“Take for example Cheetos. A few years back, Cheetos announced a gift collection ecommerce site timed with traditional retail holiday promotions. The site sold pretty much everything but the popular snack, from cologne and branded leggings to bronzer and a $20,000 jewelry set. No AI parsing through Cheetos marketing data sets would ever tell their CMO to sell pricey baubles. But with over 100M impressions, countess press pieces, social medial trending topics and a complete store sell out (yes, even the $20k jewelry set), Cheetos became the hot holiday season story. It’s these brave, and sometimes silly, choices that can resonate deeply with consumers in an age where marketing is becoming increasingly formulaic.”
I read a lot of these articles on a daily basis and it’s easy to identify the ones that are repeating the same old story to build SEO; and, I scrub those out so that you’re not stuck reading the same thing every day.
This one is clearly different with the concepts of predictive routing and sentiment analysis…check it out.
“AI-powered predictive routing engines can use historical performance data and match customer and employee attributes to predict which contact center agent is most likely to achieve targeted business goals.
Next best action
Contact center platforms such as Genesys leverage AI to suggest the “next best action” to agents in real time. This recommendation is typically based on an analysis of the customer profile, the type of inquiry they are making, and keywords being used in the conversation.
In an attempt to keep their contact center agents focused on high-value transactions, many financial institutions have started to deploy chatbot applications for lower-value interactions. However, even as chatbot technologies are rapidly improving and can effectively address basic needs, they cannot establish a personal connection that can build confidence and drive customers to share more of their needs or invest more.
Having a reliable way to identify a customer is a fundamental step in closing a financial transaction remotely, and is almost always a compliance requirement. In most cases, it includes verifications such as asking customers for their account number and other questions including address, birth date, and social security number.
Real-time sentiment analysis
Analysis of the video and audio streams will also dramatically enhance the assistance that can be provided to the agents. Facial expressions, body language, tone of voice, and keywords all reflect underlying states of mind, and uncovering them in real time feeds more informed suggestions to agents, who can then act more effectively.
Speech recognition, automatic text translation, and speech synthesis are all making rapid progress. It’s easy to envision applications in the not-too-distant future that will combine these technologies with video interaction. This will enable participants to speak their own languages but see on screen or even hear a translation of what the other party is saying.
Last but not least, contact center executives are constantly looking for ways to better collect and analyze the content of interactions to improve the quality and effectiveness of their services, provide more value to customers, and identify relevant post-contact actions. With speech recognition, the audio content of a video conversation can be transcribed into text, stored, and analyzed like any other text-based interaction channel.”
“Yet with SAP’s 2018 Digital Transformation Executive Study finding just 3 per cent of retailers have completed digital transformation projects, most of which focused on efficiency and cost cutting, there’s clearly still a long way to go before brands successfully harness digital for more disruptive innovation in the retail experience.
Again, however, only a small percentage of data is being used by retailers in decision making. While these organisations tend to collect as much data as they can, squirrelling away for future use, they’re still not able to ascertain its significance, Schneider agreed.
An example is Adidas in Russia, which used existing cameras plus RFID readers and RFID tags on garments and products to improve real-time inventory accuracy in its physical store from 60 per cent to 99 per cent. Adidas is now looking to rollout the approach globally.”
“Despite their heroic work to date, financial services firms will face the same competitive and regulatory challenges in 2019 as in the past years.
Navigating around the three-headed beast—competitive disruption from other established firms, fintechs and new digital-only banks—represents another big issue.
Thus the overarching mission for at least the next year involves digital transformation. Enter artificial intelligence (AI), which promises to forever transform banking and propel the industry deeper into the digital age.
Less analytically mature organizations are just catching up to traditional “big data” challenges
At the same time, consumers are becoming more savvy and cautious regarding the use of their data.
Banks will continue to adopt AI and machine learning technologies in 2019. Why? Perhaps the most urgent reason centers on “in-the-moment speed.” Despite having plenty of customer data, banks by and large lack the capacity for instant analysis and interpretation.
Inexpensive technology to process billions of transactions is commonplace—but extracting value and insights from that data remains difficult.”