Customer Experience

Throughout, you’ll hear from Brandwatch Research Consultant Lauren Lachowsky. She’s been at Brandwatch for two years and coordinates research projects for some of our biggest clients, particularly in the pharmaceutical sector. She’s got excellent experience dealing with companies that fall across the spectrum of DCI maturity, and has plenty of wisdom to share about using insights to create better customer experiences.

So, without further ado, let’s get into the basics for approaching customer experience with DCI.

How to approach CX

Focus and timing

Often when clients begin working with social data, they want to explore. While much can be learned from broad-reaching research studies, it’s when business questions are really nailed down and projects have a sharper focus that the most value is generated.

“When you move past that exploratory phase and clients know who they’re trying to reach and what they want to do, then you switch away from that wide scope to being agile and addressing specific business questions like how to improve key areas of the customer experience,” Lauren explains.

Timing is also key in approaching CX with social data.

As well as being more focused with the scope of the research, it’s also important not to limit it to a particular time period. One-off reports might seem like they can deliver the most value when you’ve got a specific goal in mind, but so much can be missed.

Giving an example from the pharmaceutical world, Lauren says: “If you had a huge focus group or a big quant survey that had already gone out but the drug failed, you can’t take that back. You just lost a lot of research.”

She continues: “With social data, not only is it cheap and fast to gather and analyze, you can also work in real time and be really agile. You can just pivot based on anything that happens – that’s why it’s a good choice for ongoing consumer research.”

It’s also worth pointing out here that digital consumer intelligence is about more than just social data. As you’ll read about more in this guide, social data can be combined with and analyzed alongside all kinds of customer feedback sources, from call logs through to support tickets.

Start with the audience

Lauren gives another example of a client she’s worked closely with for a few years. More recently, they’ve made the decision to proactively start by examining their target audience as opposed to looking at topics of interest. It’s a great example of how insights themselves can become more customer-centric. This research can ladder up to business decisions that are closely aligned to what consumers actually want.

This approach has also given Lauren a real window into nuance within segments of the customer journey. By analyzing the audience first, the many priorities and concerns that emerge from the data show that there is often no uniform journey from awareness through to purchase and advocacy. This is particularly true in industries like pharma and healthcare.

Digital experience

Digital consumer intelligence can help companies learn about customer experiences that occur both online and ‘IRL.’ But the increased importance of digital experiences since the outbreak of Covid-19 has meant Lauren and her clients have paid far more attention to how people are discussing their online experiences.

“Researching what influences someone to pick something up off a shelf in a store just isn’t really what’s happening right now,” she says. “The digital experience is important to understand because in some cases it’s all we’ve got.”

In practice: How-to guides for common use cases

How to create audience panels you can access 24/7

As Lauren says, starting with a key audience can be a great way to explore customer experience data. And, using Social Panels, this can be a really easy process.

Social Panels is a feature within Brandwatch’s flagship digital consumer intelligence platform, Consumer Research. It allows users to:

• Track what a group of consumers are saying in real time
• Benchmark one groups of people against other groups, or even the rest of the world
• Monitor historical conversation from a group to see how it has developed or changed over time

When studying customer experience, monitoring how key groups of consumers are describing their interactions with your brand is an easy way to quickly gather insights and react to the information to make the experience smoother.

To create a panel using Social Panels, there are three simple steps:

Step 1: You can find panelists using Brandwatch’s flexible search based on demographics, interests, or profession, or by uploading a list of individuals from outside the platform.

Here’s a simple example of how you might search for an audience. Here, we’re looking for fitness enthusiasts in the UK.

Social Panels immediately returns a list of authors (over 52k in this case) as well as some additional information about them.

Step 2: Next, you attach Social Panels to a new or existing query to start collecting relevant conversation.

For example, we could add our UK fitness enthusiasts panel to a query relating to the Pure Gym franchise.

You can look at all mentions coming from UK fitness enthusiasts (not just those relating to Pure Gym) and identify trends within conversation by attaching the Social Panel to a blank query. Depending on the goal of your research, this could be a great way to identify opportunities like new fitness trends that don’t mention your brand.

Step 3: Finally, you can begin to explore what these individuals think, whether it be about specific brands, industries, or topics – there are no restraints. Using Brandwatch’s intuitive analysis capabilities like sentiment analysis, or our AI assistant Iris, it’s never been easier to spot trends and insights in your panel data.

To continue with our fitness example, we looked at how UK fitness enthusiasts were discussing Pure Gym reopening last year after the initial lockdown. A quick search for “reopening” or “opening” within these conversations meant we were able to identify a number of different questions and areas for improvement that would improve customers’ experiences as they returned to the gym.

We’ve shown you how to get customer experience insights from a specific audience, but every customer is important. In the coming sections, we’ll show you how to cast a wider net and to deal with customer experience feedback at scale.

How to create a holistic view of customer experience data

Social posts are a vital stream of customer experience feedback, but it’s important to combine all your sources of customer input to get a full understanding and to prioritize accordingly.

With Brandwatch’s Data Upload API, you can now upload any text-based first party data to monitor all your consumer interactions in one place, whether they happen on social media, in surveys, or over the phone.

Think about all the ways your business receives feedback from customers. It might come in the form of support tickets, tweets, survey responses, phone calls, or text chat. All of these sources, provided they are text based, can be uploaded and compared side-by-side in Brandwatch.

Not only can all this data be viewed in the same place, it can also be analyzed with all of Brandwatch’s cutting edge tools and visualizations.

Let’s get into how you might use those tools to categorize and prioritize feedback,

How to categorize and prioritize customer feedback, and react quickly

You have successfully uploaded all your relevant customer feedback into Brandwatch Consumer Research. Now it’s time to make sense of it.

Sentiment and emotion analysis

Brandwatch’s sentiment analysis can be applied to all your data sources, meaning you can instantly separate positive, neutral, and negative feedback.

You might want to start by analyzing your negative feedback to find out what’s driving it. Topic components in Brandwatch Consumer Research make it incredibly easy to find the most common words and phrases that are driving a conversation, so by applying sentiment filters the most common complaints can be surfaced quickly and easily.

Categorizing aspects of your offering or customer journey

Brandwatch’s categorization capabilities allow users to sort their data quickly and easily. For example, someone might use categories to segment their data by common complaint topics. An airline might create classifiers that sorts their incoming feedback into buckets like ‘Delays’, ‘Lost luggage’, ‘Leg room’, etc by using simple keyword searches and creating categories based on them.

But some topics are harder to write keyword-based strings for. For example, ‘lost luggage’ is a fairly simple issue to search for with keywords. But what if you were interested in capturing feedback from people in the consideration phase of the buyer journey? There are so many different ways someone might express this in writing that a simple search probably won’t return the best results. Here’s where Brandwatch’s Custom Classifiers really come into their own. You can train them to recognize mentions that fit into a particular category simply by dragging and dropping relevant mentions into that category.