There’s an ongoing shift in the way retailers leverage analytics. And it’s a shift that throws out disparate data sources and demands a multi-domain approach to data analytics.
This shift to a multi-domain approach is inherent in what’s called “master data.”
In short, master data is a single source of data (sometimes referred to as your “golden data”) about your business that provides context for all types of transactions. Its effect is a more integrated approach to analytics that contextualizes and delivers data in more valuable ways.
Of course, master data is just the start. The practice of master data management (MDM) requires alignment of people, processes, and technology.
The implications for retailers are huge. A few of things retailers can do when they leverage business analytics and engagement insights from master data include:
- Empowering their employees by enabling them to make more data-driven decisions.
- Improving the customer product experience by unifying data across sales, marketing, service, and fulfillment teams.
- Predicting and informing strategies for reacting to market trends.
Wielding master data properly can give the retailers who use it a significant competitive edge. With the proliferation of MDM solutions like Riversand’s MDxP, there’s no reason you can’t leverage master data too.
The Relationship Between Master Data and Analytics
In the next section, we’ll dive into the specific advantages of using master data with embedded analytics. But first, it’s important to understand the relationship between master data and analytics.
As mentioned, master data is data about your business that provides context for transactional data. The multi-domain approach we referenced earlier refers to how master data categorizes your business data into different domains. These may include:
- Parties: Organizations and individuals, such as customers, vendors, and employees.
- Products, services, or assets: What the organization sells or manages.
- Financial structures: Accounting and reporting categories such as sales territories, business units, price lists, etc.
- Location: Geographic places such as locations, sites, subsidiaries, etc.
By categorizing data in this way, master data is foundational for a more integrated approach to retail analytics and business intelligence.
To see the gap in analytics that master data fills, consider the following example:
Imagine you run a website and a customer purchases a pair of pants. As it happens, this customer made her purchase after clicking on a social media ad paid for by you. Right when this customer clicked the ad, she became a lead in your sales database.
Then she checked out, the sale was closed, and her record was updated in your customer service database. The problem is, her lead status never changed. The sales system didn’t talk to the customer service system. So according to sales, that customer’s still a lead and should be treated as such.
As a result, your sales team is still sending her targeted emails and ads for a product she already bought, instead of additional, complementary items for potentially more revenue. Multiply this scenario across thousands of customers, and you can quickly see how wasteful not using master data can be.
Using Master Data with Embedded Analytics: Key Advantages for Ecommerce Retailers
Of course, master data isn’t just about preventing waste—though it’s very good at that. In fact, by providing cleaner, more usable data MDM provides a bevy of advantages for retailers.
MDM solutions with embedded analytics provide at least five key advantages for eCommerce retailers. We’ll detail each of these advantages below.
1. Track Your Customer’s Buying Journey with Master Data
Successful retailers understand their customers better than others. MDM solutions provide the means for them to do so.
Take the following case of customer data as an example. The Riversand MDxP and Customer Experience (CX) 360 products work together to record information about customers’ location and credit rating over time. Because Riversand’s MDM solution combines these data assets with other transaction data, you can unravel useful analytical insights.
For example, you could use these reports to illustrate how buying patterns change in relation to a customer’s credit rating. And from that, you could more effectively target the right customers at the right time.
2. Scale Personalized Retail Experiences
A customer-centric approach to master data helps you improve customer retention, make faster decisions, and gain more complete records. It does this through vastly improved data integration.
As a result of this improved data quality, personalization at scale becomes far more conceivable and effective. And in a world where marketplaces dominate global e-commerce, one of the retailer’s key differentiators is a personalized experience.
3. Make Sense of Customer Feedback with Sentiment Analysis
From website chat windows and customer reviews to support emails, customer feedback is everywhere. Complicating matters further is that this feedback is spread across dozens of product lines.
So how do you make sense of it all and extract some actionable business intelligence?
In short, MDM is how effective data management enables you to collect, consolidate, and contextualize troves of feedback to inform:
- Product marketing strategies
- Product roadmap development
- Customer service resource allocation
- And much more
4. Optimize Your Product Data for Scale
As referenced earlier, master data management integrates data from four different domains. One of those domains—parties—includes not just customers, but also retail partners. With integration into retail partner storefronts, it’s far easier to reach more customers with the right product data across all channels, applications, and devices.
You shouldn’t have to shy away from growing your business: As you scale up in your product lines and storefronts, you can keep control without adding more headaches to your growth with an MDxP foundation like Riversand’s.
5. Streamlining Inventory Management and Demand Forecasting
In a previous post, we highlighted how retailers are using artificial intelligence (AI) to optimize inventory management. The takeaway is that you can use AI to accurately forecast demand which informs inventory management and merchandising decisions.
Similarly, master data management (MDM) can unlock the ability to forecast demand for specific stores, distribution centers, or sales regions.
So, for example, if your company is planning to reorganize sales regions, it could combine regional master data and transaction data. By doing this, the company provides better context for its transaction data. In turn, forecasters are better able to predict the effect of the proposed sales reorganization.
The result is more informed strategic decision-making for everything from merchandising to corporate restructuring.
Using Riversand’s Master Data Experience Platform (MDxP), embedded with actionable insights and analytics, enterprise leaders can effortlessly leverage truly business-wide feedback at any given time, taking a major leap forward to foster greater trust between markets and customers in the retail segment.
