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Using Predictive Analytics to Smooth the Customer Journey

AvatarMercer Smith Looper | August 22, 2018

In the contact center, the queue is king. We get our data from tags in the queue to learn insights. We get our happiness rankings from surveys sent out after our customer interactions in the queue. We get metrics from the actions that we take in the queue. But, shouldn’t we start to move on? As customers become more and more savvy, and our products become more technical and nuanced, shouldn’t we, too, be trying to level up our game and dethrone the king?

There are two different types of service: proactive and reactive. Reactive, the type of service described above, is what has become the norm for most companies providing support over the internet, whether they be online stores, financial services or B2B products. Conversely, proactive support is all about identifying and resolving customer issues before they become problems. Some simple fixes to increase your proactive support game are:

    • Adding automation into your contact forms to guide customers to the right place
    • Creating more intuitive document and product design
  • Making yourself more available via chat and other modes of service, when the customer needs it

In this article we want to help you go deeper and use path analysis to create a truly remarkable, next-level proactive customer experience. According to ThoughtCo, path analysis is “a statistical analysis used to evaluate causal models by examining the relationships between a dependent variable and two or more independent variables.” But, what’s all of that science-speak mean for customer service? For us, it means figuring out all of the customer data that you have, and pulling it together in one place to analyze what your customer is going to do next. Then, using that knowledge to intercede with amazing support before they even know they need it.


But why go through all the work? Because people love proactive service. 87% of consumers want to be contacted proactively if it means it will save them from having to use support in the future. Beyond the satisfaction that you will give your customers, though, proactive support also provides the following benefits:

    • It increases customer loyalty.
    • It decreases customer contact rates.
    • It increases customer retention.
  • It reduces escalation frequency.

Tracking the customer lifecycle

So, let’s get down to it. In order to start doing any kind of accurate or useful path analysis, you’ll need to get some data. The best place to start is by understanding your customer journey. The four most important actions to track within the customer lifecycle, in terms of setting up proactive support are:

When do they come in contact with your brand?

You’d think that this would be pretty second-nature to track, but many companies don’t keep data about when their customers first learned about their product. This is the start of your customer’s journey, and it’s incredibly important to track and make note of. If you have a product that offers different versions or varieties, you may even want to differentiate between the two in your tracking. For example, when did they first become a member of your debit card services, and when did they then start using you for their credit card as well? While it may seem straightforward, the start date of when they make that switch, if you have one to make, can provide a good trigger for any time-based proactive support that you will do moving forward.

When do they buy?

The time between first signing up and buying, as well as what prompts the customer to buy, is integral in the customer experience. It shows when they have found enough value in your app to actual exchange money for goods. Tracking this both on an individual customer basis, and for your overall customer base can be incredibly useful for proactive support moving forward. Not only is paying for your product an excellent differentiator for service-level offerings, but it can also be a great trigger for any time-based flows.

What products do they view in your store?

Know what pages and parts of your product customers have used and which ones they haven’t. What actions do they take? Are there pages that they avoid? Knowing this will help you be even better at targeting on a specific individual level, but will also let you know if there are overarching areas of your product that people just do not use, or do not understand. You can use this data for individualized onboarding flows, to create better documentation, to improve parts of your app, or for email marketing campaigns.

When do some customers leave?

No one wants customers to leave, right? You probably want to do everything in your power to stop it. Because of that, this may be the most important and valuable information that you can get. When you are able to understand why an individual customer has left, you can reach out to them reactively to do damage control. But, when you understand the patterns behind why every customer that’s left has left, you can proactively attack those issues before they crop up. This is specifically helpful for targeting them with messaging in your app.

Tracking the customer service lifecycle

Just as important as what they are doing within your product is what actions a customer takes when contacting your agents for help. There are three pieces of data that can be pulled from your helpdesk software and be useful in terms of customer retention and proactive support. Luckily for you, you don’t even really need any fancy analytics tools to get this information, though matching it up with the data above can prove tricky without them.

When do they reach out for help?

Knowing when your customers reach out to support is incredible important when considering when and how to proactively support them. For this, match up your customer lifecycle data from above with your customer data from the inbox, and start to pinpoint when they first reach out to ask questions. Is it within the first day? Is it before they even become a customer? Try to navigate and identify that first point is, and create a trend analysis that you can later use to trigger your targeting campaigns of workflows.

What do they reach out about?

For this, tagging is going to be your best friend. Take a deep dive into the aggregated data that you have about what your customers are reaching out about. Then, match that up with the timelines for when they are reaching out. For example, are more people reaching out about billing within the first few weeks of their trial, and then only reaching out about shipping and handling after a few months of being a customer? These layers start to add up, and give you some additional definition to your support.

Are there more active time periods?

Are there times in their lifecycle when your customers are more active? Perhaps you have a somewhat complicated part of your application, or maybe they just need a bit more hand-holding when they just get started? For example, billing and invoices almost always create more questions. Make note of these trends and what your customers are reaching out about. Chances are that you can create an email campaign or an in-product flow that will help to deflect some of these issues.

Tracking user cohorts

The last layer of this delicious data cake is user cohorts. You know what people are doing in/with your product. You know how they are interacting with your support team about it. The last thing that you need to know is how they are grouped together, and what stories that data might tell for you.

No matter whether you are a SaaS product, you will likely have a broad swath of customers. A few different ways that you can determine the type of customer that you are dealing with (or the type of customer that they potentially represent for your company) are: by the amount of employees they have or by the amount of money that they are paying you. The main reason that identifying cohorts is so important is: it’s possible that you do not want to pay to support certain groups of individuals, in that case proactive support (and ultimately self-service) are excellent ways to lower your cost while still offering an excellent experience.

Similarly, different types of customers may need different things. The documentation and experiences that you are providing for your enterprise customers should be different from the ones you’re sending to people who have just signed up.

What Now?

So, now that we have all that data and we have it all mapped out and overlapped to have an even better contextual understanding, what do we do with it? Create excellent, intuitive proactive support experiences for our customers. The two that we’ll be focusing on in this article are through email, or via onboarding within your website/application.

Customized Email Campaigns

Customized email campaigns are great because you can set them and forget them and the only time that they need to be updated is if something changes. They also are significantly less costly than in-app messaging and, because of that, can be targeted a little bit more broadly. They are, however, opened less frequently than in-app messaging.

The main benefit of customized email campaigns for proactive support, beyond the comparison to in-app messaging, is that they can be spread out over the course of a significantly longer period of time. Here are some cases when it might be good to use the customer journey to create some proactive email campaigns for your customer base:

  • Create a newsletter campaign that triggers after your customers sign up for a new plan or purchase your product. When a customer signs up for a new plan or product, sometimes they don’t even know what they are getting—they just want to get a full, frictionless experience. What that means is that, sometimes, you’ll end up with customers who are paying you a lot of money, but not using all of the features that they have access to—primarily because they don’t know they exist. It’s up to you to tell them! Use an email marketing campaign to send information to them about the new features for the product that they upgraded to around the same time that they’d usually start using them. The points of data to use for this are: when do they buy, what do they do in your app, what do they reach out about.
  • Send them a product guide when they first sign-up. Use the data from your support helpdesk to analyze what the most frequent requests and questions are that occur immediately after sign-up, and create the campaigns around those specific issues. Try to match a cadence to the emails that matches when your customers would normally reach out. For example, if they reach out about account management two weeks after signing up, target an email about it a week and a half after their sign-up date. The points of data that this flow uses are: when did they buy, what do they reach out about, when do they reach out about it?

Targeting onboarding flows

Onboarding and user experience have become hot topics over the course of the past few years, and it’s no surprise. Why not try to make sure that your customers are able to engage with your brand in the best way possible? Triggering messaging on your website, or even interactive guides are an amazing way to help get your message to your customer right when they need to see it, in the context of what you’re trying to talk to them about. Here are some ideas for ways you can do it in your app:

  • Walk them through the dark spots in your product. Using your data of what people do and do not usually do in your application, start to use in-app messages to shed some light on those lesser-seen places. For example, maybe you get a ton of email asking about whether or not you offer a certain feature, and you do offer it, it just lives on a page in your site that is a bit difficult to find. You can direct those users to the things they need to see using in-app messaging. Make this even more potent by taking data about when people are usually emailing in asking about these features, and use that timestamp to trigger it for each individual user.
  • Trigger a message to prevent churn. Given that you now know what some of the things that people do within your product right before they churn are, start to use your knowledge for good! Create a proactive in-app message or workflow that triggers immediately after an action has been taken that usually leads to churn. For example, if you know that someone removing all of their users except for an admin is a sign that they might churn, and you have data about what reasons those customers are usually churning for, you can use your in-app message to offer assistance or help prior to the actual loss of the customer.

Put it into practice!

While none of us have magical powers and are truly able to know exactly what a customer is thinking every step of the way, using a data-driven approach to proactive customer support can make your team seem like wizards. These recommendations both for data points to collect and how to use them can help impact how your brand and company’s support are seen, as well as making the first contact with your customers one of delight instead of frustration and disappointment