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Insight

4 Ways Retailers Are Winning with Data

How data-driven companies create a competitive advantage.

In today’s hyper-competitive landscape, many traditional retail organizations are at a disadvantage as they struggle to modernize business models and technological capabilities in order to compete with digital natives.

But one area in which they have an inherent advantage is data. We asked four Publicis Sapient experts: What are traditional retailers doing right when it comes to data? How are they winning the game against new entrants and non-traditional players? In what ways are they better positioned for the future? Their response:

1. Breadth and depth

Many retailers have vast amount of data gathered over years, if not decades, from in-store transactions, web traffic, and loyalty programs. Most marketplaces and startups can’t touch the breadth and depth of data collected by retailers. What’s critical is figuring out how to synthesize that data and analyze it in a way that uncovers new opportunities for growth, before other interlopers do. Amazon’s expansion into every facet of peoples’ lives is as much about gathering more data as it is about finding profit in new categories. Retailers would be wise to embrace the data advantages they have with their own customers now, before others take them away.

- Jon Reily, Vice President, Global Commerce Strategy Lead

2. Personalization

With deep learning, retailers are approaching the point of individualized personalization, as opposed to traditional coarse-grained customer segmentation. In practice, this means that the retailers use multi-party data and multi-model insights to engage shoppers one-on-one with tailored promotions, recommendations and communication. By combining multiple AI models in a single platform, they can determine not just best products to recommend but also tailor the recommendations based on their stage of the journey, their channel preference, and time of day and day of the week that each person is most responsive to. When put together, we’ve seen this level of personalization help increase conversion six-fold—from 2 to 12 percent. 

- Rashed Haq, Global Head of AI, Robotics & Data

3. Fulfillment optimization

Data/AI plays a key role in fulfillment optimization throughout the customer journey. During the pre-order process, insights about customers, inventory availability and rate shopping can be leveraged in real time to provide the right fulfillment options to drive higher conversion in the buying process while minimizing cost. In the post-order portion of the journey, data optimization models can be used to manage multiple complex parameters like customer proximity/last mile costs, store performance, split shipments, existing and predicted markdowns and customer SLAs. These would materially impact a retailer’s e-commerce profitability—to the tune of 6-8 percent reduction of total fulfillment costs and 100 basis points uplift in customer conversion.

- Vivek Puri, Growth & Strategy Lead - Retail & e-commerce

4. Returns optimization

Profitable returns are no longer an aspiration, but a necessity. Data makes it possible to improve this process at every stage of the customer journey. Pre-order, retailers can segment customers and products based on returns history and promote or suppress marketing efforts based on the customer profile and product attributes. At the point of order, retailers can predict the profitability of each basket and improve margins through a variety of interventions, such as in-store-only returns or e-mail reminders to make returns more efficient. Post-order, retailers can fast-track returns from the customer to maximize margins before end of season.

- Nitin Dsouza, Director Strategy & Transformation, Digital Supply Chain

In today’s digital landscape, data underpins every aspect of the retail business—from customer engagement to supply chain optimization to marketing efforts. Retailers have an inherent advantage in that they have vast stores of information to draw from to support their strategies. But to create value from data, they must strategize how best to use it.

Author

Nova Halliwell

Thought Leadership

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