Using Technology to Enhance Customer Experience

For SaaS companies, customer churn is closely tied to growth. From an industry standpoint, the average churn rate for mid-market companies is between 12% and 13%. With renewal-based revenue models, churn directly affects both topline and bottom line. At Egnyte, AI and Machine Learning have been pivotal in our journey to improving customer retention and reducing churn. We have noted a 2.5 to 3 points reduction in churn rate by deploying AI programs that are actionable for both our customers and CSM teams. AI can offer powerful capabilities to help SaaS companies significantly reduce churn by enabling proactive and data-driven customer retention strategies. Some of these strategies are: 1. Predictive Churn Analytics Machine Learning models analyze vast amounts of customer data (usage patterns, support interactions, billing history, feature adoption, login frequency, etc.) to identify subtle patterns that precede churn. They can flag customers as "at-risk" before they can explicitly signal dissatisfaction, allowing for proactive intervention. It can further assign a "churn risk score" to each customer/ user, enabling customer success teams to prioritize their efforts on the most vulnerable and valuable accounts. The actionable operational data that we received by employing ML is the essence of churn analytics. 2. Hyper-Personalized Customer Experiences AI allows SaaS companies to move beyond generic communication to highly tailored interactions based on user behavior and feature adoption. AI can suggest relevant features, integrations, or workflows that the user might find valuable but hasn't yet discovered. AI can also determine the optimal timing and channel of customer-focused content, such as help desk articles, feature awareness videos, and case studies. 3. Automated Customer Support and Engagement AI can enhance customer support, making it more efficient and impactful. AI-powered chatbots can handle common customer queries 24/7, reducing wait times and providing instant solutions. Advanced chatbots use Natural Language Processing (NLP) to understand complex queries and provide personalized responses. It also helps in online enablement, reducing onboarding costs. While these strategies are already redefining the way CSM and enablement teams service customers, their significance in the cadence of customer retention strategies is going to increase hereon. Enterprises need to use AI intelligently and efficiently and focus on gleaning actionable insights from their AI strategies. #B2BSaaS #Churn #CustomerRetention

For years, companies have been leveraging artificial intelligence (AI) and machine learning to provide personalized customer experiences. One widespread use case is showing product recommendations based on previous data. But there's so much more potential in AI that we're just scratching the surface. One of the most important things for any company is anticipating each customer's needs and delivering predictive personalization. Understanding customer intent is critical to shaping predictive personalization strategies. This involves interpreting signals from customers’ current and past behaviors to infer what they are likely to need or do next, and then dynamically surfacing that through a platform of their choice. Here’s how: 1. Customer Journey Mapping: Understanding the various stages a customer goes through, from awareness to purchase and beyond. This helps in identifying key moments where personalization can have the most impact. This doesn't have to be an exercise on a whiteboard; in fact, I would counsel against that. Journey analytics software can get you there quickly and keep journeys "alive" in real time, changing dynamically as customer needs evolve. 2. Behavioral Analysis: Examining how customers interact with your brand, including what they click on, how long they spend on certain pages, and what they search for. You will need analytical resources here, and hopefully you have them on your team. If not, find them in your organization; my experience has been that they find this type of exercise interesting and will want to help. 3. Sentiment Analysis: Using natural language processing to understand customer sentiment expressed in feedback, reviews, social media, or even case notes. This provides insights into how customers feel about your brand or products. As in journey analytics, technology and analytical resources will be important here. 4. Predictive Analytics: Employing advanced analytics to forecast future customer behavior based on current data. This can involve machine learning models that evolve and improve over time. 5. Feedback Loops: Continuously incorporate customer signals (not just survey feedback) to refine and enhance personalization strategies. Set these up through your analytics team. Predictive personalization is not just about selling more; it’s about enhancing the customer experience by making interactions more relevant, timely, and personalized. This customer-led approach leads to increased revenue and reduced cost-to-serve. How is your organization thinking about personalization in 2024? DM me if you want to talk it through. #customerexperience #artificialintelligence #ai #personalization #technology #ceo

This is what happens when you prioritize customer loyalty.. you see results that go beyond just numbers. Loyalty programs aren’t a gimmick, they’re a powerful strategy for building lasting customer relationships. We recently implemented a loyalty program using HubSpot, and the impact was immediate. Not only did we see a spike in customer retention, but the feedback from our clients was overwhelmingly positive. They felt valued, appreciated, and connected to our brand in a way that went beyond just transactions. Here’s what worked for us: ➜ Personalized rewards: Using HubSpot, we tailored rewards based on customer behavior, ensuring they felt understood and valued. ➜ Automated engagement: We set up automated emails and messages to keep our customers engaged, making them feel part of a community. ➜ Data-driven insights: With HubSpot’s analytics, we tracked what resonated with our customers and refined our approach accordingly. What was the outcome? Increased customer loyalty, higher retention rates, and a deeper connection with our brand. How are you building loyalty with your customers? What tools or strategies have made the biggest difference for you? #hubspot #customer #strategy

 Every time a potential buyer leaves your website to “research a product on YouTube,” you’ve likely lost that sale. It’s a hard truth: if you’re not providing rich content like video at the point of purchase, customers will seek it elsewhere – possibly on a competitor’s site. The solution? Keep them engaged on your turf with on-site video content. Consider that 58% of shoppers say that watching user-generated video reviews increases their purchase likelihood for a product (research by BrightLocal). That’s why leading e-commerce players are integrating video reviews, unboxings, and demos right into their product pages. They understand that video content not only informs – it builds trust and prevents “bounce” to external sources. Look at the retail giants: many are now hosting live Q&A videos and how-tos on their own platforms, so customers don’t wander off to YouTube for answers. It’s about owning the customer journey. When shoppers stay on your site longer with engaging videos, they view more products and feel more confident to click “Buy Now.” 📹🛍️ According to Similarweb data, reducing bounce rate and exit-to-YouTube incidents can directly improve conversion metrics on-site. Bottom line: Don’t let your hard-earned traffic slip away due to lack of content. Provide the videos your customers are looking for right where they are. You’ll keep them in your checkout funnel and reap the rewards in higher conversion and loyalty. Is your organization already using on-page video content (product demos, customer story videos)? Or are you noticing potential buyers drifting off-site for info? I’m interested to hear how you’re addressing this challenge – comment below with your thoughts or shoot me a message! Hashtags: #CustomerExperience #Ecommerce #Retention #VideoReviews

Can technologies like Computer Vision, AI and RFID be used to improve the customer experience at a retail location? Quick answer is "of course"! But the question is how can it be done efficiently enough (getting leverage from your labor expenses) to reduce customer transaction time whilst enhancing the customer experience. At a high level, retailers who take a multi-signal capture approach to inventory management will be able to unlock the benefits of reducing transaction times and customer checkout interventions. The bigger goal is to remove customer friction, and make it easier for customers to buy by also reducing the cognitive load from multiple decisions. and maybe there should be an exchange between retailer and customer for the additional benefit. Interesting article in #CampusIDnews: "In an interview with CampusIDNews, Al Padilla, Cal Poly Pomona’s Senior Manager for Retail IT, explained these frictionless systems. He says the AI-driven checkout solution from Mashgin allows students to enter a store, grab items, and leave without physically scanning products or handing over a card. At Pomona, these stations have slashed checkout times from 90-seconds or more to just 12.5 seconds. This has virtually eliminated lines, and without lines students are less likely to pass the stores if time is tight." Any solution explored will have to take sku density, count and complexity among other factors into account. The bigger question is: Can this serve as a gateway into bringing customers into a retailer's ecosystem in order to serve then better? That can be the exchange! What an exciting time to be in the retail business! 🙏🏿 Would love your thoughts in the comments... As well as from Neil Saunders and Chase Binnie! https://lnkd.in/g8Q6vvv3

In today's competitive high street retail landscape, staying relevant to new generations and shopping trends is key. Partnering with brands and retailers daily, I witness the exciting changes taking place to drive increased share, customer retention, and acquisition through effective cross-channel personalization strategies. 1. Harnessing the Power of AI for Predictive Insights. By leveraging AI to analyze customer behavior, businesses can identify trends and preferences, enabling personalized messaging and tailored offers. This data-driven approach fosters loyalty among existing customers and attracts new ones. 2. Adopting Personalized Product Discovery (PDP). Implementing PDP customizes the shopping experience based on individual preferences. Dynamic search features suggest products aligned with past interactions online, while in-store digital kiosks enhance personalized recommendations, merging online and offline experiences seamlessly. 3. Creating a Unified Customer View. Integrating data from various channels provides a comprehensive understanding of the customer journey. This unified view enables consistent communication, real-time personalization, and effective tracking of customer engagement. 4. Cultivating Customer Loyalty through Personalized Rewards. Tailoring loyalty programs to individual spending habits and preferences using AI and customer data enhances customer loyalty. Exclusive events, early collection access, and personalized discounts resonate more with customers, fostering long-term loyalty. 5. Elevating Creativity Across All Channels. Creative excellence enhances personalized strategies. Compelling visuals, authentic storytelling, and innovative campaigns across email marketing, social media, and in-store promotions captivate customers and drive engagement. Creative design elements play a crucial role in building loyalty. By embracing these strategies, high street retailers can navigate personalization successfully, creating engaging customer experiences that nurture loyalty and attract new clientele. For further insights, feel free to reach out directly!