The retail industry has become intensely competitive, dotted with name brand incumbents, disruptive e-commerce challengers, boutique establishments and everything in between. 

Naturally, this hyper-competitive environment empowers customers to demand more choices and options than ever before, ultimately leading to downward pressure on retailer margins. 

Can Predictive Analytics Set Your Business Apart?

Given this backdrop, retailers are looking for new ways to drive competitive differentiation — from deeper customer engagement to reduced operating costs. To achieve these benefits, retailers turn to big data and predictive analytics to: 

  • Detect customer buying patterns that guide marketing campaign planning 
  • Deliver personalized promotions at the precise moment to increase purchases 
  • Predict and plan for future demand with intelligent forecasting and inventory management
  • Identify new ways to work with suppliers to minimize lead times

Whether you’re a brick-and-mortar outfit or an online retailer, this brave new world of big data can carve out greater operational efficiency, customer satisfaction, repeat purchases and, ultimately, profit. 

Creating a Comprehensive Data-Driven Approach

So retailers are unanimously achieving smashing success with this data-driven approach, right? 

Not necessarily. 

You need more than just data scientists and data visualization. You need a comprehensive approach embedded in the fabric of your organization, based on these three guiding principles:   

1. Industry Context: Understand the relevant markets, trends, competitors and business problems in your industry segment

No business decision can or should be made in a vacuum, right? Well, the same applies to big data analytics. 

Every stage of big data projects needs relevant context and expertise. They help you ask the right questions, collect the right data, synthesize the right information and, ultimately, create value out of big data investments. 

The apparel industry is different than financial services: it has different products, demand drivers, value chains, regional preferences and buyer process. Even within the apparel industry, market challenges and business problems can vary dramatically from department stores to subscription-based delivery services.

If you’re getting started with a big data initiative, make sure you’re incorporating the relevant industry context that will drive data analytics toward appropriate business opportunities. 

2. Technology Choice: Make sense of the various open and not-so-open big data technologies and choose what’s right for you

It feels as if the open source community releases a new disruptive technology every month. Some offer great promise while others seem duplicative with proven commercial technologies. 

All of this contributes to an increasingly complex technological world, where data is coming from various environments and evolving formats. Many organizations deal with this complexity by establishing Chief Data Officer or Architect roles to future-proof their technology stacks. 

How do they accomplish this? First, recognize that an overwhelming number of tools and solutions are available from the open source community as well as commercial organizations. Choose the right tool for the right job with the data pipeline. Follow the path of successful organizations to adopt best-in-class solutions along this pipeline — from incorporating the best stream processing and extract, transform and load tools to building an event messaging bus to creating a data lake and leveraging a scalable data warehouse solution with choosing a user-friendly business intelligence offering. 

Organizations that adopt best-of-breed technologies understand the evolution of these technologies and the interdependencies among them, including support for programming and modeling languages. Such organization are able to build a data pipeline that provides a scalable, robust solution with lower cost of ownership that is future proofed for the long run. 

3. Team Collaboration: Organize teams and analysts to collaborate effectively by breaking down departmental siloes and enabling data democratization 

Gone are the days of enterprise data sitting in an IT silo only to be accessed by a select few. For big data to benefit all of your organization requires the collective talents of your organization. Using their preferred languages such as Python, R or Spark, data scientists can develop and run their models in-database and tap into native machine learning algorithms to understand how various attributes impact buying behavior on the full corpus of data — without down sampling. 

Product teams can use A/B testing to quickly identify and replicate the most effective web content and promotions. Marketing can track consumer sentiment across social media outlets to detect and adapt to the latest trends. 

The list of benefits goes on, but at the end of the day, access to data is imperative. Access to data allows different internal departments to analyze it at the speed of the business and have the power to course correct, as needed. 

While this data democratization is an essential start, those retailers working at the forefront of data analytics are creating a connective tissue across teams, allowing insights to feed decisions and actions all along the value chain, from material purchases to customer retention.  

Title image "Three" (CC BY 2.0) by  Graniers