Data-Driven Retail | Pitney Bowes

Data-Driven Retail:

Extracting value from customer data

Focusing on how data can be used to better reach customers
and compete more effectively across channels.

Getting data right

A top priority for retailers is creating more personalised, customer-centric experiences, and doing that starts with knowing the customer. To get a better sense of their audience, retailers must first understand how to bring together external data sources, such as demographics, location and market data, with internally held customer data, such as transaction histories and loyalty status. Once these sources have been identified—and the data has been checked for accuracy—retailers can begin creating a detailed understanding of their customers, ideally on the way to establishing a trusted single view.
Beyond the single customer view, there are other ways data can be used to help retailers improve revenue and operational performance. Here are a few:
1

Increasing average
transaction value

2

Location-led strategy
and planning

3

Streamlining
requirements

1. Increasing average transaction value

Many online retailers have mastered the method of increasing cart values by recommending additional products they know sell well together. These are the types of decisions analytics engines can make by dynamically identifying trends and upsell opportunities and leveraging demographic and location data in the moment to offer customers the combination of products and price that is most appealing to them.
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2. Location-led strategy and planning

Location data can help businesses make strategic decisions that improve margins — analysing footfall data in combination with location and socio-economic information, for example, to optimise the mix of physical and online locations or to identify new store locations and plan franchise territories.
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3. Streamlining requirements

The rise of online shopping has created a need to understand frequently changing shipping and tax costs. External data sources on tax rates and delivery costs can be used in combination with customer address data to keep the buying experience consistent. Doing so requires that retailers have the ability to integrate data changes seamlessly and accurately to correctly estimate costs, and avoid tax underpayments or overpayments, without interrupting the customer experience at the point of sale.
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Not optimising data usage

Retailers are dealing with new buying behaviours, growing e-commerce, and ubiquitous global online marketplaces. In addition, their margins are dwindling and customer expectations only continue to rise. To respond to these challenges, retailers are increasingly turning to data and analytics to make better business decisions and guide customer marketing initiatives. While some retailers are still learning how to best collect customer data, others possess it but are unsure what to do next.

Maximising data

To get a better sense of their audience, retailers must first bring together external data sources, such as demographics, location and market data, with internally held customer data, such as transaction histories and loyalty status. A single customer view can be unified into a “golden record” of verified information and linked to a known physical or digital address for each customer. That record can then inform manual and automated decisions, such as those around intelligence-based marketing, planning and expansion strategies, and cross-channel campaigns.

The power of analytics

Once retailers have the right data and can trust it, the next question is how to best leverage it. Customer data and location analytics platforms can help retailers make sense of the information they have and then act appropriately.
Andy Reid is Global Director, Retail, Pitney Bowes Software. Andy says he sees retailers applying data to two broad purposes. “Each one of these use cases spawns many different plays, but a lot of our customers come back to these two core uses.” 

Retail analytics

This helps answer questions about the business, including how to increase revenue with existing customers, secure new customers or increase margins. Retailers can also use analytics to understand customer preferences and personalisation needs, feeding into decisions about communications delivery and customer experience.
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Customer Intelligence

This enables you to build dynamic customer profiles across online and offline powered by clean and trusted data.  Once you have a better understanding of your ideal customer, you can take a strategic approach to data segmentation.  One that provides the foundation for further analysis such as building look-a-like audiences.  Use this to bring data to life visually through maps, reports and dashboards. 
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Looking to the future

It’s clear retailers now prefer a more unified strategy that makes use of their physical and digital platforms in complementary ways, rather than in isolation. Data is the key to facilitate these strategies across channels so that a customer’s experience is seamless regardless of how they come into contact with a brand.

So how can retailers make the most of their data and continue to compete in today’s ever-changing landscape? Here are three recommendations.

1. Put your data to work:

There’s little value in collecting data if it’s not used to generate new insights and drive decisions. Targeting and personalisation efforts both require interrogation of data to understand where and how resources should be directed.

2. Understand your data:

Analytics helps answer questions, but those answers will be only as good as the underlying data and the questions being asked. So it is important to understand both the types of questions that data can answer, and the suitability of the data being queried for that purpose. A corollary is to understand where the data gaps are. Third-party market and reference data can both add value to internal data held by a retailer.

3. Seek expert advice:

It’s not always feasible to have data specialists in-house. In those cases, it can be helpful for retailers to partner with outside experts to outline how to make the best use of existing data and identify where it can be improved, enriched and contextualised to ensure it not only delivers but exceeds expectations.

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For more information about Pitney Bowes data solutions for Retail, visit

pitneybowes.com/au/data.html