Recently, the proliferation of omni-channel platforms has attracted interest in customer journeys, particularly regarding their role in developing marketing strategies. However, few efforts have been taken to quantitatively study or comprehensively analyze them owing to the sequential nature of their data and the complexity involved in analysis. In this study, we propose a novel approach comprising three steps for analyzing customer journeys. First, the distance between sequential data is defined and used to identify and visualize representative sequences. Second, the likelihood of purchase is predicted based on this distance. Third, if a sequence suggests no purchase, counterfactual sequences are recommended to increase the probability of a purchase using a proposed method, which extracts counterfactual explanations for sequential data. A survey was conducted, and the data were analyzed; the results revealed that typical sequences could be extracted, and the parts of those sequences important for purchase could be detected. We believe that the proposed approach can support improvements in various marketing activities.

We propose a method that can generate customer trajectories and purchasing behaviors in retail stores simultaneously using Transformer-based deep learning structure. Utilizing customer trajectory data, layout diagrams, and retail scanner data obtained from a retail store, we trained a GPT-2 architecture from scratch to generate indoor trajectories and purchase actions. Additionally, we explored the effectiveness of fine-tuning the pre-trained model with data from another store. Results demonstrate that our method reproduces in-store trajectories and purchase behaviors more accurately than LSTM and SVM models, with fine-tuning significantly reducing the required training data.

Since its launch in November 2022, ChatGPT, the chatbot developed by OpenAI, has taken the business world by storm. Following this success, Microsoft has increased its investment in OpenAI and has launched a new version of its search engine Bing that provides users with generated answers in response to searches, as opposed to providing them with thousands of links to choose from. Not surprisingly, Google, as the incumbent in the search engine market, quickly reacted and is launching Bard, its own attempt to create an AI chatbot leveraging the power of large language models and integrate it into the search process. Moving beyond search, both Google and Microsoft are now making their chatbots available through an API (application programming interface, a form of a protocol), thereby enabling software developers from other firms to integrate their systems with these new chatbots. From finance to healthcare and from education to travel, industry observers expect an explosion of service innovations and new digital user experiences. Leveraging the capabilities of large language models, chatbots have developed amazing capabilities to generate human-like responses, and to speak in different languages and styles.

Five9, a leading provider of the Intelligent CX Platform and Invoca, a cloud leader in AI conversation intelligence, announced that they have expanded their strategic partnership to deliver a solution that enables deeper insight into real-time data throughout the entire customer journey and brings the contact center and the marketing teams closer together to enable more "fluid" CX. "When our agents have no idea what patients may have researched online before they called, delivering a positive, seamless experience is nearly impossible. Pairing Invoca with Five9 allows us to improve our call routing and tracking, through more accurate and granular attribution, at scale, with greater efficiency than ever before." This customized solution called PreSense, combines the power of Five9 Intelligent CX Platform with Invoca's conversation intelligence technology, giving contact center agents visibility into a caller's digital journey before the call takes place. Insights from PreSense help reduce call handling times, increase contact center productivity, and help enable more fluid customer experiences that flow effortlessly through channels, and between virtual and human agents. For example, an agent could see if a customer researched a specific product or responded to a particular offer online and use this pre-call context to provide tailored recommendations.

The CX and customer service landscape continues to evolve at a rapid pace. According to reports from market leaders like Salesforce, 80% of users consider the experience a company provides to be as important as its products and services. Additionally, 66% of customers expect organizations to understand their needs and actions. To stay ahead in today's competitive landscape, especially in the face of fluctuating economic conditions, businesses must stay ahead of consumer expectations. A clear insight into CX trends by companies could be the key to standing out from the competition in the New Year.

Artificial intelligence has been around since the 1950s, but over the last several years the business potential of AI has expanded dramatically. We now live in a world where big data and powerful computational capabilities allow AI to flourish. Companies--including insurance carriers--are investing in establishing data lakes, optimizing for cloud-based operations and activating AI for targeted analytics. Insurers are seeing tangible results from their current AI initiatives. Our AI maturity research shows that carriers' share of cost savings generated through AI more than doubled between 2018 and 2021.

The shift in pharmaceutical sales from traditional, product-driven approaches to customer-centric ones is well underway. Many pharmaceutical companies are now using some kind of AI-powered Next Best Action (NBA) approach to guide marketing and sales efforts, and omnichannel is becoming the industry standard. These approaches benefit customers, be they payers, practitioners or patients, who get to enjoy a more personalised and customer-centric experience, tailored to their preferred methods of communication. When done correctly, AI-powered NBA and Omnichannel in pharma leads to increased sales, improved retention, and greater overall customer satisfaction. In fact, customers have responded so well to NBA and omnichannel practices that, in just a few short years, it has become an integral part of their expectations.

Contact centers are evolving rapidly. The days of single-channel, telephony-based call centers are long gone. This old model has given way to the omnichannel customer experience center. In legacy call centers, the customer's pathway through sales or service was relatively linear. Call in, speak to an agent, and (hopefully) resolve the issue.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. Many organizations are challenged with finding strategies to deal with rising customer volume and changes in expectations, while facing an uncertain business market, according to Genesys, a provider of contact center services. While they are pressured to deliver better experiences with less, many organizations are hamstrung by legacy business processes, siloed point solutions and insufficient technical resources. This is where artificial intelligence (AI) technologies have the potential to help, since most lack the data scientists and resources to implement and deploy technologies orientated around their customers and employees while still supporting business objectives, Genesys said. In a move to help organizations optimize customer journeys with new experience orchestration capabilities, Genesys last week unveiled Cloud AI Experience.

The relationship between machine learning (ML) and marketing has been strengthening in the past years, resulting in the emergence of a new set of strategies and tools that optimize the process. A modern marketer is left with no choice except to jump on bandwagon to stay in competition and maintain the required skill-set in the industry. One the other hand, we have all been wishing to have this revolutionary way of performing marketing tasks. Your customers are at the center of your business, and you can finally deliver on this fact by using ML to your advantage. With the ability to track and analyze data with the purpose of driving customer engagement, ML has numerous uses in marketing.