Why customer data is the enemy of CX
Identifying what customers expect
Modern consumers expect a lot more from businesses than they did 10 years ago. When customers contact customer service, they expect businesses to properly identify who they are, and to immediately know what issues they’re having.
This isn’t mind reading. It’s proper use of customer data. Applied properly and integrated with supporting systems that facilitate the expected level of customer experience.
And customers expect this level of highly personalized experiences across all channels.
Convenience is king
The customer of today and tomorrow wants convenience. They want businesses to come to where they are.
If businesses want to provide these experiences, they need to know where their customers are, and where they spend time.
And the only way they can do that is by leveraging customer data.
That’s the first step. So far so good.
Identifying who customers are
Once you, as a business, know where your customers are, the next thing you need to know is – who they are.
And one of the key problems that businesses try to solve is being able to properly identify who the customer is when they interact with them.
Doing this is challenging, because customers can contact businesses from any address belonging to any channel. And it’s not uncommon for the business to be missing that specific piece of data.
Being able to identify customers is a very cumbersome process with a lot of pieces of data. It’s really a puzzle.
For example, when a customer calls their bank or telco provider, they might need their ID number or other official identifier. And if they send an email, then there’s a third set of functions that come into play towards identifying the customer.
So, this is the first problem. And I say “problem”, because of how most systems are set up to identify customers.
Identity resolution systems use a set of unified data to attempt predicting who the customer contacting them is.
And I say “attempt predicting”, because all modern CRM systems more or less rely on static customer data profiles.
The problem with customer data
This is the wrong approach if the goal is to provide good customer experience. That’s because data shouldn’t be viewed in a binary manner, i.e. you either have data or you don’t.
Instead, data profiles should be dynamic, constantly developed and updated by collecting data over time.
By failing to collect data over time, businesses won’t be able to enrich their using touchpoints across the customer journey.
So, the first step towards providing better CX is building dynamic customer profiles that get updated and enriched over time across all new interactions.
But this is just the first step towards being able to accurately identify who your customers are using customer data.
After being able to accurately identify customers, your next challenge is being able to accurately predict why they’re reaching out to you.
There’s two ways of going about this – you can be either reactive or proactive.
- Reactive: The customer tells you what they want
- Proactive: Leveraging customer data to predict what the customer wants
Reactive customer service
There are a few problems with the reactive approach.
The most common problem happens when an agent is talking to a customer and can’t solve their problem. In these cases, the conversation needs to be transferred to another agent who is better equipped to solve their issue.
And this is a big problem for most businesses – especially when dealing with customer service over the phone. The biggest problem with this is that the transfer happens – without transferring context.
When an agent transfers a complex customer query to a specialized customer service representative without transferring the context, the customer needs to repeat themselves. And customers don’t like that. It’s inconvenient and inefficient.
Proactive customer service
Being proactive requires data.
And this data can come from anywhere and any point throughout the customer journey.
For example, a bank client may be contacting customer service over a valid transaction being flagged as suspicious. Or a telecom customer could be complaining about a high phone bill or service interruption in their area. And a retail customer could be reaching out over a late delivery or an item arriving broken.
Businesses that want to be proactive need to leverage consumer and behavioral data if they want to be able to predict with any amount of accuracy what customers want.
We had an ISP client who wanted to transform their call contact center into a chat contact center. This was in response to internet usage soaring throughout 2020. And when internet use rises, so do problems associated with internet usage.
The client expected that customers would find the chat customer center to be more convenient. But what they didn’t expect was a sharp rise in the volume of inbound chats.
The ISP didn’t predict that increased convenience would result in increased inbound traffic.
This meant that they didn’t predict that their users were having internet connectivity issues and proactively offering them solutions, or filtering them according to their needs, or even managing expectations (i.e., displaying a status message over the chatbot rather than have agents message users) for high-volume queries.
This example just illustrates that a fundamental component of providing good CX is the ability to leverage customer data to predict why customers are contacting you to offer proactive customer service.
The above are just two crucial elements of the most important component of CX – personalization.
Leveraging customer data for personalization
Once you can reliably identify who your customers are and predict what they want, the next challenge is using customer data for personalization.
From a customer service perspective, personalization comprises knowing the customer, knowing their history and connecting them to the agent that the customer has historically had success dealing with.
This results in connecting a customer with an agent they understand and collaborate well with, judging by historical outcomes for the customer and their satisfaction with the interaction.
And again, this relies on the ability to transfer context. This is because context can be based on likeliness.
For example, a customer visiting an online retailer an completing a transaction might get an error on a page. After this, they’re likely to contact customer service, and that error code can provide context based on likeliness.
The other way to transfer context is from agent to agent. And any way you look at it, it’s all data.
When customer data becomes the enemy of CX
The basic problems regarding customer data are that customer-facing roles don’t always have access to all of a customer’s data, or how the data is stitched together – if at all.
Compounding this, each team has a set of tools that they use. These tools:
- May or may not be integrated
- The data models may or may not be compatible across tools
- Could be missing data people are hiding from each other
And if you look at the individual tools, and what can be integrated – most of what is needed to provide CX, just isn’t there.
There are two main problems that contribute to this state of things.
First, there are problems with how businesses are structured from an organizational perspective. There are a bunch of silos. Marketing, sales, customer service, etc. all these departments are wired into their respective technology stack – none of which collaborate. This results in incomplete customer data profiles across the company.
In this sort of function-based organizational structure, each function has a set of tools. The problem is that none of these tools work together across functions. So, any changes that an organization undertakes with the aim of improving CX requires coordination across all these functions. This doesn’t happen quickly.
Organizational challenges that make customer data the enemy of CX
The problem with how most businesses are organized is that there isn’t one single department that can act as the single source of truth. This is regardless of whether a business is a B2B or B2C.
Solving the problem of data departmentalization through collaborative data sharing across functions is key.
Changing this requires the involvement of managers and team to come together. Then, they need to all pull in the same direction to efficiently solve these problems.
This requires departments to align and adopt changes across all their functions. And this just takes too much time from the perspective of customer experience demands.
The adaption of enterprises towards consumer demands isn’t happening fast enough. The existing hierarchical organizational models will never improve CX. Even though this is exactly what customers are demanding.
This all brings us back to the issue of convenience, which enterprises are proving too slow to deliver on. And this will lead to decreased loyalty and increased customer churn.
The best way to address this is through composable business organization.
The composable enterprise stands as an alternative to functional organizations. In them, you have a group of people in charge of a business area instead of an internal-facing function end-to-end.
In reality, this is a way to quickly tackle, solve, and optimize for customer outcomes, since everything you need for this is available in the composable unit. That, and the ability to make decisions that drive change.
How technical solutions turn customer data into the enemy of CX
Next, there are technical problems. Most CX solutions are designed to address only one pillar of the problem.
For example, a solution implemented by a marketing team that collects customer data off the back of campaigns may not work with a solution used by a sales development team. When a customer reaches out to sales development in response to a marketing campaign, the sales development representatives won’t be able to know why the prospective customer is contacting them. This is bad CX.
If these CX solutions don’t contribute to data sharing, then you won’t have accurate data since there’s no sequential collaboration. As a result, you’ll have broken customer experiences. No department in the chain knows what comes next and there’s no reliable data to make a decision. CX flow is broken, departments don’t know what comes next.
And if you don’t have complete, accurate data, then it’s hard to use any of it well. Else, you could make the embarrassing mistake of sending product offers to someone who has an open ticket with your customer service department – complaining about the same product you just offered them.
This isn’t just about having a single source of truth. It’s about every department looking after their section of the data puzzle, collaborating, populating and enriching the data. When that starts happening, then they can have the data live on one, two or three platforms where it’s accessible for everyone in the broader organization to use in all contexts.
This is because most data can’t be used in contexts that lead to improved CX.
How customer data and CX can go from enemies to allies
The key to getting customer data to work in service of CX is by applying new organizational models to become more focused on customers instead of internally.
Most of the existing functional organizations in enterprises struggling with CX simply are not organized to serve customers well, but are rather focused internally. This is primarily because the existing organizations inhibit CX.
Then there’s the technological approach to consider. Technology needs to work in service of new organizational setups to facilitate execution.
Most businesses seem to believe that simply buying tools advertised to solve their problems will indeed solve their problems. And the problem with most tools is that they don’t get integrated across functions. It’s like an archipelago of islands without any bridges or boats connecting them.
Businesses need to understand that the organization and people are what facilitate data driven CX. Technology should just serve to support the new way of working.
To meet customer demands for hyper-personalization, enterprises will have to adopt new organizational models and choose the right tools to support them in working with customers better.
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