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How will Augmented Humanity Techs Disrupt the World of Customer Success?

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In the future, human capabilities will be enhanced by a mix of existing, emerging, and nano-technologies. As technology becomes widely used and also due to rapid digitization partly drived by the COVID pandemic more and more is being written and talked about “Augmented Humanity”. The need to add an augmentation layer to human skills or to humanise interactions between humans and machines is driving attention toward technologies such as exoskeletons, ingestibles, implantables, injectables, biometrics, brain-computer interfaces, affecting computing, wearables, augmented and virtual reality (ARVR), and smart devices.  The more specific question we try to answer today is how customer success will change as we adopt the idea of augmented humanity. Of all the technologies listed above the one that we are already seeing happen is technologies and investments in better understanding the human emotions as expressed in their voices, faces, text and other online behaviors to better understand the

How Important are Tools/Tech for Customer Success

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The short answer is - Not very important. It might be an over simplification but for startups and small but growing companies it is true. As growth drives more customers to your portfolio you should start looking for tech/tool help. For the small business (what this post is focused on) - It’s about partnering with customers throughout their journey so they realize great outcomes and achieve their goals. To manage best customer experiences focus on the following three pillars: 1. Allow customer to select the right solutions for their business needs The customer journey typically begins with online research, so you should look to provide an exceptional experience starting well before you are pulled into an engagement. Through an intelligent website, serve up the relevant information potential customers need, including information related to all your solutions. Once customers are ready to engage, provide experts—both within your company and via your partner community—who have deep

3 Ways Customer Success Teams Can Use Data Analytics

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Today we will discuss why your customers success team needs data analytics to succeed in today's world, where everything is quantified and measured. Through analytics, you can gain insights into various aspects such as customer health and customer product adoption. Customer success managers use different data sets, including sales, on-boarding, usage, etc. in the customer life cycle. This data is important to better understand customer behavior. Through proper data analysis, customer success managers can understand the needs of customers effectively. With the help of big data methods and tools, such as predictive analysis, artificial intelligence, and machine learning, it can help increase customer retention and provide positive impetus to customer upsells. Data Analysis in Customer Strategy  Data analysis simply extracts information and insights from data to enhance human decision-making. Providing solutions through data insight into customer success will help customer s

Using Process Mining Techniques for Customer Journey Maps

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Process Mining Framework Proposed in Paper Today will take a quick look at this paper published recently by two leading business school professors. Reference is listed below. The authors in the reviewed paper provide a model that is intended for any one who is interested in mapping customer experience by clarifying the components of Customer Journey Maps (CJMs) . By purposefully drawing upon a standard born within the process mining arena, the model presented exhibits the following features:  it is easily exploitable by data analytics tools,  it is extensible to fit a domain- specific application, and  it is not tool-dependent.  By bringing process mining techniques and CJMs closer together, the model closes the gap between actual and expected CJMs and the authors propose a potential new area of research, which requires further investigations with real-life collections of CJMs. The authors anticipate that new techniques and metrics are needed to cluster journeys and their repre