Improving Customer Engagement With AI

We in pharma are all about improving our customer experience and customer engagement. A lot is being done in this area. However, if you really want to put your customer experience and customer engagement on steroids, you need to consider using AI in the process.

For the past decade, pharma companies have employed one-to-many communications using social media in their customer engagement efforts. That is now being improved even more thanks to these trends:

1. Conversational AI is being used everywhere
With Alexa, Google Home, Siri, Cortana and the vast array of conversational bots used in homes and on phones these days, people are comfortable interacting with conversational AI. There is a great opportunity for companies to use conversational AI for ongoing customer engagement rather than just a one off transactional point in time.


2. People want real conversations
When I say real conversations, I don’t mean with a human. I mean conversations that use natural language with a conversational flow, not ‘Push 1 to discuss X, Push 2 to discuss Y’ prompts. Those are using such old technology and should be outlawed. Modern, AI-powered bots like Amelia can read conversational content and emotional tone as well as answer appropriately. You can utilize them on web sites, phone lines, call centres, and even as medical reps.

3. Personalization with AI-powered technology is on the rise
AI-powered sales enablement can provide a precise roadmap through the sales The studies are in, and the AI bots beat humans in this kind of engagement. That’s because they have access to more information and can solve most issues very easily. With deep learning and NLP providing more personalization of language on top next best action modelling, we are seeing better customer engagement than a human company representative can provide. 


4. Customer insights from data offers a wealth of information
You could argue that many tech companies such as Google and Amazon play fast and loose with the spirit of GDPR and other privacy laws. We don’t really want all conversations in our house recorded and transcribed by people at these companies. (I don’t fully understand why humans are involved in the process when AI could handle all those tasks, but there must be reasons.) But we use those devices anyway in large part because of the experiences Google and Amazon are able to deliver with those insights.
Successful Application

Superior customer experience and customer engagement is the main competitive driver of growth when executed well, and a big risk when done poorly.

Now customers want, and increasingly expect, more of a 1-to-1 personalized experience or a smaller curated group chat, especially with issues that require high involvement such as a medicine choice.
Providing these type of personalized, authentic conversations and experiences to individuals would be a mammoth task without AI. There’s no better way to automate the process and still provide a strong customer engagement. In fact, studies have shown that when done well, the engagement is stronger with AI than with humans alone.

To successfully deliver AI in customer experience and customer engagement companies need three things:
1. Data unification
Datasets have to be combined to create a single customer view. This is, of course, easier said than done. Since datasets collected together are often very different and messy, this task can be time consuming, tedious, and expensive. However, there are more and more platforms coming on the market that can make this process easier and faster – depending on the data sets.
2. Real time data insights
Real time insights and actions is the name of the game. You need to engage the customer at precise times in his or her decision making process. If you leave it till the next day or the next week, you have lost them. This is where next best action modelling comes in. It automates this process to serve up the right content, in the right channel, in the right sequence, at the right time for each customer.
3. Business context
Cross-channel, omni-channel analytics allows each unique customer’s journey to be an engaging experience for his or her individual needs. But it needs context. It needs to have an understanding of the significance of each event in the journey, what each touchpoint will help to shape, and what KPIs should be used to measure and track these to constantly improve the customer experience.
Uses
There are various ways these are being used today in these main areas.
    1.    Customer service with AI-powered chatbots and robots replacing humans.
    2.    Predictive personalization to predict what is needed in the customers’ daily life and their interactions with the brand.
    3.    Enabling high impact insights from customer data to change the way things are done. This allows mining insights from billions of data points to not only remember who that customer is and what they like, want and need, but to also predict what they will need next. This can be achieved by adding AI into the customer journey analytics.