- AI & NLP
- Churn & Loyalty
- Customer Experience
- Customer Journeys
- Customer Metrics
- Feedback Analysis
- Product Experience
- Sentiment Analysis
- Surveys & Feedback Collection
I’ve just returned for Corinium’s Chief Customer Officers and Influencers conference in Atlanta. As someone who just joined the customer experience and insights industry four weeks ago, this felt like a capstone project where I was forced to “talk the talk” and “walk the walk.” It was humbling to meet these experts. The experience inspired me to share some of the Customer Experience best practices that we discussed.
Are you doing customer engagement right? Too many marketers assume that their strategies and offers are engaging their customers. This assumption is often wrong. Unless you are engaged in two-way dialogue and providing value-added communications, you have no idea what customers think about your business and your marketing tactics.
Do you have a lot of customer feedback collected but don’t know exactly what to do with it? Maybe you’re debating whether to hire a data scientist in-house to analyze it all manually, or go the agency route? Perhaps you’ve heard about text analytics and wonder what it’s all about, and what you need in-house to get started.
Are you responsible for measuring the progress in improving customer experience? If yes, I’m sure you needed to come up with a rationale on which metrics to choose for this: Is it an all ubiquitous Net Promoter Score (NPS), the traditional customer satisfaction CSAT, or a more recent invention Customer Effort Score (CES)?
A great example of a company who has transitioned from time-consuming, manual customer feedback analysis to AI-powered, fast text analytics, practically in no time, is Greyhound. Greyhound is the well-known bus transportation network, with services across the United States, Canada and Mexico.
Have you heard of Thematic analysis? If you are working in insights or data analysis, you will have heard of Sentiment Analysis. It answers the question “Is this mentioned in a positive or a negative light?”.
Here, we’ll give some examples of how to tell the story of your VOC compellingly to your stakeholders that you are trying to influence, to ensure you have a successful VOC programme. This is part 3 of our blog series from our webinar “5 practical ways to influence managers for Voice of Customer (VOC) success”, by myself and Dr. Alyona Medelyan.
Here, we go through important factors to consider when using insights from your voice of customer (VOC) programme, looking at customer lifetime value, in particular, to motivate your internal stakeholders to take action. These factors can also be helpful for prioritizing which tasks to act upon first using your VOC insights.
Is your growth plan to acquire more new customers? What’s wrong with that? Nothing, if you have all the time and resources in the world, but there’s a smarter way to maintain growth whilst achieving a high customer retention rate. It’s simple, keeping your customers coming back for more will result in a greater ROI.
Continuing our blog series on artificial intelligence AI (see earlier blog posts), we share some examples of everyday AI applications and commonly used AI analytics. If you look hard enough, you’ll find plenty of everyday examples of how businesses have used AI to make your (and their) life easier, thus improving the customer experience.
Last week, ThePointsGuy published the 2018 “The Best And Worst Airlines In America”. According to Forbes’ interview with Brian Kelly, the author of the report, 9 airlines were reviewed using 10 objective criteria. Here, we extend this report by adding the element of customer perception according to online reviews.
Often, AI is portrayed in the media as this ever-growing frenzy that will ultimately lead to robots stealing our jobs. And, how we should fear computers that are more intelligent than we are. Really? But as 2018 comes to a close, it’s clear some businesses aren’t paying attention to Hollywood’s ominous depiction of artificial intelligence. Instead, they are embracing AI whole-heartedly with the adoption of AI text analysis.
Are you getting the most out of your customer feedback? How can you ensure your feedback will transfer to solid actionable insights that make a difference to your business?
To investigate the public perception of fashion brands, we’ve analyzed over 8,000 customer reviews of four brands and applied Thematic to find insights in this data. We chose three brands that directly compete in the US fast-fashion world, H&M, Forever21, Charlotte Russe, and one brand that targets the same audience but operates as solely an online store, Lulus.
There is no argument that AI is here and it’s here to stay. AI has been a hyped-up term for quite a while and is now a reality. This blog is a start of a series of blogs focusing on AI and how it can help improve your customer experience. To get a full insight, download our e-book “How to leverage AI to improve Customer Experience”.
Artificial intelligence (AI) tools make it possible to easier anticipate customer needs in multiple ways. For example, marketers can analyze vast volumes of customer data, identifying the characteristics of high-value past customers which allows businesses to create highly personalized campaigns. Sales teams can quickly identify customer purchasing patterns and customer service reps can deliver relevant actions and sales offers.
Open-ended survey questions often provide the most useful insights, but if you are dealing with hundreds or thousands of answers, summarising them will give you the biggest headache. The answer lies in coding open-ended questions. This means assigning one or more categories (also called codes) to each response. But how to go about it?
I was attending a conference at a resort hotel in Orlando, Florida, standing in the lobby trying in vain to connect to a client who needed to discuss the results of a range of multivariate output. To my chagrin, my cell would not pick up a signal. Imagine my surprise when the nearby concierge, viewing my angst, permitted me to use his phone to complete the call. He even dialed the number for me.
Earlier this year, I have written about why word clouds suck. Is there a better way of visualizing customer feedback? Yes, there is, and the best thing about it is, you can even use Excel to create these visualizations - if you represent the data correctly.
Most people believe that text analytics solutions fail because sarcasm in customer feedback is very common. Somebody writes “Great service, yeah right!” and the dumb algorithm tags it as positive. So, whenever I speak on text analytics, someone in the audience will always ask:
But how do you deal with sarcasm?
Understanding customer comments, on a large scale, needs to be automated. So, it requires Natural Language Processing (NLP) or Text Analytics. Unfortunately, most open-source NLP tools were developed on text researchers have easy access to. These are typically news articles, research papers and movie reviews. I learned that the analysis of customer comments is quite different, and here is why.
Recently I had an interesting discussion with Ron Stroeven, one the founders of Infotools, about open-enders, short for open-ended questions. In 1990 Infotools was established, but Ron has worked in market research far longer than that. He has a wealth of experience in survey design and data analysis, so it was fascinating to hear his view on open-enders, their history, and future.
Four people and several automated solutions were tested on a task of coding open-ended questions in a Net Promoter Score (NPS) survey. Their task: figure out the five key reasons behind an NPS survey and the five areas that could be improved. Here, we compare their performance using an academic metric of consistency. 250 students at a Swiss business school responded to a classical NPS survey with a scale question How likely are you to recommend the business school to someone choosing their third-level studies? and two open-ended questions Why? and What should the business school improve?
If you missed our presentation at the Sentiment Analysis Symposium in New York last July, read on to see it in full with accompanying slide notes.