Pharma Customer Journey Mapping

Use AI to engage people - not personas.



The majority of Pharma companies talk about being customer-centric and patient-centric and everyone has a customer journey map for their patients and physicians. However, is it valid for their customers? Unlikely! Each of your target customers (be it payers, physicians or patients) will come to your brand in a different sequence of touchpoints, and this sequence will differ from person to person.

Part of the reason the sales engagement process can be less effective is because your customers come to your product in their own unique way and, in reality, it is far more complicated than the lovely linear customer journeys that we see within our client companies. Everyone’s aim should be to deliver the right content to the right person in the right channel, or touchpoint, at the right time.

Another thing to keep in mind is that the customer engagement journey has changed dramatically from the old analog age to the digital age we are in today. You need to understand the key interactions, channels, touchpoints and sequences, as well as the messaging architecture required, to meet the overall customer engagement objectives.

In fact, the bottom line is that you must be creating and distributing relevant content, in the right touchpoint or channel, in the right sequence, to both engage every individual person and deepen their relationship with your brand, within their individual journey to using your brand and becoming loyal to your brand.

In the days of small data, this was not possible. However, today everything is possible. In several projects this month alone, we have combined all data sources from our clients’ multiple platforms in multiple countries with multiple brands, integrating, restructuring and combining the data, adding a temporal aspect to it, and then applying Artificial Intelligence techniques to map the individual customer journeys.

So, we are able to map trillions of customers digital journeys and engagement levels, and pull out what that individual person – not a persona (a fictional character to represent a particular customer segment) – needs to become more engaged. This then feeds back into the system to serve up the right content, in the right channel, at the right time, straight into the CMS.

The data is constantly updated by what is happening in the data sources, which gets fed into the AI and from there into the CMS to automate the process for digital touchpoints and to recommend the other channel sequences and content for non-digital channels.

In a quick skim read, digital teams may think that this doesn’t sound that new as most companies strive to do that already. However, despite sophisticated AI-powered CMS systems being available, they are only tracking the digital journeys and not linking them with all the other data and journeys they possess on their customers.

Before, our clients had data on customers from various places and used that to profile their clientele (physicians and patients mainly) in a static way into a persona by using a combination of psychological factors and linear approaches. However, this is grouping people together into a profile (or persona) and we really do not need to be so rough anymore. We can do this to the level of the individual now, and that is what digital teams should be helping their brand teams do.

What has changed now is that we can have big data constantly refreshed; we also see that even individual customers do not stay the same – their journeys also evolve with new experiences and information.

Big data is the core of the digital economy and supports innovation and deeper customer engagement. By applying Artificial Intelligence to the big data, we can map the individual journeys to ensure we are serving up the right content in the right channel at the right time, and additionally, as the customer changes and their journey evolves, we stay on top of the changes needed.

Let me give you a very simple example.

I was in a pet website wanting to buy an automatic pet feeder. I went to 3 sites and made a choice and bought one. However, they clearly used cookies, a content management system serving up content to convince me to buy based on their customer journey mapping because, for the next 6 months, I received emails, ads, pop-ups and more, all essentially saying ‘buy this pet feeder’.

Now, my buyer journey was not in their map. I am fairly decisive. I will do some research and then make a decision quite quickly. However, they clearly had me as a ‘will take some time to decide’ kind of person/persona. If they had been doing what our clients are now doing with our AI, they would have seen that I bought a pet feeder elsewhere online and, now knowing I have a pet and what kind of pet, should have sent me information on things more relevant to my pet’s lifecycle (flea treatments etc.) linked to related things that I may be interested in purchasing online at the time I am looking (e.g. clicking on ‘pet food’ while buying groceries online).

This way, it is not an interruption when my attention is elsewhere but relevant to the task at hand and will get my attention. We can now be that specific, and actually far more specific. So, they failed with me as they had just used a content management system and a persona. In most CMS systems, what they do to solve this is use assumptions, pre-defined rules and it cannot handle the levels of detailed tracking, multiple data sources, nor complexity, that we are now handling with AI as it is not designed for that level of data.

The same mistake is being made by many Pharma companies who are not powering up their CMS with an extra layer of integrating all their data (big and small, structured and unstructured) and using AI to track and analyze the big data available to them. Of course, with content management systems, all you have is an online journey, so it is not tied to prescribing, your CRM system or the plethora of other data you have.

However, we are now tying it all together seamlessly, and feeding that back automatically into the CMS. That way you have a very different proposition, one that is valuable to your customer and to you.

The sort of questions we are now answering for our clients are:

  • What is the unique digital customer journey for each customer?
  • How does this tie into what we know about them from other sources such as our CR system or meetings attendance etc?
  • What is the best optimal sequence of content for that customer to drive brand adoption?
  • What are the optimal touchpoints or sequences of touchpoints to drive brand adoption faster?
  • Which profiles of customers are best predictors of potential for increased business?
  • Which tactics drive more customer adoption in this journey?
  • What is the optimal resource allocation across digital and non-digital channels?
  • When a customer drops off the journey, which are most valuable to re-engage and what is the best way to re-engage them?
  • Which customers should we not engage reps with?
  • Which customers use a competitor brand but are vulnerable to switch with the right content and touchpoints?
  • What is the portfolio cross-sell for any specific customer (i.e. given a large portfolio of brands, we can determine the optimal sales and profit outcome)

Conclusion

Although CMS systems were a wonderful leap in technology, we are now able to add a layer of power to them using Artificial Intelligence: to analyze all the data possible, uncover the changing nature of the customer’s relationship with the brand, to ensure that we disrupt and innovate in a positive way, and fulfil all the customer’s expectations in order to bring customer engagement to the next level.


For a confidential discussion about your data and how this could be achieved, contact the author at Eularis: http://www.eularis.com


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