Few data analytics initiatives in the eCommerce space have so much potential to achieve business impact as focusing on understanding and improving digital shelf performance. Tracking, managing and optimizing the way brands meet their consumers online (and being better at it than competitors) is paramount to driving sales and market share in eCommerce, as well as driving efficiencies across eCommerce business operations.
However, many brands struggle to realize the full potential of their digital shelf presence, mostly because they oversimplify what it takes to get there. In this article we outline the 6 best practices that are essential for brands to optimize their digital shelf presence in ways that maximizes business impact.
#01: Start by identifying what you are solving for, not by choosing a tool
The first step when starting a digital shelf improvement journey must be the clarification of the business questions that are being solved for – at its core, digital shelf analytics should be a business initiative, not purely an analytical one. Questions should consider the identification of improvement opportunities and be as precise as possible. Some examples of good business questions are “Which products are out-of-stock online, even though they are physically available in-store?”, “How much are out-of-stocks costing us in lost sales?”, or “How can we ensure the accuracy of our syndicated content?”.
#02: Combine digital shelf data with other data to maximize impact potential
The power of data to provide valuable insights increases significantly when we combine it with other sources of relevant data – an analytics ecosystem can be orders of magnitude more impactful than isolated data solutions, and digital shelf analytics is no exception. A good example of data to be combined with digital shelf data to achieve results is historic sales data, which can be used to prioritize opportunities and understand their dollar value. Other examples include product distribution data, brick-and-mortar availability data, and market share, to name a few.
#03: Focus on end-user needs by combining analytics and design
Data analytics needs to work for its end-users, and not the other way around. The end product should be fueled by powerful analytics, and it should also be designed so that insights are clear and intuitive and the user is able to get to what they are looking for fast, in a fairly attractive interface. Teams will respond better to a solution that blends analytics and design to meet their needs in a seamless, well-thought-out way.
#04: Think of adoption as the ultimate goal
Having the best designed and most insightful analytics solution in the world will not have an impact if it’s not adopted and acted on. Adoption is the key to ensure intuition is not prioritized over data, and to really drive business value. The best way to achieve this will vary depending on several factors, however, building capabilities to ensure end-users will effectively incorporate the insights to their work is paramount. Doing this might include the design of new business processes, designation of new responsibilities, and the re-design of monthly performance reporting routines, to name a few.
#05: Evolve your solution and processes with your business
As brands evolve in their strategies and in the way they manage their eCommerce operations, so will the associated business opportunities and the analytics solutions that will help realize them. In order to continuously create value and stay ahead of competitors, it will be key to embrace digital shelf optimization as an ever-evolving process. Improvements may be requested as users continue to adopt the solution, new features will likely need to be gradually incorporated, or new or improved data sets might be made available.
#06: Govern digital shelf data very closely
A common mistake brands make when contracting a digital shelf data provider is assuming that the information required for set-up is self-evident – this is never the case. It is important that configuration parameters, such as zip codes or search terms, results in data that is representative of the business. A good example of where this comes into play is in zip codes: digital shelf data trackers are typically able to track 5-10 zip codes, of the more than 40,000 existing in the US. Selecting these limited zip codes in a way that is representative of the brands’ sales and distribution will be key to have representative availability data, for example.
Once the tracking is set-up, factors like changing product assortment and distribution, retailer platform changes, and retailer bot protection will inevitably result in data gaps. Processes and systems need to be put into place to ensure anomalies are identified and acted upon promptly – this validation should be independent from the digital shelf data provider to ensure objectivity. A good example of governance best practice is cross-checking the assortment being tracked by comparing it with UPC-level consumption data – this will handle any assortment changes due to innovation, distribution or seasonal changes.
Bottom line…
Establishing a digital shelf ecosystem to power eCommerce business results is an initiative that should be a priority for any organization intending to maximize their eCommerce sales and share. However, it is important that the initiative is approached in the right way, to ensure that it really creates value for the organization, and doesn’t become a sunk cost. Being mindful about the approach to get there is crucial to ensure that the result is not the “illusion” of a solution. As Stephen Hawking famously said, “The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge”.
