So.… you’re thinking about a Customer Success or customer analytics solution for your team. And you know that the quality of the data you integrate into your Customer Success system will determine the benefits you get from it.
18 months since launch, with dozens of customers and mountains of data later….I’m confident in saying we’ve seen every data quality issue there is. Here’s the three most common “gotchas” we’ve seen. Spend some time on these upfront and you will greatly accelerate your Customer Success system time to value.
Get your data from the “systems of truth”
So much of deploying a Customer Success system is accessing useful customer information as it lives in source systems.
Follow a simple premise:
- your best financial data is in your financial systems
- your best support ticket data is in your support CRM
- your best usage data is in your back-end and/or your clickstream tool
- and so on…
Beware of using copies of data or derivative data that you get from a system other than the source. I’m talking about you, Salesforce CRM. Too often, in order to put things into Salesforce, the data is somehow aggregated or manipulated to the point where it loses its detail and accuracy. Bypass Salesforce and go to the truth.
Pay attention to “data curation”
Data that is machine-generated is great, if only because it’s inherently accurate. Think usage data, for example.
However, a lot of your customer data is input by you, the vendor. This data is “curated” by your employees. Some curated data is very accurate, because there are strong incentives to make it so:
- support ticket data is generally good because status and backlog are closely inspected by the head of support
- revenue data from the finance team is good because it drives financial reporting. Nobody wants to report inaccurate data to the Board or the Auditors
- sales opportunity can be good if there’s strong VP Sales inspection and/or commissions are driven from opportunity records
Conversely, beware of data that must be maintained but for which there is no incentive to do so. The result is a lot of missing data or inaccurate data. Some examples:
- account fields in Salesforce like Tier, Stage, custom fields
- support ticket fields like “reason code”
- anything else where the data isn’t closely inspected by leaders all the time
If you really want a field of data to be accurate, work to create the incentives and inspection that would make it so. Or, forget it and focus on the art of the possible.
Keys must join your data
The elusive “Customer 360” enables a Customer Success Manager to know exactly how a customer is doing. I wrote about the value of it here.
By definition, a Customer 360 is created by joining data from various sources like your Sales CRM, Support CRM, usage data, billing data, survey data, etc. This means you need a “key” to match up those records to a common customer record. A key is unique identifier, often a long string of letters and digits. For example, an Account I.D. in your Salesforce in instance looks like this: “999bb7c9999f27d11d09a5e”
Done well, you would know that a support ticket created by “MegaCorp” is the same company that has a sales opportunity under the Account name “Mega Corporation” in Salesforce CRM. Why? Because they share the Account I.D. in common.
Do you care about the people in your customer base? Then you’ll need to associate those people and their data to their respective companies. One approach is to embed account I.D.’s into your user / contact records. Or, use email addresses to match the URL domain at the company for whom they work.
Nobody’s data is perfect. And never will be. However, you will probably need to invest in data quality in order to maximize the benefits of your investment in the Customer Success team and the tool they use. Pay attention to the three gotchas and you will remove most of the impediments to success.