We all know everyone is talking about being customer-centric and, of course, everyone has a customer journey map, but is it valid for all 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.
Understand Today’s Customer Journey
Another thing to keep in mind is that the customer engagement journey has changed dramatically from the old analogue one in 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 in the approach is that you must be creating and distributing relevant content in the right touchpoint or channel in the right sequence to both engage your customer and deepen their relationship with your brand within their customer journey, to using your brand and becoming loyal to your brand.
In three projects this month alone, Eularis have been combining all data sources from our clients’ platforms, restructuring and applying Artificial Intelligence techniques to them to map the individual customer journeys. 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.
This may not sound that new as most companies strive to do that already. Before, our clients had data on the customers and profiled them in a static way into a persona, using a combination of psychological factors and linear approaches. What has changed now is that we can have this data constantly refreshed as we see customer do not stay the same – their journeys also evolve.
By applying Artificial Intelligence to the approaches, we can map the individual journeys to ensure we are serving up the right content in the right channel in the right time, but on top of that, as the customer changes and their journey evolves, we stay on top of the changes needed.
An Example
Let me give you an example of old versus new. So 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 had a cookie in my system and 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. If they had been doing what our clients are now doing with our AI, they would have been notified that I bought a pet feeder elsewhere. Also, the AI would have assumed I had a pet and would have possibly sent me information on things more relevant to that pet’s lifecycle (flea treatments in summer, etc) at the time. So, they clearly were trying to do something good but they failed. The same mistake is being made by many Pharma companies who are not powering up their CMS with an extra layer of AI.
It is critical to ensure that you have identified the right persona at every stage of the journey and to predict the next behavior. This is not an easy problem to solve using traditional CMS methods. You do need large volumes of heterogeneous data from different sources and channels as well as contextual information in real-time. Also, these personas need to be changed at a very granular level, depending on that person’s behavior.
In most CMS systems what they do to solve this is use assumptions, pre-defined rules and correlate linear static links between behaviors. Sadly, correlation only picks up linear relationships (most are not linear), and more importantly, it cannot handle the levels of complexity that we are now handling with AI as it is not designed for that level of big data. It utilizes limited datasets and does not really delve into the complexities possible in the real world. Neither does the technology used in CMS have the ability to handle that level of instant data-streaming, which is why the outcomes are less than ideal.
This has been an area we have been asked a lot about this year and we dove in to solve it. It has been an exciting time and we have had to increase our system engineers on staff and with a few recent hires from Google and the world of IOT (Internet of Things), we have found we can achieve this now. What we are doing when using Artificial Intelligence is identifying patterns in one person’s behavior from different sources and different devices, matching that to their personas in real-time, and then analyzing these in context of bringing them to the outcome desired for our brand, reliably and accurately.
We use a combination of supervised machine learning and unsupervised machine learning for this part of the process. Anyone coming to my Masterclasses about AI transforming Pharma will know why. Our clients now have various data on their channels, CRM systems and many other platforms. By collating and integrating these, we are getting enormous amounts of data. With this data we have created an event engine which we program for the client and it adds in the temporal aspect, which then helps identify sequencing. Thereafter, it goes through the complex event processer and is restructured for AI. The correct sequence, content, touchpoint and journey is then mapped for each customer individually and updated as that customer acts, and all this is automatically fed into the CMS to serve up a far more relevant customer journey.
The sorts of questions we are now answering are:
• What is the unique customer journey for each customer?
• What is the best optimal sequence of content for that customer to drive brand adoption?
• What are the optimal touchpoints to drive brand adoption?
• What are the optimal sequences of touchpoints to drive brand adoption?
• 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)?
