These are the data, customer experience (CX) and artificial intelligence (AI) stories we’re reading here at Finalytics.ai.
They Need to Talk to Us
There are three main challenges to making the most of data troves to better influence the customer experience…
While the technology to capture data and analyze it in real time is available, many organizations don’t yet have in place the necessary data pipelines and analytical tools.
Many firms don’t have sufficient in-house data science, data analysis or data engineering functions.
Banks must have a customer-centric culture in which data drives product decisions. Without this.. it doesn’t matter how good your technology is, you won’t shape the (customer experience) in the most meaningful, impactful way.
It’s a good article but we wish they all would just come talk to us.
AI Transforming Digital Marketing
AI is developing the ability to take care of the entire content generation process itself, creating copy and images that it knows are likely to be well-received by its audience. A huge buzzword in this space will be personalization – where individual customers are served content that’s specifically tweaked to them, perhaps using information and reference points that the AI knows are relevant to them, intertwined with the overall marketing messages.
Gee, no wonder our virtual ears were ringing. He’s singing the Finalytics.ai song.
Marr explains the various ways that AI is being used in digital marketing, not just for personalization but also to automate other marketing processes.
AI Transforming Banking
Historically built to last, today banks need to be built to change. If traditional players want to reposition themselves as lifestyle partners, in tune with the modern banking needs of their customers, then they must evolve rapidly – and without fear. Key to this will be their embrace of AI technology which has broad-ranging applications from fraud prevention and risk management to delivering personalised customer experiences and driving efficiencies through greater automation.
We endorse his message. Check out the free whitepaper.
Building Scalable AI
Most community banks and credit unions lack the scale to build a machine learning (ML) team. However, we should look at how the big boys are trying to do so. There’s probably no better example than Capital One.
In an article in ZDNet, Stephanie Condon gives three steps that CapOne has followed to build their ML team from a discussion with Abhijit Bose, head of Capital One’s Center for Machine Learning and Enterprise ML Platforms.
Bose is quoted about the difficulties to scale,
You can always have pockets of people who are building things on Amazon or experimenting with a machine learning model with their own data, and then everything just falls flat. When it’s time to actually go to production, and then keep that model up and running 24-7 in some mission-critical application like transaction fraud, that is the hardest part.