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It’s understandable for some organizations to not grasp the importance of merging customer experience with customer success.

For one, the two initiatives seem an awful lot alike. They both involve customers, after all. Companies might also feel little urgency to merge the two because customers themselves don’t usually recognize or even care these are separate initiatives.

But customers do notice the two efforts when they work well together. Take a B2C customer. It feels satisfied when a software vendor delivers a product on time and without a hitch, enabling its own staff to use the software to meet deadlines. That’s an element of customer experience (CX). The same B2C customer also notices when that vendor offers free training programs that demonstrate how its business customers can align their goals with the software. …


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There seems to be a lot of buzz around the Julia programming language lately. After nearly ten years of development, Julia 1.0 was released in August 2018. And according to the KDnuggets software poll published in May 2019, Julia is one of five major analytics, data science, and machine learning tools with a significant increase in usage compared to the 2018 software poll. This post takes a look at the Julia programming language and highlights a few of the advantages and disadvantages of Julia compared to Python.

What is Julia?

Julia is an open-source, dynamically-typed, programming language designed to excel at scientific and numerical computing. The Julia programming language can be used for specialized domains such as machine learning, visualization, and data science. Julia can also be used for general-purpose programming. The language supports parallelism out of the box, offering three main levels of parallelism which are categorized as Julia coroutines (green threading), multi-threading (currently experimental), and multi-core or distributed processing. …


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It’s one thing to want to please customers. It’s a whole other thing to actually deliver a consistent experience that satisfies customers and keeps them coming back for more.

Thankfully, the delivery of customer experience (CX) no longer relies on outdated, often generalized perceptions of what it takes to give customers what they want. With consumers leaving thick bread crumb trails on their digital devices, organizations can capture real-time, irrefutable insight on what they’re thinking and how they’re behaving.

Consider consumer data as evidence and analytics as the crack detective that pieces together all of the clues. That’s the thing about data: Consumers generate so much of it that there is no way a company can make sense of it all without help. That’s where the detective — the analytics component — should be called into duty to sort all of the information and then break down the data into small groups that reveal consumer history, sentiment and a host of other detailed insights. …


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Marketing teams at any company can fall into the commodity trap- where the competition for products and services are based solely on pricing. And many companies specialize in selling commodity products, making it all the more difficult for their marketing teams to avoid falling into the commodity trap. Today, however, companies selling commodity products have access to a wealth of data- data that often contains information about customers, manufacturing processes, operations, logistics, and worker expertise.

This blog post highlights several examples of how companies are leveraging multiple sources of data and applying machine learning (ML) and customer analytics in ways that allow them to avoid the commodity trap. …


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As of this writing, there are over 7,000 martech solutions available, compared with only 150 in 2011. Having a big martech stack isn’t that big of a deal anymore. If you’ve got the budget, you can purchase loads of martech to do just about anything. But going on a martech shopping spree will cost you more than money: it may disrupt your focus. Try telling your customers, “we’re too busy buying and implementing martech to focus on you,” and see how that goes. Martech exists to help your B2B marketing team drive improved engagement with customers.

You need a martech stack that provides you with a personalization engine which you can use to drive more relevant content to your customers, something a customer data platform (CDP) helps give you (more on CDPs later). …


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Over the course of two prior blog posts, we’ve explored a major trend in data science: the emergence of the citizen data scientist (CDS) and how a CDS can effectively collaborate with professional data scientists to unlock the massive business potential of data. This post describes the 7 key traits of a successful CDS, traits that enable them to drive unique business value through data analytics.

Let’s begin by defining exactly what a CDS is. Gartner says a Citizen Data Scientist is “a person who creates or generates models that leverage Predictive or Prescriptive Analytics, but whose primary job function is outside of the field of statistics and analytics.” …


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The concept of predictive modeling has been around for decades, and it involves collecting data, formulating a statistical model, making predictions, and then revising the model as more data becomes available. It is only in recent years that the use of predictive modeling techniques in marketing has taken off- thanks to the abundance of customer data available. There is a wealth of internal and external data that data scientists and marketers can leverage to make predictions about customers such as the propensity to engage, convert, buy, and churn. This post highlights two common predictive modeling techniques used in marketing.

Clustering…


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While the number of B2C companies using machine learning is skyrocketing, the adoption of machine learning by B2B companies lags far behind in comparison. This post highlights two ways machine learning makes a difference for B2B companies.

Augments Account-Based Marketing (ABM)

Lead generation is fundamental to business growth, whether the business is B2C or B2B. For B2B companies, ABM tactics allow marketing teams to focus on creating highly targeted campaigns that address the specific needs of each set of accounts. Because ABM focuses on the best-fitting accounts, it is an ideal approach for both new and existing customers. …


On our blog, we highlight the many ways machine learning allows marketers to gain valuable insights from data- insights such as products customers are most interested in, when customers are most likely to buy, and the propensity for customers to churn. Today, we thought we would highlight how machine learning and data from connected devices are a powerful combination for marketing. When we talk about connected devices we are talking about the Internet of Things (IoT), a worldwide network of devices all connected to the internet. …


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Businesses are swimming in customer data. To make the most of that data and in order to create highly personalized experiences for their customers, business and marketing technologists are investing in CDPs (Customer Data Platforms). But having automation alone isn’t enough to drive successful interactions with your customers, CDPs need to be optimized in order to get the best results.

Where can you use a CDP?

Business and marketing technologists recognize Customer Data Platforms for the value they deliver in creating lasting relationships with their customers. CDPs that are combined with machine learning, offer a complete view of your customers that also enables you to engage them across the channels of their choice in real-time. …

About

Zylotech

AI-driven customer intelligence platform that enables clients to cross-sell / up-sell to their customers.

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