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Omni-Channel Retail Analytics: How to Start Your Journey

Elena Prokopets

B2B Freelance Writer

Omni-Channel retail involves delivering a uniformed, frictionless shopping experience regardless of which channel consumers use to complete the purchase — e-commerce website, online marketplace, social channel, or a brick and mortar location. 

Modern consumers don’t settle for a single approach to shopping. The global pandemic further blurred the lines between offline and online commerce: 

  • BOPIS (buy online-pay in-store) order volume grew by 554% in May 2020.
  • Online personal appointments and digital queues are what 81% of consumers and 59% of consumers now expect from retailers
  • Social commerce — purchases via popular social media networks such as Facebook, Instagram, Pinterest — are projected to reach $90.4 million in 2021. 

Such digital consumer journeys provide retailers with a plethora of previously untracked data. But to decode those clicks into insights, businesses will also need better omni-channel retail analytics solutions.

Why Omni-Channel Retail Analytics is Mission-Critical for Retailers

Omni-channel commerce stands for consistency and excellence at every touchpoint with the customer. 

However, few retailers truly excel at providing a frictionless shopping experience across channels. Why? Because 71% don’t know where to start when it comes to understanding their users’ behaviors as Amplitude reports. Furthermore, 59% move forward with decision-making based on instinct, rather than evidence.

Our customer research at Indellient confirmed the above too. Many retailers still use ad-hoc omni-channel analytics methods and Excel spreadsheets in particular. 

Surely, Excel is an inexpensive, available and familiar tool with a host of simple analytics dashboards and formulas for running basic calculations of customer lifetime value (CLV) or total sales volumes per channel. But this software was never designed to support big data analytics or advanced statistical modeling scenarios. 

Consequently, you are very limited in your ability to analyze complex customer journeys and run advanced data modeling scenarios. 

The Two Options for Enabling Omni-Channel Retail Analytics 

Modern omni-channel retail analytics tools help you learn not just the baseline shopping preferences, but also unravel:

  • Customers’ preference for product assortment at different locations
  • Regional price sensitivity and response to discounts 
  • Local micro-trends and changes in preferences per channel 

The above intel can help you optimize your marketing, merchandising, and inventory management strategies, so that you could fully satisfy each cohort of shoppers.   

Sounds promising? Here are two types of retail analytics solutions that can provide such insights. 

SaaS Platforms

Proprietory SaaS platforms offer subscription-based access to a set of data storage, transformation, and analytics tools to crunch the numbers. Most provide pre-configured connections to connect with common business systems — CRM, ERP, or e-commerce platforms — to operationalize available data. 

Popular options include Tableau, InsightSquared, and Adobe Analytics among others. 

Benefits

  • Unified access to data from various channels
  • 360-degree view of customer journeys 
  • Fast access to market and consumer insights
  • Access to pre-made reporting dashboards and visualization tools 
  • Low learning curve for non-technical users 

Shortcomings:

  • Vendor lock-in: sensitive data remains stored with a vendor whose operations and performance you cannot control. 
  • Features are limited to what the vendor believes is important, little ability to influence what is provided or how it works
  • Limited compatibility and interoperability: the tool may not integrate with other business software you are using, and may be difficult to extract. 
  • Challenges to growth: some vendors impose limitations on how quickly data can be loaded or how frequently, or require higher paid tiers for those services
  • Higher total cost of ownership (TCO).  SaaS tools charge on a per-user or per-license basis, so the costs can inhibit benefiting from your analytics.

Custom Data Analytics Solutions

In-house analytics solutions are an emerging in importance as an alternative to off-the-shelf software. You can create a fully customized, omni-channel retail analytics setup to suit your specific needs. In this case, you have complete control over the data, computing resources and tools that you choose to access and share the information. 

The “build” route may also be better suited for retailers early into their analytics journey. In most cases, to benefit from off-the-shelf tools, you’ll need to configure them to integrate with other data sources, both internal and external. If up to this point, most of your customer intelligence was stored in an ad-hoc, siloed manner, some tools may not be able to process such data without additional transformations. 

For that reason, we encourage retailers to start with data warehousing — the process of consolidating all data sources in a centralized, secure repository.

data warehouse is an analytics layer, sitting atop of your standard databases. The data collected in it is already cleansed and transformed for analytics purposes. All that is left is to connect a Business Intelligence (BI) tool of your choice to mine insights. 

Benefits:

  • Scalable and flexible data storage  
  • Connect any type of BI or analytics tools you need to enhance data visibility across the organization 
  • Create custom data models and machine learning-powered analytics engines
  • Customizable for reporting on different benchmarks/KPIs
  • Fully secure and compliant — you remain in full control of the data 
  • Cost-effective in the long-term as you have fewer licenses and renewals to deal with
  • Future-proof your data analytics solution by allowing growth in scale, new functionality and data sources
  • Solve deeper level business questions by building custom data models using the full suite of cloud tools available

Shortcomings:

  • Higher upfront investment to set up a data warehouse and connect required data sources (this can be mitigated by working with an experienced team).
  • Technical experience is required — you will need in-house IT resources or external partners to build and maintain the new system
  • You will need to ensure that all data is stored securely and breaches are virtually impossible. An experienced technology partner can help here. 

To Sum Up 

As the online commerce landscape becomes more crowded retailers will need to step away from guesstimation and spreadsheet-powered analysis. Or else they risk losing the race to more “customer intelligent” competitors. 

Custom data warehouses provide a future-proof foray into omni-channel retail analytics, scalable alongside your business needs and operation size. By consolidating your data and maintaining it in an analytics-ready state you can rapidly apply different modeling techniques to procure insights for decision-making and stay atop of the emerging consumer trends across all sales channels. 


Turn Data into Actionable Insight

Indellient takes a customer-first approach to help you bring all your Analytics data into one place. Our team of Data Analysts, Data Engineers, and Data Scientists make sure you get the most out of your data.

Indellient is an IT Professional Services Company that specializes in Data AnalyticsCloud Development ApplicationManaged IT SolutionsDevOps Services, and Business Process Management.

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About The Author

Elena is a freelance B2B tech writer for software companies and their technology partners.