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What Your Customer’s Tweets Can Teach Your Brand About CX Strategy

Megan Wells

Brands know the value of a positive customer review. After all, 90% of people read reviews before visiting a business. Brands will often share glowing customer comments from their official handles - mostly under benign hashtags like #CustomerExperience, #CX, or #CustomerSatisfaction.

 

However if you browse the hashtags #BadCustomerService, #BadCustomerExperience, #BadService and others on Twitter, you'll see a laundry list of customers reaching out to customer support with cries for help. Complaints of delayed flights, lost packages, failed user experiences, and plenty of botched food orders aren’t simply isolated calls for immediate attention - they hold valuable lessons for any company looking to improve their CX strategy.

 

To help you develop the customer experience strategy your brand deserves (and avoid a PR disaster) learn how social media signals and continuous intelligence technology can turn the most common Twitter complaints into CX gold.

Why use social media for customer experience?

Social media is a key customer service channel for brands and an important part of their customer experience strategy. Especially as a Facebook Messaging survey by Nielsen found 64% of people would prefer to message a business rather than call a business.            

                                                                                                                                    

People love social media as a way to reach out quickly and easily on the platforms they already use (and avoid the dreaded customer service hotline). A major brand may get mentions from over 2000 people per day on social media, many of those customer service-related. It shouldn’t surprise you to learn then that companies may spend anywhere from 12 - 23% of their marketing budget on social media, and those numbers are only going to keep rising. 

 

Resources to meet customers where they’re at is one thing, but creating an organized response that aligns with a broader customer experience strategy is another thing entirely.

Challenges in using social media for CX

  • Real-time response expected: When using social media as a customer service outlet, people want a response and they want it fast. 79% of customers expect a response to their social media posts within 24 hours. 59% of brand replies to user Tweets occur within 15 minutes. Despite this, many customer complaints (like the one below) are going unreplied or unresolved. 

 

 

  • Disjointed support experience: Marketing teams spend a ton of resources on social media listening and social intelligence tools, but they don’t always work together with other contact channels, leaving the customer disappointed. Large companies may have hundreds of social handles that include regional accounts and individual location accounts, further complicating the process.
  • Lack of front-line feedback integration: From a business perspective, when complaints or praise are siloed in your social media channel of choice, the brand misses out on opportunities not only to save that customer, but to learn exactly where they’re going wrong to ensure it doesn’t happen again.

 

Social media usage will only continue to grow. Forrester predicts digital customer service interactions will rise 40% in 2021. To keep up with this rapid pace of growth, your business will need to work smarter, not harder. Information across channels and departments doesn’t have to be difficult to synthesize - in fact, it could be where your competitors are headed next.

 

So, let’s imagine for a minute - how might you use social media data and a continuous intelligence tool to optimize customer experience management strategies?

Use Case #1: Delivery & logistics

UPS delivered an average of 21.1 million packages per day in Q2 2020. Meanwhile the postal service, Amazon, and independent retailers process millions more. While the volume of deliveries is truly a modern wonder, there are bound to be some logistical errors.

 

Twitter tends to be the place to detail items not delivered, delivered late, or lost. In many cases, Twitter also chronicles the customer’s stress and frustration in trying - and failing - to resolve these problems via customer service channels. 

 

 

Angry tweets like these are  a chance for brands to make things right. Many times, this can lead to an even better position for the brand before the problem occurred - 47% of US consumers have a more favorable view of brands that respond to customer questions or complaints on social media. Beyond that though, it also offers brands a chance to create an even better customer experience moving forward.

 

With an analytics tool that combines data from multiple sources, you can layer tweet sentiment over data from multiple sources - for example from your product or logistics department - and deduce any correlations in seconds.

 

For example, you might start by tracking patterns in time of day, week, month or year to see when the most mistakes occur, then do some more data visualizations to find out why.  But you could also look for trends in anything that might impact the delivery process, including:

 

  • Sent-from and sent-to locations
  • Shipping method used
  • Purchased via third party vs direct
  • Device used
  • First time purchaser or not 
  • Seasonality 
  • Weather patterns
  • Current events   

 

You can also answer questions like: 

 

  • If a particular user often has problems, why? Is there a piece of the shipping process that’s confusing? Can messaging be tweaked or more assistance provided to this and other customers?  

 

With some legacy BI tools, the process of preparing and querying large datasets to answer these questions has been time consuming and expensive. What if there was a way to bring all the data sources together under one roof? If the company could have a single, complete, and up-to-the-minute view of every customer journey, they could already see ALL the touch points where the customer tried to get help and ideally:

 

  • Try to intercept that customer before they churn 
  • Fix the broken touchpoints and try to eliminate them 
  • Look at churn patterns against these types of bad experiences and try to retroactively learn where they need to be working to improve CX 

 

With a continuous intelligence tool, time-to-insight is much faster. Querying your data is easy, and you don’t need to spend a lot of time prepping it. With faster time to insight and a super flexible tool, you can get more heads on the problem and get to solutions faster. 

Use Case #2 - Customer service with airlines

Airline companies receive frequent tweets related to late departures, unfriendly gate agents, and other customer service fails. The industry consistently struggles with poor CX ratings, though some airlines are working hard to change their image. 

 

Most major airlines have a social media team working to monitor and engage with customer comments. They find ways to surprise and delight customers who Tweet and message them on Facebook. Or, they may offer upgrades and discounts to soften the blow of a bad experience - United recently made headlines for gifting a case of White Claw to a customer who asked for it in a tweet.

 

But what if they could go further?

 

 

Tweets like these present an opportunity to do more, both from a customer experience and an operations point of view. If airlines could take a tweet and quickly create a full user profile and customer journey map,  suddenly your options become much more impactful. You can use data to:

 

  • Analyze customer profile for common flight destinations and frequency of trips, then create a super-personalized offer (whether as a surprise and delight or to fix a mistake) 
  • See which airports get the most complaints and about what
  • Observe what times of the day/week/month experience the most delays
  • Are there patterns in employees who receive complaints or kudos?
  • Do peak complaint times correlate with break times, shift changes, or other significant events?
  • Proactively provide useful updates to repeat customers, such as wait times for the most stressful parts of the journey, like parking and check-in.
  • Anticipate needs, for example if a customer tweets that they’re going to miss their connection because of a delay, look up the customer’s flight and help them find rebooking options while in the air

 

When you have a complete view of customer interactions across all platforms and touch points, you can begin to proactively solve problems, not just react. At that point, you have time to focus on bigger fish like CX strategy, customer loyalty, and pushing ahead of your competitors. 

Use Case #3 - Contact centers across all industries

Let’s not ignore the glaring common thread between customer complaints across industries - the customer service center. Twitter is littered with comments like these from frustrated customers:  

 

  • “The automated response said I would get a call and I didn’t”  
  • “I can not log into Manage my booking and your password reset isn't sending me an email?”
  • “Would be great if your contact numbers worked and didn’t keep pushing people to automation and WhatsApp.”
  • “I have been on hold now for an hour and the agent decided to end the call. Now they want me to complete a survey for a call which was on hold” 

 

Unfortunately, data silos are a common challenge for customer service/customer experience teams meaning that many of these tweets simply live and die on the channel. Marketing, sales, operations teams often use different platforms, with customer feedback coming in on all of them. 

 

This “frankenstack” approach of disparate data sources and platforms that don’t talk to each other leads to disjointed service for the customer. They are forced to try multiple communication methods and speak to multiple different support agents. They may get different answers every time, and it’s frustrating. (We’ve all been there, right?)

 

In the case of call centers, the potential is there to not only improve staffing efficiency by predicting seasonal needs, but also unclog backlogs in real time. 

 

  • Find out why password reset errors, site errors, or dropped actions are happening, when, and why (and assign IT people to fix problems in real time)
  • Get tipped off to these events faster and develop a proactive response
  • Where are the disconnects in your service chain?
  • Which parts of the automated response system are repetitive or ineffective?
  • Do you have too many channels to monitor?
  • Do you need to improve your self service help channels like help center, chatbots, etc. 
  • How many calls, tweets, and messages are being missed? 
  • How do changes in customer service models affect customer contact rates, journey success, and perception?

 

While it may feel impossible to get granular answers to these questions, rest assured it is possible.

 

Take it from Comcast, who used Scuba to map out 30,000,000 individual customer relationships in a detailed journey format. Organizing this data revealed insights that were incredibly useful for their 40,000 support agents (not to mention other business teams across the company). Equipped with a comprehensive intelligence tool, Comcast’s team could finally get to the root of problematic events, decipher trends, and answer complex questions on the fly. And, they reduced the number of support calls by 11.3 million!

 

Want to see what’s possible with your organization's customer experience strategy? Request a demo from Scuba today!

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