two people standing in front of a wall with multiple arrows circling
PHOTO: Charles

Let’s 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: “What’s the best product to upsell to this particular client?” (“and should I even try to upsell that person?”); “What’s the right promotion to show an engaged shopper on my ecommerce site?” and “What’s 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 you’ve 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.

The Next Best Action Is Strategically Important

Companies put a wide 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? It’s 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 you’re helping elicit interest and drive conversions. Present the “wrong” (e.g., outdated, irrelevant, mismatched to sales cycle stage, etc.) one, and you’re losing customer interest or even turning them off your brand.

Related Article: Good Personalization Hinges on Good Data

A Rule-Based Approach to Next Best Actions

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:

  • Validity rules. In other words, is a promotion or offer active/valid, or has it expired? If it is still valid, then the next best action could be for a website to display that offer to a visitor or prompt a call center agent to discuss it with a caller, etc.
  • Eligibility rules. Is the individual eligible to receive the offer or take the next prescribed action? For example, perhaps a promotion is just for members who’ve achieved a certain loyalty status. Or for people who’ve purchased two or more products — they may be eligible for a discount on additional purchases. Sometimes eligibility rules are applied to people who are already customers, or to those who are not. (The cable provider in my area doesn’t seem to apply any rules in its promotions, and I’m always frustrated to receive offers for super-low monthly rates that are allowed only for new customers!)
  • Priority rules. Sometimes there are multiple relevant paths or actions you could recommend to an individual. How can you and your system choose? One way is through rules, again, with marketers manually governing prioritizations. For example, at a financial services institution, the marketer might determine that upselling logged-in customers trumps encouraging those people to go paperless. Setting prioritization rules in this fashion is important.

Related Article: Why Personalization Efforts Fail

Shortcomings of a Rules-Only-Based Approach

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.  

There’s 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 you’re wrong in the next best action you’ve 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 isn’t optimal or scalable in the long-term.

Related Article: Refine Your Personalization Efforts by Ditching Tech-First Tendencies

Where AI and Machine Learning Fit in Personalization Efforts

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, there’s 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 what’s best for the customer with what’s 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 bank’s 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

A Central Brain

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 customer’s unfinished loan application — letting the agent know to provide information on interest rates or help set up an appointment at the customer’s local branch as that person is calling in.

Given the wide range of activity and vast quantities of data, it’s 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.

From Next Best Actions to Must-Take Actions

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. It’s 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.