How data-driven optimisation can improve brand profitability

The business landscape of the 2020s is filled with uncertainty. With the constant changes in the business climate, it’s hard to know what the future will hold. However, optimisation is key to weathering the storm.

Even the smallest enterprise relies on insights backed by data to make strategic changes to their operation to better suit the needs of their customers. You need to utilise this data to optimise your efforts.

With stats that tell you the percentage of purchases done via mobile (59%) and the proportion of consumers who shop at bargain-driven retailers (89%), it’s no wonder that for retailers, data is the key to revenue and profitability. You now need to use this data to optimise more than ever to help determine which products resonate the most strongly with customers, which are the most profitable and which customers represent the greatest Customer Lifetime Value and need to be retained at all costs.

Marketing departments are also heavily reliant on data to inform their branding strategies and get an overview of how well their brand measures up against competitors. In fact, a study by Snowflake Computing and Harvard Business Review found that companies making decisions driven by data have the best chance for longevity. Ensuring you’re doing this right now is vital. By using data to mine actionable insights, all departments can do their part to ensure profitability both now to survive and grow in the future.

What is optimisation in retail?

The essence of leveraging data effectively is looking closely at what has happened to ascertain how you can optimise going forward. Brands need to position themselves in front of prospective customers at the right time and with the right proposition.

You need to use the data you have and are collecting day to day in order to develop more optimised methods that can stabilise your position and help you grow when the time is right. Retailers need to account for external factors which influence consumer behaviours, such as the natural expansion and contraction of markets and the broader economy.

Understandably, accurate optimisation requires no stone to be left unturned when it comes to analysing your data. That can represent a huge investment in time and resources. Retailers commonly find that they have an abundance of data and that distilling it into actionable insights creates a world of headaches. Rush this process, however, and you could find yourself plotting a course for disaster.

The danger of inaccurate optimisation

In an effort to make their data more manageable, retailers often draw from a more limited stream of data. Specifically, not only their own historic customer behaviour data, but also the day to day data they’re collecting. In a landscape that’s as fast-changing as retail right now, it’s vital you use your data smartly.

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For example, as inventory continues to build up, retailers are stuck with the cost of warehouse space for products that cannot be sold.  Retailers need to be using their data wisely and doing everything they can to ensure online purchases increase so their products are continually moving off the shelves.

A lack of alignment across departments

Many retailers are facing issues with their ROI and ROAS on marketing spend — it’s decreasing, but they have little visibility as to how or why this is happening. While this may be impacted by external factors such as changing consumer spending habits, many departments neglect looking inwardly to solve these problems.

In a survey we did in partnership with The Retail Hive, we found that only 4.4% of retailers believed that all of their departments were aligned around key metrics. Failure to align around key metrics within a company can easily lead different departments to focus on different objectives — damaging company revenue and profitability.

In order to optimise better, retailers need better visibility of their data so they can make informed decisions. Brands and their constituent departments need easy access to real-time actionable data at a SKU level. This can ensure the correct margin performance is shared across departments, informing their strategy while also ensuring that the whole organisation is sufficiently well informed. This can be done more easily with the right AI-driven technology…

AI to the rescue

The fundamental problem with optimisation is that for it to be effective, it requires brands to parse a lot of data. More than any one team can realistically be expected to sift through while still keeping operations running. But given the potential problems that inaccurate optimisation can cause, are retailers stuck between a rock and a hard place?

This is where AI platforms like Upp can come to the rescue. While some retailers are using AI-driven platforms, very few are using ones that do more than your standard product listings optimisation. By using Upp, retailers can make sense of their data on whatever level of granularity is needed. These platforms can even provide an end-to-end solution for retailers looking to uncover their metrics and unlock the insights they need to improve revenue and profitability.

Here are some ways in which AI can help to deliver more accurate optimisation while lessening administrative headaches and improving brand profitability:

Giving brand instant access to the bigger picture

No longer do companies need to gather data from multiple silos to get access to the big picture. AI-driven platforms like Upp can pull data from numerous sources instantaneously, allowing decision-makers to examine a wide array of causal factors in real-time.

AI technology like Upp helps brands to cut out the white noise of data and pull from the causal factors that influence the behaviour patterns of their customers.

Actionable insights

The right platform can provide a full end-to-end solution so you can not only have visibility over all of your data across all departments, but this AI can make smart recommendations based on product performance and external factors. This allows teams to take actionable insights based on this, ensuring every decision is furthering the brand’s revenue and profitability.

Providing analysis on a granular SKU level

What good is data if you don’t have access to it? Without access to data analysis on a granular/SKU level, departments or locations can’t hope to act with agency. By providing analysis on an SKU level, Upp allows for more specific item planning while still offering total automation.

Identifying the causal factors that really matter

Different causal factors can have different implications for different brands, different products and different target audiences. And pulling data from irrelevant sources can muddy the waters and impede agile decision-making. AI allows brands to identify the causal factors that really matter to them and act upon them accordingly.

What’s more, because causal factors can change with the current environment we’re in — as well as with the seasons, with the weather or even with what a celebrity is tweeting — AI technology like Upp can replace outdated or irrelevant causal factors with newer, more significant alternatives. Track the right metrics in real-time on a regular basis and you’ll be on the right track to optimisation.

By leveraging AI-driven platforms, brands can sidestep the pitfalls of inaccurate optimisation, unshackle themselves from unnecessary costs and maintain a healthy cash flow while charting a course for greater revenue and profitability.