How to Take Charge of Your Omnichannel Customer Experience

Omnichannel marketing focuses on the entire customer experience and not the customer’s individual experience on different channels. This includes connecting with digital touchpoints such as social media, web properties, offline access, digital assistants and even things like chatbots, IoT devices, and other applications. For omnichannel it is important to deliver a strong customer experience and in today’s world that means providing what people want, when they want it, and personalized to them, and immediate. The challenge for the marketer is to find the right set of actions for an individual that will move them to the next step in a more complex process or induce them to purchase, if they are ready.

Therefore, omnichannel marketing is not only about being on all the channels that your customer is on, but also about creating a seem-less customer experience irrespective of which channel and what stage in the customer journey they are in. It’s about meeting their individual needs at the time they need it. Most companies analyze their customer journey to assist planning what communications are appropriate, which channel, and when. However, in today’s world there are many customer journeys that can be as unique and individual as the customer themselves. As marketers, we need to be able to meet the customers where they are and with what they need, when they need it.

Because of the increased sophistication of customers, 70% of the buying decision journey is completed without any sales involvement. This is also true for pharmaceuticals. What this means is that for that 70% of the journey, we as pharmaceutical marketers’, need to get it really right to keep them engaged on that journey.

Therefore, to build a successful marketing strategy and reach consumers on this modern personalized immediate customer journey, understanding the micro-moment within each individual journey is very important. These high-intent touch points occur when customers have an immediate need that they want addressed. They are intent-rich moments when decisions are made, and preferences shaped. This is a very complex challenge when you have hundreds of thousands, or more, customers, each with unique customer journeys and needs. Fortunately, AI can not only help us understand our customers a lot more also right down to a segment of one but also be used to serve up the right content, to the right person at the right time. Therefore, where we are now has moved from customer journey to micro moment, or context, marketing and the only way to deliver this level of personalization is with AI.

Is your current approach out of date?

The way many pharma currently approach this is out of date. We are moving from the traditional customer journey to one that capitalizes on their individual needs in every single moment. Your customers want the content they want, in the channel they want, at the moment they want and they want it personalized to them and they want it now. As I previously stated, these are intent-rich moments. They have a specific need in that moment that you need to meet. We need to be shaking up the customer journey mindset from a journey to a series of context rich personal moments.

The impact of user‐centered innovation and the delivery of unique digital buyer engagement journeys are two key criteria often missed across sales management processes in Pharma, with disparate digital efforts all being conducted in a piecemeal way. This needs to change.

Context is critical to understanding these events. Marketers investing in “personalization strategy” may not understand that they are really investing in relevance. If it’s not relevant, it’s not a personalized experience. This concept is critical to personalization.

Let’s say you have a customer who has taken a certain action: downloaded a whitepaper, filled out an application, called your call centre or asked to see a sales rep, to name a few. What content, 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? 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. Sometimes this is called ‘Next Best Action’ (NBA) because that is exactly what it is. However, with some exceptions, if one investigates most so called NBA tools they are mostly human rule based so humans decide ‘if this happens, then I will serve up this content in this channel.’ Although better than nothing, it is not ideal as the rules could be wrong as they are human led. The assumptions could be completely flawed.

In the past, and in many of these rules-based NBA approaches, cookies and user tracking was one way to link a consumer across channels and properties but now with privacy legislation forcing the hand of those who used to rely on these technologies, the time is ripe for using AI to make large scale analysis possible. Fortunately, Artificial Intelligence (AI) can help as AI can be used to understand all the digital footprints and what variables each individual exhibits and therefore what new people coming into the system are likely to need for the best customer experience. Machine learning (ML) techniques in particular, enable much deeper insight and actions than cookies and tracking ever could. What this does is essentially allow automated and personalized service to individuals in terms of giving them what they need, when they need it, and where they need it. It is almost like having a concierge who knows all your history and can offer you exactly what you need, in that context and in that moment, to give you an outstanding customer experience.

In our company we successfully created a system like this for a global pharma company a few years ago across all key brands in 30 markets. The case study can be read here. It was not an insignificant project and took quite some time to implement. https://eularis.com/resources/digital-transformation-results-in-extra-4-55bn/

Currently we are working on the creation of a version of this for a large global healthcare agency who will be using it for their pharma clients, as well as for a pharma company directly themselves. This approach is the future of omnichannel. It is not simple, and is very dependent on data, but it is very doable using AI.

On top of the mechanics of using AI for this task, it also must be transparent and explainable AI – i.e. white box. There are millions of interactions happening every second but if you wanted to delve into why something happened, you can. Most AI is now moving to white box and explainable AI unlike the old Black Box inexplicable AI and this is important especially in an industry like ours with such strong regulatory and compliance needs.

We, as a company, first got into white box or explainable AI with a project for a hospital in oncology for complex cancer patients in which multiple cancers were at play making it difficult to identify the optimal treatment regime for the patients. We had to develop a platform to search, and retrieve relevant knowledge regarding a set of pathologies from a variety of sources (scientific literature, clinical trial data, clinical guidelines etc), to analyze and understand what aspects and combinations of a patient’s individual situation from their longitudinal EHR to be able to analyze specifics of a patient’s condition. This was to provide an optimized treatment recommendation for that individual patient’s personalized attributes, and this recommendation was provided electronically to the treating oncologist with links to the evidence supporting that recommendation. This had to be white box or explainable AI as the consequences of error were extreme. A customer journey may not have the same life or death consequences from an error as the system described above, but nonetheless in a strong regulatory environment, the need for explain-ability is increasingly important.

Where are we today?

What is possible to create these days is very sophisticated. Yet despite access to sophisticated tools for this kind of work, many pharma marketers continue to struggle to apply data-driven insights to their marketing strategies and campaigns. They are hamstrung by disparate data sources that are often incomplete and inaccurate, and by internal policies and procedures that treat data as a static asset. This makes it difficult to set go-to-market strategies, and acts as a barrier to implementing these systems and compromises the ability of companies to leverage advanced technologies like predictive analytics and artificial intelligence, build true omnichannel marketing capabilities, or simply get the right message in front of the right people at the right time. Fortunately, these systems can be custom built for your customers, their data, and their needs as well as yours.

Where to begin?

1. Planning – the key to success with any AI project is thorough planning – and a big part of that planning is understanding what you are trying to achieve, how to achieve and what data to use, and how to tie it together in the tech stack
2. Invest in the right data – Even with limited channels, you’re likely sitting on enough data to help better understand your customers’ channel and content preferences. Making this easy to access, properly tagged and cleansed makes it easy to learn from, and helps enable campaigns.
3. Change your mindset – Enabling personalization and coordinated campaigns at scale requires a very different mindset than the brand-led, siloed approach of old. Instead, teams will need to become comfortable with the complexity of dealing with machine learning while being agile enough to respond to demand.
4. Start with a MVP prototype and get that working before rolling it out across all brands and countries.
5. Test and adapt – start with a pilot with one brand and one country for example, before rolling it out. See what parts are working well, and what could be enhanced, and adapt with the Real World Data as it is bring done.