In the article “For eBay, AI is ride or die” Japjit Tulsi, VP of engineering at eBay, makes it clear that, according to eBay, companies must be investing in Artificial Intelligence, or they will face a tough ride downhill in the not so far away future.
I agree, although we have to acknowledge that the version of AI we are mostly talking about is an automated version of the big data and predictive analytics hype we’ve seen a couple of years ago. I sincerely hope that the current turmoil around the topic will speed up the process. I’m saying this because I’ve heard many talk about the personalization of customer experience, yet few (besides the behemoth examples we all know) have actually made that happen. At least not to an extent that consumers actually notice it as such and as beneficial to them.
It is only logical that companies invest in the personalization of commercial offerings first, much like eBay is. This is what the powerful execs of companies are responsible for and this is where innovation and growth has originated for decades. It’s a safe bet. Or is it?
What’s the customer’s role?
I have long propagated that value is not created in the transaction, but co-created in the experience (before, during and after the transaction). In order to maximize value co-creation in the experience both parties need to put their competences to work for benefit of the other. In other words: I need to be able to tell what I need and the supplier needs to be able to ask me relevant questions to understand me better. Or, in order to design my new key-note presentation I need a variety of skills and resources. The company providing the software to do so, should help me understand the skills and resources needed and provide me with ways to learn or access them. And it would be even better if the company understands the level of my current skills and resources in order to provide me with customized learning.
Point to be made here is: companies cannot offer the best individual experiences without the use of the individual knowledge (which includes intent) and skills of the customer himself. Much of which is not available in the vast data-lakes currently being deployed.
Throughout the Customer Journey
Another aspect to take into consideration is the Customer journey. Research I did on the consumer decision journey for car insurance taught me that many companies focussed their capability development on consumers being able to find them in the early phases (the “locate” step in the universal job-map of the job-to-be-done framework. See image below) of the consumer decision journey and on the last inch of converting consumers (the “execute“-step). The research clearly showed that there was huge opportunity to improve the consumer decision journey by offering help throughout all steps of the journey.
For starters most consumers started the journey without a clear view on what they need or want from a car insurance. Helping them define their need is a gap that still exists today if you ask me. And if you are the company that solves that the best, how would that increase your chances of actually making the transaction? I think by a great deal.
Imagine what would happen if you could assist, or guide your customer or prospect in each of the above steps in an individualized and seamless way? By continuously fine-tuning your understanding of the consumer’s intent, his knowledge- and skill-level and maybe even personality traits, as the journey progresses?
Conversations that capture intent
Analytics on (contextual) data may provide you with a good enough guess. And putting that in front of your customer with A/B-tests will help you better understand when your guess was good enough. Truth is that you will never (ever!) be able to do this most accurately on an individual level without accepting the fact that you need your customer’s input to better understand him. And this goes beyond consumer input to fill the slots of a transactional dialogue (e.g. the desired time of departure when booking a flight). This requires the capability to have conversations with consumers in each step of the journey. Conversations that assist customers with help, advice and notifications when they need it. Conversations also that capture missing information to understand the customers intent and his (level of) knowledge and skills needed to execute his job to be done. This is what conversational commerce should be about. If not, AI will be another tool to amplify the megaphone to shout more offers at customers.
Conversational commerce to get the job done
To get back to my question of above whether it is a safe bet to focus AI efforts on bringing customized offers to the customer’s screen? It probably is a good place to start learning and understanding machine learning techniques. I think though that such applications of AI could be so much more powerful when these techniques are combined with the capability to turn your company’s knowledge about the customer (journey) into automated conversations that capture the customers knowledge, skills and intent to an unprecedented level. A level that stretches beyond search (locate) and transact (execute) and allows companies to turn accidental and unsatisfying touchpoints into seamless, personalized journeys the customer loves and that get his job done.
What do you think?