Omnichannel orchestration is critical to delivering a customized and relevant experience to each customer. If designed and executed correctly, responsive omnichannel marketing programs can significantly improve customer engagement and brand equity, feeding into the lifetime value of each customer. Although, ensuring that such models evolve with changing customer preferences is critical. If pre-defined omnichannel programs do not respond to each step in a customer’s journey toward a purchase, there will be many missed opportunities for potential sales.

All traditional marketing models consider the customer journey to be pre-defined. They rely heavily on static and intricate customer journey maps that include multiple scenarios of a customer’s journey and assign corresponding actions to each possibility. Historically, marketing and brand leaders have used such models to define their marketing strategies. While such approaches were relatively more practical in the past, the advent of digital channels and changing customer behaviour have made it difficult to map a customer’s route options towards a purchase decision.

“Your customer does not know that they are on a customer journey”

In today’s age, it is impossible to predict all action possibilities of each customer along their purchase journey. They can choose to interact with (or not) any channel at any time, leading to infinite possibilities of interactions leading up to the decision. Excessive digital information has reduced customers’ attention spans, making it even more difficult to penetrate their consciousness and thought processes.

How can a just-in-time customer-response model drive omnichannel Optimization?

Machine learning-based algorithms need large volumes of historic data to train from. Business rules can be defined based on analyses and expert knowledge.

Developing responsive models that respond quickly to customer actions is essential to driving omnichannel orchestration. Such models exist in many degrees of sophistication across life sciences companies without a universally prescribed blueprint. Based on the volume of customer data and other factors influencing the analytics capacity of organizations, such just-in-time algorithms can be straightforward, complex, business rules-driven, or AI/ML-informed. Different companies can choose the best-suited omnichannel program for their unique requirements.

Customer-response algorithms in any shape or form need to address one common objective – recommending the next best action (NBA) to follow with all possibilities of customer behavior. The two straightforward approaches to achieving this objective are:

  1. Alerts and micro-journeys (NBA)
  2. Dynamic scores and segments

1. Alerts and micro-journeys (NBA)

Alerts are triggers that can deliver quick action recommendations for immediate customer requirements without missing out on an opportunity. NBA alerts range from elementary rule-based processes, such as a notification to a Key Account Manager that a Medical Scientific Liaison just visited a targeted healthcare physician (HCP), to sophisticated machine learning algorithms that monitor complex patient data for signals that an HCP has recently diagnosed a patient with a rare disease.

Micro-journeys are short sequences that can pre-determine recommended responses to standard customer journey fragments, such as recruitment, reminders, and follow-up messages around a webinar.

It’s important not to over-use NBA to control every contact. Customers will take complex and unpredictable journeys that make it difficult or impossible to model and apply NBAs to every step. Furthermore, some channels, such as sales teams or specialist marketing partners, cannot execute the actions in the planned timeframes of the journey plans.

2. Dynamic scores and segments

Dynamic scores and segments can contribute to continuously optimizing each customer’s channel mix and messaging. These powerful analytics tools can help sales and marketing organizations understand customer behavior and preferences and use that information to rank-order various channels to interact with the customer segments. Here’s an example:

  • An HCP can be assigned segments for attributes such as target tiers and adoption ladders. These segments infrequently refresh with changes in the sales data.
  • The HCP is also given scores against appropriate engagement options, such as face-to-face (F2F) meetings, marketing emails, and other digital channels. These scores guide channel priority and frequently change based on daily interactions with the HCP. For instance, the F2F score will decrease drastically immediately after a sales rep visit. The digital channel scores will increase to emphasize follow-ups, such as relevant emails to build on the sales rep’s visit.
  • As the HCP progresses through their journey, the synchronized changes in the dynamic scores will guide the sales and marketing teams on the best-suited promotional content and the interaction channel to eventually nurture the HCP into a customer.

The dynamic scores of various channels can augment the NBA-driven interaction with the customer. Some of these scores may not frequently change (customer target potential and attitudinal segment), but others might frequently change (even daily). As the customer progresses through their journey, these scores will change, guiding the sales and marketing organization on the timing and priority of messaging and continuously improving the channel mix for the customer over time.

The highlight of such algorithms is that they are effortless and efficient in guiding sales and marketing teams in the right direction, but without fully taking control of getting things done. The operators can also intervene with the algorithms’ outcomes and make preferred changes.

Read Axtria’s white paper on “Optimizing your biggest promotional channel — a responsive, data-driven approach to empower field teams” to learn more about this dynamic scoring methodology!

Putting it Together

Dynamic scores, segments, alerts, and micro-journeys can fit well in a federated control model when efficiently used in a well-coordinated omnichannel system. While dynamic scores can be controlled centrally and guide the expert operators on recommended actions, alerts can fulfill pre-defined and automated rule-based tasks.

In context of omnichannel orchestration, a federated control model is one where some actions are directed from the central teams, some activities are recommended by the central teams, and some actions are decided entirely by the expert operators in each channel.

Both these components need to integrate seamlessly into any organization’s omnichannel platform for well-coordinated recommendations along each customer’s journey. Efficiently managing customer data is of limited use if the omnichannel orchestration engine does not accommodate changing customer preferences, intelligence-driven responses, and a degree of autonomy for the sales and marketing operators. AI/ML-driven customer scoring and segmenting models can regulate channel mix decisions, while automation workflows can assist sales and marketing personnel to deliver the desired customer experience. Therefore, it is critical to ensure a steady algorithm production line, from customer evaluation to providing the message, to ensure a robust omnichannel orchestration framework.

Charles Rink is an Omnichannel Capabilities leader at Axtria. He has over 20 years of experience in strategy consulting, technology, and analytics for the Life Sciences industry. Based in London, he brings extensive experience advising clients in commercial strategy, analytics automation, and Omnichannel operations globally.

Suraj Gupta is a thought leadership expert at Axtria. He has over 9 years of experience in analytics and consulting, with more than 6 years in life sciences.

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