The Power of AI in ‘Next Best Actions’ – CMSWire

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Lets say you have a customer who has taken a certain action: downloaded an ebook, filled out an application, added a product to their cart, called into your call center or walked into your branch office, to name a few. What content, offer or message should you deliver to them next? What next step should you recommend? How can you best add value for that individual, while nurturing the person, wherever they are in their relationship with your business?

Based on your history (or even lack of history) with a given individual, you and your company might also have questions such as: Whats the best product to upsell to this particular client? (and should I even try to upsell that person?); Whats the right promotion to show an engaged shopper on my ecommerce site? and Whats the right item to promote to someone logged into my application? The list goes on.

These types of questions are all important to businesses today, who often talk about next best actions. This customer-centric (often 1-to-1) approach and sequencing strategy can take a number of forms. But at a basic level, the concept means what it sounds like: determining the most relevant or appropriate next action (or offer, promotion, content, etc.) to show a person in the moment, based on their current and previous actions or other information youve gathered about them across your online and offline channels. Next best actions can also include triggering messages to call center agents or sales reps to alert them of important activity, or to suggest the next best action they should take with a customer.

Companies put awide variety of thought, time and effort into establishing sequencing paths from none at all (with a one-size-fits-all message, promotion, offer, etc.) to a lot. At a majority of organizations, though, determining the next best action for their customers is very important, involving multiple teams of people across functions and divisions.

There are teams of marketers and designers, for instance, who create elaborate promotions and offers with different media for different channels. And there are customer experience teams who devote many cycles to thinking about call-center scripts and next best actions.

So when it comes to deploying those next best actions, it can devolve into an inter-departmental war about who gets the prime real estate. For example, when new visitors hit the homepage or when customers log into the app, what gets displayed in the hero area?

Why all the effort and involvement? Its because next best actions are strategically important to engagement and the bottom line. Present the right, relevant offer or action to a customer or prospect, and youre helping elicit interest and drive conversions. Present the wrong (e.g., outdated, irrelevant, mismatched to sales cycle stage, etc.) one, and youre losing customer interest or even turning them off your brand.

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For many years, organizations have taken a rule-based approach to determining the right next best action for a particular customer in a particular channel or at a particular stage in their journey. Rules are manually created and structured with if-then logic (e.g., IF a person takes this action or belongs to this group, THEN display this next). They govern the experiences and actions for audience segments which can be broad or get very narrow.

Three types of rules are the most frequently applied to next-best-action decisioning. These can be used on their own or, typically, in concert:

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But one problem with rules is the more targeted and relevant you want to get, the greater the number of rules you need to make. With rules, personalization of the next best action is inversely correlated to simplicity. In other words, to deliver truly relevant and highly specific actions and experiences using rules only, you quickly enter a world of nearly unmanageable complexity.

Theres also the time factor to consider. As you have likely experienced, it takes a lot of hours to create and prescribe sequencing via rules for the multitude of scenarios customers can encounter and the paths they can take. And unraveling a heavily nested set of rules in order to make minor adjustments (and make them correctly) can take many more hours.

Another problem with rules is that they are just a human guessing. Suppose youre wrong in the next best action youve set up for a customer to receive in fact, it may actually be hurting revenues or customer loyalty.

So while rules do play a vital role in determining and displaying next best actions, a rules-only-based approach generally isnt optimal or scalable in the long-term.

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Machine learning, a type of artificial intelligence (AI), can supplement rules and play a powerful role in prioritization and other next-best-action decisions: pulling in everything known about an individual in the channel of engagement and across channels, factoring in data from similar people, and then computing and displaying the optimal, relevant next best action or offer at the 1-to-1 level. Typically, this all occurs in milliseconds faster than you can blink an eye.

Across industries, theres an enormous amount of behavioral data to parse through to uncover trends and indicators of what to do next with any given individual. This can be combined with attribute and transaction data to build a rich profile and predictive intelligence. Machine-learning algorithms automate this process, make surprising discoveries and keep learning based on ever-growing data: from studying both the individual customer and customers with similar attributes and behaviors, and from learning from how customers are reacting to the actions being suggested to them.

In addition, when multiple promotions or next actions are valid, you can apply machine learning to decide on and display the truly optimal one, balancing whats best for the customer with whats best for your business.

Optimized machine-learning-driven next best actions outperform manual ones, even when what they suggest might seem counter-intuitive. For example, a banking institution might promote its most popular cash-back credit card offer to all new site visitors. But for return visitors located in colder climate regions, a continuous learning algorithm might determine that the banks travel rewards card offer performs much better. Only machine learning can pick up on behavioral signals and information at scale (including seemingly unimportant information) in a way that humans simply cannot.

Related Article: 5 Drivers of Personalized Experiences: A Walk Through the AI Food Chain

Determining and displaying next best actions involve integrations and interplay across channels. One system is informing another of an action a customer has taken and what to do next. For example: a customer who joined the loyalty program could be eligible to receive a certain promotion in their email. Or a shopper who browsed purses online can be push-notified a coupon code to use in-store, thanks to beacon technology. An alert might get triggered to a call center agent based on a customers unfinished loan application letting the agent know to provide information on interest rates or help set up an appointment at the customers local branch as that person is calling in.

Given the wide range of activity and vast quantities of data, its important to have a single system that can arbitrate all these actions, apply prioritization and act as the central brain. This helps keep customer information unified and up-to-date, and aids in real-time interaction management and experience delivery.

In the end, everything organizations do when communicating and relating to their customers could be viewed as next best actions. In fact, personalization and next best actions are closely intertwined, as two sides of the same coin. Its hard to separate a next best action from the personalization decisioning driving it, which is why the two areas should be (and sometimes are) tied together from a strategy and systems perspective.

By effectively determining and triggering personalized next steps, you can tell a cohesive and consistent cross-channel story that bolsters brand perception, improves the buyer journey and turns next best actions into must-take ones.

Karl Wirth is the CEO and co-founder of Evergage, a Salesforce Company and a leading real-time personalization and interaction management platform provider. Karl is also the author of the award-winning book One-to-One Personalization in the Age of Machine Learning.

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The Power of AI in 'Next Best Actions' - CMSWire