What MBA Students Need to Know about Customer Experience, Data Science and Surveys

I was asked to give a two-hour lecture at the University of Washington to an executive MBA class on the topic of measurement and analytics in customer experience. I covered such topics as Big Data, data science and how they can help you improve customer understanding. I’d like to thank the instructor, Sasha Frljanic, for letting me share my work with an exceptional group students (they asked some really good questions!). In today’s post, I’d like to share my slides and provide a summary of my talk, highlighting content that I think is particularly important to customer experience and customer success professionals.

3 Critical Questions Customer Success Executives Need to Answer

In today’s subscription-based economy, customers are no longer trapped in long-term contracts and are able to jump to competitors easily when they become dissatisfied with their current vendor. Consequently, many subscription-based and SaaS companies are turning to the practice of Customer Success to keep their customers. Customer Success is the function in a company that manages the relationship it has with its customers to ensure the customers receive value from the product or solution. Customer Success is about making customers as profitable and productive as possible.

>> Take the Customer Analytics Best Practices Assessment

5 Steps to Better Customer Retention Analytics

This blog describes a five-step process to improve your customer retention analytics efforts. This process leverages existing data to draw useful insights to show you how you can improve retention rates, increase purchasing behavior and maximize the lifetime value of your customers.

Four Ways to Optimize Your Customer Survey [INFOGRAPHIC]

Companies, in support of their customer experience management (CXM) programs, rely heavily on the use of customer surveys as a means of collecting customer feedback. An optimal customer survey maximizes the value of the survey to both the businesses who use them and their customers who complete them. Specifically, businesses need customer surveys that provide reliable, valid and useful information to help run the business more effectively. Customers need surveys that let them give quick, yet meaningful feedback about their experiences.

How Datameer Is Helping Businesses Improve the Value of Their Data

Big Data analytics is at a crossroads; Gartner reported that, through 2017, 60% of Big Data projects will fail to go beyond piloting and experimentation, and will be abandoned. I had the opportunity to talk to Andrew Brust, Sr. Director of Marketing Strategy and Intelligence, about how his colleagues at Datameer are trying to improve how companies extract value from their vast amounts of data. Datameer is a self-service big data analytic platform that makes analyzing data simple. Their platform helps their customers perform rapid data discovery and quickly find business insights in those data.

How to Improve Customer Service

Improving customer service is not an easy task. It's an endeavor that has many moving parts. You need to track the right loyalty metrics like retention; you need to ensure your customer service staff are well-trained and have the tools to deliver that service; you also need a culture of customer-centricity.

5 NPS Myths and How to Overcome Them

I first heard of the Net Promoter Score from Fred Reichheld, one of its co-developers, in a talk he gave at a vendor conference. When he stated that “the NPS is the best predictor of business growth,” my interest was piqued. Why? I have never found evidence in my 20+ years of experience to support that statement. Since his talk and the release of his book on the same topic, I have conducted many studies to examine the merits of the NPS claims. Additionally, other researchers, from both industry and academia, have conducted research on the NPS. Our basic conclusions: the NPS claims are not true and there are a lot of problems with their research claims.

4 Reasons Why Customer Retention Matters to Your Customer Acquisition Efforts

Business growth depends on acquiring new customers and keeping them around for a long time. Yet businesses are over 2x more likely to focus on acquisition efforts than they are retention efforts. In today’s post, I want to discuss why businesses need to increase their focus on customer retention efforts and why the are imperative to your customer acquisition efforts. Here are four reasons why customer retention is important to customer acquisition.

Analyzing Big Data Using an Integrated, Customer-Centric Approach

Businesses are trying to leverage their vast amounts of data to stay ahead of the competition and move their business forward. The application of Big Data analytics refers to the idea that companies can extract value from collecting, processing and analyzing vast quantities of data. Businesses who can get a better handle on these data will be more likely to outperform their competitors who do not.

4 Criteria for Evaluating Your Customer Feedback Metrics

Both Customer Experience Management (CXM) and Customer Success Management (CSM) programs necessarily require the collection, synthesis, analysis and dissemination of customer metrics. The quality of customer metrics necessarily impacts your understanding of how to best manage customer relationships to improve the customer experience, increase perceived value and customer loyalty and grow your business. How do you know if you are using the right customer metrics in your customer program? This blog will help formalize a set of standards you can use to evaluate your customer metrics.

For a more complete discussion on customer metrics, download the free white paper, "The 4 Pillars of Customer Feedback Metrics."

Beyond the NPS: Try These Loyalty Questions in Your Customer Survey

Customer loyalty is the degree to which customers feel positively about and engage in positive behaviors toward your company or brand. While some people argue that all you need is one loyalty question in your survey (ala NPS), we believe that the NPS is not sufficient in capturing different types of customer loyalty. In this post, I will provide several loyalty questions you can start using in your customer survey today.

Take the Customer Analytics Best Practices Assessment and Receive the Free Report

How well does your customer-centric program leverage your customer data to reduce churn, increase engagement and grow your customer base?

Take the free Appuri Customer Analytics Best Practices Assessment to learn if your company adopts best practices in customer analytics and learn how to effectively use your data to grow your business.

The assessment takes less than 10 minutes to complete. At the completion of this assessment, you’ll receive a free report of your results to see how you compare against others, and our executive summary ($499 value) of findings.

Does Your Customer Success Manager Need Data Science Skills? Part 2

In the last post, I discussed the general idea that Customer Success Managers (CSMs) need to be knowledgeable in business and technology and possess good interpersonal (customer-facing) skills. We now live in a world of Big Data where everything is quantified. This statement holds true for customer success managers who have access to many different data sources that contain metrics that are used to measure the health of the customer relationship. Furthermore, customer success managers often rely on Excel as their main technology to analyze their data.

Despite the increased role that data play in customer success management, only half of CS teams have a data analyst. Without the proper analytics support for your customer success managers, they are leaving a lot of potential insight trapped inside their data, insights that could help them more efficiently and effectively manage their customers.

Does Your Customer Success Manager Need Data Science Skills? Part 1

Customer Success Managers (CSMs) play an important role in today's subscription-based economy. Typically employed by Software-as-a-Service (SaaS) companies, CSMs are responsible for ensuring customers receive value from their solutions to decrease customer churn and grow the existing relationship. To accomplish this goal, CSMs require a solid foundation in business and technical skills to help identify their customer’s needs and employ different company resources to improve the health of the customer relationship. Additionally, due to the data-intensive nature of managing customer relationships, CSMs now need to possess some degree of proficiency in data science skills or, at least, have access to those skills.

The 7 Customer Experience Questions You Need in Your Survey

Sign up to view our free Webinar, The Optimal Customer Relationship Survey. To register, click here. I will be addressing the content of this post in this free webinar.

A formal definition of customer experience, taken from Wikipedia, states that customer experience is: “The sum of all experiences a customer has with a supplier of goods or services, over the duration of their relationship with that supplier.” In practical terms, customer experience is the customer’s perception of, and attitude about, different areas of your company or brand across the entire customer lifecycle (see Figure 1.)

Optimizing Your Customer Survey: Free Webinar

Companies, in support of their customer experience management (CXM) programs, rely heavily on the use of customer surveys as a means of collecting customer feedback. An optimal customer survey maximizes the value of the survey to both the businesses who use them and their customers who respond to them. Specifically, businesses need customer surveys that provide reliable, valid and useful information to help run the business more effectively. Customers need surveys that let them give quick, yet meaningful feedback about their experiences.

We held a free Webinar, The Optimal Customer Relationship Survey, that presents questions you need in your customer survey and an analytics approach to help you optimize the ROI of your CX improvement efforts. To register for this free Webinar, click here.

Customer Experience Management Best Practices: Free Report

Companies that have customers who are more satisfied with the customer experience (CX) outperform companies that have customers who are less satisfied with their experience. But what makes CX leaders different than CX laggards? Download our free report on CXM best practices.

IBM Offers Free e-Book Explaining Why Everybody Needs to Be Excited about Data

Data, whether they be big or small, can provide a lot of value to businesses, people and society as a whole. IBM recently released a free e-book illustrating why everybody needs to be excited about data. This e-book talks about how data can 1) power your business, 2) impact your personal and professional life 3) make an difference to others and 4) even help you dominate your fantasy sports league.

Four Data Science Imperatives for Customer Success Management

Business growth depends on ensuring customers recommend, stay and expand their relationship with you. Businesses are implementing customer success management (CSM) programs to help improve their relationship with customers to improve their chances of success. In our Big Data world, Customer Success Management (CSM) programs are now able to leverage large amounts of customer data to help them better understand their customers’ needs to decrease customer churn and increase up/cross-selling opportunities. In today's post, I will discuss the intersection of Big Data and CSM and illustrate how the adoption of data science practices can improve how CS personnel can improve the health of the customer relationship.

The Problem with Net Promoter Score Segments

The NPS is a popular organizational metric used by customer experience professionals. I have noted several problems with the NPS elsewhere (see Stop Listening to the NPS Dogma and Follow the Evidenced) related to the claim that the NPS is the best predictor of growth (research shows it is not) and the use of net scores (mean and top box scores are better summary metrics). In today's post, I want to discuss another problem regarding the meaning of customers' ratings and the subsequent customer segments based on those ratings.

Customer Loyalty Definition - Part 2: Customer Loyalty Measurement Framework

Last week, I reviewed several definitions of customer loyalty (Customer Loyalty Definition - Part 1) that are being used in business today. It appears that definitions fall into two broad categories of loyalty: emotional and behavioral. Emotional loyalty is about how customers generally feel about a company/brand (e.g., when somebody loves, trusts, willing to forgive the company/brand). Behavioral loyalty, on the other hand, is about the actions customers engage in when dealing with the brand (e.g., when somebody recommends, continues to buy, buys different products from the company/brand). Generally speaking, then, we might think of customer loyalty in the following way:

Customer loyalty is the degree to which customers experience positive feelings for and engage in positive behaviors toward a company/brand.

Calculating and Improving Customer Retention [Infographic]

Business growth depends on customer loyalty. Customer loyalty is the degree to which customers feel positively about and engage in positive behaviors toward your company/brand. Businesses that have customers who are more loyal outperform businesses that have customers who are less loyal. There are three types of customer loyalty (i.e., retention, advocacy and purchasing), each responsible for different types of business growth. In today's post, I present an infographich from Salesforce that addresses retention.

Customer Loyalty Definition - Part 1

Customer-centric professionals would agree that business growth/success depends on improving customer loyalty. They, however, show little agreement in how they define and measure customer loyalty. In this post, I will review many current definitions of customer loyalty, incorporating them into a single, unifying definition. Next week, I will review research findings to further clarify the meaning of customer loyalty and present a customer loyalty measurement framework that will help crystallize how companies need to think about loyalty.

Media and Publishing Industry Leaning on Customer Experience

The media and publishing industry is an evolving one, shifting from old-school print to new-school digital media. In this transition, publishers are confronted by two important business imperatives. First, they need to be able to leverage the revenue from their print media to fund their transformation into the digital world. Second, they need to ensure their customers’ digital experience helps drive customer loyalty in the form of subscriptions and repeat visits. Publishers can look to customer experience management principles to help them in the transition process.

Stop Listening to the NPS Dogma and Follow the Evidence

You are probably already familiar with the Net Promoter Score (NPS), a metric used to gauge the health of the customer relationship. Although it is widely used by companies, most people don’t know that it actually has three serious problems. First, the “research” behind the NPS claims is flawed. Second, the calculation of the metric (a difference score) results in an ambiguous score that is difficult to interpret. Third, the NPS is insufficient in measuring the multidimensional nature of customer loyalty.

The Complementary Roles of Customer Experience Management and Customer Success Management

Companies are adopting customer-centric management disciplines to help them stay ahead of their competitors. While several customer-focused disciplines exist, two popular ones are customer experience management (CEM or CXM) and customer success management (CSM). In today’s post, I will discuss the similarities of and differences between CXM and CSM and why companies need to consider both disciplines when managing their customer relationships.

Our Nightmare on Amazon ECS

Here at Appuri, we have a large number of small, single-purpose services that make up our ETL pipeline, API and UI. We started from large, monolithic repos and gradually migrated to this microservices pattern, not because of any philosophical bias but because it fit our work style. By and large, this has worked well with all the known pros and cons of microservices. But I'm not here to debate microservices. I'm here to tell you about our nightmare on Amazon EC2 Elastic Container Service (ECS) and how we saved ourselves by moving to Kubernetes.

How Data Integration and Machine Learning Improve Customer Loyalty - Part 2

Last week, I introduced the notion that businesses can gain deeper customer insights if they connect their disparate data silos. Similar to how oncologists can leverage information from genome sequencing to tailor cancer treatments for a specific patient in order to improve health outcomes, businesses can use all customer data from disparate data silos to personalize interactions with their customers to improve customer loyalty.

How Data Integration and Machine Learning Improve Customer Loyalty - Part 1

In this Big Data world, a major goal for businesses is to maximize the value of all their customer data. Most customer data, however, are housed in separate data silos. While each data silo contains important pieces of information about your customers, if you don't connect those pieces across those different data silos, you're only seeing parts of the entire customer puzzle.

Engage your Users Early and Often to Increase Retention – Part 1

Regardless of what business you are in, it’s not news that your users’ experience with your products starts from the very first day they interact with your company. For some this happens during a free trial or the first time they open your mobile app or game, for others it may begin when they receive an email regarding their subscription. Whatever form this first contact takes it is critical to ensure that every part of it is working in a way to optimally engage the user.

Data Recipe: Comparing Groups of Users - Part 2

Understanding the difference between two different groups of customers is very important for many aspects of a business. The information gained from understanding these differences could be used to understand retention, give you insights into campaign customization or even identify features to add to a product or service.

In Part 1 of this topic I covered example business questions we are answering, how to define groups of users, and how to get all of the variables that describe the users.

In this post -- Part 2 -- I will demonstrate a methodical, scientific process by which to understand the significant differences between two groups using a t-test. Part 3 will wrap up the topic by demonstrating the significant difference between two groups using a Chi-Squared test.

Running Spark in Production: Choosing where to host Spark

Appuri helps customers diagnose and predict churn. This, by its very nature, is a Big Data problem because user behaviors is predictive of churn. A popular game title, for example, can easily collect 4 billions events a day. Over the last few months, we decided to move our core ML pipeline over to Apache Spark. This series of blog posts covers real-world lessons in learning how to run Spark in production.

How Product Teams Leverage Customer Behavior Data For The Best Customer Experience

There are all sorts of customer analytics products out there today. These tools provide insights into what your customers are doing with the product, but where many of these products fall short is tying this data in with other data sources such as your CRM, marketing automation, acquisition campaigns, and customer success software, support software.

Unlocking Business Value from your Mixpanel Data using Advanced Analytics

Sophisticated business users often have questions that cannot be answered by the Mixpanel UI. This webinar shows the data team how to get access to the rich set of information captured by Mixpanel, but only accessible when you can run SQL queries.

Mixpanel is capturing your users’ raw event (sometimes called “telemetry”) data. These events are sent to Mixpanel by code running in your mobile app or on your website and are stored in their original, raw form. The Mixpanel UI enables you to view this data in pre-defined ways and segment along pre-scripted boundaries.

Data Recipe: Comparing Groups of Users - Part 1

Understanding the difference between two different groups of customers is very important for many aspects of a business. The information gained from understanding these differences could be used to understand retention, give you insights into campaign customization or even identify features to add to a product or service.

The Four Horsemen of Churn and What Retention Marketers Can Do About Them

Churn happens all over your product - that's the bad news. The good news is that as a retention marketer you have access to a treasure trove of user data in Appuri - literally every click and tap the user made is available for analysis. Appuri's churn drivers feature uses all this data to diagnose and predict churn. If this magic 8 ball told you why users are churning, what would you do about it? In this article, I make an argument that not all types of churn is equal, and provide a framework for prescriptive actions and decisions for different types of churn drivers based on examples from our customers.

April 2016 Product Update: Tight Salesforce Integration, Churn Drivers

Now you can sync data from Appuri to Salesforce, and use certain Appuri UI features directly from Salesforce. The Churn Drivers view is enhanced, with a focus on prescriptive actions and next steps. Lastly, you can manage your team and user access levels from within the UI.

How to load billions of JSON events into Amazon Redshift every day using Go and Kafka

Amazon Redshift forms a crucial part of our ETL pipeline. For our larger customers, we need to load 4 billions+ JSON events into hundreds of tables in Amazon Redshift every day. In this blog post, I dive into how we identified a bottleneck in our ETL pipeline, removed it and launched a new Go service with horizontal scalability and zero downtime.

B2B Lead Scoring Isn't Enough

Predictive analytics isn't new for B2B companies, but it hasn't yet reached it's potential. Even in marketing functions predictive is still immature, with 89% of B2B marketers planning to implement or expand capabilities in 2016. But adoption isn't the only indicator of maturity when it comes to predictive analytics -- it's also application. For most B2Bs, the only application is lead scoring. And lead scoring just isn't enough.

How Mobile Game Companies are rewriting the rules for Brand Advertising

Given the ability to run Cost Per Install (CPI) campaigns on mobile devices, why would anyone advertise a mobile game on a TV commercial? In this post, we explore the emerging world of mobile brand advertising.

Building a scalable job scheduler for Amazon Redshift

Job scheduling is as old as computers - the idea of "do this at that time" is really powerful and useful. We have all used cron to run scripts on a janky old Linux box (AWS Linux AMI, I stand guilty as charged). At Appuri, we are huge fans of Amazon Redshift but one feature we found sorely missing was the ability to run scheduled jobs on the data warehouse. Once you have your data in a central store like Amazon Redshift, you want to do something with it - transform it, aggregate it, create a report, email it etc. We built a job scheduler that saved our team hundreds of hours of work, and delights our customers with what it can do.

3 Things Your Customer Success Software Must Do

Retention is a key metric for SaaS businesses of all kinds, but B2B SaaS companies have some distinct characteristics that affect the way their customer retention is managed. Larger deals, longer sales cycles, and customers at both the corporate account and individual user levels translate to greater urgency and complexity. Assigning retention goals to product or marketing teams, as is typical with B2C SaaS, isn't enough.

How to have a Successful Mobile Game Launch

You've spent many sleepless nights developing and polishing your game and are ready to launch… now what? Should you just put it in the app stores and watch it fall flat as users pour out of it like a sieve? You need to have a really addictive, fun game - but that (alone) is not enough.

Why Retention Matters

You've probably heard the stats:

  • Acquiring a new customer costs 5X more than keeping an existing one1
  • A 5% increase in customer retention can increase profitability by 75%2
  • 80% of your future revenue will come from 20% of your existing customers3

Clearly, acquisition isn't everything - it's retention that really sustains growth.

Edge Analytics Cache vs. Data Sandbox

After our first post introducing the Edge Analytics Cache and our last post comparing the Edge Analytics Cache vs. a Data Mart, we got a few questions from readers about the notion of a data sandbox. How is an EAC different than a data sandbox (aka analytics sandbox)? It’s a great question worth exploring in this week’s post.

Edge Analytics Cache vs. Data Mart

We got a lot of great feedback on our first post introducing the Edge Analytics Cache. The questions we got most were along the lines of What does an EAC consist of? and How is this different than a data mart? Answering the former will help clarify the latter, so we’ll get more descriptive about the components of the EAC first.

Data Recipe: Calculating User Periodicity to Define Churn

It's important for a business to understand how often users engage with its product. This understanding opens up possibilities to create increased engagement -- the difference between desired engagement and actual engagement becomes a benchmark for campaigns designed to change user behavior. But understanding user engagement with your product is also a necessary step in defining a common KPI: churn.

Expanding the Data Lexicon: Introducing the Edge Analytics Cache

Ten years ago, the phrase “big data” was rarely used outside of a few select academic circles, even though the quest to quantify the volume of information in the world - and the rate at which that volume is growing - dates all the way back to 1941 (Forbes has fairly succinct historical timeline here, if you’re interested). The term made its way into mainstream only when the problems it referenced affected a larger population, and those problems increased in severity. Nowadays, even my 15-year-old nephew knows what “big data” means, and my 64-year-old mother has a fairly good idea too.

Best Practices for Retention in Games, Part 2

In Part 1, we walked through a typical game and discussed an analytics maturity model for shoring up retention of your game. We talked about using Descriptive Analytics as a way to find out where users are leaving your title. Here in Part 2, we will drill down into Diagnostic Analytics portion of the analytics maturity model.

Visualizing Retention Cohorts with Appuri

Cohorting is a powerful tool in the modern retention manager's toolkit. By grouping similar users, you can compare them to other groups and tease apart differences in user behavior. Appuri's Retention Cohorts feature let you create powerful cohorts to diagnose retention problems - all within a slick visual interface that makes it easier than ever (and maybe even a little beautiful too).

What Online Schools Can Learn from SaaS Businesses

For-profit schools can only be as nimble as the laws that govern them in order to stay profitable. In the last 5 years, new laws have come into effect that control how online schools acquire leads that eventually turn into enrollments. They are now competing on the same playing field as traditional brick-and-mortar schools that have decades to centuries of branding, alumni, sports teams, etc. For-profit schools are looking towards retention solutions to help. Interestingly enough, there is a definite similarity between how software-as-a-service (SaaS) companies work to retain their customers and how these schools can work to retain their students.

The Flip Side of Seasonality: Retaining Customers Beyond their New Years’ Resolutions (Part 3)

January just might be the most important month of the year for the health and fitness industry, which is why we’re taking a look at some best practices for retaining all those new customers you acquire this peak season, using one of our own fitness tracker customers as an example.

The Flip Side of Seasonality: Retaining Customers Beyond their New Years’ Resolutions (Part 2)

In our first post in this series, we talked about the importance of evaluating seasonal campaigns by how many new customers were retained, rather than just acquired. But how do you measure retention? In this post, we’ll deep dive into data-derived definitions of retention and churn to help make these metrics more meaningful.

The Flip Side of Seasonality: Retaining Customers Beyond their New Years’ Resolutions (Part 1)

While the holiday season means an explosion of ecommerce activity for retailers, customer acquisition for fitness and food apps actually peaks in January. The two main drivers of this post-holiday acquisition boom are pretty intuitive:

Data Recipe: Using Mixpanel data to compare retention cohorts

Understanding how you interact with your customers and how it affects their behavior is a major part of retention management. In order to track these changes and effects, a powerful technique is visualization of cohorts and comparisons of retention over time.

Best Practices for Retention in Games, Part 1

We talk to a lot of online and mobile game companies about how they can keep their players longer – we do this all day, every day. I'd like to take this opportunity to write about is what we are seeing as Best Practices from leading companies in the industry to keep their players longer.

Data Recipe: Different Definitions of Day N Retention

Retention is critical for businesses as it determines who continues to use their product or service. It costs approximately 5 times less to retain a current user than acquire a new one. Although the word retention itself has a straight forward definition, it can be defined and analyzed in many different ways.

Data Recipe: Using Appuri to generate cohorts and calculate Day N Retention

A cohort is a group or set of users who share a specific event in a specific amount of time. Analyzing cohorts and understanding its retention over time is essential for businesses to understand their customers and trends over time.

This Data Recipe shows how to generate cohorts and calculate Day N Retention based on cohorts.

Data Recipe: Using Mixpanel to generate pin-point accurate counts of Daily Active Uniques (DAUs), Monthly Active Uniques (MAUs) and "Stickiness"

Daily Active Uniques (DAUs) are the number of unique users who visit your website or mobile app each day. The aggregation of those unique users into months gives you your Monthly Active Uniques (MAUs). Dividing your average DAU count by your MAU count gives you a ratio that can be used to see how engaging your product is and is often referred to as "Stickiness". These are key metrics for any online business.

Games are not a hit-driven business

The common wisdom is that "Games are a hit-driven Business" but after several of our titles hit Top 10 grossing, I view successful in this business differently. In fact, I would say games are not a hit-driven business!