Customer-centric principles and AI are both a part of marketing’s future. How might they work together?
When it comes to marketing’s future, two things are clear:
- Customer-centric principles are increasingly important to maximize the relevance of a business.
- Technologies like AI, machine learning, and various automated processes are giving businesses a competitive edge.
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Depending on your perspective, these two developments might seem at odds. How can a business abide by customer-centric principles if its services can be conducted without human intervention? Yet these principles aren’t as opposed as you might think. As customer-centric AI solutions emerge, it’s clear that, together, these ideas could expand the scope of interactions with consumers.
What does customer-centric AI accomplish?
Before we dive into AI capabilities, it’s worth considering what customer-centric AI actually accomplishes. By and large, these solutions augment a company’s customer engagement capabilities, allowing them to deploy individualized experiences at scale without any manual programming or logic building.
Why is this important? While any company — large or small — can offer high-quality customer service, deploying personalized interactivity at scale is a unique challenge. For example, businesses may not have the resources to individually respond to messages in a timely manner (especially when customers rely increasingly on quick social media posts). Customer-centric AI addresses this problem by automatically responding to common inquiries and determining the market group to which a given customer belongs. In the future, customer-centric AI could even contact customers preemptively to address them at any point in the buyer’s journey. This could include offering relevant products or highlighting services that address their interests.
While customer-centric AI has yet to be fully explored, many businesses are enthusiastic about its potential. For example, one survey found that 87% of retailers believe AI can improve customer services. The same report suggested that 59.4% of retailers would adopt AI as is a money-saving tool, while 47.8% would use it to enhance customer lifetime value.
Automated trading is a possible model for customer-based AI
Customer-centric AI, as described above, is still a relatively new concept, though comparable AI systems do exist today. Of these, automated algorithmic trading offers the likeliest model for how customer-centric AI could operate in the future. For years, these algorithms have allowed prominent financial traders to take part in high-frequency trading — automated processes that trade and manage entire portfolios of stocks. These algorithms are not intended to replace human traders. Rather, they are built to maximize profits on opportunities that would vanish in the seconds it takes for humans to make a decision.
AI-based trading typically functions by taking the combined parts of a large trade and breaking them down into smaller lots. This allows each smaller lot to be executed for the right price over time while mitigating the risks of a single mass trade. While humans traders still assign the conditions that initiate a given trade, the actual execution is implemented by algorithms once such conditions are met. Trading data can also be used to predict future opportunities or risks, informing traders of ideal strategies.
Despite these benefits, AI trading has drawbacks. These high-frequency trading algorithms don’t often fail but, when they do, the consequences are severe. One prime example is 2010’s “Flash Crash” when mutual fund house Waddell & Reed sold $4.1 billion futures contracts, drawing liquidity from the market until the Dow Jones Industrial Average collapsed by 9%. More importantly, algorithms that do function as intended don’t always enhance performance. Take the Eurekahedge AI Hedge Fund Index that used machine learning techniques to grow by 8.3% between 2014 and 2019 — which was below the 10.9% rise of the overall S&P 500 Index.
That doesn’t mean automated trading models don’t have value. What they lack in performance, they often make up in terms of scale: They can execute a higher volume of decisions with increased complexity. This allows for lower trade costs, higher probabilities of trade execution, and increased market liquidity. These benefits have encouraged traders to continue pursuing AI-based algorithm solutions — provided that humans offer oversight and crucial data interpretation.
From this implementation, we can see how automated solutions could lend themselves to customer services:
- Customer-centric AI would function by treating customer service as a series of smaller-scale interactions instead of a larger, difficult-to-classify experience.
- Customer-centric AI won’t be perfect, nor will it always have higher performance than human-based interactions. That being said, it will make it easier to manage customer experiences at scale, allowing a baseline level of quality for common inquiries.
- Customer-centric AI would still be managed and programmed by humans, but manage the execution for a wide range of standardized interactions.
Capabilities that make customer-centric AI possible
For all the technological benefits and futuristic imagery the phrase implies, no AI — customer-centric or otherwise — is going to develop itself. Any business looking to implement automated solutions must ensure several features are in place in order to manage its development properly.
When it comes to customer-centric AI, the following elements are particularly important:
Data
You could have the most advanced, customer-friendly AI platform in the entire universe, and it wouldn’t help you without reliable data. As we’ve previously discussed, machine learning is essentially a pattern recognition system. When you feed it comprehensive volumes of reliable data, it generates relatively accurate insights. If you don’t have enough data — or even worse, have lots of bad data — your automated solution won’t go anywhere fast.
For a customer-centric business, AI solutions must be equipped with accurate consumer data. This data could include anything, from basic demographics to buying habits, but it will generally reflect the background of your selected market. Everyday customer interactions, such as emails, tweets, or service calls, will likely be the most important data sources. As it stands, most customer-centric businesses that collect this data usually delete it shortly thereafter. When implementing a machine learning platform, it will be crucial to collect interaction data and aggregate it in a database for long-term reference.
Conversational technologies
Going beyond data, future customer-centric AI solutions will likely include some kind of conversational element. This could include a messenger chatbot or a voice-recognition feature, such as Alexa or Siri. Whatever the method, these conversational interfaces must be customer-facing so that consumers can directly interact with them. This will unlock scale, from an operational perspective, and benefit from your communications platform as a whole.
Even with technological advances on the horizon, AI bots may not be equipped to analyze all customer interactions without restriction. Such a sweeping integration is a monumental task could limit the efficacy of individual interactions. For the short-term, customer-centric AI should be focused and strategic. For example, a business could map common customer inquiries to the most frequent resolutions to offset the workload for its service team. Once a common response resolves these inquiries, human representatives can focus on unique and more specialized tasks.
Technological developments such as these are always exciting, but for businesses, investments must also be practical. Customer-centric AI has the potential to make a measurable impact on a business’s bottom line by increasing operational efficiency and unlocking scale. These solutions could even be implemented gradually, as customer-centric insights emerge, and work alongside existing customer service protocols. In the coming years, we can expect customer-centric AI to become as widely available as any automated solution in use today. In the meantime, businesses may consider investing in scalable data infrastructures, so they can take advantage of these solutions as they emerge.