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This article is part of a VB special issue. Read the full series here: Building the foundation for customer data quality.
In the era of digital technology expansion, companies are accumulating large volumes of data on consumers’ activities both online and offline. At the same time, recognition of customer data’s immense value and potential to drive revenue growth is rising as companies realize that without the appropriate data — and data management — initiatives risk falling short of their potential.
Delivering a comprehensive customer experience holds immense importance. It plays a crucial role in acquiring new customers and retaining existing ones in today’s crowded digital landscape. In a world of increased globalization and an abundance of choices, customer experience assumes even greater significance.
According to Salesforce’s Connected Customer report, 88% of customers say their experience with the company is as important as its products or services. Meanwhile, 8 in 10 business leaders say data is critical in decision-making at their organization.
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Consequently, business leaders are prioritizing data-driven decision-making, aiming to deliver an exceptional customer experience that establishes a competitive advantage.
But to meet and surpass evolving customer expectations while optimizing return on investment (ROI), brands must possess high-quality data for their marketing and advertising efforts.
“Gathering the right customer data increases a company’s ability to be built to deliver tailored products to customers as opposed to producing generic inventory. Companies today need a hybrid approach,” Nathan Saegesser, principal and head of data and analytics at KPMG’s deal advisory and strategy practice, told VentureBeat. “Having a wide range of well-processed data empowers companies to have a greater understanding of their clients, and they can use this information to provide a better product or service to drive sales.”
Achieving personalization necessitates having the right customer data, including a comprehensive understanding of the customer’s location and their interactions with the organization or brand, and in particular a real-time view of customer behavior.
To address modern data management challenges, companies are incorporating AI into their customer data management strategies. This proactive approach empowers them to foster authentic interactions and develop hyper-personalized experiences that align with customers’ evolving demands.
Companies can now provide personalized recommendations and deliver relevant content through the use of sophisticated algorithms. This not only improves customer satisfaction but increases sales and reduces customer churn.
The essence of effective data management for mapping the customer journey
Traditionally, organizations have relied on customer segmentation as part of their marketing strategy to ensure customers receive relevant communications and offers. However, they often struggle to achieve deeper levels of personalization using this tried-and-true method. While increasing segmentation efforts may seem like a viable approach, it does not necessarily yield the best return on investment (ROI) or maximize program effectiveness.
The key to optimizing a customer’s journey lies in data. Unfortunately, the era of one-size-fits-all mass emails is long gone.
“We found that fewer than 10% of organizations have reached advanced maturity on their insights-driven capabilities,” Kim Herrington, senior analyst at Forrester, told VentureBeat. “The underlying goal of becoming insights-driven is shared by chief data officers (CDOs) and chief executive officers (CEOs) — to help employees make smarter business decisions faster. To do this, employees must be able to move from knowledge seeker to insights, to action.”
One of the main challenges that brands encounter today is developing and implementing a comprehensive strategy for gathering and organizing data — data that, traditionally, has remained highly compartmentalized.
Data is often dispersed across spreadsheets and platforms, and there is a growing discussion around the complexities associated with data, including its vast volume and lack of organization, which presents challenges for enterprises and their teams.
“More data isn’t necessarily better — unless it’s connected and easily accessible,” said Claire Gribbin, head of SMB Worldwide at Amazon Web Services. Gribbin emphasized the significance of a unified data infrastructure in enabling businesses to effectively collect, store and access data.
“A well-planned data strategy, coupled with unified and clean data, has the potential to generate measurable value and drive tangible improvements for a business,” Gribbin added.
According to Raj De Datta, co-founder and CEO of commerce experience platform Bloomreach, organizations should begin by thoroughly understanding the various types of data they already collect and their respective sources. He said that typically, companies have distinct silos of customer data. This necessitates adoption of a customer data platform to consolidate all this data into a unified location.
“This not only helps with more efficient collection and storage, but it also makes it easier to analyze that data for more customer insights,” said De Datta. “With GDPR and other compliance regulations, third-party data is becoming increasingly difficult to use. Making more of an investment into collecting zero-party and first-party data will allow businesses to give consumers the option to reveal data about themselves in return for value — an exchange that’s ideal for both parties.”
Likewise, Chris Comstock, chief growth officer at marketing data analytics platform Claravine, said that a strong customer data strategy can help build a trusted relationship with your customers and make them loyal customers for life.
However, to fully embrace the potential for data standards to alleviate common procedural issues, he suggests that companies should first align on and apply the right data naming conventions and taxonomies.
“Where companies tend to misstep is by trying to achieve high-quality data outputs by increasing the amount of time put into adding inputs. However, if the inputs aren’t consistent across datasets, then the conclusions drawn on the other side won’t be trustworthy, and thus [will be] unusable,” said Comstock. “In cases like this, it is a better allocation of resources to devote time to standardizing data across all aspects of the business as opposed to focusing twice as hard on fixing faulty outcomes.”
Comstock emphasized that data consistency is crucial across all industries. Inconsistent or non-standardized data can result in unclear and unhelpful conclusions, which can lead to major problems.
“Without proper data standards to ensure coherence among a brand’s first-party data, marketers may find themselves starting over from the beginning,” he warned.
Driving customer loyalty through hyper-personalized experiences
Behavioral data can help predict customers’ purchasing decisions. Real-time access to these behavioral insights also enables businesses to identify customers who are at high risk of churn. By combining effective data management with business intelligence (BI), companies can reduce churn rates and better understand how customers engage with their products.
Companies are increasingly adopting AI-powered hyper-personalization techniques to achieve exceptional results and deliver seamless omnichannel experiences. This approach enhances their BI capabilities and enables them to personalize their marketing efforts to the individual level by using carefully collected customer data.
“Customers get frustrated by offers and emails they get that aren’t specific to their needs, and if unchecked, such inbounds can lead to damaged brand reputations and long-term sales impacts. A client-centric personalization approach driven by AI can increase customer loyalty,” said Claravine’s Comstock. “New customers can be difficult and costly to find, so customer-centric approaches are the key to reducing churn; by offering superior service, personalized product offers and an all-around great experience.”
By harnessing the power of AI, companies can optimize their targeting strategies and tailor their messaging to each customer’s unique preferences and behaviors, in turn enhancing customer loyalty to the brand.
“At Walmart, we use a predictive basket algorithm driven by deep learning models to predict a complete order containing multiple items for a repeat customer, along with the reorder quantity,” Mangalakumar Vanmanthai, VP of data and customer analytics engineering at Walmart Global Tech, told VentureBeat. “This is displayed for online customers on the Walmart.com homepage and app. These recommendations and substitutions are personalized as needed using high dimensional neural embeddings of customer behavior.
“By combining customer data science technology with customer behavior, we aim to create delightful experiences whether customers are shopping with us in our stores or online — ultimately increasing customer lifetime value.”
Vanmanthai further explained that Walmart utilizes Smart Substitutions, one of Walmart Global Tech’s specialized recommendation algorithms. This algorithm assists store associates and customers in making improved item substitutions, taking into account their personalized preferences as well as the preferences of similar customers.
“It is built on a graph convolutional neural network that is trained using historical data pertaining to substitution acceptance and semantic models of the catalog,” said Vanmanthai. “After Smart Substitutions was introduced, we found that our customer acceptance of item substitutions has increased to over 95%.”
Aaron Lee, CEO and founder of Smith.ai, a platform providing AI-driven virtual agents, believes that advanced analytics tools and machine learning algorithms are key in making sense of the vast amounts of customer data. He said that Smith.ai utilizes technologies like generative AI and natural language processing (NLP) to scan customer data, which has helped the company identify customer sentiments and understand their pain points.
“Using AI has allowed us to provide better support and anticipate future requirements. By understanding each of our customers’ unique situations, we offer more tailored and suitable solutions,” explained Lee.
Lee highlighted a notable example of gaining insights from customer data during the COVID-19 pandemic. As a result, the company actively identified decreases in call volumes and proactively provided customers with smaller plans and flexible payment options.
“Our customers responded positively to this proactive approach, which greatly contributed to post-COVID customer loyalty,” said Lee.
He further emphasized the potential of data-driven strategies, where companies can use accurate and timely data to determine customer renewal timing, utilization patterns and product satisfaction. With this information, businesses can reach out to customers with targeted promotions and discounts, minimizing customer attrition and boosting revenue.
Key considerations for driving revenue through data management
Pedro Arellano, senior vice president and general manager of Tableau at Salesforce, told VentureBeat that the challenge businesses encounter today lies not in collecting data but in comprehending its significance. To optimize the efficiency of data collection, storage and analysis, he advises organizations to align their data management strategy with their business objectives.
Arellano emphasized that approaching data management from a business perspective can provide clarity on the appropriate processes, tools and governance required. This alignment ultimately leads to improved revenue generation, saving valuable time and resources along the way.
“The right tools — from software and hardware to platforms and tech solutions — are essential to building a data management strategy. The technology that fits an organization will help it manage the data within its existing analytics environment and streamline processes so people have access to the information they need when and where they need it directly in the flow of work,” he said.
“Other big challenges in using data effectively often come down to data literacy — data owners aren’t always data experts,” explained Arellano. “Businesses must invest in the training that employees need [in order] to do their work and become data-driven decision-makers, and ensure everyone understands the company’s data management strategy.”
Arellano also predicts that generative AI will revolutionize work processes, and says it has already proven effective in alleviating pain points and eliminating operational bottlenecks across various industries. However, he highlighted the importance of establishing robust data governance architecture when adopting such technologies.
“Robust data governance programs can empower businesses and IT teams to interact with data — from making faster, smarter data-driven decisions to data security. Every benefit of having actionable insights comes from having sound data governance,” he said. “Businesses must invest in developing and communicating policies for proper data usage regarding data quality, security, privacy and transparency.”
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