Today, 81% of customers interact with brands that provide personalized customer experiences, and 76% of consumers are frustrated when interactions aren’t personalized and relevant. Find out more about these stadts at Forbes.com
A recent McKinsey research study highlighted that organizations who excel at creating personalized experiences yield 40% more revenue from personalization activities than those that don’t. However, personalization at scale requires more than just a deep understanding of customers and their needs. Organizations must also determine the best method to leverage the underlying data available.
Accurate and comprehensive data is the foundation for effective personalization. Without high-quality and relevant data, efforts to create meaningful and personalized customer experiences fall flat.
In this article, we will highlight how incomplete and poor data can undermine personalization. We will also share actionable strategies and tactics to build impactful personalized experiences and fix bad data.
Table of Contents
What Are Personalized Customer Experiences?
Personalized customer experiences involve providing tailored messages, offers, social media ads, and product or service recommendations to individual customers based on the data you have about them.
Personalization customizes the entire customer journey by harnessing the customers’ preferences to adapt the experience at every touchpoint. This may include location-based landing pages on a website, targeted marketing outreach and recommendations based on purchase history. It may include even providing better customer service at call centers.
How Can Bad Data Ruin Your Ability to Deliver Personalized Customer Experiences?
Today, companies struggle to create personalized experiences despite having access to vast amounts of data. Managing information from different sources and systems can be challenging due to inaccurate, outdated, incomplete, or duplicated information.
Bad data can hurt your personalization efforts by delivering irrelevant content to your customers, damaging customer loyalty and business credibility. Bad data can also lead to disengaged customers and unrecognized returns on ad spend.
For instance, if a customer bought a coffee machine from your online store but the information hasn’t been updated in the marketing department’s customer purchase information, you might send them a promotional offer for an accessory incompatible with that model.
Poor data quality can disrupt personalization efforts in many other ways such as:
- Reducing the effectiveness of your marketing campaigns. Inaccurate or incorrect contact information can prevent marketers from creating audiences and segments of similar customers for participation in a marketing campaign. Furthermore, bad data can result in a promotional email bouncing or being delivered to the wrong customer. This can impact your sender reputation and the campaign’s results.
- Negative customer service interactions. When a customer’s support history is incomplete or outdated, they may have to repeat their problem multiple times, resulting in frustration and wasted resources.
- Costly compliance issues. Storing or using outdated or inaccurate information undermines compliance with data privacy regulations such as GDPR, CCPA, and industry specific laws. This could lead to fines and a poor reputation.
- Generating unreliable AI & machine learning (ML) insights. AI and ML models are powerful allies for forecasting customer behavior, detecting churn risk, and predicting customer lifetime value. However, feeding poor-quality data to these models will undermine their reliability, leading to incorrect decisions and lost profits. Too many organizations focus on fine-tuning their AI algorithms without considering that AI model outputs are only as good as the underlying data.
How to Get Started With Personalizing Customer Experiences
Despite all efforts, 69% of customers still feel that the ads they receive are mostly irrelevant to them. Follow these steps to transform poor data into good information.
1. Understand Your Current Data Ecosystem
- Understand your data landscape. Build a shared knowledge base of the first-, second-, and third-party data in your ecosystem. Analyze the data quality of existing data sets. Do you have all the relevant demographic, firmographic, geographic, purchase, behavioral, preference, and other information you need? Is the data trustworthy?
- Document your assets. Catalog the data across your enterprise to understand where data is stored, shared, and authored across systems and business units.
- Govern your data. Evaluate the cross-section of people, processes, and technology to improve how you manage, protect, and leverage your enterprise information across brands and business groups.
2. Clean and Validate Your Data
- Historical data cleanup. Leverage data standardization rules for historical data cleanup to ensure uniform format, updated information, and deduplication.
- Improve upstream data capture. Capture better and more valuable data at the source of entry by using data validation rules and third-party validation services (e.g., address and phone verification tools). Be thoughtful and strategic about requiring additional customer information throughout their journey.
- Map data attributes to value & priority use cases. Prioritize data cleanup and collection efforts to align with business objectives.
3. Build a Single View of Customer (SVC)
The digital landscape has rapidly expanded over the last decade and only continues to grow. With such a vast number of customer touchpoints across channels, a single customer view (SCV) becomes crucial for effective personalization.
Many tools and solutions promise a single customer view but the best tool for a task is dependent on specific business needs. Some common solutions for creating a single view of customer include:
- Customer Data Platform (CDP). Purposefully built to service marketing and CX teams with an actionable SCV, centralizing segmentation and audience building, omnichannel activation, journey orchestration, predictive analytics, and more. Vendors include Treasure Data CDP, Adobe Real-Time CDP, Salesforce Data Cloud, and Tealium.
- Master Data Management (MDM). MDM solutions enable the creation and synchronization of an enterprise source of truth across different domains (e.g., Customer, Product, Employee, Location, Supplier, etc.) with a limited set of data points that define those business entities. Vendors include Informatica Multidomain MDM, Stibo STEP, and Boomi Master Data Hub.
- Enterprise Data Warehouse (EDW). EDWs are repositories that collect and aggregate data from multiple sources to facilitate data access, business intelligence, and analytics across the organization. Vendors include Amazon Web Services, Google Cloud Platform, and Microsoft Azure.
- Marketing Automation Platform (MAP). Designed to optimize marketing operations and empower teams with automated campaign execution, lead nurturing, personalized email marketing, multi-channel campaign management, real-time analytics, and more. Vendors include Salesforce Pardot, Adobe Marketo, HubSpot, and Braze.
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Examples of Great Personalized Customer Experience
Netflix and Hilton are two brands that have an incredibly large user base and vast amounts of data and complexity in their business. This complexity poses a significant challenge in achieving personalization outcomes at scale.
Nevertheless, both businesses have succeeded in tapping into the value of their data by prioritizing data-driven personalization, thoughtful data capture, and transparent data usage.
Netflix
The media streaming and entertainment platform is a master in maximizing the value of high-quality data to create effective personalization and refined recommendations and advertisements. Netflix uses analytical models to personalize every movie available on the platform, from the artwork to the trailer and synopsis, and fully capitalizes on customer satisfaction through its state-of-the-art recommendation system.
By helping customers discover valuable new content, Netflix keeps customers engaged, reduces churn, and increases customer satisfaction.
Hilton Hotels
Hilton is another company that has successfully embraced personalized customer experiences. Customers can benefit from a highly curated booking process that lets them search and book pet friendly hotels and select connected or adjacent rooms, all without having to contact the front desk directly.
Hilton loyalty members are greeted with a personalized welcome at check-in and, in some cases, are even presented with a check-in gift. This may be simple on the service, but requires thoughtful behind the scenes interactions between customer loyalty and booking systems with check-in desk associates.
The marriage of the right technology, data foundation, process, and people allow Hilton to leverage personalization efforts to increase customer loyalty, improve customer satisfaction, and drive incremental revenue.
Meet Your Data-Driven Personalization Goals With High-Quality, Trusted Information
In today’s competitive landscape, aligning your data strategy with your overall business strategy isn’t just beneficial – it’s essential. High-quality data are the backbone of effective personalization, enabling businesses to meet and exceed customer expectations at scale.
However, no organization is immune to bad data, and if left unaddressed, a lack of high-quality information can severely impact your brand. Keep bad data out of your systems by understanding your current data ecosystem, cleaning and validating your data, and building an actionable single customer view (SCV) for your marketing and CX teams.
Get in touch with Infoverity today to meet your personalization goals and become a data-driven business.
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