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How to Design an Effective B2C Data Analysis Process

A productive data analysis process enables marketing teams to correctly measure their performance, both present and historical, as well as make reliable predictions and optimize strategies accordingly.

This has been a key factor in the success of top B2C brands like Amazon, Netflix, and Walmart.   As consumers continue to explore digital avenues for meeting their daily needs, B2C marketing executives across all industries are recognizing the importance of data analysis for delivering quality experiences to customers and boosting ROI.

This guide will discuss the importance of having a data analytics setup, as well as walk you through the process of designing and implementing it at your company.

The Rise of Customer Journey Complexity

The need for a comprehensive data analysis setup comes from the ever-growing complexity of the customer journey and customers’ expectations of a personalized experience.

In fact, 71% of customers see personalized interactions as standard, and 76% get frustrated when they don’t get them. Brands that fail in personalization risk losing 38% of their customers, according to a study by Gartner. Let’s break it further down. 

Why personalization matters

In the US and many parts of Europe, the average household has access to at least 7 connected devices, many of which can be used to engage with brands through search, email, and social media, among others. While this presents B2C companies with opportunities to reach more customers, it also makes marketing and sales more time-consuming and challenging. 

From the discovery stage to conversion, a customer goes a long way, typically averaging eight touchpoints. Imagine, 92% of customers visit online stores with no initial intention of making a purchase. In fact, 25% of these customers visit to compare competitor prices and features, while 45% visit to learn more about specific products and services. Marketing activities continue even outside the online store—on social media, comparison sites, search engines, and other platforms. Even after a purchase is complete, the customer journey continues, and those people crave personalized recommendations and offers. 

That said, marketing to customers across multiple touchpoints takes and generates enormous volumes of data. This data contains information on consumer behaviors at different stages of the conversion journey, their unique needs, and how to create personalized offers that will most likely appeal to them.

Handling large data volumes from multiple sources can be time-consuming, expensive, and error-prone. Companies often end up with siloed and low-quality data, which lowers the quality of experiences they provide to their customers. This, in turn, leads to losing about $4.7 trillion in global consumer sales.

To break the cycle, companies need to leverage modern technology and data management practices. 

Data-Driven Operations: Data Accessibility and Clean Data

In a webinar by InfoTrust and Forrester, Senior Analyst Richard Joyce said: “just a 10% increase in data accessibility will result in more than $65 million additional net income for a typical Fortune 1000 company.”

💡 Data accessibility is about making data accessible for use within an organization. This means that people from various departments and with different experiences in processing data know where or how they can access or request data and get it in a usable state. 

Build a strong data culture in marketing analytics  with our free guide

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Accessibility to clean data is one of the core aspects of a data-driven B2C company. It enables customer-facing departments to tap into mission-critical insights, leading to higher conversions and an increase in net profit, as stated above. The many benefits of data accessibility also include the following.

Improved Decision-Making

When data is accessible and usable by executives from various departments, it is easier for each leader to understand the company’s overall business performance and how their team’s activities contribute toward the end goal.

This information is crucial for helping them make decisions and implement strategies that produce positive results while moving the company closer to its objectives. It is important to stress that the quality of data used in decision-making should never be ignored.

According to Gartner, companies lose an average of $15 million per year due to decisions based on low-quality data. 

👉Learn how to measure and improve data quality 

Enhanced Data Quality

Silos are a primary culprit for low-quality data in businesses. When data is siloed in various departments, duplicates and inconsistencies are bound to occur, and it becomes difficult to build a holistic view of the company’s customers, partners, and products. According to MIT, low-quality data can make a company lose 15% to 25% of its revenue.

However, when data becomes accessible, the situation turns around. Teams get more up-to-date data, duplicates and inconsistent information are eliminated, better insights are generated, and the company makes more profit.

More Effective Budget Allocation

When you have access to properly organized data, it becomes possible to identify the channels and strategies that yield the best results. Knowing this will allow you to justify each expense and allocate more budget to high-performing areas.

Better Customer Experience

The cross-pollination of consumer data among customer-facing teams enables the various departments to get deeper insights into how customers behave and their unique needs at every step of their journey. This is instrumental in generating sales enablement content, creating personalized offers, and establishing better relationships with clients.

Designing a Data Analysis Process for B2C Companies

Data analytics involves six main phases, widely referred to as the data analytics life cycle.

6 data analytics life cycle phases

This section will discuss how to build a B2C analytics process using the various phases of a data analytics life cycle.

Discovery & Preparation

The discovery stage focuses more on your business needs than the data itself. Here, you will need to set clear goals for your team and strategize on how to achieve that. You will need to examine trends in your industry and make an assessment of available resources and technology requirements.

Afterward, you will identify what your company’s data sources are and the story you want your data to tell. This data usually passes through a hypothesis test, where you resolve your business needs based on current market scenarios.

After the discovery stage, the preparation stage ensues. Here, the focus moves from business objectives to data requirements. Data preparation involves capturing, processing, and cleaning business data inbound from internal and external sources. The collected data can be structured (having defined patterns), semi-structured, or unstructured. 

As a B2C brand, your data sources might include Amazon Advertising, Facebook Ads, and Shopify.

Model Planning & Building

Now that you’ve captured the data you need, the next step would be to load and transform the data. That’s what the model planning phase is all about.

There are several techniques you can use to load your data into the analytics sandbox. The two main types are:

  1. Extract, Transform and Load (ETL): This procedure extracts and transforms data using predefined business rules before loading it into the sandbox.
  2. Extract, Load and Transform (ELT): Here, you load the raw data into the sandbox and transform the data afterward.
👉Read our beginner’s guide to ETL processes 

Dirty data can be either filtered or completely removed in this phase. Other techniques you might employ include data aggregation, integration, and scrubbing.

The building phase involves developing datasets for training and production purposes. Here, you’ll rely on techniques like decision trees, logistic regressions, and neural networks. This stage also covers the execution of the designed model, and the nature of the execution environment is defined and prepared so it will be easier to expand if a more robust environment is required.

Results Communication

This stage involves making the results of your model execution known to stakeholders within the company. The stakeholders will scrutinize your report to determine if it meets the business criteria stipulated in the discovery phase. This involves identifying critical findings from the analysis, measuring business goals associated with the results, and generating a digestible summary for the company stakeholders.

Operationalization

This stage involves moving the data from the sandbox and implementing the model in a real-life environment. The data is constantly monitored and analyzed to ensure the generated models return the expected results. You can always come back to make tweaks if the outcomes aren’t as expected.

Automating Data Analysis with Improvado

Manually building and managing data pipelines can be a time-consuming, resource-intensive, and error-prone process, especially for enterprise-level companies with petabytes of data. 

On average, data engineers at enterprise-grade companies spend 40% of their work day fixing bad data and broken data pipelines.

The error-prone nature of manual ETL is worsened by the slow pace at which data engineers detect incidents within the pipeline. According to Wakefield, engineers take an average of four hours to detect errors and about nine hours to fix them.

This leads to the frequent occurrence of bad data, which in turn impacts 26% of these companies’ revenue. To curb the menace of bad data, companies need to leverage automated ETL platforms like Improvado.

Improvado is a revenue data platform that automates omnichannel marketing analytics and reporting at scale. The platform automates the crucial areas of your company’s data analytics life cycle (aggregation, transformation, and cleansing), delivering clean, analysis-ready data to your desired warehouse, BI, analytics, or visualization tool.

This saves up to 90% of reporting time, gives you more control of your company’s data, and ultimately boosts your ROI.

Get our guide on sales and marketing data centralization

Getting Ahead of the Curve

With the consumer landscape getting more complex by the day, data-driven organizations have continued to stay ahead of the curve by reinforcing their analytics stack with automated omnichannel revenue platforms and leaving manual ETL behind.

This enables them to centralize existing data, scale with new data sources, and focus on uncovering impactful, growth-oriented insights.

If you would like to know more about how Improvado can help establish a robust and scalable data analysis process for your company, feel free to reach out.  We’d be happy to help!

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