I’ve had numerous conversations recently, including a couple of web shows (CRM Playaz Roundtable, In the Hot Seat) about the biggest trends in 2023+ for CRM and customer experience (CX). I believe we’re approaching an inflection point that will create some radical departures from our current approach to CRM and CX solutions in the near future. I believe this in part because of these factors:

• The growing seller - buyer gap. I’ve talked about this issue for a few years, starting with my first buyer behavior study back in 2015. In a nutshell buyers’ have changed their buying behavior in many ways, but sellers continue to sell in the same way they always have. This gap has created a sales crisis for many companies. The following diagram provides some context for the diverging behaviors.

• The potential for moving to real time streaming data.

• The rapid evolution of AI tools including the use of large language models like ChatGPT.

• Consumer fatigue with contextually irrelevant personalization.

• Lower engagement with several communication channels caused by overuse.

• Growing dissatisfaction with the poor experience created by outdated tiered support models.

Reimagining Business Applications with Real Time Data

Real-time streaming data can provide significant value across a broad set of business applications by enabling organizations to make better data-driven decisions. Moving from a historical only data view to a holistic data view (historical + real-time) lets companies respond to changing events quickly, and optimize business processes. Here are some ways businesses can use real-time streaming data:

• Real-time Decision Support: Analyze data as it streams into your systems, allowing you to detect trends, anomalies, and patterns immediately. This enables businesses to make proactive decisions and respond to issues or opportunities as they arise. The capability enables an organization to utilize business data and systems to provide continuous intelligence.

• Customer service and support: Leverage real-time data from customer interactions, social media, and support tickets to quickly identify and address customer issues or concerns, improving customer satisfaction and loyalty.

• Sales and marketing optimization: Analyze streaming data from multiple sources, such as web analytics, ad platforms, or CRM systems, to optimize marketing campaigns, sales strategies, or pricing in real-time.

• Contextually Relevant Personalization (or individualization): Use real-time data to understand user behavior and preferences, allowing you to offer individualized content, recommendations, discounts and promotions based on their current context, activities, actions and behavior. Consumers show signs of growing fatigue with “contextually irrelevant personalization” (see the section below) that is based on historical data instead of real-time data streams that more accurately represent current needs, desires, context and behavior.

• Fraud detection and security: Analyze streaming data to identify suspicious activities or transactions, enabling businesses to detect and prevent fraud, security breaches, or other malicious activities in real-time.

• Healthcare and telemedicine: Use real-time data from wearable devices or remote monitoring systems to track patient health, provide timely interventions, and improve healthcare outcomes.

• Supply chain and inventory management: Monitor real-time data from IoT sensors, GPS trackers, or other devices to optimize supply chain processes, manage inventory levels, track high value items, reduce counterfeiting and reduce costs.

• Predictive maintenance: Collect and analyze data from IoT devices and sensors to predict when equipment or machinery might require maintenance, allowing businesses to avoid costly downtime and improve efficiency.

• Smart cities and IoT: Use real-time streaming data to monitor and control various aspects of urban infrastructure, such as traffic patterns, public transportation, or energy consumption, leading to more efficient and sustainable cities.

• Financial services and trading: Analyze real-time financial data to make informed investment decisions, monitor market trends, and identify opportunities for arbitrage or risk management.

There are a number of tools available for ingesting, processing, managing and distributing real-time data streams. On the data end of it you can use tools and platforms like Apache Kafka, Amazon Kinesis, Google Cloud Pub/Sub, or Microsoft Azure Event Hubs. Additionally, a business could use data processing frameworks like Apache Flink, Apache Storm, or Apache Spark Streaming to analyze and process the data in real-time. Once available to stream, the data can then enhance applications across functions from sales and marketing to service and finance. Customer Data Platforms (CDP) have a role in the use of streaming data to enhance the customer experience as well, particularly in implementing a real-time interactive and dynamic customer journey.

Real Time Interactive and Dynamic Customer Journeys

I have a confession, for several years I’ve had a growing feeling that the old exercise of customer journey mapping is dead. All the research I’ve done on buyer behavior shines a light on how ineffective the process and concept is today. Maybe it never was the most useful exercise, but at least in a pre-smartphone world it was reasonable to assume that your company was an important source of information on your products and services, and how a purchaser could use them to their benefit. Once the prospects had access to the entirety of the Internet’s information in their pockets, the buying process changed. Gone is the need for the vendor to be the only source of information on their products and services. Buyers can research, read reviews online for almost anything they want to purchase; connect with others that have experience with the potential solutions, in their industry, and with similar challenges; and generally eliminate the need to interact with a vendor until the decision to purchase is nearly decided, or is already made. I’ve written quite a bit on this subject, (here for example) so I won’t spend more time on it. The only other thing I’ll add is that the concept of providing a map to what you think a customer will do, in a linear and sequential way, is simply not accurate in almost all buying journeys today.

The answer to these issues is dynamic customer journeys; or using technology to create personalized and adaptable paths that drive interactions with customers. These journeys are shaped by a variety of factors such as customer preferences, real-time behaviors, and previous interactions with the company. The concept of dynamic customer journeys acknowledges that each customer's experience is unique and constantly evolving, requiring businesses to be flexible and responsive in their marketing and sales strategies. Dynamic customer journeys involve the following key elements:

Data-driven: By collecting and analyzing real-time and historical customer data from various touchpoints, businesses can gain a better understanding of their customers' needs, preferences, and behaviors. This information allows them to tailor the customer journey accordingly.

Personalization: Dynamic customer journeys focus on delivering relevant content, offers, and experiences to individual customers based on their unique profiles and preferences. This enhances customer engagement and increases the likelihood of conversion. I like to call this “individualization” instead of personalization to differentiate the concept that real-time data can radically change the level of specificity of the interactions.

Real-time adaptability: As customer preferences and behaviors change, businesses need to adapt their strategies accordingly. Dynamic customer journeys involve monitoring and adjusting marketing, sales and service efforts in real-time to ensure they remain effective and relevant to each customer's needs.

Omnichannel integration: To create a seamless and consistent experience, businesses must integrate all channels (online and offline) and touchpoints, such as websites, social media, email, and in-store interactions. This ensures that customers receive a cohesive and relevant experience throughout their journey with the brand.

Continuous optimization: Dynamic customer journeys involve ongoing testing, analysis, and optimization to ensure that businesses are consistently meeting their customers' needs and expectations. Integrating real-time data and behavioral analysis enables the vendor to continually refine their strategies, maintain strong customer relationships and drive ongoing engagement and loyalty.

Digital Twinning

Digital twinning, or the creation of a virtual replica of a physical object or system (or behavior), can be used to create a dynamic customer journey by allowing businesses to model, analyze, and optimize the customer experience across multiple touchpoints. By integrating digital twins with customer data, analytics, and marketing automation tools, businesses can create a more personalized and engaging customer journey. Here are some ways digital twinning can be used to create a dynamic customer journey:

Personalized experiences: Digital twins can be used to create virtual replicas of customer profiles, preferences, and behaviors. By using these virtual representations, businesses can provide tailored experiences and content that resonate with customers on an individual level, ultimately enhancing the experience and increasing conversion rates.

Real-time optimization: By simulating customer interactions and touchpoints, digital twins enable businesses to continuously analyze and optimize the customer journey in real-time. This allows for the rapid identification and resolution of bottlenecks, friction points, and underperforming campaigns, ensuring a seamless and satisfying experience for customers.

A/B testing and experimentation: Digital twinning facilitates rapid testing and experimentation of various customer journey scenarios. By creating multiple digital twins of the customer journey, businesses can quickly iterate, test, and compare different approaches, enabling them to make data-driven decisions and optimize their strategies.

Predictive analytics: By integrating historical customer data and leveraging machine learning (ML) algorithms, digital twins can help businesses forecast customer behaviors and preferences, allowing them to anticipate and respond to changing customer needs proactively.

Cross-channel integration: Digital twins can be used to create a holistic view of the customer journey by integrating data and insights from multiple channels, such as social media, email, web, and in-store interactions. This enables businesses to deliver a consistent and cohesive experience across all touchpoints, increasing customer satisfaction and loyalty.

Training and support: Digital twins can be used to train customer support agents and sales teams, providing them with a deep understanding of the customer journey and equipping them with the knowledge and tools needed to deliver exceptional service.

Digital twinning can revolutionize the way businesses create and manage customer journeys. By enabling real-time optimization, individualization, and cross-channel integration, digital twins help businesses deliver engaging and seamless experiences that drive customer satisfaction, loyalty, and ultimately, revenue growth.

Contextually Irrelevant Personalization

Contextually irrelevant personalization occurs when personalized content, recommendations, or promotions are delivered to users in a way that doesn't align with their current context, interests, or needs. This can lead to a poor user experience, reduced engagement, and lower conversion rates. Ultimately implementing a dynamic journey map strategy can create relevant and contextual interactions. Companies can also employ the following tactics:

Enhance user profiling: Improve your understanding of users by collecting and analyzing more relevant data points, such as browsing history, purchase history, preferences, demographics, and location. This will help you build richer user profiles and deliver more relevant personalization.

Utilize real-time data: Make use of real-time data to understand users' current context, actions, and needs. This will enable you to deliver personalized content, offers, or recommendations that are timely and relevant to users' current situations.

Implement contextual targeting: Consider factors like time, location, device, or users' current activity when delivering personalized content or recommendations. This ensures that the personalization is relevant to the specific context in which users are interacting with your application or website.

Employ ML and AI: Use advanced algorithms and ML techniques to analyze user behavior, identify patterns, and predict user preferences. This can help you deliver more accurate and relevant personalization.

Digital Twinning: (see section above)

Offer user control: Give users the option to customize their own experience, such as choosing their interests, content preferences, or opting in or out of certain types of personalization. This ensures that the personalization is tailored to their specific needs and preferences.

Balance personalization and privacy: Ensure that you respect users' privacy and comply with data protection regulations when collecting and using their data for personalization. Be transparent about how you use their data and provide them with control over their data.

By implementing these strategies, companies can improve the relevance of their personalization efforts, resulting in a better user experience, increased engagement, and higher conversion rates.

Customer Communication Channels and Overuse

I’m just finishing a project for a client on consumer communication preferences. The report, which comes out mid-April (check the “Research Reports” section of the site after 4/19/2023) is based on a survey of 1200+ consumers, 18 and older in 14 countries in North America (NA), Europe, Middle East and Africa (EMEA) and the Asia Pacific (APAC) regions. I don’t want to pre-disclose the data, but suffice it to say that consumers are showing a lot of signs of fatigue with some specific (and very popular with companies) channels. Ultimately customers want to communicate on the channel they prefer and trust, and this preference can change by activity (healthcare, banking, entertainment, eCommerce, etc.).

Modernizing the Sales Process

We often think of sales from an internal view: “The sales function is responsible for company revenue and value is defined as the revenue generated for their company”. Flipping this concept though, you can see a very different view of sales. Sales teams are ultimately successful if they grow revenue, but they do that by understanding and filling customer needs. The old linear sales process, with at most a single “discovery” step, is not sufficient to interact with a modern buyer. Understanding comes from an effective discovery process, which must underpin the entire sales strategy. A modern sales strategy should resemble this diagram:

Aligning how the customer wants to buy with the execution-level sales process is critical to closing the seller - buyer gap. Think about approaching this problem digitally rather than trying to force the buyer journey back in time to its analog predecessor. Dynamic customer journey maps (discussed above) can help address the gap of course, but complex sales will continue to involve a sales team to facilitate finding the best solution for a customer. Modern sales thinks about the sales process and the buyer” journey” as helping the buyer make a high-risk, complex decision with the desired outcomes, driven by customer needs and expectations. The modern sales strategy then, is powered by a complete discovery process that identifies customer needs and builds trust. One of my clients, SpotLogic, Inc., pioneered the productization of this concept and calls this model the opportunity’s “Information Position™”. The discovery data model is the digital underpinning of a data-driven approach to sales.

AI, ML and Large Language Models

It’s hard to publish anything on CX and CRM without putting anything in about the use of AI, ML and large language models. The discussion of how these technologies can and will be incorporated into a CX strategy is much larger than I can address in this post. We’re launching a 3 part series on AI and ChatGPT on our bi-weekly show, In the Hot Seat (ITHS) on the Playaz Production Network (PPN) starting 4/10/23. Check it out.

Collaborative Incident Management

I wrote about this concept last Summer here. The approach is fairly straightforward, instead of the old tiered support model where agents passed more difficult or unusual customer problems to another level(s) of agents, the incident has an owner (usually the first person to talk to the customer), and brings in team members as needed to collaborate on solving the customers’ problem. The model is underpinned with some form of a collaboration system like Slack or Microsoft Teams. There are many advantages to moving to a collaborative team approach to support. Having the ability to provide the customer with a single, consistent point of contact greatly improves the experience. Incidents often are resolved much quicker due to issue ownership and the ability to rapidly bring in the correct skills from team members.

Modernizing your CX strategy can be a huge undertaking, but adopting an incremental approach and working through each process can accelerate the change. For assistance on redefining and implementing a modern CX strategy, contact us here.

Note: ChatGPT4 was used to support the research behind this post as well as generating some of the content, which I then fact check, edit and refine.