In my last Blog post I talk about some things not to do in B2B customer surveys. A big one for me is asking questions where the answer is already known to your company. Business data is a huge area of customer questioning that fits the "Don't Ask" profile. Some examples of survey questions I've seen where business data is asked for:
- "How long have you been a customer?"
- "What region are you located in?"
- "Please select the [Company] products you have purchased or use?"
- "When did you make your last purchase from us?"
When asking this kind of question, the surveyor is taking a "short cut" by having the customer fill in or validate data that exists in the company's databases.
The solution is to create linkage between customer surveys and business data. By doing so, it is possible to avoid asking for business data from your customers and focus can be placed on the actual information that is desired. Fortunately, it's usually fairly easy to get business data for the customers you plan to survey. And, by organizing your survey process to take advantage of business data, your surveys can be shorter yet still effective from an insight perspective while being considerate of customer time.
Four techniques for incorporating business data into customer surveys
Needless to say, linking survey responses to business data is not a new challenge. And, companies often go to great lengths to do it. Larger firms often integrate customer surveys with other systems, so that the information derived from surveys becomes part of the customer record and can then be extracted, aggregated and analyzed along with other customer data via the company's normal reporting mechanism. But, even in this scenario, data gathering via surveys that are not integrated with other systems (because I/T has to do it on a survey by survey basis) still suffer from the linkage challenge. So, companies have evolved four techniques to link business data with customer survey responses.
1. Brute force. In this popular approach (often deployed when using low-end survey tools) a customer list, in a spreadsheet, is developed where business data is included for each individual to be surveyed. An identifier is "coded" to each individual customer in the spreadsheet. Each e-mail to be sent is also coded with the same identifier (hopefully) to match the data in the spreadsheet. Most survey tools allow this kind of coding and will "kick" out resulting survey response data in a spreadsheet file. If coded correctly matching up survey responses with business data later is pretty easy. Reporting can then be done in via spreadsheets or other data manipulation tools. The downside is that this is a time consuming and error prone process. Data has to assembled, coding assigned, used properly, re-matched after the survey, then handed off to a spreadsheet guru to generate the analyses and reports. The time investment often outweighs the cost savings of using a low-end survey tool.
2. The multiple mailing method. This is similar and slightly more sophisticated approach to the brute force method. Instead of coding respondents and then matching responses later, in this method the spreadsheet is filtered in advance by the needed business data. Each filtered subset is then sent the survey. When responses come in, you already know that batch #1 is from "RegionA large customers", Batch#2 is from "Government accounts in California", Etc. Survey responses organized and sent this way are easy to interpret and report on but hard to do subsequent analyses on. Needless to say, this method is also somewhat cumbersome in that several or possibly many e-mailings must be set up and scheduled. And, care must be taken to ensure that no individuals are in multiple subsets (or they'll receive multiple survey invitations).
3. The Customer Panel. In this method business data is stored within the survey tool in a "panel" (a separate database). Surveys are sent to panel members and responses are automatically tagged with the information stored about them in the panel. This is a good approach generally, its only real flaw is that the panel needs to be refreshed or updated periodically so that its business data is relevant. A second potential flaw is that the survey tools with built in panel support are often at the high end of the market or panel support is an extra cost feature of the tool.
4. Pre-load business data into the survey. In this method business data and customer names are loaded into the survey tool, the survey is designed and e-mailed out. When responses are received they are already tagged with business data and responses are filterable based on the business criterion loaded to the survey. Slicing the data becomes fast, easy and fairly painless. If the survey tool has good analytics and reporting tools this approach can save lots of time and provide immediately actionable data for follow up action taking. It also doesn't require panels, integration with CRM or other systems, spreadsheet gurus for data analysis, and its not subject to data matching errors post survey.
I view method#4 as having the best combination of affordability, flexibility, analytics and reporting power, time conservation for customers and time conservation for the surveying company. I am not aware of many survey tools that support method#4. QuestBack is a feedback management system that does.
- "How long have you been a customer?"
- "What region are you located in?"
- "Please select the [Company] products you have purchased or use?"
- "When did you make your last purchase from us?"
When asking this kind of question, the surveyor is taking a "short cut" by having the customer fill in or validate data that exists in the company's databases.
The solution is to create linkage between customer surveys and business data. By doing so, it is possible to avoid asking for business data from your customers and focus can be placed on the actual information that is desired. Fortunately, it's usually fairly easy to get business data for the customers you plan to survey. And, by organizing your survey process to take advantage of business data, your surveys can be shorter yet still effective from an insight perspective while being considerate of customer time.
Four techniques for incorporating business data into customer surveys
Needless to say, linking survey responses to business data is not a new challenge. And, companies often go to great lengths to do it. Larger firms often integrate customer surveys with other systems, so that the information derived from surveys becomes part of the customer record and can then be extracted, aggregated and analyzed along with other customer data via the company's normal reporting mechanism. But, even in this scenario, data gathering via surveys that are not integrated with other systems (because I/T has to do it on a survey by survey basis) still suffer from the linkage challenge. So, companies have evolved four techniques to link business data with customer survey responses.
1. Brute force. In this popular approach (often deployed when using low-end survey tools) a customer list, in a spreadsheet, is developed where business data is included for each individual to be surveyed. An identifier is "coded" to each individual customer in the spreadsheet. Each e-mail to be sent is also coded with the same identifier (hopefully) to match the data in the spreadsheet. Most survey tools allow this kind of coding and will "kick" out resulting survey response data in a spreadsheet file. If coded correctly matching up survey responses with business data later is pretty easy. Reporting can then be done in via spreadsheets or other data manipulation tools. The downside is that this is a time consuming and error prone process. Data has to assembled, coding assigned, used properly, re-matched after the survey, then handed off to a spreadsheet guru to generate the analyses and reports. The time investment often outweighs the cost savings of using a low-end survey tool.
2. The multiple mailing method. This is similar and slightly more sophisticated approach to the brute force method. Instead of coding respondents and then matching responses later, in this method the spreadsheet is filtered in advance by the needed business data. Each filtered subset is then sent the survey. When responses come in, you already know that batch #1 is from "RegionA large customers", Batch#2 is from "Government accounts in California", Etc. Survey responses organized and sent this way are easy to interpret and report on but hard to do subsequent analyses on. Needless to say, this method is also somewhat cumbersome in that several or possibly many e-mailings must be set up and scheduled. And, care must be taken to ensure that no individuals are in multiple subsets (or they'll receive multiple survey invitations).
3. The Customer Panel. In this method business data is stored within the survey tool in a "panel" (a separate database). Surveys are sent to panel members and responses are automatically tagged with the information stored about them in the panel. This is a good approach generally, its only real flaw is that the panel needs to be refreshed or updated periodically so that its business data is relevant. A second potential flaw is that the survey tools with built in panel support are often at the high end of the market or panel support is an extra cost feature of the tool.
4. Pre-load business data into the survey. In this method business data and customer names are loaded into the survey tool, the survey is designed and e-mailed out. When responses are received they are already tagged with business data and responses are filterable based on the business criterion loaded to the survey. Slicing the data becomes fast, easy and fairly painless. If the survey tool has good analytics and reporting tools this approach can save lots of time and provide immediately actionable data for follow up action taking. It also doesn't require panels, integration with CRM or other systems, spreadsheet gurus for data analysis, and its not subject to data matching errors post survey.
I view method#4 as having the best combination of affordability, flexibility, analytics and reporting power, time conservation for customers and time conservation for the surveying company. I am not aware of many survey tools that support method#4. QuestBack is a feedback management system that does.
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