Close Menu

How to Turn Your Retail Analytics Into Data-Driven Strategy

Analyzing disparate data sources can be challenging for retail. Learn how retailers can get a 360-degree view and create a true data-driven retail strategy. 

With the rapid growth of e-commerce, the retail industry has plenty of data, but brands lack the right resources and tools to leverage it effectively and link it to their in-store customer activity.

Retail owners tracked sales, inventory, and customer behavior long before microchips or retail analytics software existed. Retailers use this information to optimize their business decisions and satisfy their customers – what products to stock at what time and how best to market them. Even though they look quaint today, these methods were very effective for retailers to boost sales and gain a competitive advantage.

Nowadays, retailers have access to a vast amount of data. Thanks to mobile devices and social media, they possess millions, if not billions, of data points about their current and potential customers.

They have all the historical data they need to produce highly personalized customer journeys using big data analytics. Innovative brands are already using customer analytics and retail sales data to create highly personalized customer journeys based on those insights. 

The apparel industry lags behind when it comes to embracing analytics, often relying on merchant- and designer-driven “gut feel” rather than analytics-based decision making. Players in the apparel industry cite several challenges as barriers to investing in their analytics capabilities, including poor data quality, a rapidly changing assortment and competitive landscape, high SKU and logistics complexity, and limited analytics expertise among current employees. 

Some companies hesitate to transform because they believe it requires a three- to five-year tech overhaul and Amazon-level resources or capabilities. It can be difficult, expensive, and time-consuming to build a customer journey that is truly data-driven. Now, thanks to a new approach Gartner calls smart hubs (i.e., CDPs), brands can regain competitive advantage much more quickly. 

To illustrate, consider the following:

  • Access to third-party customer data may (and likely will) disappear
  • Making customer data useful is a challenge
  • Modern retail data analytics rely on a variety of capabilities
  • Partial solutions are not enough

Managing the entire customer lifecycle with smart hubs addresses the challenges holistically.

Concentrate On Customer Engagement That Generates Opt-Ins and First-Party Data

Marketers are also losing access to critical tools, cookies, and third-party data, which have helped them understand consumer behavior and target retail customers accordingly for decades.

The availability of third-party customer data is dwindling rapidly due to tougher regulations like the General Data Protection Regulation (GDPR) of the European Union and the California Consumer Privacy Act (CCPA). Google has recently decided to block third-party cookies in its Chrome browser, which will impact digital businesses, consumer products, and technology.

Data analytics for retail apparel: what does this mean? The most insight can be gained from first-party data. Thanks to historical data collected through the supply chain, POS systems, social media, retail sales, and more, brands have huge amounts of rich data. Since you know the data firsthand and can ensure its accuracy, that kind of data is generally of a higher quality than data obtained from third parties.

Understand That Simply Collecting Data Isn’t EnoughAnalyzing disparate data sources can be challenging for retail. Learn how retailers can get a 360-degree view and create a true data-driven retail strategy. 

Data collection is a top priority for most apparel players. Purchase transactions, web browsing behavior, interactions with mobile apps and social media, and responses to email campaigns are all captured automatically by systems. Yet petabytes of customer data are worthless until you can turn them into actionable insights to formulate a data-driven retail strategy.

To solve this problem, most brands have invested in enterprise warehouses and/or big data lakes to gather customer data in one place. The problem is only delayed by putting all this data into one rigid system.

When retailers tried to solve this problem, they sometimes spent a significant amount of money and time (often a year or more) building enterprise warehouses to organize big data in a way that marketers could use. By the time the enterprise warehouse was built and all relevant data was cleaned, the entire solution was already outdated.

The data produced by new marketing channels and other systems that came online in the meantime was too large for it to handle. Retail is changing at an astonishing rate in the digital age. Due to this, the analytics solution of new retail analytics software was often not equipped to provide the advanced analytics needed to adapt to the constant changes in retail.

As a result, many retailers, even global luxury brands, still struggle to unify siloed data and make actionable insights. Retail businesses with larger budgets may have the IT and advanced analytics expertise to help automate retail on the operations side. Marketing, however, must queue up for those scarce, expensive data analytics resources. By the time marketers have the customer insights they need, the opportunity they were chasing has already passed.


Build a Foundation In Data Analysis

Exactly what does it take to convert all that data into meaningful insights? You’ll need the right people and the right software toolsets to unlock the valuable and actionable insights from that data. There is no doubt you need smart, dedicated marketers. In addition, you need historical data and streaming information as much as possible. Also required are business intelligence solutions and predictive analytics, as well as AI and machine learning solutions.

You wouldn’t want to end up with valuable customer insights if you then don’t use them. Why? Marketers lack the capabilities to apply those insights in campaigns at scale or speed. This is a problem faced by every marketer in the retail industry.

Understand the Available Tools and Their Capabilities (and Limitations)

You don’t necessarily need to have all capabilities, but as much as possible it’s recommended to connect your business analytics, analytics tools, and actionable insights. By doing so, you can truly personalize the customer experience at scale and speed.

At the moment, many technologies and analytics tools are available on the retail market that provide some or all of these capabilities. Despite this, many fail to connect the entire process from beginning to end. Let’s examine a few retail data analytics tools:

  • Customer data infrastructure solutions (CDIs). CDIs use prebuilt data connectors to gather and share data from across point channel solutions (e.g. email, web, call centers, etc.). They serve as pipes that move data from one place to another. Nevertheless, they have limited capacity to store, process, or analyze that data. Additionally, they are primarily designed for technical rather than business users and do not offer interactive access to data for marketers.
  • Data management platforms (DMPs). To better target both paid advertising and their web content, DMPs were designed to manage anonymous profiles and enrich those profiles with data from third parties. In recent years, however, increasing regulation of third-party data and the gradual disappearance of cookies have reduced the value of DMPs.
  • Marketing clouds (MCs). MCs provide orchestration capabilities and offer multiple channel solutions. These solutions are, however, largely acquired through acquisition. Despite offering some or all of the following capabilities, even leading MCs still suffer from some or all of the following limitations:
    • Disconnected data. It is often necessary to unify the underlying data of each channel, which the MC does not provide. To stitch all your cross-channel data together, you will need extensive IT or consulting resources. 
    • Limited marketing self-service. It has historically been difficult for marketers to interact with underlying customer data in MCs. 
    • Agility is lacking. Due to the limitations of MCs, you cannot deploy best-of-breed solutions from other vendors without risking fragmentation of data as well as execution across channels.

Consider an Integrated Solution

It is clear from our discussion thus far that each of these analytics solutions cannot seamlessly connect all four capabilities for personalization at scale. The market for CDPs has grown rapidly, with sales expected to grow from $900 million in 2018 to more than $3 billion by 2023, according to Research and Markets.

A CDP isn’t just for creating one-off campaigns. As a result, they drive value across the entire customer lifecycle, including:

  • Acquisition. If you know your retail business’s current best customers and their behaviors, you can target consumers that share the same key traits and behaviors. POS systems, acquired business analytics software, supply chain data, and other retail analytics software can provide this information.
  • Activation. Nowadays, the acquisition is simply getting a customer to share an email address or credit card number. However, if you can personalize messages based on the valuable information you just collected about them during the acquisition process (e.g., demographics, pages browsed, carts abandoned, etc.) you are more likely to win their business.
  • Repeat purchase. Around 80% of customers remain one-time buyers, while only 20% become repeat buyers, according to the Pareto principle. You can craft much more effective messages to drive that key second purchase by using the data and insights gathered during a customer’s initial purchase (e.g., price, category, season, whether it was a gift, etc.).
  • Retention. All brands need to measure and manage churn risk, regardless of whether they’re subscription services or a more traditional retailer. It is simply not sufficient to use standard churn risk models. Creating business analytics algorithms that yield a holistic view of at-risk customers will help you craft enticing offers that will keep them coming back.

Rapid changes are taking place in the apparel industry. Consumers are looking for an end-to-end, personalized experience; assortments and channels are becoming more complex, and competition is increasing from nimble and digital-native brands. Analytical approaches can help apparel companies address these challenges, transforming even the most complex use cases into growth opportunities.