Earlier this week, ShopperTrak’s Chief Product Officer, Chetan Ghai, was a guest columnist at Innovative Retail Technologies, where this article was originally published.
Ghai’s column, Why Do Retailers Struggle To Optimize Business Intelligence Returns?, acknowledges some of the newfound difficulties that can accompany a retailer’s adoption of a business intelligence (BI) solution, and outlines three solutions to common scenarios that “cloud judgement when investing or implementing BI tools in a retail operation.”
Read the article below to better understand how retailers can make smart decisions when employing a BI solution.
Why Do Retailers Struggle to Optimize Business Intelligence Returns?
Due to the rapid emergence of technology, touchpoints along the customer journey have spread far beyond the physical store. Now, retailers are tasked with aligning their operations in order to create an enhanced experience. Such an evolution has placed a strain on each of the functions within a retail operation — including marketing, operations, finance, business development, merchandising, etc.
At the same time, Business Intelligence (BI) tools have evolved over the last several years: they’ve gone from helping retailers examine past actions to providing rich, forward-looking insights that optimize current and future business practices. This shift makes BI the ideal solution for both physical and online retailers, as they work to streamline cross-functional processes and remain competitive.
In that vein, it’s worth highlighting that adequate implementations of BI solutions provide individual metrics, while thorough implementations build a cohesive story about what the whole team needs to do. As companies are making significant decisions such as how to invest capital, where to align resources, etc., BI enables them to do so with a fully integrated view.
Recognizing the positive impact that BI can generate, it’s important to note a few common scenarios that cloud judgement when investing or implementing BI tools in a retail operation. They include:
- Positioning BI with thoughts such as “I’ve done this in marketing. Now how can I do the same in merchandising and finance?” Business intelligence solutions are costly and typically originate in a single function of an organization as the result of a pilot. For example, a retailer wants to better understand customer loyalty, so they deploy a BI solution that is applicable to marketing and engagement strategies. Consequently, the tool is designed around that specific function, which then poses a challenge when working to use it across departments.
Solution: Business Intelligence is best implemented as an enterprise tool. In doing so, retailers should examine what is needed in each function of the operation, as opposed to thinking singularly, and choose tools that will meet the needs of the entire organization.
- Allowing BI to serve as a company-wide tool without a cross-functional support process. For example, if you’re in finance and have ideas for reporting around better cross-functional planning, you need approval from a group that’s not involved in your function. So, even though the BI solution is positioned to solve an integrated problem, the organization is not prepared to operate in this way.
Solution: Develop a steering committee for BI investments that include representatives from the key departments that have requirements for BI. Make sure this team has a voice to make changes in how the organization works to optimize the benefits of a BI investment.
- Investing in BI solutions before examining business operations and identifying a specific set of problems that should be solved. In this scenario, companies make a significant financial investment in a solution and do not equip their employees with explicit direction. As a result, leadership essentially expects a team to develop interesting concepts and solutions without a clear view as to how they can take action. Initial ambiguity is a catalyst for failure and ensures multiple iterations for an implementation.
Solution: Key decision makers within an organization need to outline specific issues, or a hypothesis, that are provable/measurable, and clearly communicate the goal of a BI tool prior to making a monetary investment.
The vast majority of purchases are still done in a physical store, and recent stats reinforce the notion that brick-and-mortar isn’t going anywhere (e.g., 90 percent of all retail sales are transacted in store per ATKearney). Thus, the store should be the central focal point of any BI solution, especially when considering the importance of omnichannel retailing. As customers browse online and pick up in-store, it’s vital to align resources. Further, the delivery mechanism of BI intelligence must be rendered in a way that allows the regional, district, and individual store talent to consume it easily and act upon it; otherwise, it simply won’t matter.