The Top 10 ways AI can improve Retail Store Operations.
The Top 10 AI use cases that Retail Operators at every level told us would transform their daily work. AI for Retail Operations is...
One of the common hurdles companies face when digitalizing their performance management processes, is starting with ‘messy data’. Cleaning it up seems like a daunting task; tearing up systems, disrupting business as usual and above all, costing time and money. It’s not difficult to see why operational transformation projects fail to get off the ground.
The reality, however, is not quite as painful as you might think. It’s more like getting fit – much more doable than you first expect. As long as you have a sustainable action plan, you can whip your performance process into shape.
For data to be useful, it needs to go through four steps. If your data is messy, then there’s a problem somewhere within this process. This is what the four-step process looks like:
Often people think that their data quality issues originate right from the source. However, our client work shows that for most companies this is not actually the case. The quality of Point of Sale, Staffing and procurement solutions means more data than ever is being accurately recorded.
For around 85% of companies, the issues start at the aggregation level. Sales have a different system to merchandising, procurement, and finance. The key is to ensure that everything is consistent. The answer is not consolidation – there is still some benefit to keeping some team-specific specialisms. It’s all about aggregation. To get it right, you need:
This often makes companies nervous – it seems like a huge undertaking. It doesn’t have to be, however. One of the companies we worked with, in the food services industry, has nine different EPOS systems. It took on a three-year project to try and consolidate everything onto one system. Unsurprisingly it failed, huge inertia of rip-up and replace putting off any process.
What the customer really wanted was to be able to look at information in a consistent way. The EPOS was capturing that information, just storing it with different codes. The best way to solve this was:
By taking these steps, it only took three weeks with an aggregation approach.
Around 80% of a business finance analyst’s time is spent pulling together pretty standard analyses, such as variance analysis. Poor quality aggregation means that these reports are often bespoke and not replicable. They also consequently lack sufficient time to actually fix the anomalies they have uncovered by connecting their analytics into the action workflow.
The good news is that 75% of this type of analysis can be automated. At one of the top retailers in the UK, an army of analysts was producing variance analyses around which food lines were underperforming each week. However, the store managers couldn’t connect their actions to improvements. By automating variance analysis in a strong data hierarchy and connecting it to the workflow in stores, we could map the financial result directly to operational actions and impact over time.
At one of our clients, we’re running 38 billion calculations a week, which wouldn’t be possible in excel (or BI). It allows you to continuously look at all of your sites at a granular level. Not just how revenue indexes by store but down to, for example, how are bananas indexing by store. It provides much more specific and qualifiable areas of action.
Reporting data needs to do three things to be useful for performance:
Once you’ve found the source or sources of the problem, the next step is to put this data into a habitual weekly routine in your organization. You need to be doing four things:
We’ve been helping many medium-to-large retailers and hospitality companies spot the issues within their four data steps, solve those problems and digitalize the performance process.