaders both hold critical and complementary roles in a company’s analytics culture. Burciaga writes:
“Data scientists and CX leaders have divergent but complementary talents. Data scientists are able to organize and synthesize complex information to draw conclusions that are valuable for an organization’s strategy. Yet while they are high on IQ, data scientists are not always so adept with EQ. In contrast, to be successful, CX professionals must design with empathy, developing a deep understanding of their customers and business processes through journey mapping. But they lack the highly technical skills data scientists possess.”
As I’ve explained in my previous article, this combination of skills (CX, Analytics & Data Science) is creating a new field called Big Data MR (Market Research). The challenge now is all about getting started. How do you take that first step? Well, contrary to popular belief, you don’t have to wait until everything in your marketing analytics ecosystem is absolutely perfect to get started. In fact, you really shouldn’t! Instead, take smaller steps by working with smaller problems around chunks of data.
For example, my team developed an agile, piecemeal approach we call practical AI. The idea is simple; instead of launching large-scale projects with long, risky and expensive cycle times, we pressure-test smaller ideas inside short sprint cycles we loosely call hackathons. To find ideas for new algorithms, our data scientists collaborate with both clients and subject-matter experts at our agency in various fields like Digital Media, SEO, Social Media, CRO, etc.
When subject-matter experts come to us with new concrete problems, our challenge is to find already established models, techniques and algorithms used in other fields like engineering, medicine or finance. From there, we see if the existing algorithm is well suited to address a new problem in marketing. If it is fit (pun intended if you’re a data scientist), then we proceed to refine the prototype and ultimately put it into production. As it scales, we can begin to automate the process by working with analytics developers and data engineers on the APIs and data pipelines needed to streamline and optimize the model’s output.
You can start in a similar way by either hiring a data scientist or working with a data science agency or consulting firm. With the help of an in-between expert with enough technical IQ and marketing EQ, organize meetings between CX leaders and data scientists. This person could be a consultant or a BI/Analytics specialist close to both the worlds of data and business. McKinsey calls this resource an Analytics Translator, in other words someone capable of bridging the gap between analytics, AI and marketing subject-matter experts. The objective should be to find problems with your current Customer Experience program, where legacy BI techniques alone are simply not adequate to design the right solution.
Here are just a few questions you can tackle to improve your CX:
- Does our marketing team speak to the right personas? If so, how well do we understand those segments and their customer needs?
- Is our website or mobile app creating an optimal experience? What aspects of the user flow would we prioritize and optimize first, if we looked at more data to tackle this question?
- What is the root cause of customer churn? Do we understand the variables and factors having the most impact?
- How can we design an omnichannel personalization experience across platforms? (that last one is admittedly a doozy)
By augmenting your CX capabilities through analytics and data science, you can begin to discover new questions to ask in order to improve your marketing performance. You can isolate, through insights from data, the most important areas of the customer experience that need your attention and care. The more you can focus on what really matters for the consumer, the faster your brand can grow.