Part 3: Data and the Customer Journey - Help along the way

This is part 3, the final part of a 3 part article on data and customer journeys. The first 2 parts can be found here:

1. Data and the Customer Journey - a 3 part article
2. Part 2: Data and the Customer Journey

Before we get started, let's summarize the first 2 parts.

Part 1: The Why and How of Customer Journey Analysis

• Businesses analyze customer journeys to understand customer experiences and optimize touchpoints for better satisfaction and loyalty.
• This analysis provides valuable insights like pain points, preferred channels, and purchase decisions, leading to: Personalized marketing and offers. Improved customer experience. Increased sales and ROI. Data-driven decision making

Part 2: Assembling the Customer Journey (like using puzzle pieces)

• Customer journeys are mapped using various data sources and tools:CRM systems, web analytics, and surveys provide quantitative data.Personas represent different customer types with unique needs and expectations.Touchpoint tracking captures interactions across all channels.Visualization tools present the journey in a clear and concise way.Collaboration across marketing, sales, and service ensures a holistic approach.

Challenges and Solutions:

• Data silos, privacy concerns, and dynamic journeys require innovative solutions: Customer Data Platforms (CDPs) unify customer data from various sources. Omni-channel marketing tools deliver personalized interactions across channels. AI and machine learning predict behavior and personalize experiences. API integrations break down data silos. A customer-centric culture fosters collaboration.

The Cookie Crumbles: New Approaches to Customer Journeys

• The demise of third-party cookies necessitates alternative data sources: First-party data like email addresses and purchase history gains importance. Consent-based data collection builds trust and gathers valuable insights. Contextual targeting delivers relevant ads without individual tracking. Identity solutions connect data across platforms while preserving privacy. Server-side tracking processes data without cookies. Advanced analytics extract meaningful insights from available data.

Emerging Trends:

• Customer Data Platforms (CDPs) consolidate and analyze first-party data.
• Cookieless measurement solutions are being developed.
• Focus on building deeper customer relationships and collecting data through direct interactions.

The Future of Customer Journeys:

Today we'll seek to answer the question, does Entity Resolution (ER) have a role to play in assembling and combining the data required for an organization to understand its customers journeys better?

The answer is Yes, entity resolution technology has the potential to contribute to more accurate and real-time customer journeys, but it's important to understand its limitations and potential uses as well:

Benefits of ER for Customer Journeys:

1. Improved Data Accuracy and Consistency: ER can identify and merge duplicate customer records across different systems, eliminating inconsistencies and ensuring a single, accurate view of each customer's journey. This leads to better personalization, targeted marketing, and overall customer experience.
2. Enhanced Segmentation and Personalization: By uncovering hidden connections between customers based on shared attributes or interactions, ER enables more effective segmentation and personalization. This allows businesses to tailor messaging, offers, and recommendations to individual customer needs and preferences, leading to increased engagement and loyalty.
3. Privacy-Preserving Insights: While ER deals with sensitive customer data, modern solutions prioritize privacy by employing encryption techniques and adhering to data privacy regulations. This ensures customer data remains protected while still enabling valuable insights into their journeys.
4. Real-time Journey Analysis: Some ER solutions (like Senzing 's, for example) offer real-time data processing, allowing businesses to analyze customer journeys as they happen. This enables them to trigger personalized interactions and interventions at crucial moments, potentially influencing purchase decisions and boosting satisfaction.

Limitations of ER:

Not all entity resolution, or even MDM solutions are equal, while each boasts its own strengths and capabilities, ER tools can be valuable in a customer journey project, it is important to be aware of the potential limitations of some. Here are some key points to consider:

Accuracy:

• Limited data sources:?Some ER tools might not handle diverse data formats or integrate well with various systems,?leading to incomplete customer profiles and inaccurate journey maps.
• Data quality dependence:?The accuracy of ER results heavily relies on the quality of the initial data.?Inaccurate or incomplete data can lead to flawed customer profiles and misleading journey insights.
• Limited entity matching capabilities:?Not all ER tools are equally adept at identifying and matching entities across disparate data sources,?potentially missing important connections and hindering journey analysis.

Speed and Performance:

• Scalability issues:?Some ER tools might struggle with large datasets or real-time processing,?leading to delays in generating customer insights and hindering real-time personalization efforts.
• Computational resource demands:?Complex ER algorithms can be computationally expensive,?impacting performance and potentially increasing costs,?especially on large datasets.
• Lack of real-time capabilities:?Not all ER tools offer real-time data processing,?limiting their ability to analyze ongoing customer journeys and trigger immediate actions.

Implementation and Cost:

• Complex setup and maintenance:?Some ER tools require extensive technical expertise for deployment and ongoing maintenance,?increasing costs and resource allocation.
• Limited customization:?Pre-packaged ER solutions might not offer the flexibility to adapt to specific customer journey needs,?potentially hindering personalization efforts.
• Vendor lock-in:?Some ER tools might have proprietary data formats or require specific infrastructure,?limiting flexibility and potentially increasing costs when switching providers.

Additional limitations:

• Privacy concerns:?Improper data handling and lack of transparency in ER processes can raise privacy concerns and erode customer trust.
• Limited feature sets:?Basic ER tools might lack features like advanced analytics or machine learning capabilities,?limiting their ability to extract deeper insights from customer data.
• Lack of domain expertise:?Generic ER tools might not have the specific domain knowledge relevant to your industry,?potentially leading to inaccurate interpretations of customer behavior.

Remember, the best ER tool for your customer journey project depends on your specific needs, data landscape, and budget. Carefully evaluate different options and consider their limitations before making a decision.

If you've recognized the need to investigate entity resolution for your customer journey data analysis, what else can you use it for?

ER vs. Cookies:

• Complementary roles: ER doesn't directly replace cookies, but it can enrich first-party data and improve insights gained from other tracking methods.
• Focus on relationships: ER excels at discovering connections between entities, while cookies mainly track individual browsing behavior.

ER in Recommendation Engines:

• Potential for personalization: ER can be used to identify similar customers based on shared attributes or relationships, informing personalized recommendations.
• Challenges in real-time: Utilizing ER in real-time recommendations might be complex due to data processing requirements and privacy considerations.
• Not a standalone solution: ER should be combined with other data sources and analytics for effective recommendations.

Overall:

ER is an incredibly valuable tool for building more accurate customer journeys, but it's not a magic bullet on its own. Its success depends on responsible data practices, integration with other data sources, and a clear understanding of the limitations of the tool you are working with. Consider also that while real-time recommendations using ER alone might be challenging, its potential to identify customer connections can enhance existing recommendation engines.

Remember, the best approach to building accurate customer journeys involves a combination of technologies, data sources, and a focus on ethical and privacy-conscious practices.

If you stuck with me for all 3 days, THANKYOU. I'm always happy to talk with you about what we are seeing in this marketplace, from myriad organizations. Feel free to drop me a line.