Omnichannel Analytics in 2023 (Everything You Need to Know)

Omnichannel Analytics in 2023 (Everything You Need to Know)

Omnichannel analytics has never been more important than it is today. Not only are customer expectations greater than ever (with advanced fulfillment options like BOPIS becoming a must-have), but sky-high inflation and economic uncertainty means that consumers have less discretionary dollars to spend. 

Consumers expect more but they can spend less. 

In turn, this means that retailers must find ways to satisfy consumers through all channels without overcommitting on inventory – all while keeping their costs low and their cash flows high.

This is a tall order, but one that can be more easily achieved with the help of omnichannel analytics. This is perhaps why more than a third of retail executives are actively investing in omnichannel technologies.

But before we explain how omnichannel analytics can solve your problems, let’s discuss what it actually is. 

Example of Omnichannel Data Analysis

What is omnichannel analytics?

At its simplest, omnichannel analytics describes a specialized set of tools or software that allow retailers to combine and analyze data from across all of their sales channels in one place. 

Instead of generating multiple reports from multiple platforms (POS systems, ERPs, ecommerce platforms, Excel spreadsheets etc.) and manually combining all of the data for the purposes of analysis – omnichannel analytics integrates all of this data into a single platform. This not only saves time on manual data analysis, it reduces human error and gives retail leaders a more accurate view of their business. 

More sophisticated omnichannel analytics (especially platforms leveraging AI and predictive analytics) can also use omnichannel data to:

  • More accurately predict demand for each SKU at each location
  • Quickly diagnose and flag issues like out-of-stocks and overstocks across all channels
  • Automatically create POs and inventory transfer requests based on anticipated demand for each SKU at every location
  • Proactively recommend opportunities for reducing operational costs, improving margins, and boosting revenue 
  • Break down team silos and enable cooperation (e.g. ensuring each channel has enough inventory when marketing runs a promotion)

Why is omnichannel analytics important?

The effect of COVID-19 on the omnichannel customer journey

Omnichannel analytics is important because large multichannel retailers have become too complex to effectively manage without modern tools.

Take, for example, a legacy retailer that seeks to implement BOPIS (buy-online-pickup-in-store). 

From the customer perspective – all she wants is to find an item online and pick it up on her way home from work later that day. 

From a retailer’s perspective, there are a dozen small miracles that have to take place to ensure the customer gets her wish. For example:

  • Integrating ecommerce with POS to ensure inventory is up-to-date and accurate
  • Accounting for ecommerce demand in B&M inventory to ensure regular shoppers don’t see empty shelves due to BOPIS orders
  • Revising inventory allocation across all channels to minimize transfer and shipping costs for the new fulfillment method
  • In case of a location-specific out-of-stocks, calculating the most cost-effective transfers to maintain margins (is it more cost effective to transfer from the warehouse, DC, or another local store with less demand for the same SKU?)
  • Using data to optimize BOPIS policies, picking times, abandoned orders, etc.
  • Accounting for and optimizing returns from ecommerce sales (especially for fashion & apparel)

If a retailer doesn’t have all of the above points dialed in and optimized, they’ll quickly discover that BOPIS is hurting them more than helping them – as lack of inventory forces customers to leave for competitors, unoptimized fulfillment and shipping eats into margins, and ecommerce returns decrease revenues. 

Dialing these points in is precisely why omnichannel analytics is important. And the benefits are not simply limited to fancy fulfillment options like BOPIS. Retailers that use omnichannel analytics can elso expect these benefits:

  • Data becomes more accurate, retailers can streamline business operations from planning to fulfillment
  • Demand forecasting becomes more accurate, improving in stocks, and makes inventory management more efficient
  • Cross-business visibility leads to deeper insights, better internal communications, and more effective decision-making
  • Customers’ journeys improve as they find, buy, and return products through whatever sales channel is most convenient for them

It’s no surprise that retailers who leverage analytics are surging ahead in the industry. In fact, Forrester Research estimates that retailers leveraging advanced analytics will win $1.8 trillion in market share from less savvy retailers this decade. 

Omnichannel data challenges 

Visual representation of omnichannel data storage

Do you face a flood of real-time omnichannel data that you can’t make heads or tails of? You’re not alone.

Forrester research found that “74 percent of firms say they want to be data-driven, but only 29 percent are successful at connecting analytics to action.”

This is because data management methods designed for nightly rollups of store sales aren’t up to the task of true omnichannel analytics.

  • Manual processes and spreadsheet analysis – Small retailers and startups rely heavily on manual inventory processes. Even with the data they have, spreadsheet analysis isn’t responsive enough to support a complex omnichannel strategy.
  • Siloed databases – Data for inventory, point of sale, and other functions sit in application-specific databases that are difficult to bridge. There may be no consistent policy on data-labeling, date and numerical standards, contribution codes and SKUs, etc. Worse yet, different channels may use different applications for the same functions, further siloing retailer data.
  • Processing limitations – Analyzing retail data (especially for the purposes of calculating demand and making predictions) requires a lot of computational power, as each individual SKU at each individual location has its own demand profile – impacted by dozens of different variables (like price, seasonality, competition, cannibalization, geography, sociodemographics, etc.). 

Omnichannel data is messy, difficult to find, and impossible to action without the proper tools. This is why many omnichannel retailers struggle with the same issues like:

  • Buying too much or too little inventory
  • Lacking visibility into over-performing or under-performing products
  • Poor allocation between and within channels
  • Lower margins due to markdowns and manual reallocation

The issue of poor data management is widespread. More than a third of retail executives said data silos keep them from optimizing customers’ experiences.

Solutions for omnichannel data issues

Dedicated omnichannel analytics systems (like Retalon) have three major advantages in addressing data challenges.

Firstly, they are designed to integrate with a wide variety of data types from a wide variety of platforms (including ERP, POS, inventory management, order management, etc.). This is achieved through a combination of purpose-built APIs (applications that allow different software to talk to each other) and perfected retail data-cleaning procedures.

Secondly, beyond simply combining and cleaning your data, the most sophisticated omnichannel analytics platforms use AI and ML algorithms to produce actionable analysis. For example, Retalon’s omnichannel analytics solution can:

  • Produce an accurate demand forecast for every SKU at every store
  • Flag, diagnose, and fix inventory issues, lost sales, and out-of-stocks
  • Recommend profitable inter-store transfers by moving inventory from areas of high supply to areas of high demand
  • Generate optimized purchase orders (quantity, estimated delivery time, shipping cost, etc.)
  • Account for returns in demand forecasts, and optimize the reverse supply chain

Thirdly, omnichannel analytics solutions leverage cloud infrastructure to compute large quantities of calculations (like predicted demand for each individual SKU at each individual location) – something even the most sophisticated Excel spreadsheet would never be able to achieve. 

Bottom-line benefits of omnichannel optimization

Omnichannel Optimization

More accurate forecasting

Omnichannel analytics can account for hundreds of variables and calculate demand across millions of store / SKU combinations. 

Better informed retailers:

  • Buy more of the products with the highest demand.
  • Properly stock the locations with the highest demand.
  • Order the right amount to meet demand at each location without going over.

Improved margins

Combining accurate forecasting with actionable recommendations helps retailers achieve true omnichannel optimization:

  • Earn more revenue by decreasing lost sales
  • Improve inventory margins by reducing unnecessary markdowns
  • Reduce total inventories by only bringing in SKUs that sell
  • Lower operational costs, including storage, picking, and shipping

Unified business operations

Bringing all sales channels together under one analytics system tears down silos. Everyone shares the same source of information. Decisions are made using the same business rules. 

With everyone on the same page:

  • Communications improve across departments and channels.
  • Inventory shrink declines as fewer products get lost in the system.
  • Inventory becomes more consistent across channels.
  • Markdowns and stock-outs decline.
  • Wasted time and cost get squeezed out of retail operations.

Improved omnichannel customer journey

Engaging, obtaining, and retaining customers is central to a retailer’s existence. And today customer expectations are higher than ever before. Omnichannel analytics can help retailer’s stay competitive and improve the customer journey. For example, retailers can ensure:

  • Websites give customers correct in-stock information
  • Every location has enough inventory to meet customer demand
  • Stores improve customer service by fulfilling orders quickly

Moreover, omnichannel analytics can ensure that new fulfillment improve customer experience while maintaining profitability, including:

  • Buy-online-pickup-in-store (BOPIS).
  • Buy-online-pickup-at-curb (BOPAC).
  • Reserve-online-pickup-at-store (ROPAS).

Winning retail with omnichannel analytics

The benefits retailers gain from using analytics in omnichannel retail are profound. Traditional data and inventory management systems aren’t up to the task. As a result, retailers lagging behind on implementation are losing sales, profits, and customer loyalty.

To execute a winning omnichannel strategy, you need an analytics solution that helps your retail business:

  • Increase capacity to forecast every SKU at every location
  • Improve forecast accuracy in every location to minimize overstocks and understocks
  • Leverage existing systems without adding costly infrastructure
  • Improve internal communications by replacing silos with a shared analytics platform
  • Improve the customer journey by offering better information and more choice

Omnichannel analytics optimizes every aspect of your business, streamlining operations and removing the inefficiencies that erode revenues and profit.

Want to learn more? Book a demonstration of Retalon’s omnichannel analytics solutions on your own data.