How to empower your customer success team to improve customer experience
In this podcast episode, join special guest Philipp Wolf, Founder of Custify, and our host Claudiu Murariu, CEO and Co-Founder of InnerTrends, as they dive into proactive and data-driven customer success.
Here’s what they talked about:
- What is Proactive Customer Success?
- Defining the customer in a B2B environment
- What data your customer success team needs
- Setting yourself up for success with implementation
- Avoiding disaster during implementation
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Proactive customer success
[P]: The concept of proactive customer success means that you’re actively identifying where your customer may run into problems based on your data, and reaching out to your customer to solve these problems together. This is to be done before they run into the problem themselves and reach out to your support department.
You’re not waiting for the problem to accelerate, or until the problem makes your customer want to leave or not have a proper onboarding experience with your product. Instead, you’re getting in touch with the customer as soon as you’ve identified the problem based on the data, and are working together to fix it.
In an ideal scenario you can fix the problem without any involvement of the customer, but more often than not you’ll need the customer to work with you to resolve the issue.
For example, many times throughout onboarding there isn’t anything that you can do to fix it; it’s not you or your product’s fault. It’s the customer’s turn to do something with the product. The customer might not understand what it is they need to do to get there, be it because your product is complex, maybe your product isn’t mature enough to have an entire onboarding flow or guided onboarding. Regardless of the cause, the customer doesn’t know what steps to take next and you’re helping them actively get there.
I like to describe proactive customer success as actively getting in touch with the customer with the right message, at the right time.
If you’re reaching out to the customer regarding the problem, you can be straightforward and say: “Hey, I recognize that you’ve taken steps a, b, and c, but to experience the full power of our product, you would also need to complete step d; maybe we can do it together.”
You could also send an email saying: “This is a knowledge article that we have published on our knowledge base. Why don’t you go through it, it’ll show you how to set this specific part, or whatever is missing.”
[C]: It means your focus is on solutions to problems you see the customer is having without the customer actually writing to you.
When the customer writes to you “I know I have a problem, help me fix it.” The actions you take moving forward are reactive. But when you see that the customer has a problem in the data, you can be proactive; you can investigate and gather as much data as possible about the problem.
If you can fix it automatically in the product, you can write to the customer and say, “Hey, the problem was fixed.” Or if you have a solution to offer, you communicate the problem: “Hey, this is what needs to happen.” And then the customer is on their own again, back on their path towards success.
Defining the customer in a B2B environment
[P]: It’s not always easy to label or define what a “customer” is. But in the majority of cases, it’s simple.
Let’s say I’m selling to InnerTrends. InnerTrends is the company, InnerTrends is the account. And then there’s Claudiu, and probably five other users within this account who can use the application. This default module is representative of two-thirds of cases we see.
In typical customer success approaches, the definition of the customer is the account (InnerTrends) and the definition of the user is Claudiu.
You can also measure what we call “health scores” in Customer Success terms; in the data world you call them KPIs. The KPIs you measure have been defined together with your product team or management as what a successful customer looks like: they have to log in twice per week, they have to use a certain feature once a week, they have to publish this article, etc.
Whatever is necessary to get to the value of your product or service. Those are the KPIs around it, and then you measure: how well does this client compete against these KPIs? How much value does the client get?
In the majority of cases, these health scores are evaluated on the company or account level – per our example, the InnerTrends level. It doesn’t matter who performs actions there (Claudiu or others); as long as the benefit and value is there for the account overall, it’s good.
Then we have B2B and B2C environments, which is where it becomes a bit more unclear. A practical example would be when you have very small businesses as your customers: hair salons, restaurants, etc. where you typically have one, maybe two users, but they don’t really use the product that much. They are B2Bs, but they act like B2Cs. So who is the customer? Is it the pizzeria, or is it Luigi, the primary user of your product?
And then it can become even more complicated in the case of a transactional business model. Take Uber, for example – you enter the car, they drive, you pay. Uber still wants you to have a lot of rides, so it’s kind of a subscription… but it’s not a subscription that I pay per month, Uber just wants you to come back all of the time.
The health score of our customers that are based on a transactional-based business model is typically tied to the transaction itself. Going back to the Uber example, the likelihood of a customer returning is very much dependent on the customer experience – if it’s a great experience, they’ll likely use Uber again in the future.
So the customer has now become clear: it’s Philipp, who entered the car. But measuring the success of the product is very much tied to that transaction.
[C]: What’s very important for everyone is to have a clear definition of a customer within your company. It may be simple, it may be more complex, but you need to have it.
Customer success’ data needs
[P]: The product data is the most important source of information for measuring the health scores, or KPIs. For example, we can’t tell if someone has been successful with the onboarding or not without the product data, so it really is essential.
But there are other factors that tie into the overall health of the customer and are relevant to customer success:
- Support data. What’s happening in support is an important piece of the puzzle because even though you have proactive customer success, the customer will most likely still have support and reactive questions that reach your organization.
- Customer Relationship Management (CRM) Data. This is, of course, dependent upon your business model and how you define your customer success team. But as soon as you have a sales team involved in your customer acquisition and it’s not a self-serve product, you typically have information in the CRM gathered by the sales team throughout conversations before they close the deal.
- Billing and Accounting Data. When is the renewal date? Were there any problems with payments, if you offer a monthly subscription? Do you frequently run into issues with charging their credit card? This data is an essential element for the customer success team.
Depending on your organization, the data will lie in different systems. Some customers will have them very organized, others will have them in four different systems that are not connected.
The hardest case is when you have no unique customer identifier and you have some data in your billing tool, you have your own product data, and neither of these are connected to your CRM.
[C]: Product data, billing data, CRM and support data – everything needs to be available to customer success because it’s an ecosystem.
It comes back to the initial definition of a customer. It’s critical because if you don’t have it well defined, systems won’t communicate between them. If you have it well defined, and every system understands the definition, it will be very easy to connect them.
Everyone – the product, support, and marketing teams – all want the same thing. You want to get the customer to success. And customer success is another big piece of the puzzle that tries to influence that.
Examples of tools that make it easier for people to bring over data
[P]: Data warehouses are a great starting point; the minority of companies that we work with have the data in a data warehouse where all of the data comes together and you pick out what you want, or can distribute the data to other tools. Most SaaS companies develop their data warehouse at a late stage, which is okay; if you’re in your scaling or growth phase you don’t necessarily need to have that.
A good foundation can be built from the early stages of growth within the company if someone makes the decision to get your data in order; it will help you so much later on.
You can do this by having your own database that is already tracking all of the major events that happen throughout the product journey, and is connected to the billing and CRM data. You can interconnect all of these elements in advance with an identifier that you’ve already marked in the database and the CRM.
So I wouldn’t say there’s “the” tool, but rather it’s about how the data is organized within those tools; it can be stored and used later on, and eventually be learned from.
[C]: That’s actually something we covered in a previous episode of the The Data-Led Podcast called The Modern Data Stack for Growth.
For companies that are just starting out in the growth stage, and it’s not easy for them to set up a data warehouse because they don’t have the resources or the people to do that, we recommend developing a clear and detailed tracking plan that’s shared across the organization, and having a customer journey metrics map so that the KPIs are shared across your organization.
So that means whatever tool you implement, you know what data is going in, what metrics and how do I have them aligned with the other tools, and then look for all of the tools that make that data back available to you either through data or through APIs.
What you want is to not have tools that get you locked in and allow you to communicate with other tools in the future. So whenever you want to put a new tool in the modern data stack, it will be able to communicate easily with the other tools.
A recipe for disaster in a customer success platform implementation
[P]: No commitment from top-level management: The customer success platform is not a business priority.
Customer success platforms are never an easy implementation – you have to invest time and effort into making them work well because they are based on the data.
If your data is well organized, it can come together very quickly. If your data is all over the place or you don’t have any tracking whatsoever, you need to define what you want to track and go from there.
The customer success team will need to work with the product team at some point to get to the data. And even if the data is there, they’ll still need someone from the product team to send it to the customer success platform, or make it available for the API, etc.
So you need the sources in a technical team – if you don’t have this, and it’s not a business priority, then they’ll work on something else and develop features for which you don’t have the data.
- The Chicken vs. The Egg: We run into this a lot towards the beginning of implementation – you can’t go into it saying “I don’t want to invest the time into building the customer success platform before you’ve shown me the first results.”It can’t work like that; you have to invest the time upfront to track the major events and have a primary tracking system in place. It’s the Pareto Principle: 20% of your events will probably give you an 80% insight into the health of the customer.
- Data Inconsistency/No data: Another major problem we see is what we already talked about – the data is in multiple systems, but it’s not interconnectable. So I have something in the system, but I have no idea how to find this customer from my billing system in the CRM. I can search for the name, but then some of the data isn’t there.
- Wrong Data: Wrong or bad data will lead you to wrong conclusions. When you’re trying to judge and make decisions based on the data, which is incorrect, you won’t be able to make the best decisions possible.
Conclusions:
- Organize your data well from the beginning – it will help you later with customer success and beyond.
- Have a clear definition of what a customer is within your company, and how you define customer success – then tie that to revenue and retention.
- Understand that customer success platforms only work well if they get data from multiple sources: support, billing, CRM and product!
- Having multiple sources to get data from requires commitment from top-level management.
- Aim for proactive customer support, rather than reactive – your customers will thank you for it.
Have any additional questions about the role data plays in proactive customer success, or any other topics you would like to hear covered on The Data-Led Podcast? Comment below! We can’t wait to hear from you.
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