Data Analytics Role in Improving Customer Experience

Forrester previously predicted that organizations either need to learn to leverage customer insights and quantify the business impact of customer experience (CX) initiatives or find themselves in a vulnerable position. Another report from Adobe revealed that companies considered “CX leaders” were three times as likely to have exceeded their business goals. While many forecasts are little more than an alternate reality, Forrester, Adobe, and others are spot on about CX. Customer experience is a competitive advantage in every industry, from finance and insurance to healthcare, logistics, and retail.

Big Data is critical in creating the personalized experiences customers have come to expect—at scale, without friction, and with the human element intact. Companies are ramping up investment in smarter CX tools powered by AI, machine learning, and advanced analytics that help them understand customers—through how they shop and what they buy—to improve the customer experience.

In these next few sections, we explain how customer-centric data analytics strategies are the new competitive advantage.

Growing Datasets Enables Better Customer-Centric Data Analytics

According to Salesforce, 80% of customers say that the experience a company provides is as essential to them as a brand’s products or services. But capturing, analyzing, and acting on customer insights has long been a challenge for organizations. While so-called “small data” has been accessible for years in the form of sales reports, web analytics, and social media reporting dashboards, many companies struggle to connect transactional insights with the emotional.

And as companies have become more digitized, the amount of customer data available has increased exponentially in just a short time. Mobile app usage, location data, chatbot interactions, and social media feeds can now be combined with data from phone calls, transaction histories, chats, email conversations, and in-store interactions.

These channels combine to capture tons of raw data containing valuable insights into where and how customers like to engage with brands. Here are some examples of how brands use Big Data analytics to improve customer experience:

Understand Evolving Consumer Expectations. Customer behavior and expectations have changed dramatically over the past few years. In the midst of the COVID-19 pandemic, consumers evolved in response to the uncertain, unprecedented time.

• Tear Down Customer Experience Silos. Today’s marketers, sellers, and customer service professionals face tremendous pressure to reimagine traditional roles, break down silos, and apply new technologies to provide differentiated, digital-first customer engagement. According to Salesforce, 69% of marketers say traditional marketing roles hinder customer engagement. As a point of reference, only 37% of marketers felt that way in 2018.Traditional organizational models often had marketers focused on one stage in the sales funnel or one area like email marketing or lead generation, while sales and service teams each did their own thing. Now more than ever, it’s critical for all customer-facing teams to have a cohesive understanding of a customer’s journey to ensure a consistent end-to-end experience.
• Using Big Data Analytics to Improve Service. Customer feedback can be used to inform processes for handling issues moving forward or for improving products and services. This Convince & Convert article mentions a credit reporting agency that analyzed their database to determine which complaints made customers most unhappy. The agency then measured those impacts against their impact on the organization’s bottom line. From there, they were able to prioritize issues based on magnitude.
• Personalization. According to McKinsey, personalization can increase revenue by 5-15% and marketing efficiency by 10-30%. Big Data analytics allows brands to use online behavior insights to create customized landing pages, email campaigns, and offers, and to serve up personalized recommendations to drive more purchases.

Customer Experience and Data Analytics Are Essential for Understanding Customer Needs Throughout Complex Journeys

According to the Adobe report mentioned above, CX leaders are increasingly prioritizing content strategies that align with the specific touchpoints in the buyer journey. Big Data analytics removes the guesswork when it comes to understanding customer needs, pain points, goals, and interests, and it creates total visibility into the buying process.

Companies can now review thousands of data points in real-time that help them understand their customers in context. For example, AI-enabled analytics tools can reveal why some products are more popular with millennials vs. baby boomers, or why marketing campaigns aren’t generating as many leads as anticipated.

Organizations can also gain complete visibility into the buyer journey by drawing on transaction data, purchase histories, social media insights, as well as things like market trends and environmental conditions. This lends insight into which factors triggered certain events. Companies now create super-specific customer segments and target each group with precision, optimizing customer interactions by serving up personalized content and communications at every touchpoint.

Customer-Centric Data Analytics Helps Brands Prioritize and Act on Issues Based on Revenue Impact

A McKinsey report highlights an important point. While many executives understand the benefits of embracing a customer-centric strategy, they often fail to quantify what great customer experience is actually worth and how it can generate value. According to KPMG, the relationship between CX and financial gains can hit a point of diminishing returns.

For example, companies often overspend on efforts to attract or delight customers, which typically cost more than improving retention rates or taking advantage of upselling and cross-selling opportunities. Big Data analytics tools can help organizations make sure they allocate their resources where they will have the most significant impact.

Another report from Deloitte offers a framework for measuring the business value of CX and breaks it down as follows:

• Does the customer realize business value? What external factors (positive reviews and referrals) indicate this?
• Is the customer experience positive? Here, you look at complaints, review interaction data, NPS scores, and surveys to gauge sentiment.
• How are products/services performing? How are products selling? Are certain products/services linked to more positive experiences than others?
• Internally, you want to measure revenue, churn rate, and average order size, as well as how much you’re spending on marketing efforts and processing returns.

Big Data allows organizations to connect what customers say to what they do and analyzes satisfied, neutral, and dissatisfied customers to identify what factors into a good or bad experience. Big Data can also be used to target and acquire customers who share characteristics with a company’s most profitable customers. This allows sales and marketing teams to focus their efforts on audiences with the highest propensity to buy and the highest potential lifetime value.
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How AI & ML Change Customer Experience and Data Analytics

Artificial intelligence has changed the way brands connect with customers, making it easier to create personalized experiences and nurture long-term relationships. According to Adobe’s 2020 Digital Trends in Asia Pacific report, more companies are embracing AI and ML as a way to bridge the gap between the high-volume, high-velocity data being generated and the ability to use that information to engage customers in real-time.

For marketers, bringing more automation into the mix is huge. The report touched on an important point: over the past several years, marketers have amassed more and more customer data, along with a growing list of responsibilities. As AI-enabled platforms become more accessible, there’s an opportunity for marketers to gain some of their time back by eliminating ad-hoc manual efforts that prevent them from creating real value for consumers.

The Salesforce 2020 State of Marketing report found that AI adoption has grown by 186% since 2018, and 92% of marketers now have an account-based marketing (ABM) program. AI tools allow organizations to leverage real-time insights to engage customers as they browse a brand’s website. Brands might serve up content that speaks to specific pain points. In sales, AI is becoming an essential tool for lead scoring, and can also help determine the best steps to take next based on what tactics have historically been successful at various stages in the sales cycle.

In financial services, banks use Big Data analytics to improve the customer experience by tracking milestones like weddings, births, college, home purchases, and retirement, and then using those insights to deliver relevant offers. In healthcare, Big Data analytics are being used to provide personalized patient journeys and improve health outcomes.

Customer Insights and Data Analytics Set the Stage for Success

Technology is redefining the current relationship between brands and consumers. As customer expectations continue to reach new heights, a data-driven customer experience is no longer the exclusive domain of tech giants and well-funded startups.

Organizations in banking, retail, insurance, healthcare, and countless other sectors are now competing on experience. Without Big Data analytics, rising to emerging CX challenges won’t get any easier.

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