A couple of weeks ago I wrote about the eCommerce customer experience in general. This week I am digging deeper into the five and a half factors that form the eCommerce customer experience.
We have analyzed millions of webshop customer’s comments. This has taught us how customers talk about eCommerce. I have gathered in this blog post the key insights on how to analyze eCommerce customer comments and understand their loyalty.
Before starting extracting actionable insights from customer feedback, you need to set up an enterprise insight process that:
- Gathers feedback: you actively solicit feedback through many channels and touchpoints, crawl social media, and analyze contact and support center feedback; and
- Analyze feedback: you've implemented a feedback analysis system that detects the sentiment and categorizes all open-ended customer comments according to an industry specific categorization system (Codeframe).
This gives you an excellent starting point: all the comments are categorized by topic and sentiment. But turning that information into actionable insights isn't a straight foward process. It requires quite a bit of creative data analytics and visualization work.
Follow these 12 steps and you will be able to monitor the customer journey, identify the loyalty drivers and improve your bottom line.
When many web stores sell the same or comparable products within the same geographical area the only thing that sets you apart from competition is the shopping experience. The shopping experience becomes even more important when you don’t sell your own products. And when it comes to an excellent eCommerce experience, there is a benchmark: Amazon has set the bar. Do you know how you compare to the one-click wonder?
Here are a three real-life examples where real-time text analysis service has been successful in identifying systemic issues in our customers’ products and services.
Systemic issues–like login not working on your web service or eCommerce shipping problems in a warehouse–may cause lost revenue and customers. That is why it is important to quickly pinpoint this type of problem clusters. Manual ticket tagging is slow, expensive and the results are inconsistent. Automatic feedback categorization service fixes this problem in real-time, accurately and feasibly.
Open-ended customer feedback analysis is not a stand-alone application because it needs to be fed with text. And that text comes from outside, from another application or platform. Feedback analysis is a feature that enhances the functionality of an application.
Most CEM, CRM, BI and Contact Center platform vendors have the necessary competencies and skills to solve their core domain problem with their own software development team. Additional functionality–embedded analytics services like advanced text analysis or image recognition–can and should be provided by specialized 3rd party software vendors.
Extracting actionable insight is difficult. It takes a lot of work and requires serious thinking and planning. One of the most important things you need to do is to design and implement a CX database.
You, the CX professional, need to own this data. Don’t let BI or IT people set restrictions. Making compromises will greatly hinder your ability to do your work well. Good data is paramount!
This blog post was written by Maurice FitzGerald, who recently retired as VP of Customer Experience for Software at Hewlett Packard Enterprise. His career with HP, Compaq, Digital Equipment Corporation and Wrangler Jeans concentrated on customer-centric business strategy and process improvement. He is currently documenting his experience in three books that are expected to appear in early 2017. You can find more information about Maurice's books here.
We talk a lot about asking customers what to improve, and improving it. In my previous post for Etuma, I covered the same approach for employees. Now I want to introduce a new concept: ask employees how to improve customer experience. Let’s call it Customer-Employee NPS, or ceNPS. (If I could think of an appropriate word beginning with an ‘a’, I could call it ‘aceNPS’, which would be cool.)