The Power of Retail Data Analytics: Increase Sales and Improve Operations
Leveraging retail data analytics can empower retailers to make better decisions. According to research by global management consultants McKinsey & Company, successful apparel retailers improved their bottom line by 2-10% as a result of applied analytics. Those that implemented larger applications achieved returns over tenfold on their analytics investments. Let’s take a look at what retail data analytics is and how it can increase sales and improve operations.
Retail Data Analytics
Retail data analytics refers to the analysis of information like customer behavior, store performance, inventory, and other retail data in order to uncover patterns and trends. Insights derived from AI-powered retail data analytics allow brands to make smarter decisions to boost sales and improve operations.
McKinsey reported the following benefits enjoyed by apparel retailers who implemented specific analytics applications:
- Growth in sales (4-5%)
- Improvement in digital and omnichannel cost-effectiveness (15-25%)
- Growth in digital sales through marketing and personalization (30%)
- Decreased inventory costs (10-15%)
- Decreased churn among high-performing staff (50%)
Though McKinsey’s research focused on a specific segment of retail, it’s clear that analytics can help solve operational problems and uncover opportunities. Below are three ways retailers can use analytics to improve retail operations and sales.
Personalized CX and Marketing
Personalization has proven critical for building customer loyalty and influencing conversion. When done well, it can also help differentiate brands from the rest of the competition.
Retailers can personalize customer experiences by using analytics to segment audiences. Audience segments need not be limited by demographic data (e.g., age range, postal code), but should include actions buyers take during pivotal moments in their journey. Below are some examples of segments defined by behavior:
- Customers who visited the brand website three times in the past 24 hours.
- Users who subscribed to a newsletter.
- Users who made a purchase in the past two months.
Using customer data and analytics, retailers can tailor experiences to a particular segment in order to achieve the desired outcome—whether that goal is to encourage conversion, combat churn, or build loyalty.
The same principle applies to marketing actions. Retailers can customize campaigns and promotions according to their audiences’ journey stages. This means targeting new prospects with educational content about a brand while offering upgrade discounts or loyalty memberships to converted customers. By personalizing customer experience and marketing, brands can push the right actions at the right time to the right people, increasing their chances for successful conversions.
Optimized Logistics and Supply Chain Operations
Retail data analytics can help retailers optimize their logistics and supply chain from a few different angles:
Starting point: Prevent supply issues by mitigating supplier risk. Retail data analytics help brands avoid supply chain problems by managing supplier risk. Retailers can analyze data and find risk indicators like delayed shipments, imprecise deliveries, order fulfillment errors, and compliance or regulatory issues.
Midpoint: Plan strategic delivery routes to streamline logistics. Retailers can collect and analyze information like delivery routes, weather updates, driver schedules, fleet data, and more to plan the most efficient routes to consumers.
Endpoint: Manage inventory with accurate supply and demand forecasts. Brands can more accurately predict sales and adjust stock capacity based on analyses of historical data, inventory turnover ratio, behavioral trends, sales performances, and other relevant information.
Retail prices need to strike a balance between staying competitive and making a profit. Retail data analytics makes use of the following data to help retailers manage pricing:
- Consumer demand
- Product seasonality
- Competitor prices
- Retail channels (online, on-site)
- Purchasing behavior
Advanced analytics also enable retailers to factor in broader variables like market conditions, industry predictions, competition, and the historical performance of specific products to determine the right pricing strategy.
Retail data analytics empower brands to make data-driven pricing decisions for different channels, audiences, and seasons. Retailers can also use data to avoid creating prices for inaccurate customer personas or artificial demand fueled by fleeting trends.
In summary, retail data analytics can help retail players boost sales and improve operations by using data to identify trends, patterns, and opportunities. Retailers can use analytics to personalize CX and marketing through audience segmentation. Retail data analytics also enable players to optimize their logistics and supply chain via accurate forecasts, strategic delivery routes, and mitigating supplier risk. Finally, analytics use customer data, historical performances, and competitive analyses to help retailers implement the right pricing strategies.
Power Retail Data Analytics With Treasure Data
More data means better analytics and better decisions. Treasure Data‘s enterprise-grade customer data platform (CDP) collects and centralizes data from all sources: website, mobile, social media, PoS, IoT, offline, and second- and third-party sources. Our CDP comes with 120+ pre-built connectors for omnichannel data collection with bulk and streaming data ingestion. Keep all your data—customer, prospect, and product data—in one place, with unlimited access, for as long as you want.
Treasure Data Customer Data Cloud is an integrated suite of cloud-based customer data platform solutions. Treasure Data provides insight by collecting and centralizing customer data, unifying profiles, and analyzing journeys to surface hidden customer behavior trends.
See what you can do with our platform:
- Collect and centralize customer data from all sources
- Unify customer profiles using online + offline data
- Analyze customer journeys
- Derive actionable customer insights using Machine Learning techniques
- Personalize customer experience at all customer journey stages
- Seamlessly connect to existing martech and business intelligence tools
- Manage data pipelines at the lowest cost
- And more
To learn more about how you can use Treasure Data Customer Data Cloud to power your retail data analytics, consult an expert today. Want to learn more? Request a demo, call 1.866.899.5386, or contact us for more information.