Feedback collection can occur through either the upload of feedback that has already been collected. This can be accomplished by clicking on the 'Upload Data' link on the Main Dashboard page and then uploading a file using the upload button. Feedback collection can also occur by implementing the MachineCore feedback collection tools on your website. This can be accomplished by clicking on the 'Feedback JS Tracking Code' link on the Main Dashboard and then following the instructions.
Only when using the MachineCore feedback collection tools can you benefit from real-time alerts and analysis. These collection tools are designed to collect the right data from customers, and then analyze any feedback immediately. You will recieve alerts, if you configure alerts. In addition, you will see real-time/immediate and automatic updates to the customer feedback analysis as each comment is collected.
If you choose to collect feedback data using another company, that is totally fine. However, we recommend that you ask customer's specific questions that can be used to understand where improvements can be made. We find that so much of the customer data that other customer insight company's collect on your behalf is just not helpful. We believe you should really focus on understanding how your business is performing against customer expectations first, and then you can design specific survey's to dig deeper into what you learn. Just about everything else, is interesting to know but not really meaningful for your business.
While MachineCore's CXAI platform can analyze just about any customer feedback, regardless of source, the analysis is much more useful for generating business insights when it is collected in a very specific way. In particular, we recommend that you seek to gain a broad and general understanding of the experience of your customers. In this way, you will learn about their experience across all interactions with your products and services, and not just the specific and limited areas that you think you should ask about. The CXAI platform performs the best with this more general feedback. We recommend using the following types of questions, but feel free to adjust the wording for your specific business:
Each feedback comment is analyzed and categorized into at least one out of twenty pre-determined categories. Sixteen of these categories were selected because they have been proven, through many years of academic and business research, to be predictive of customer satisfaction. Through their connection to customer satisfaction these categories have been individually and collectively shown to help predict the performance of a business. The remaining categories are there to help identify comments that are not useful for understanding where to take action to improve your business, or for identifying other specific types of information.
The following section will explain each category, including why the CXAI platform categorizes each comment the way it does. If you have a need for further information on any of these categories please contact us.
Product Information
This category relates to any information that customers are looking for regarding your products or services. It will pick up things like feedback on product descriptions on your website, care instructions, size charts and how a product serves its purpose. This type of information has been show to be especially important to new customers as they are trying to determine whether it meets their needs or not, having not purchased from you before.
Product Value
This category is focused on the perceived value your products and services have with your customers. The AI will tend to categorize comments here regarding initial ticket prices or the value of your offering compared to the competition. Value perceptions are central to customer satisfaction and whether they will purchase with your company or not.
Product Quality
This category focuses on the perceived quality of your products. The AI will pick up any comments related to the performance, wear and tear, longevity of your products and services here. Perceptions of quality are especially influential over a customers desire to conduct a repeat purchase and are often intertwined with value perceptions.
Product Images
This category relates to the imagery you provide to your customers to describe your products, services and experiences. The analysis will try to identify when customers are talking about how your pictures and videos are influencing them. The ability to visually communicate your products and services effectively to your customers is critical in the purchase and branding process, and poor performance in this area will impact your business.
Product Selection
This category focuses on customer wants and needs from your product and services offering. The analysis will identify when your customers are talking about your offering and categorize them here. This analysis is particularly helpful for identifying where you have gaps in your offering, and when your customer tells you that, you know you are leaving sales on the table!
Product Availability
This category identifies any feedback relating to your products and services being unavailable when the customer wanted them. This generally means that this category will contain feedback that identifies where product out-of-stocks are occurring, or where products are unavailable when they were expected to be available. This category directly identifies where your business is likely to be losing sales due to lack of product or service availability.
Product Search
This category is focused on capturing insights into the customers ability to filter through the available options to identify their preferred products or services. The analysis will generally identify feedback relating to website search and filter capabilities, as well as some similar brick and mortar store-based product search activities. This category is helpful because it helps identify where customers are seeking specific products, but is not able to find them. This typically does not indicate that the product was unavailable, but more often that you need to improve the support you provide to help them find their preferred products.
Navigation
This category identifies where customers are having trouble understanding how your website or store is configured. The analysis will pick up customer feedback related to the ability to find certain section or types or products easily. This category can help to identify faster way to connect your customers with your products, and ways to remove customer frustrations so that their experience is faster, more enjoyable and less confusing to them.
Price and Promotions
This category focuses on the promotional offers that you offer to your customers. The AI will identify any feedback where the customer is talking about their opinions on your promotions, coupons and other offers. This category is especially helpful for understanding whether customers feel good about the promotions you are offering to them, and whether there are any improvements you can make to improve the performance of these promotions in the future.
Order Fulfillment
This category relates to the post-purchase fulfillment process most commonly associated with eCommerce orders. The AI will look for any feedback on customer experiences related to the receipt of orders and the immediate post-purchase process. This category is helpful for identifying if delivery of your products is happening as you, and the customer, expects. It can also be helpful for identifying delivery issues, third party fulfillment issues, and areas where customers would like you to deliver products, but that you do not currently.
Site Performance
This category focuses on feedback related to the technical performance of the website. The analysis will listen to feedback from customers related to technical issues, bugs and speed of performance. If you use the MachineCore collection tools you can even see how this feedback relates to different device and browser types. This feedback area is critical to any web business as any technical issues are usually a direct impediment to the customer's ability to accomplish their tasks and purchase from your business.
Check-out Problems
This category focuses on any feedback related to the check-out process. The AI will identify feedback related to website and store check-out problems so that you can quickly find fixes to those problems. Any issues with check-out lead to a direct and immediate impact to your business. Fixing anything identified in this category is usually a gold mine for a business.
Password
This category identifies any feedback related to a customers ability to use their password. Usually this means that they cannot reset their password or need other assistance. This is often due to specific technical or web design issues. If you want to have repeat customers, collect their information, and keep them engaged, a strong password management process is essential.
Security and Privacy
This category identifies any feedback related to data security, the sharing of personal information, or the policies of your business in this regard. It is essential that you protect the privacy of your customers, and if they have concerns, they won't be customers for long. This feedback category will identify if any of those concerns exist, and if so, where and how to address them.
Service Quality
This category encompasses a few different areas. Generally, any feedback about the customer service process, whether that is over the phone or in person, will be categorized here. In addition, if customers are requesting new features and functions from your business, they will also be categorized here. Examples of new features and functions would include customers seeking additional features on your website, or that you provide them with an app. This category is important because it identifies how well your business is doing in providing the experiences and services that you customer is expecting from your business.
Store Location
This category will identify feedback specifically related to brick-and-mortar store locations. This feedback could range from feedback on the experience in a store, to the need to find a store. Often this category will be combined with another category. For example if customers believe you have less product availability in your brick-and-mortar stores this category would be combined with the 'Product Availability' category. This is helpful for identifying when feedback relates to a physical experience or a digital experience. Only if the feedback clearly relates to a physical store experience will you see this category included.
Unsubscribe
This category will identify feedback relating to amount of marketing your company does. In particular, if customers are commenting on the amount they receive, including the desire to receive less, or to unsubscribe entirely, you will find it in this category. This is very helpful for monitoring whether your customers have communication fatigue.
Log-In
This category identifies any feedback related to the ability to log-in or register with your business. Understanding this feedback can be helpful for identifying fixes to the registration process that can allow you to more effectively engage with your customers through a registered visit. If they are unable to login or register, your business is probably unable to provide the best possible experience to them.
Non Actionable
This category is not displayed on the dashboard. However, the AI reads each comments and determines whether it is a valid comment and then whether it is helpful for your business. If it is a valid comment, but not particularly helpful for understanding how to improve your business, it gets categorized here. Examples of this feedback include feedback like "I love your company, keep it up!". We keep this feedback in the analysis when calculating sentiment scores, but otherwise remove it from the action recommendations and analysis.
No Comment
This category is not displayed on the dashboard. The AI assigns this category if it believes the comment has no value to your business. To fall into this category the comment provides no insight into customer sentiment, and provides no value from a business improvement perspective. These comments are removed from the analysis and customer counts entirely. If your business subscribes to a per comment processing fee subscription with MachineCore, these comments are excluded from the comment processing charges.
The MachineCore Recent Experience Score is a sentiment score that is proprietary to MachineCore. The sentiment analysis performed by the CXAI platform identifies the sentiment of the customer only bases on the feedback that they have provided. Therefore, it does not rely on any self reported levels of satisfaction with their experience. The analysis is to assess their sentiment with the specific experience they are reporting on.
Here is how to interpret the results of the Recent Experience Score:
Net Promoter Score (NPS) is a standard method for measuring whether customers are willing to recommend your business or not. This measurement has widespread adoption. It is most often based on asking the customer to rate how likely they are to recommend your business on a scale of 1-10. We have included this measurement in our dashboard given its ubiquity. However, we don't find that it is a measure that is helpful in any way for understanding why a customer would or would not recommend your business. As such, we don't believe it offers any actionable value. It is provided as a courtesy and only if you provide this customer rating information to us. The MachineCore collection tools do collect the information required to calculate an NPS score. We believe that the MachineCore Sentiment Score is actually more insightful than NPS because it is based on a direct analysis of the customers feedback. We often see that customers misreport NPS information (due to filling out the form incorrectly) or feedback collection providers overly emphasize the need to collect positive recommendations, resulting in biased scores.
We believe that the analysis of customer feedback is the only way to actually understand what your business can do to improve.
If you would like to learn more about how NPS is calculated you can click here to go the public wiki page. MachineCore uses the standard definition and calculation in its dashboard.
