Next-best-action is a marketing paradigm that leverages data and predictions to determine (and deliver) the most relevant content to an individual to maximise engagement and drive desired outcomes.
In a two-part series on next-best-action (NBA) marketing, we’re looking at the strategy from a CXM perspective. This first article breaks down the considerations leading up to the customer interaction, helping marketers design effective next-best-action strategies.
The follow-up, delves into delivering content in response to interactions.
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Next-best-action marketing is based on three core tenets of relevance, context and responsiveness.
Relevance: Content that responds to, continues or enhances the previous interaction
Context: Adapting to the customer’s current situation, whether that’s purchase-readiness or browsing device
Responsiveness: Delivering a timely experience, ideally in real-time
Notice there’s no mention of buying, converting or subscribing? In our experience as CXM consultants, focusing on conversions limits the potential to deliver mutually rewarding experiences. Instead, we look for the overlap between customer preferences and organisational objectives.
In reality, there are several possible pathways. But there is only one optimal pathway for each individual. And we can find it through an iterative, interrogative, data-driven process.
The benefits of next-best-action marketing
Become more responsive to customer needs
Provide personalised customer experiences
Boost conversion rates
Increase customer loyalty
Improve marketing campaign results
What should the customer do next?
Next-best-action marketing aims to provide the most appropriate content or offer at any point in a customer’s journey. It won’t always be a purchase.
For example, the result could look like:
Interacting with more content so we can home in on the customer’s interests and preferences
Providing first-party data, either directly or indirectly (by listening to their behaviour)
Downloading an app
Completing their user profile
Selecting communication preferences or interest categories
Defining these next-best actions early is essential to success. From there, you can reverse-engineer the customer pathways and identify gaps in data.
How to gather data
Much of the legwork that enables personalised marketing is in progressive profiling. Whether asking customers for input or prompting them to tell us something about themselves, the goal is to gather data that illuminates their preferences and interests.
Implementing a test-and-learn cycle is the best way to gather the data to inform next-best-action content. The strategy and tools each company uses for this process will vary. What’s important to keep in mind is that 360° customer profiles come together through an iterative approach.
The discipline tends to be the same between B2B and B2C marketers. We know X, we’re missing Y; therefore, we need them to Z. By testing, learning and improving, we can get the insight we need little by little.
What isn’t the customer telling us?
Inactivity can be as informative as activity. After repeated unopened emails, it becomes clear that a customer doesn’t like interacting with a brand over email. So we test different methods, learn from our experiences, and enrich the customer’s profile.
This situation can lead to a cognitive dissonance that’s common in customer experience management. What a user tells us doesn’t match the behaviour we observe.
One way to dissolve this tension is to ask the customer. Marketers usually don’t like to ask pointed questions, preferring to infer or extrapolate, but sometimes the direct approach is the most effective.
Predicting the next-best action
If you look at one user’s interaction history, you can predict what they’re likely to engage with. If you rank marketing channels by ROI, you can identify which channel saw the most success. Somewhere in between is next-best-action marketing.
Using predictive modelling, a test-and-learn approach and progressively smaller segmentation, you can see how different audiences respond to different prompts and nudges.
The follow-up to this article will go more into depth on delivering next-best action content.
We predict your CXM strategy could use a performance boost. Our CXM consultants blend data-driven insight with practical strategies to maximise ROI.
Increasingly complex customer journeys create a risk of fragmentation, with each platform collecting different types of data. Conversely, multiple touchpoints give marketers more potential avenues through which to engage.
The key here is to look at the customer as holistic and human.
Single customer view
The remedy for fragmented or siloed data is an integrated customer interaction hub. Somewhere data from disconnected platforms can meld together, enriching an individual’s profile to create a single customer view.
Customer-centric marketing
Communicating on the customer’s preferred channel will naturally lead to a higher likelihood of the desired outcomes occurring. The key is to consider each stage in the customer journey – and every interaction – from the user’s perspective.
Connected data
Data quality is crucial for NBA marketing. Disconnected data leads to disconnected customer experiences; incomplete data leads to incomplete journeys. Marketers need to listen to a user’s signals, not in isolation but in the context of a cross-channel journey.
So much of customer experience management is data management. Without a good data pipeline, you can’t hope to predict customer behaviour or launch a sophisticated campaign.
Ultimately, marketers are trying to predict the best course of action, deliver a seamless customer journey and personalise experiences at every touchpoint.
The role of martech and AI in next-best-action marketing
When digging into next-best-action marketing, you’ll see a lot about automation tools and AI. Technology plays a significant part in data analysis, predictions, segmentation and modelling.
For example, Adobe Analytics features AI-powered contribution analysis and anomaly detection tools that inform predictive models. Marketo Smart Lists use cohort analysis to segment target accounts based on the likelihood of purchasing. Adobe Campaign’s predictive user engagement capabilities allow marketers to optimise send times and model the probability of a contact engaging or unsubscribing.
But, as mentioned earlier, martech is only as useful as the data it receives.
NBA marketing always comes back to strategic goals. Before working with clients on next-best-action techniques, predictive modelling or personalisation, we look for a clearly defined purpose. That through-line which connects an individual customer interaction to a long-term business goal.
So, what next?
Let’s recap the components that enable marketers to build a next-best-action strategy:
Clear marketing objectives
Data integration
Enriched customer profiles
Visibility over the customer journey
Data analytics
With these elements working together, you can implement a test-and-learn approach to move customers closer to the end goal.
Next, we’ll look at the best practices for delivering next-best-action strategies that point users in the right direction.
TAP CXM enables brands to deliver personalised customer experiences that build loyalty and boost profits. See our work in action.
