Missed the first two parts? Check them out here
5 AI Techniques to collect VOC data: https://www.trustinsights.ai/blog/2020/07/how-can-ai-improve-voc-part-2-5-ai-techniques/
The Impact of AI on the 11 Key Components of VOC
The Voice of the Customer is broken out into eleven different components. Some companies are set up to collect data from each component, some from only a couple. Ideally, you could find data from all of these but aim to start with at least one and build from there. Let’s walk through each phase of VOC and see where there are opportunities for AI to assist.
11 Key Components of VOC
1. Advertising and Marketing
With ads, you’re spending money to catch someone’s attention at different stages of the customer experience. When you’re putting ads out to your customer base, you want to make sure that you’re providing the right messaging at the right time – where the customer is in their journey with you. You can accomplish this with A/B testing. A/B testing is creating variations of the same thing and running experiments to see which version resonates. With manual A/B testing, you can manage a handful of iterations. You might test subject lines, images, copy, or audience segments.
A really great example of using AI to do A/B testing is Coca Cola. While most people execute one or two tests simultaneously, Coke figured out that they could use AI to run millions of permutations of ads across the world and knock out the underperforming campaigns until they had a set that would be used. They had to give the AI a training data set of keywords, images, and audience profiles, as well as thresholds of success or failure for each ad campaign. Once the planning was done, the AI took over and Coke monitored what was working and learned from the experiment how to reach their audiences globally.
For advertising, you’ll use regression analysis most.
2. Market Research
It’s tough to pull opinion data out of quantitative information, so why not just ask your customers what they like and don’t like? Market research allows you to dig into specific preferences, thoughts, and feelings that you would otherwise just infer from your static data. Market research can come in the form of 1-1 interviews, panel surveys, or general population surveys. This kind of research is generally the springboard for understanding what your specific audience voice will look like.
For market research, you’ll use regression analysis and exploratory data analysis most.
3. Social Conversations
People do NOT hold back their opinions on social media. You can find people from all walks of life that have thoughts about your company, whether you’ve asked or not. If you want to know exactly how people feel about your company, start a conversation on social media, sit back, and wait. You’ll get the good, the bad, and everything in between. There are a lot of social listening tools on the market already that can pull some of this information, but the limitation of the tools are the platforms that they integrate with. If your customers are on the major social channels but are also found on less conventional platforms you might be missing out on key information about what your customers want.
For social conversations, you’ll use text mining and natural language processing most.
4. Product/Service Reviews
Similar to social conversations, people are not shy about saying how they feel about you in online reviews, especially if they have had a negative experience. This data is a gold mine in terms of learning where to improve and what to keep doing. In addition to the conversation, you can extract the sentiment of reviews to create a model that will help you understand the elements of a five-star review, versus what garners only one star.
For reviews, you’ll use a combination of text mining and regression analysis most.
5. In-person/private messaging
A lot of people are more comfortable interacting digitally than directly with someone in-person or over the phone. That’s where chatbots become very helpful for your business. They can take on the role of interacting with your customers and collecting intake information that can be passed along to a service representative for further investigation and problem-solving. On the flip side, chatbots can help answer repetitive questions that your customers may have. To get this information, you would look at your other VOC components, like social listening, product reviews, and customer service calls, to understand what questions are asked repeatedly and where you can have AI step in.
Email is another way that customers feel more comfortable interacting with you. AI can help with standard responses and setting up nurture campaigns that will move your customers through a pre-defined experience. Both chatbots and email can be set up with predictive text and responses to ensure a consistent customer experience. You can then collect information about the responses and conversations to help inform your products and services.
The big thing to remember is to ALWAYS disclose your data collection policies – so if you’re going to mine the content for information, your customers need to know this ahead of time so that they can decide what they share and what they don’t.
For messaging, you’ll use text mining and natural language processing most.
6. Search Intent Data
Search Intent data is a good place to start when you’re not sure what your audience looks like. Often used at the awareness stage of the CX, search intent data can inform your content marketing and advertising plans. Starting with keyword research, you can use AI to help with permutations of words to get a better understanding. Once you know what keywords you want to be known for, and what keywords your audience is using, you can create predictive calendars to serve up content when they want it. You can combine your data sources and other VOC components to really hone in on the language that your audience is using to talk about you, and what questions they have that you can answer in your content.
For search intent data, you’ll use a combination of predictive analytics/forecasting and text mining.
7. Sales Interactions
You can learn a lot about the behaviors of your customers by what they buy, and how often they purchase. You should have access to your e-commerce data, online cart information, and customer relationship management (CRM) software. Within your CRM you can chart out your sales funnel and see how quickly your customers take to convert. With your e-commerce and cart data you can see if people start the process and then abandon part way through. Understanding these behaviors can shape your customer experience and help expedite the experience for your audience.
For sales interactions, you’ll use a combination of text mining and regression analysis most.
8. Customer Service
People typically don’t call customer support because they are happy with what they have. Your customer service calls, emails, and chats are a treasure trove of information – specifically what your customers don’t like and where you can make improvements. Accompanied by a data collection disclaimer, you can mine this data for keywords and topics that would otherwise get lost in the shuffle. If you’re recording your customer support calls, you can have AI transcribe the audio and then you can run text mining against the content to see what bubbles up. Using AI, you can also parse out the sentiment and tone of your interactions. Knowing the trends of positive or negative sentiment can help you train your service reps for all kinds of situations that may come up and allow them to provide even better support.
For customer service, you’ll use a combination of text mining and regression analysis, along with exploratory data analysis.
9. User/Owner Groups
User and Owner groups tend to be more focused on how people are using your products, which helps you to better understand your user experience. User groups can highlight when your audience creates “workarounds” or “hacks” when features or functionality don’t exist or work as expected. Getting this first-hand information from your customers about how they use your product (often in conjunction with other products) is invaluable and will help inform how you should innovate moving forward. A lot of companies fall into the trap of “I know what our customers want” and miss out on creating exactly what they really need.
For user groups, you’ll use a combination of text mining exploratory data analysis most.
10. Customer Metadata
A lot of companies rely on ONLY this information to determine the profile of their customers. While this information is important, it’s also too broad and tends to lead to assumptions. A great example of this is with My Little Pony. If the marketing executive had solely focused on the young girl ages 4-11 segment, they would have missed out on the “Brony” segment, which is adult men who are interested in the lore and fantasy of MLP. This segment also has more disposable income and is willing to spend it with MLP and Hasbro.
Customer metadata is a great place to start your information gathering about your customers but should not be the only thing you collect. Systems like Google Analytics and your social media platforms will have the inferred demographic data around your website visitors and followers, such as age, gender, location, and interests.
For customer metadata, you’ll use exploratory data analysis most.
Many companies have the missed opportunity to ask people quick surveys such as, “did you find what you wanted?” “did we answer your questions?” or “was the information helpful?” These surveys can be quick, can be one question, and can tell you a lot about your customers. Using AI, you can program these surveys to be on your website to be triggered when a customer takes a certain action, or to be sent to a customer’s email address at the conclusion of an interaction. This information adds an additional layer of information to inform your VOC to understand if you’re taking the right actions with your customers, and how they are feeling at the moment.
Customer feedback surveys also give you the data you need to calculate your net promoter score (NPS). Companies rely on this number to gauge their customer’s loyalty, as well as demonstrate to other potential customers that they are worth doing business with. A company with a higher NPS is more likely to attract new business.
For surveys, you’ll use regression analysis most.
Up next: How to bring it all together.
Need help with your marketing data and analytics?
You might also enjoy:
Get unique data, analysis, and perspectives on analytics, insights, machine learning, marketing, and AI in the weekly Trust Insights newsletter, INBOX INSIGHTS. Subscribe now for free; new issues every Wednesday!
Want to learn more about data, analytics, and insights? Subscribe to In-Ear Insights, the Trust Insights podcast, with new 10-minute or less episodes every week.