From rock and hard place to retail revolution: the power of decision intelligence

This article was co-authored by Alex Castello-Cortes of CCW Learning Consultants.

Leveraging data and AI to turn retail’s toughest challenges into strategic advantages

Today’s retailers face a dilemma: how can they provide customers with better value yet keep prices competitive (read: low)? In tough economic times, this can be even more difficult. Making changes normally means increased investment. But this can be difficult to justify when retailers are trying to reduce costs.

They are stuck “between the rock and the hard place”: needing to save money to deliver better customer value and yet having to spend money to create that value—and simultaneously make a profit?

The data challenge in retail

In this situation, making intelligent, informed decisions about the overall business is even more important. The best way to do that is to use the available data optimally and empower front-line employees all the way up to the boardroom to do this.

Yet according to the BRC, “only 46% of retailers are actively investing in enabling technologies.” So, what are the obstacles standing in the way of retailers making better use of data and analytics?

Legacy data estates: A legacy data estate—here defined as the older infrastructure used to store, manage, and process data for enterprise retailers—can complicate data management because of the variety and age of the sources, as well as poor performance due to duplication of data and a lack of trust in what is seen. These estates also entail high costs to maintain and run, not delivering what modern retailers need.

Data volumes: A retailer may have thousands or even hundreds of thousands of SKUs, each with its own set of data. Add to this supplier, customer, and distribution data, and it can be overwhelming—and not surprising—that only 50 percent, according to BARC, of the available information in organisations is actually used for decision-making. So, the important question is, “How can data be accessed and streamlined to ensure it is meaningful and relevant?”

Data speed and accuracy: Many firms, roughly half according to the BRC, admit that they cannot process ‘Big Data’ fast enough, which prevents them from being able to move quickly to realise potential benefits. And when data is not captured in real-time, users cannot make timely and informed decisions, resulting in decisions based on gut feelings or traditional experience—which is risky in this post-pandemic period, where trade and the economic environment are very different and volatile.

The pitfalls of spreadsheet reliance

Retailers struggle with reports that have been centrally distributed periodically (i.e., not in real-time) and thus contain outdated information. What’s more, most analytics systems don’t let users proactively set alerts, so decision-makers are often left in the dark when circumstances change.

Against this backdrop, the increased demand for insight grows ever more urgent, introducing yet more complexity and driving up costs. For example, using multiple systems to manage data increases costs in licensing and the resources—in-house and external—to run them. Data is often migrated from one system to another or to third parties, which means all customisation is lost.

What’s more, trust in the data erodes because it has not been obtained from the original source. Many organisations also (incorrectly) believe that they must migrate all their data into one central data store before benefiting from analytics or AI techniques that will increase insight.

One all-too-common workaround is to download the data into spreadsheets. In some organisations, spreadsheets are the backbone of analysis and decision-making at all levels. Although spreadsheets have many valuable uses, retailers must be aware of the unintended consequences of spreadsheets’ uncontrolled proliferation. These include:

• Poor governance and control: Businesses now face increased scrutiny from regulators, clients, and third parties and therefore need strong data governance and tighter controls. The problem with spreadsheets is that once distributed, it becomes almost impossible to control their contents or accuracy, and you end up with multiple versions of the truth.
• Security: Once data is downloaded from source systems and/or put into spreadsheets, it is exceptionally difficult to manage and track it. In addition, version control is an issue, and, going back to our initial list of challenges, the data is not updated in real-time, so it is likely to be out of date and a security/compliance risk.
• Lack of training: Many firms expect employees to arrive with spreadsheet knowledge, but this is often not the case. An inability to use spreadsheet packages and/or a lack of understanding of how an existing spreadsheet has been set up can lead to errors that take time to fix and have huge cost implications for the business. For example, up to 90% of spreadsheets contain mistakes, leading to unreliable data at best and loss of profit and revenue at worst.

All these things can lead to poor decision-making, thus introducing the potential for costly errors and increased risk exposure.

Empowering retail employee with data

Different types of employees across retail organisations now require better data to make decisions in their day-to-day roles. This could be to: identify which high-margin products to promote, manage peak delivery times, and check for store and DC availability on the shop floor, to name a few.

However, per a study from New Vantage Partners, more than 80 percent of employees do not have access to business intelligence tools to answer these and other queries. Instead of widespread access, retailers have typically relied on a small group of people to create reports which can also create delays in making timely and informed decisions.

The promise of decision intelligence

What if the opposite were true? What might some of the benefits be of making high-quality analytics accessible to more end-users? And how might a business case be put together to balance the cost of inaction and the advantages of increased insights against investment in enhanced data and analytics capabilities?

With the advent of AI, data analysis is not as intimidating or complex as it used to be. For example, the ability to use AI-driven chatbots to ask questions on dashboards makes interpretation easy for a wide range of users. With 77% of retailers planning to enhance business intelligence platforms by introducing AI, firms may encourage less technical users to be bolder in their research and empower them to explore previously unavailable data. When this is enabled in a controlled way via governed self-service, users get the benefits of no-code, easy-to-use solutions without the problems associated with disconnected tools and spreadsheets.

Accurate, consistent data can be shared from a single data platform—via any device with a web browser—across all teams in the business, from head office (HO) trading teams to distribution and shopfloor users. This creates a cohesive unit, with all teams working towards the same business goals based on accurate data.

Quick wins with decision intelligence

Decision Intelligence (DI) has the potential to unstick retailers from the uncomfortable rock (needing to save money) and hard place (needing to spend it to create value). It is a discipline (and technology) that leverages human, organizational, and technological capabilities to foster timely and effective decision-making, granting broader access to data-driven insights for both technical and business professionals, streamlining decision-making processes, and providing an advantage over traditional analytics methods restricting data accessibility to technical experts.

But how to get to this point?

The best approach to adopting new investments is always to identify and quantify some quick wins. The potential benefits of decision intelligence can bring these in a few different areas involving data and how it is shared. Imagine enabling your teams to do some or all the following (and more!):

•  Identify by channel (clicks and bricks) which product sells better or drives more margin so they can refine assortment by channel or store accordingly.
• Improve visibility of ‘purchase order’ quantity versus ‘receipted order’ quantities into the business.
  • Improved management of actual stocks in the supply chain
  • Quicker response to discuss oddities with suppliers and agree on action.
• Weekly Stock, Sales, and Intake (WSSI)
  • Plan “what if” scenarios for future sales and stocks to establish changes needed to drive stocks and derive an accurate open-to-buy (OTB).
• Tackle flow planning.
  • Plan the stock in and out of the warehousing, enabling management to work out staffing needs, enabling managers to work out staffing needs and the optimum number of booking slots for supplier deliveries.
• Returns by channel by categories to understand true cost implications.
  • Impact on business in terms of handling, damage, logistics/process, etc.
  • Enable better visibility to make informed decisions (i.e., is there a brand often returned to a particular branch that doesn’t stock it?). Does this indicate that perhaps customers should try them on in-store, reducing returns?
• Visibility of irregular weekly sales patterns and being able to react/understand why they are happening.
• Identify products that are starting to decline and manage their exit strategy, thereby reducing risk and loss of profit.
• Forecasting stocks based on predicted sales patterns.
  • Create predictive scenarios to test out ideas before committing to orders.
• Price optimization to maximise margin across a mixture of categories and products.
  • Creating bespoke pricing strategies for different categories/products/locations/customers rather than sticking to a generic, all-encompassing strategy.

Those are just a few examples. In addition, your organisation will benefit from more fluid and quicker decision-making at all levels as confidence grows and all stakeholders are able to make more intelligent decisions.

Investing in the future of retail

Although it feels counter-intuitive for retailers to invest when trying to save money, this may not be the case! It is, in fact, the ideal time to invest in decision intelligence to give retailers the best of both worlds. Overall costs, especially with a platform such as Pyramid Analytics, will reduce running and long-term costs and save money due to faster response times. At the same time, due to improved data, retailers can drive sales as they provide customers with better-value products.

Contact us at CCW or Pyramid Analytics to help determine the quick wins for decision intelligence in your retail organisations and how to deliver them in a cost-effective, timely, and optimised way that will work with your priorities. And, of course, help you to get out from between that rock and the hard place.