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Is There Such Thing As Too Much Customer Data? Featuring Insights From Salesforce And IBM Watson

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Matt Wujciak
11/02/2020

data

When it comes to imagining the future, customer service often gets painted in a dystopian light. Take the 2002 sci-fi film Minority Report. Tom Cruise’s John Anderton walks into the Gap, an identity recognition system scans him, and a hologram asks about a recent purchase.

There’s something unsettling about this portrayal—an unsolicited bot seems to know everything about you (or, as in the movie, mistakes you for someone else). But the truth is, customers today expect this kind of sleek, personalized service, which requires data of course.

According to CCW Digital’s research conducted in August of 2020, when consumers were asked: 

“following COVID-19, do you care more or less about the customer experience when deciding which companies to support and buy from?” a staggering 59% of consumers now care more about the customer experience when deciding which brands to purchase from, making the customer experience arguably the most competitive differentiator in modern marketing.

Increasing expectations in customer experience is good and bad, depending upon how you look at it. It requires the need to invest in better technology and data insights to deliver this sleek, personalized customer experience. It also can boost long-term sales from the contact center.  But is there such a thing as too much customer data?

The problem with customer data isn’t having too much of it. It’s what we’re doing with it.

We can never collect enough customer data. The problem is in the methods we use to make it actionable

Businesses constantly commit to make or break operational decisions such as: pricing adjustments, product evolution, new marketing campaigns, and inventory management to name a few. When an organization prepares to make decisions, it is crucial that relevant information is readily available to be analyzed. Without a clear view of relevant points and considerations, businesses may be making uninformed decisions.

Envision this scenario: a business is gathering feedback directly from surveys, reviews and social media, but also wants to pull insights from contact center logs and other documents such as field reports. It is challenging to sift through tens of thousands of documents and extract meaningful and actionable insights. The business wants to know: what are the top concerns? What emerging issues do we need to address?

Customer-centric organizations are presented with a concise, data-driven list of information that they can quickly act on. 

For example, a financial services organization wants to improve the customer experience for credit card holders. Using Key Point Analysis, they could assess their customer complaint-related data (call logs, social media, incident reports, etc.) and easily identify top complaints. Key Point Analysis would offer them the following view:

  • Incorrect information on credit reports, including payment dates and amounts owed (17%)
  • Repeated calls and unwarranted contact (15%)
  • This account is fraudulent (7%)
  • My debts were paid in full (6%)

With this information, the financial services organization could decide which challenge to address first – in this case, incorrectly reported credit information – and quickly work to improve based on customer feedback.

Turning support into sales

The data used in the customer experience also serves another function. 

Customer-centric companies are increasingly realizing that when customers contact a business with an inquiry on a new product or service, it’s a missed opportunity to let them leave without offering other products to help up-sell the deal. But that is also true of customers who contact a business with a problem; often they, too, are ready for an up-sell. 

How do we use AI and data to predict what the customer wants, deliver support, and then increase personalized sales?

Read More: Special Report Series: Generating Revenue In The Contact Center - Where Marketing, Sales, And Customer Data Align (Sponsored By Salesforce)

For example, a customer who has contacted a tech company for the third time trying to resolve a performance issue might react positively to an offer for 50% off the newer model, plus access to upgraded service for one year. Likewise, a mother calling into a clothing company to ask if she can replace a dress coat she ordered for her son with a larger size so he’ll have it in time for the school play might want to hear about a music- themed tie or respond positively to an offer for an annual reminder that her growing son might be ready for a larger jacket. AI can suggest these options to the agent, who can then turn a potentially painful situation into a profit for the company.

Today, advancements in AI, machine learning, and all of the capabilities we use to create and record data are enabling deeper levels of customer engagement and service than ever before. Powerful and trainable algorithms can parse through massive amounts of data and learn patterns to automate and assist customer service processes, and then recommend next best action, which often includes cross-selling. This technology is changing the face of customer service and helping organizations understand customers’ needs—often before they even do—providing the service they need at the right moment, says Jayesh Govindarajan, VP of AI and machine learning at Salesforce.

“AI being used in nearly all aspects of customer service, starting with auto-triaging customer cases to agents with the right skill sets, and followed by assistive AI that steps in to surface information and responses that help agents resolve cases faster and with precision,” says Govindarajan. There’s even AI that can use context in a conversation to predict a response. “If I say ‘I’m hungry—it’s time to grab some …,’” Govindarajan says, “it knows I'm probably going to say ‘lunch’ because it's mid-afternoon.”

For many organizations, it may not make sense for the contact center to be the one doing the cross-sell and upsell, as that primary function lives elsewhere in the company. But the front line team has never had more of the customer’s attention, so it may be the right time to ask questions or gather missing data, and record actionable information in the customer record that can additionally be utilized by sales and marketing, rather than operating in cross-department silos. 

The maturity from classical marketing to personalized CX and sales offerings is indeed the right path. Marketers are increasing adoption of and enhancing digital, AI-driven touchpoints, while ensuring that they complement each other collectively through customer data.

Read More: How-To Guide: How To Scale Conversational AI (Sponsored By IBM Watson)

Keeping the consumer in mind is an opportunity to analyze your data in your most common customer interactions: gleaning insights from what typical products are sold as complementary or services that may support the loyalty and repurchase behaviors of your customers over time.

In fact, Salesforce research shows 30% of Americans contact customer service more than they did prior to the pandemic. As consumers have become more comfortable contacting customer service departments, the contact center has gained tremendous opportunity in monetizing data through increased customer interactions across different channels - for all departments.   

As Jim Roth, Executive Vice President, Customer Support at Salesforce recently told us:

“We see a path for more contextual support. Meeting customers where they are is our evergreen goal. Messaging is the backbone of our strategy to enable this effectively. Support engineers’ responsibilities will continue to be similar, it’s just a matter of channel choice from a customer’s perspective. For us, this opens the door for Salesforce Support for...ServiceNow, Jira, iOs, and beyond.”

At the very least, using the contact center as a data hub serves as an opportunity to create contextual, personalized offers that the contact center team, specifically, can use to sell to customers. Is there a set of add-ons, special warranties, renewals, or entitlements? Revenue generating offerings depend on personalization, which depend on the technologies used for data-driven insights.

The contact center is only as profitable as both the quantity and quality of their customers’ data. The customers’ data is only as useful as the technologies used to measure it. When management, technology, and data is aligned, contact centers are equipped to deliver quality customer services, increasing CSAT metrics, and of course, long-term sales.

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