Earlier this month, I attended a great MIT Sloan Alumni Association event on “Crossing the Chasm from Big Data to AI in Customer Centric Enterprise Business.”
The panel highlighted how “today, customer data-driven businesses are both anxious and experimental. To meet and exceed customers’ high expectations, companies must strategically apply innovative automated technologies,” as well as how “artificial intelligence (AI) (has become)particularly important.”
The panel was moderated by Abhi Yadav, co-founder and CEO of Zylotech with panelists:
● Ajay Kapoor; VP of Digital Transformation + Strategy, SharkNinja
● Andy Thurai; Chief Digital Strategist, IBM Cloud CTO
● Scott Brinker; Co-founder & CTO, ion interactive, editor, chiefmartec.com, program chair, MarTech
With this all-star panel steeped in years of experience in implementing innovative, effective solutions, I found the discussion fascinating and practical. Below is a summary of my notes from the event in order as they were discussed by the panel.
Customer Centricity – Innovate or Die
● Companies keep telling their managers “innovate or die,” especially around improving customer centricity.
● But, while there’s a lot of lip service around customer centricity, companies, and marketing in particular, struggle with leveraging innovative technologies, dealing with data barriers, and taking advantage of the big data boom in order to make customer centricity a reality.
● One of the biggest drivers for change, especially within more traditional companies, is the fear created by this shift of power from the company to the customer. Fear of how easy it is for consumers to switch vendors, and fear that they must adapt or become obsolete.
● But companies should realize that by becoming customer centric, they have a tremendous opportunity to drive up margins and generate more revenue. In order to do this, however, they must know their customers through-and-through.
Why isn’t Customer Centricity happening faster?
● It seems that even with all the talk around customer centricity, marketing is still treating everyone generically; sending out spray and pray email campaigns and being generally disconnected from their customers. The panel identified marketing schizophrenia, culture change, and reliance on manual efforts as key issues.
● Many marketing organizations scaled by breaking their data out into more and more specialized silos. In essence, for these companies, marketing isn’t a coherent entity, but a whole bunch of parts that can’t act in a coordinated way. “Oh, you guys run the website, you guys run the email program, you guys run the advertising.”
● These groups were able to grow by not having much coordination or overlap between them. But, today, all the excitement revolves around the consumer, who basically expects to deal with one, non-schizophrenic, brand–regardless of what their touchpoint is.
● Trying to find ways to connect these previously disparate groups is incredibly challenging. It’s certainly challenging from a systems point of view. There’s a lot of time and money required to integrate systems that weren’t originally designed to talk to each other. But this issue is solvable. You just need a plan to get from where you are to where you want to be.
Culture Change, The Elephant in the Room:
The bigger problem is the cultural change. Apart from re-thinking how these groups collaborate together, companies also need to accelerate the clock speed at which they do their engagement.
● Speed of Execution: It used to be that a yearly marketing plan, broken into a quarterly cadence, worked fine. But, many companies now need to operate on a weekly, bi-weekly sprint cadence, a process that’s easy to conceptualize, but hard to culturally shift to.
● Marketing’s Shadow IT – Impacting both culture and Speed. Fortunately, the culture is changing. Marketing historically has relied on IT, but now shadow IT is emerging. The CMOs have the money, and instead of waiting for IT to execute something in an ad hoc or clunky manner, they’re saying, “You know what? I’m going to bring the best of the tools and I’m going to change the cultural behavior. I’m going to get technology where it’s going to work for me.”
Experiment Then Expand
To help facilitate the changes discuss above, it’s beneficial to consider establishing an experimental group. This group job is to:
● Be on the lookout for new approaches and technology that are relevant or potentially relevant.
● Quick learn, understanding that the vast majority of experiments are not going to work out.
● Let the rest of the organization stay focused until you’ve got some things that demonstratively say, “Okay, this seems to have promise.”
It’s important to remember, that other portion of the organization is actually bringing in the money today, with the hopes that this new portion of the organization is going to make the money tomorrow, and keep the business competitive.
While there’s been a lot of noise around AI and machine learning for years, it wasn’t really happening until recently. The panel shared their thoughts on the opportunities for AI and machine learning:
● Personalization through automation: Leading marketing organizations are now trying to elude the manual element, personalize it, and address marketing processes without having a human in the channel.
● Letting the machines handle a lot of this exploration is great because we couldn’t keep track of it ourselves manually.
● Clean data sets, doing ETL, doing the merges, enriching, getting a large enough dataset to get to faux probabilistic and deterministic insights, and doing the necessary hyper targeting requires AI.
● The manual approach is too slow. Today, even when marketing speaks about personas, we are not talking about an individual, but rather a group. And the only way to go from group thinking to individual personas is through automation.
I hope you found the panelists comments and my notes helpful. We appreciate your thoughts on the subject.