How the best retailers design their customer data strategy
Retailers need to put in place a successful data strategy as soon as possible if they want to stay relevant in today’s high paced environment.
This means they need to manage the data adequately so they have reliable and quality data that helps them provide better customer journeys and take better business decisions across the entire organization.
But this is not always an easy task.
Retail data is often disparate, unconsolidated, unstructured, and even hidden. But there are solutions available today to help retailers manage and leverage data as a critical asset for the company in a coherent strategy.
What is a retail data strategy?
A retail data strategy is a long-term plan that defines the technology, processes, people, and rules required to manage a retailer’s information assets. All types of businesses collect large amounts of raw data today, but retailers specifically need to handle the challenges that come from high-frequency purchases, high-volume product catalogs and deep product taxonomy.
Essentially, a retail data strategy should focus on 2 goals:
- Increasing top-line revenues by improving customer acquisition and loyalty, and
- Creating bottom-line value through operational efficiency and insights.
What is data first strategy?
A data-first strategy means a business strategy that is built on the foundation of objective data.
Data first organizations prioritize business innovation and risk models built around multiple sources of retail data, whether it comes from online or offline sources.
In order to be data-driven, a retailer must embrace data as a corporate asset. Data first organizations expand upon API first approaches using a multitude of technologies.
Why do retailers need a data strategy?
Data strategies enable innovation and value creation in line with current and future market trends which support long-term business objectives. Furthermore, experts say that most companies fail today due to an inadequate data strategy to support accurate decision-making.
Quality customer data can help retailers break down data silos, make data-informed decisions, improve customer loyalty and retention, stay compliant with regulations and leverage new monetization opportunities.
Who designs and manages the data strategy?
A chief data officer (CDO) in retail organizations is a C-level executive that holds a range of strategic data management responsibilities related to the business, including data governance, data quality and data strategy, to extract maximum value from the data available to the company.
There are two main approaches a Chief Data Officer (CDO) can take towards the company data strategy: defensive and offensive.
- Defensive data strategy has as a primary business objective of minimizing risk, ensure compliance with regulations, use analytics to detect problems and build systems to prevent them.
- Offensive data strategy bets for using data to increase revenue, profitability and customer satisfaction, and all the activities are designed to be customer-focused and bring new and compelling insights to sales, marketing, innovation, etc. for better decision making.
What tools help execute a successful data strategy?
The central tool an enterprise retailer needs to perform a successful data strategy is a customer data platform (CDP), which helps collect, cleanse, enrich, store and activate customer data. By providing a unified customer profile and a Single Customer View that functions as a single source of truth for all interactions with your company.
A CDP helps improve retail customer engagement, loyalty and customer support activities.
What should retailers consider when building a data strategy?
PRINCIPLE #1: Not all data is critical.
It’s important to recognize that more data isn’t always the answer to better business outcomes or customer experiences in retail.
Collecting too much data can result in huge complexity, expensive infrastructure and undue risk. Retailers need to make sure they are only collecting customer and transactional data that’s actually useful.
Collecting unnecessary data leads to a retailer’s customer data platform (CDP) becoming overloaded. Audit every piece of data you collect and ask yourself these questions:
- What department or team needs this data?
- What’s the use case for this data? What strategy or outcome will it enable?
- If we didn’t collect this data, could we achieve the same results in a different way?
PRINCIPLE #2: Comply with data regulations
As data privacy becomes more important to consumers, retailers will see more governments enacting laws such as GDPR and CCPA. These laws have already changed the way companies collect and store their customers’ data, specially in Europe.
It’s now crucial for retailers to manage consent and preferences across channels – for which they use a Single Customer View.
PRINCIPLE #3: Avoid data silos
Data silos happen when retail data is being collected by different departments with different tools and platforms, and those tools are not connected or integrated with each other.
This usually isn’t intentional. Data works best when it’s shared across departments because that can promote collaboration and problem-solving across the company.
Data silos occur because of the true complexity of retail business needing to handle huge numbers of customer and transactional data, plus a lack of a data governance or data orchestration strategy.
As an example, a retailer’s finance department might accidentally be holding back useful data from the purchasing team. The marketing team might be using a customer data platform that has data that customer service needs.
PRINCIPLE #4: Data security is essential
Data security has a simple definition: the protection of data from unauthorized access, use, change, disclosure and destruction.
But it’s a very complex challenge. It’s one of the most important parts of customer data management. No matter what type of data a retailer is collecting from its customers, they need to keep this information safe. Not only will a data breach give a retailer a lot of negative press, but it can also be very costly.
How does Loyal Guru handle retail data?
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To ensure that our client’s data strategy covers all critical elements and helps them to make the most out of their data, our platform takes into account the following areas:
Data Governance
First of all, we define the framework that will rule our client’s data strategy. In this way, we consider what the policies, procedures, roles, and responsibilities will be in our client’s data strategy, and what is the tech stack involved.
Data governance often entails training teams to understand and leverage the data available to them. This often means putting data analysis and data activation tools in the hands of departments outside of IT.
Data Architecture
Another important thing that we will decide in our retail client’s data strategy is their Data Architecture. This architecture needs to match with our client’s current business needs but be flexible enough to adapt to future needs and uses of this data.
This delimits how we are going to store data, the architecture of our metadata, the data model, the data integration, etc.
Data Quality
As the retail data we collect will be used to take decisions and launch communications and promotions, we must be sure that the data our retail clients use has integrity, reliability, consistency, completeness and quality.
Data Security
Every company has data privacy and data security needs.
Incorporating best practices for data security from the very beginning, helps our retail clients avoid security gaps and leaks. Our client defines the access, roles and permissions of different teams and team members over data and databases, so they can avoid access by unauthorized users and prevent the inappropriate use of our corporate data.
Data Integration
Retailers have been collecting huge amounts of customer and transactional data for decades, but have often not been able to process and activate this data. That is why we help our retail clients understand if this data has already been stored and where, and activate the right integrations to make this data useful.
Integration is a key step to create a single and complete source of truth for retailers.
Conclusion
If your retail organization wants to be more purposeful with data and harness the power of technology to grow customer acquisition and customer loyalty, laying a strong foundation starts with a good customer data strategy. Loyal Guru helps enterprise retailers with an unbeatable data, loyalty and personalization solution that will increase performance across your organization.
If you’re a retailer and you’d like to talk about any of the loyalty program strategies outlined in this article in more detail, then get in touch with our team to learn more. We’ll be happy to show you examples or talk through your brand’s unique challenges.
