Originally published on the Infopulse blog.
Data is key to strengthening relationships with your customers. But merely collecting data isn’t enough. There’s a big gap between being a data-driven company and an insight-driven business. While most organizations have access to plenty of data, determining what matters the most is a true challenge.
When 89% of businesses say that they are already competing on customer experience (CX), it may be tempting to start tracking every metric existing. But being truly data-aware means that you are not just seeing your customers as entries on a spreadsheet. You are actually listening to them. And that is where the voice of customer analytics kicks in.
Just What is Voice of Customer (VOC)?
Voice of the Customer (VoC) is a research methodology used by businesses to collect necessary data for describing the needs and requirements of their customers. The process assumes collecting every piece of feedback, sentiment, review, etc., that the target audience expresses about a certain product, service, or experience. Afterward, the data is used to close the gap between customer expectations and their actual experience with the brand.
[VOC provides] a common language for the team going forward in the product development process
Abbie Griffin and John R. Hauser MIT scientists
VoC research allows gaining new insights for making informed product and business development decisions. And as this post will further show, the gathered intel can help your company massively overhaul the product’s UX and overall customer experience.
Typically, all voice of customer programs consist of three stages:
- Collection — different methods for gathering customer feedback are used to gain necessary data for analytics. These can be direct ones such as surveys/polls or in-direct ones like customer data mining from review platforms.
- VoC data analysis — algorithms are deployed to examine the collected raw data and determine certain patterns, e.g., commonalities among customer expectations within a certain demographic.
- Implementation — applying the obtained insight to improve relevant areas of the business.
Wondering whether your company needs to go to such lengths with customer feedback collection? The following voice of customer statistics should help you shape up your business case:
- Per Salesforce, 80% of customers indicate that company experience is as important to them as its products or services.
- HubSpot research states that 80% of consumers would stop doing business with a company because of poor customer experience.
- Walker study suggests that in just one year customer experience will overtake price and product as the key brand differentiator.
- Businesses that lead in CX outperformed the laggards on the S&P 500 index by nearly 80%.
- Forrester states that last year experience-driven companies increased revenue by 1.4X and customer lifetime value 1.6X more than other companies.
Finally, if you look at this year’s top list of the most admired global companies, you’ll see that the top spots are reserved by CX leaders such as Apple, Amazon, Walt Disney, Starbucks, Microsoft, Alphabet (Google), and Netflix.
To wrap it up, companies that excel at customer experience tend to:
- Grow revenues faster than its competitors;
- Generate higher customer lifecycle value;
- Majorly improve client retention, reduce customer churn over time and boost loyalty.
What’s even more interesting is that CX leaders can set up higher prices without any backlash as 86% of people are ready to pay more for great customer experience.
So, if your business is ready to capture those benefits, it’s time to get started on your voice of customer strategy.
How to Collect Voice of Customer Data
- Customer interviews
- On-site and off-site customer surveys
- Live chat data
- Call center data (phone, email, support tickets)
- Social media
- Website analytics
- Online customer reviews
- Net Promoter Score (NPS)
- Feedback forms
- Focus groups
As you can see, there’s a lot of data that could be gathered. However, operationalizing such volume of unstructured information is problematic without the right tools and technology in place.
How to Apply Data Science to VoC Analytics to Gain the Most Value
When information is aplenty, the voice of customer methods based on manual data analysis no longer bring the best results.
The VoC collection tools can produce a multitude of scattered data entries that won’t tell you a comprehensive story. In fact, analyzing data from different angles can lead you to contrarian conclusions.
Data science can help you “weed out” exactly what kind of improvements are the most in-demand and how they can generate additional value for your business. Below we’ll explain how to collect customer information that makes the most sense to your business, plus how to transform it into insights.
Step 1: Start with a Hypothesis.
VoC programs generate the most value when they are targeted at answering specific questions and exploring the outcomes that underpin value in your industry. For instance, as an auto manufacturer, you may be interested in reducing the number of recalls; and determining what car features generate more sales and lead to higher customer satisfaction. Telecom companies would want to know how to reduce customer churn, handle issue escalation calls and what upsells work best with a certain customer segment.
The hypothesis you develop will inform your choices regarding:
- The voice of consumer data;
- The voice of customer analysis tools you’ll use;
- The types of analysis you’ll perform.
Step 2: Take an Inventory of Your Current VoC Sources.
Perhaps, your company has already been gathering some voice of the customer data or not. In any case, before performing any analysis, you will need to determine:
- Which of the data sources are in place.
- Which ones are no longer being used (or never were).
- Where this data is collected and warehoused.
- How you can access those data lakes.
Preparing your data for analysis may take some time and technological efforts as most sources like those can be “owned” by different departments including Customer Service, Sales, Marketing, PR, etc. All of the sources will need to be consolidated and cleansed before any VoC software can be installed.
To ease up the process, it’s best to label each of the data sources using the following codes:
- Globally used — the data is all over the place;
- Partially used — data stashed in parts of the company;
- Rarely used — the data is not collected frequently;
- Formerly used;
- Never used.
Step 3: Align Data Sources with Your Objectives.
Next, you’ll want to label the VoC sources in terms of the value you can receive from those. You can create a global taxonomy across all 5 scores mentioned above and one or more of the following objectives:
- Reduce operating costs;
- Increase customer experience;
- Reduce customer churn;
- Boost revenues;
- Improve issue resolution time.
This way you can determine if you have enough data sources to make comprehensive conclusions, plus determine where else you can gain the necessary data.
For those extra insights, you can try linking what customers say to what they do. Because, as David Ogilvy famously pointed out: “Consumers don’t think how they feel. They don’t say what they think and they don’t do what they say.”
Hence, you will also want to build a data set of past surveys’ results that, for example, focus on the customer’s willingness to recommend your product to others. You can link survey results back to your databases and pull down some data for each outcome measure for further analysis. For instance, you can match the self-reported customer satisfaction within a certain period with data on customer retention, revenues, product upgrades, referrals, etc.
Building this type of data link is integral for gaining comprehensive insights and measuring the ROI of your VoC program and CX initiatives.
Step 4: Perform an Analysis of the Historical Performance of Real Customer Cohorts.
Once you have consolidated all of the available VoC sources into a centralized database, you can start modeling and predicting how they will contribute to your objectives. Specifically, you can use the historical data at hand to determine:
- How does the increase/decrease in customer satisfaction levels contribute to expensive contact center calls?
- How do the churn rates drift depending on satisfaction levels?
- What is the correlation between an improved feature release and customer churn?
Continue reading this post on the Infopulse blog where it’s been originally published.
