With eCommerce now Mainstream, Digital Analytics for Retailers is High Priority

Introduction

For the past few years, retailers have increasingly experimented with various alternative digital and eCommerce models to evaluate their applicability and profitability for certain markets and customer segments. This has included online ordering for curbside pickup or home delivery, as well as an array of mobile shopping options.

Current Landscape

Prior to the recent emergence of COVID-19, adoption of digital commerce technology by traditional brick and mortar retailers has been somewhat underwhelming, and even painfully slow. This is because many of these options have not yet proven themselves to be adequately profitable to the retailer, especially when compared to traditional in-store visits. As a result, in the search for a profitable model, the industry has been more focused on the pros and cons of using proprietary retailer-operated eCommerce solutions, outsourcing to third party delivery companies such as Instacart or Shipt, or a combination of in-house and outsourcing.

In 2017, online grocery sales were predicted to capture 20% of total grocery retail by 2025 to reach $100 billion in sales, according to a Food Marketing Institute (FMI), with a study conducted by Nielsen. These figures were subsequently revised and accelerated to reflect that the industry would achieve that $100 billion sales volume target hit by 2022. It is now likely that this milestone will be achieved much sooner due to the extraordinary eCommerce volume attributed to COVID-19.

With the recent virus-related stay-at-home orders forcing so many people to be socially distanced, this has unexpectedly accelerated, and stress-tested the technologies and operational constraints of these eCommerce models. Some retailers have consumed all of their existing eCommerce capacity and are unable to satisfy new shopper demand for these services. At the same time, virus-related increases in traditional in-store shopper traffic has likely obscured an undeniable point that these eCommerce models have been artificially turbo-charged and shopper comfort with these digital experiences has changed forever.

Focus on Digital Commerce Models

While some retailers are focused on the fundamental enablement of these digital commerce models, others who were earlier adopters have realized that they now need to shift their focus. While they have been focused on enablement, they have underinvested in the necessary analytics to run these new channels in a sustained and profitable fashion. During the nascent state of eCommerce initiatives, most channel-related decisions have been made instinctively, rather than in a data-driven fashion. The timely emergence and affordability of AI-powered analytics for retailers now seeks to help remedy the online blind spots that currently exist.

What is AI and why is it an important enabler for eCommerce?

Artificial intelligence (AI) is the ability of a computer program or a machine to think and autonomously learn on its own. AI encapsulates a broad set of technical capabilities designed to provide better outcomes, augmenting what humans would otherwise need to do. More clearly, AI is software that mimics and automates the tasks that humans previously had to do exclusively. These tasks include learning, reasoning, problem-solving and even understanding language. Common AI terms that you may have heard include machine learning, deep learning, robotics, natural language processing (NLP), and more.

Benefits of AI-Powered Analytics for Retailers

Below are some examples of how AI-powered analytics for retailers can help bolster eCommerce performance:

Online Product Assortment

Just because a retailer may have 20,000 to 50,000 items sold in a given store, does not mean that the entire online product assortment must match what is physically sold in that same store. To the contrary, a retailer’s online assortment should be tuned to provide product coverage on those items that are most in demand, and most commonly available-to-promise with inventory stock on hand to fulfill orders. AI-powered analytics for retailers can assist in curating what the ideal online assortment should be. This can be tuned location by location to ensure alignment with unique demographics that may exist in those locations.

Online Intelligent Pricing

Just because a retailer has optimized in-store shelf pricing does not mean that the online pricing must be – or even should be – the same. In fact, all experts acknowledge that online fulfillment costs including order picking costs and delivery costs are much higher, no matter how efficient a given retailer may be. AI-powered analytics for retailers can help them understand what the optimum pricing should be for items purchased in online orders, as well as their respective pick-up fees and/or delivery fees.

Online Intelligent Promotions

Most grocery retailers have three primary promotional vehicles that are used to shape shopper behavior; an ad/circular which may be print-based, digital, or both, in-store temporary price reductions (TPRs), and manufacturer-sponsored coupons. AI-powered analytics for retailers can help them figure out how to optimize promotional offers to achieve the desired shopper-specific behaviors including securing bigger baskets, increased profitability of those baskets.

Online Demand Forecasting

From the retailer’s perspective, avoiding lost eCommerce sales attributed to out-of-stock merchandise is a key benefit of highly accurate AI-powered forecasting specifically for online orders. If these lost sales attributed to out-of-stock merchandise could be reduced by 50% or 75%, it would significantly help retailers grow their eCommerce top line revenues.

Online Personalization

AI-powered personalization also allows retailers to segment their shoppers by frequency, recency, category level participation, total spend and product attribute preferences, and then tailor the many offers in their “offer banks” into highly relevant, basket-building experiences.

Set high benchmarks with AI and monitor KPIs across functions

Beyond Advanced Planning for online Merchandising and Marketing, there is an assortment of general and operational Key Performance Indicators (KPIs) that are highly relevant for eCommerce retailers who seek to run a tight ship. Here are examples:

Sales & order volume by channel (curb-side, delivery)

Order size in revenue and items per order by time of day and day of week.
Category diversity analytics
Customer profitability analysis
Identify causal relationships or attribution on what factors can drive sales outcomes
Customer purchase patterns by channel

Operational metrics

Picking efficiency and accuracy
Picker and Driver productivity analysis
Out-of-stocks and substitutions by order

Legacy BI Analytics for Retailers are Not Sufficient in the Digital Era

Over the past two decades, in-store-centric decision making has been supported mostly by legacy tools including data warehouses, report generators, business intelligence (BI) solutions and even Microsoft Excel. The inadequacy of traditional, legacy reporting and business intelligence tools results in three common challenges:

Decision making based on Legacy systems is too slow, laborious, and inaccurate.
Source data required for eCommerce optimization is derived from many legacy systems and consequently is siloed and not accessible.
Spreadsheets and traditional reporting tools are not sufficient, insightful or actionable enough for digital operations.

Conclusion

eCommerce has become a critical-mass component of retailer revenues. This will only grow for the foreseeable future. AI-powered analytics for retailers can play an important role in making eCommerce channels a highly profitable and attractive part of the retail enterprise.