The human component of marketing remains as important as ever to make technology more effective over the lifetime of a brand-consumer relationship.
In the ever-evolving age of digital marketing, marketers are competing against many things: time constraints, a noisy marketplace, endless channels and formats for content, just to name a few. But the most challenging is delivering contextually-relevant content. Brand marketers are beginning to see help come from artificial intelligence. The talk of AI and martech isn’t new, but real use cases are now becoming a reality. Specifically, AI lends itself well to analytics and predictive data that marketers are now using to deliver what people care about most at any given moment. By taking into account all of the detailed data points across every digital channel over the lifetime of a brand-and-customer relationship, marketing is becoming increasingly relevant and personalized.
Data is the fuel for AI
AI has proven most useful in analyzing data and putting meaningful optimizations to work in real-time. It has the potential to greatly collapse the time-to-market for optimization as compared to traditional operational processes that take weeks or even months to deploy, analyze and improve based on the results. It helps brands understand what, when and how to serve customers to drive the dream state marketers have been aspiring to reach for a long time: real-time contextual relevance.
To get to contextual relevance, AI finds its fuel in data. There are a couple of different types of data that brands can lean on to make AI more intelligent:
- Implicit data – This is data collected from behaviors such as clicks, purchases, engagement with certain types of content/products, etc. It’s the bread crumb trail of data exhaust that is naturally left behind by digital engagements without having to ask consumers for it. It’s the largest data set that brands have and it’s also the hardest to manage and put to use in meaningful ways.
- Explicit data – This is data collected by soliciting and receiving direct feedback from customers. This can be profile data points, NPS score or other survey response data, feedback during customer service interactions, etc.
Implicit data is the most powerful data set because it’s massive and the more data AI has to feed it, the smarter it becomes. Explicit data is incredibly important when it comes to AI too, though, but you don’t get a full picture of that data set without the merging of digital and customer service inputs.
The human channel and AI
The customer service and digital marketing gap are still wide, but closing that gap is more important now than ever. Customer expectations are rising as are the chances for people to abandon brands due to poor service and marketing. Customer service is the place where one-to-one personalization is king, and arguably where the most impactful interactions between brands and customers happen. When it comes to salvaging and strengthening relationships by creating unique, personalized experiences, service can be the definer.
While real human interaction will become the differentiator, AI can optimize these interactions. Just as a marketer would orchestrate email, SMS, push, app and web experiences, brands can add the “human channel” to the mix. AI can help determine when a human should be the channel of choice and suggest what the message from that human could be. As interactions become more digitally focused, including the emergence of chatbots, speaking with a knowledgeable human at a brand will anchor any brand-and-consumer relationship.
And regarding the explicit data that is needed to fuel AI? The information flow needs to be bi-directional to ensure that data can be captured by the human channel and fed back into the brand’s data ecosystem, ultimately to be used by other channels. While the human interaction is a cost center for a brand, the value is unmatched, bringing dual benefit to both the customer and the brand; valuable data is collected to make the next engagement even better. It’s a win-win.
Things to consider
Three things to consider when implementing AI solutions:
- Scale: Unfortunately, many brands that fine-tune their data and marketing strategies do so without the ability to truly scale, so they stop at ‘walk,’ so to speak, and can’t get to run. If that applies to you, reconfigure your scaling strategy and work AI components in from the start, with the clear goal that it is there to boost your ability to operate exponentially.
- Keep it personal: Leaning too much on machines and AI make brand-to-consumer relationships impersonal. Clearly define your engagement strategies and prioritize the touchpoints that benefit from the human touch. Sincerity in building the relationship is paramount.
- Lean on your customer data: Unless they opt out, every customer is leaving a breadcrumb trail of feedback, and those data points are clues to how you can better serve them. AI can be instrumental in understanding a customer and strengthening those relationships by providing a contextually relevant view of the customer and generating recommendations on how best to move forward.
As an industry, we get excited about AI and how it can change the way we interact and understand our customers. But at the end of the day, the human component of marketing remains most important and is required to make our technology, including AI, more effective. Customer service reps, data scientists and digital strategists with an eye for emerging tech will be valuable players that will ensure the entire ecosystem is operating to its fullest potential.
Opinions expressed in this article are those of the guest author and not necessarily Marketing Land. Staff authors are listed here.