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Gone are the days of random blasts and generalization. Today, a company must be specific, personal and strategic in its efforts towards its clients. And to establish and maintain a long-lasting relationship, all you need is data. The more data you hold, the better relationships or partnerships you can have to deliver a top-notch, transparent user experience.

Most customers want all of their interactions with a brand to be personalized. According to the 2022 Gartner Customer Service and Support Survey, 71% of B2C and 86% of B2B customers expect companies to be well informed about their personal information during an interaction.

No matter how ambitious the personalization, the experiences a brand is able to deliver will only be as good as the data it has in place. If a personalized customer experience is a well-prepared meal to serve, then customer data is the raw ingredients. To deliver personalization that drives business impact, a brand needs to start with the right data foundation. 

Companies collect 3 main types of customer data:

  • First-party data is provided directly to the organization by customers through its owned channels, such as a website or mobile app.
  • Second-party data is provided to the organization from another source, for whom that data is first-party.
  • Third-party data is bought, collected and sold by a data aggregator (who doesn’t interact with customers directly).

The biggest turn-off for customers is when a business doesn’t understand who they are and what they want. To overcome this obstacle nowadays, it is easier than ever to personalize the customer journey, e.g., by creating customized offers and messages with the tools of digitalization and AI (artificial intelligence), which are fast becoming among the most important methods in delivering an enhanced customer experience. For example, using predictive call routing improves customer experience by examining past customer behavior and preferences to predict what customers need. Here, AI reduces escalation time, gives customers the answers they want faster and improves overall customer satisfaction.

This type of predictive analytics also extends to increasing sales. Because AI can analyze large amounts of data extremely quickly, you can leverage data, statistics and modeling to make predictions and generate real-time, actionable insights. This can help facilitate a sale by using suggestions for related products and accessories, for example. Furthermore, in addition to making it more likely the customer will buy something else from you, the personalized nature of the customer experience engagement also strengthens the emotional connection the customer feels for your brand.

Due to the ability of AI to process large amounts of data in real-time, brands are able to use AI to personalize content for each specific customer based on their purchase history, customer service tickets and browsing patterns. By using this customer data, AI applications are able to provide customers with the content that would be most appealing to them, including factual information, images, videos, instructional material or community discussion forums. Moreover, AI is being used for customer sentiment analysis by analyzing voice, image recognition and behavior in order to better understand customers’ emotional states and what they need and expect from the brand. Customer sentiment analysis refers to the automated process of interpreting emotions in communications in order to determine how customers feel about a brand’s products or services. Typically, the customer communications come from online surveys, product reviews, customer support tickets and social media posts.

Consumer Privacy in Question

There’s an inherent tension between personalization and privacy. A data-driven approach to designing personalized experiences benefits the customer and the company, but when done wrong, it risks violating customers’ trust as customers become alarmed by how their data is used. Thus, to be successful and trustworthy, proper consent and data management are necessary.

High-profile data breaches and data ethics scandals elevate privacy concerns and accelerate the desire to withhold personal information. Government regulations around the world redefine the standards by which data is collected, stored and used. For instance, in 2018, the European Union (EU) began enforcing the General Data Protection Regulation (GDPR), and 150 additional privacy regulations followed across the globe, including the California Consumer Privacy Act (CCPA) in 2020 and similar bills in more than a dozen U.S. states. 

“By 2025, 75% of the world’s population will have its personal information covered by modern privacy regulations,” says Brad Fager, Gartner senior director analyst. “This reiterates the need for brands to leverage customer data with a proper understanding of customer preferences, terms of service and relevant regulation.”

Below are four ways to properly manage customer data:

  1. Be explicit with consent management and preference settings to give customers better control over how their data is used. Delineate these settings by functional area (e.g., marketing, sales, customer service), and use cases to avoid conflating applications of data.
  2. Prioritize transparency in privacy settings so customers know how and why you intend to use and manage their personal information. Communicate proactively about preference and consent.
  3. Make ethics a core component of your data management strategy by creating data use cases based on how these use cases bring value and benefit to the customer, not just to the company.
  4. Limit data collection to what’s actually needed by defining each data use case so as to collect the minimum data required through the least invasive methods.

Data Complexity: A Challenge to Overcome

With greater responsibility over the data sources collected and processed, however, come challenges related to complexity. Today’s consumers don’t always follow a linear path of interaction. Neither does their data. A consumer may visit your website, complete a purchase and finish their customer journey. Or, they may go to a desktop website and add an item to their cart, continue browsing on their mobile phone, then jump onto a tablet to complete the purchase. If the data collected throughout this multi-channel, non-linear journey isn’t managed correctly, you can find yourself in a state of data chaos.

A Customer Data Platform (CDP) is one solution for preventing such chaos and simplifying data complexity. CDPs ingest customer data from across your tech stack, eliminating data silos and creating 360-degree customer profiles. They solve for data quality and support lawful compliance by centralizing data from all your sources in one place, validating its accuracy and sharing it with tools throughout the stack.

According to the CDP Institute, there are currently well over 120 different companies that offer CDP solutions. Sorting through the noise of the CDP market has been difficult for even the most industry-savvy teams. That said, several distinct categories of CDPs have begun to emerge: customer data infrastructure, marketing cloud offerings, application-layer marketing hubs and CDP tool kits. While categories can share some overlapping capabilities – identity resolution, segmentation, API integrations – the deployment choice between them is significant.

CDP’s Important Value as Market Conditions Change

Inside most companies:

  • Websites and apps change, with landing pages and app screens being added, optimized or removed.
  • New campaigns are run, often requiring new data flows to measure success. New tools are required to optimize performance, thus requiring new integrations. 
  • Event tracking changes as developers across different platforms work in silos over time.
  • Customers toggle between known and anonymous states and across different devices, which requires dynamic identity resolution capabilities. 
  • Users update their consent preferences to opt out of personalized experiences, and their information will need to be extracted from certain flows and tools. 
  • Models are built and experiments are run, which force several of these steps to be repeated. 

And in the market:

  • New privacy regulations, such as GDPR and CCPA, fundamentally change the way you can collect, manage, and activate data.
  • Apple and Google create new platform rules that change how you can access cookies and device identifiers.
  • API requirements change as vendors continually update their offerings and specs.

Soon, the challenge evolves from establishing a data strategy to being able to adapt your data strategy to internal and external changes. 

Customer Data Platforms provide a data pipeline that can connect customer data to and from internal systems as well as the digital ecosystem, enabling you to address complexity as conditions change.

Conclusion

Nobody knows exactly what will be possible in the future, but it’s certain to be driven by data. For this reason, it makes sense to collect as much data as possible now, even if you can’t use it straight away.

Businesses are expected to not just meet the needs of customers, but anticipate and exceed them. Today’s marketplace is constantly fluctuating and its vital organizations adapt by harnessing the power of analytics and artificial intelligence to make the necessary changes to survive and thrive.

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