Data Strategy in Retail: How to Use Data to Influence Sales and Customer Loyalty
As the retail industry continues to undertake a significant and rapid transition, data strategies play an increasingly important role in helping create a competitive edge. It’s no longer enough to simply provide more products and a more convenient shopping environment to remain competitive. Companies must develop data strategies to ensure they are able to increase or maintain their market share. Today’s consumer is looking for more – they want experiences and personalized recommendations. With the right technology and a winning strategy, data can help retailers develop this personalized experience.
Let’s get into what data strategy is and the steps retailers can take to get started.
What is Data Strategy?
Data strategy is a comprehensive plan that identifies how data will be used to create value for an organization. The purpose of a data strategy is to support an organization’s objectives, such as improving efficiencies, reducing costs, or gaining a competitive edge.
The Importance of Data for Retailers
Data has become increasingly valuable over the past few years. In a survey by McKinsey, companies using advanced analytics are likely to achieve additional margins of 10% or more compared to their competitors. Not surprisingly, in this increasingly competitive digital market, access to good quality data (emphasis on quality) has become a key differentiator in retail businesses’ ability to survive and thrive. Retailers can use data to personalize their marketing offers, optimize the customer experience across multiple platforms, and offer better products at lower prices.
Develop a Data Strategy. Why?
As many business leaders invest heavily in technology and analytics, gaps are now surfacing in their wider infrastructure – particularly in their staff skills and business processes. Having a clear understanding of how to use data will help address these gaps and prioritize investments. A good data strategy focuses on delivering real benefits quickly over time, a great data strategy leverages principles in the agile process over a considerably longer period of time.
Company Challenges vs. Data Goals
To explore how organizations are using artificial intelligence (AI), PwC surveyed 1000 business leaders and tech executives in its 2022 4th Annual AI Business Survey. While many companies embrace AI, they struggle to put in place the right data strategies and infrastructure. Here are a few other common obstacles keeping organizations from maximizing their data. Among the respondents, 37% cited unreliable or inadequate data as a barrier to AI goals. This is followed closely by a lack of time or resources (35%), a poor user experience (32%), and a lack of knowledge about how best to utilize AI (30%).
How can Retailers Address Gaps in Their Data Strategy and Infrastructure As They Unlock The Value Of Their Data?
The most common gap in data strategy and infrastructure is the need for simple, intuitive data tools. Lack of proper data tools is often a symptom of a lack of good data management practices.
Data management best practices include:
- High-quality, properly tagged data: Data should be properly labeled and stored in a way that aligns with your business structure. This makes it easy to find and interpret the data you need.
- Timely access to accurate data: Data should be stored in a way that makes it easy to update regularly and quickly produces reports for decision-making purposes.
- A culture of data transparency: Everyone in an organization should be able to access and interpret the same datasets to make decisions based on accurate information from the same source.
Retailers must support this shift in mindset by providing employees with access to data and analytics tools, introducing new processes and roles, embedding analytics in the organization, and setting up the right governance structure.
Retail leaders can then focus on building the right infrastructure, which includes core technologies such as data management platforms, advanced analytics tools, AI algorithms, and enabling technologies such as cloud services, data lakes, and visualization tools.
How can Data Increase Sales and Customer Loyalty In Retail Businesses?
Data is a key asset for retailers looking to differentiate themselves in a crowded market. However, the sheer amount of data available — and the speed at which it’s generated — can be overwhelming to companies without the right resources in place.
Companies can use data to increase sales and customer loyalty in retail businesses by:
- understanding what customers are looking for from their business
- understanding what their competitors are offering
- developing products, services, or promotions that make their business more attractive to customers than their competitors.
- encouraging customers to tell them about their experiences so they can keep improving their products
- making it easy to access your business and buy (or otherwise use) the products or services you offer
- communicating with customers in ways they will find helpful and interesting (e.g. through social media)
These can be achieved by incorporating AI, cloud computing, and big data.
1. Several retailers have incorporated cloud computing and big data
Cloud computing and big data are two of the major trends in retail today, and they are both essential to a company’s data strategy.
Cloud computing is the model used to store data securely over a network of remote servers, making it accessible from any place on any device at any time. Big data refers to large amounts of information gathered by companies that can be analyzed and used for marketing or other purposes.
Cloud computing offers small retailers the ability to manage big data easily while having a secure place to store it—all while saving money.
2. Companies that use big data properly can see significant gains in their sales figures
Marketers can use big data to calculate the lifetime value of a customer. Customers can be targeted with customized offers and incentives, increasing sales and improving the customer experience.
Additionally, retailers use data to decide where to open new stores, which products will be bestsellers, and how much stock they need for each item.
According to McKinsey, big data can help increase sales by around 60% across the entire retail supply chain. However, the report notes that there is a risk of overestimating its power and not utilizing other tools available to retailers.
3. Data-driven Personalization
Companies can also use data to tailor offers to individual customers based on their preferences. This will positively impact sales because the right offers are given to the right people at the right time.
On the other hand, data can help improve our customer service to be more convenient and enjoyable for customers to shop with us. We can do this by analyzing past customer feedback and data from our call center.
4. Competitive Analysis
Another way to use data is to understand what their competitors are doing and how they are differentiating themselves from others. This could give them important insights into improving their marketing strategies.
Some of the world’s leading brands, such as Estée Lauder, L’Oreal, and Procter & Gamble, have realized the impact big data can have and have invested in technologies that implement its findings.
L’Oréal is using cloud data integration across functions to develop innovative products.
In the cosmetics industry, customer demands evolve quickly, and beauty products change accordingly. L’Oréal is a global leader in consumer product design and manufacturing, with roughly 37,000 employees and operations in 130 countries.
A cloud-based data integration platform enables access to real-time information. L’Oréal can create new products faster than the competition and deliver more personalized services to its customers.
For retailers, it is crucial to understand how to use data effectively to get the best results.
When looking at customer behavior and shopping patterns, retailers can make educated decisions on how to improve their customer experience, make their marketing more targeted and relevant, increase sales, and make smarter decisions about inventory management.
Data gives retailers a clear view of their business across all channels and locations. It shows their customers’ real needs and desires to create an engaging customer experience that will keep them coming back for more.
Retailers need the right team mix, process and tech to support customer analytics.
Data collection is only the beginning of a thorough data strategy. This can be done by using customer data platforms. The next step is to analyze the data. This will help them make better decisions and improve their product or service.
Data can help a retailer:
- Understand their customers better
- Improve their customer service
- Expand into new markets
- Improve their marketing
It’s important to know the value of different customer segments, ensuring traceability to marketing spend.
The importance of big data in retail cannot be overstated. Retailers need to determine, among other things, which customer segments are worth marketing to and how to price and allocate their goods to maximize profits.
Data can help with all of these things. For example, in the case of segmenting customers, it is important that they first figure out how valuable different customer segments are.
Then they can spend their marketing funds wisely to engage high-value customers who already know about your brand instead of low-value ones who wouldn’t appreciate or pay a premium for your offerings.
Ultimately, retailers need to understand the data they have at their disposal and how best to align their marketing efforts with their business objectives. By defining the purpose and value of data, finding the right tools, and building a data strategy from the ground up, retailers can arm themselves with the tools necessary to reap the benefits of data and gain the upper hand in the competition.