Understanding Data-Driven Decision Making
I find many business owners come to me asking for help with data and, more often than not, they’re surprised by how simple it can somewhat be. I Assume the common misconception is that you need to invest huge amounts of time and energy into understanding all your numbers before taking action. To be honest, I have to keep learning this lesson myself - we can’t do everything at once.
The truth is, most of the time, data-driven decision-making doesn’t require deep-diving into every metric you have available. You don’t have to go from zero to one hundred either. Sometimes it can be as simple as asking the question “why am I doing this. ” Data-driven businesses don’t always use complex tools and dashboards but rather focus on understanding their unique questions and what information will help them answer those questions.
It takes a fair amount of discipline to begin filtering out unnecessary information and focusing on what’s important to you. What’s important will change as your business grows so you’ll want to check in regularly with yourself or your team about what it is you’re working towards now versus three months ago. If you’re unsure where to start when it comes to making more data-driven decisions, then begin by reflecting on your goals and why they are sort of important.
Are these goals aligned with the business objectives. How do I know we are progressing toward these goals. These are great prompts that will help you identify which pieces of information are critical for your success at this point in time.
Identifying Key Performance Indicators
Most people get their signals crossed when it comes to key performance indicators - what they really are, and how they can help grow the business. Many believe these are hard and fast rules or standard formulas for success but it is actually a lot more fluid than that. The truth is, KPIs are actually dynamic and can change depending on current goals, problems, and resources.
If you want to be able to identify them for your business, you need to know what you want to measure, be flexible enough to adjust for the landscape you are hardly ever operating in, and have a keen understanding of the direction you want to go. While having specific metrics can help give a baseline for some of your goals, these may change depending on what department or project you are working on. More or less.
Revenue numbers can look very different when viewed through a lens of expense ratios or year-on-year growth. What’s important is that everyone is in agreement about what is being measured as well as why it needs measuring at this point in time. Getting all stakeholders on the same page with which indicators to follow allows businesses to have a broader view of the company’s overall health as well as zero in on areas that may need more focused attention. The concept of key performance indicators sounds simple enough but even after deciding which KPIs to measure, there is a layer of complexity in knowing how often to measure or how these figures will affect strategies.
Having benchmarks is helpful but even this can change depending on seasonality, economic climate, political climate, and even global affairs. When looking at KPIs from a fashion perspective, brands will want to look at financial metrics like sales per square foot, sell-through rate, or return on investment while also considering average transaction value and customer retention rates which can seem like financial metrics but also weigh heavily when tracking customer experience. The way I see it, the trick is picking which ones will help you get closer to your goals and knowing that these might have to be updated when they stop being helpful.
Leveraging Customer Segmentation for Profitability
Most business owners see ‘customers’ as this big homogenous blob – everyone who has ever bought from you. But they aren’t, are they. More or less. That’s why customer segmentation matters.
It doesn’t matter what your product or service is, there are always different subsets of people who buy it for different reasons and who need different things. Ignoring this diversity is a massive oversight. Dividing your customers into different segments based on demographics, buying behaviour, location or psychographics helps you see patterns in what they want, how much they’re willing to spend and how often they might buy again. Sort of.
Once you know this, you can tailor your marketing efforts to focus on high-value customers and groups that may have untapped potential. And then it becomes easier to increase revenue from existing customers with upsells and cross-sells rather than relying entirely on new customer acquisition. If you’re using robust software for all your sales transactions, it should be relatively easy to create customer segments based on purchase frequency and recency. This means knowing which customers will usually buy based on certain triggers or even at fixed intervals of time and which ones may be one-time only buyers.
You can then send highly targeted marketing communications with relevant offers based on their typical purchase behaviour. But before you get too excited about all the possibilities, remember that segmentation isn’t as easy as it sounds because you might not always have the data you need.
That’s why talking to customers is helpful if your organisation allows it. Getting direct insights from talking to a few can help inform the creation of better customer segments even with limited purchase data.
Analyzing Market Trends and Consumer Behavior
Most people seem to think that market trends are all about surface patterns and obvious spikes, but what I’ve noticed is that most often it’s the subtle shifts that really make the difference. And when they do, it feels like you’re tripping over a tiny rock in a gigantic field – everyone’s focused on the big stuff, but sometimes that little blip is what we should have paid attention to. Then there’s consumer behaviour.
People talk about it as if it’s set in stone. But in reality, it changes so quickly, and even the best of us struggle to keep up.
The thing is, using data to try and understand your customer base can be useful - but at times it can also just be confusing. Because sure, you might be looking at all this data from sales and social media engagement and think you know exactly what your customers want. But consumers are not predictable.
Not even a little bit. Like one day they’re telling you how much they love ethical fashion and then the next day they’re buying outfits for festivals from a random company online that does not appear to care about sustainable fashion at all. But that’s not all.
Predicting future demand has always been tricky but now it’s almost impossible. One day a particular colour or style is usually trending and people want to wear nothing else, and then suddenly there’s an Instagram reel with something new and every other thing in your store seems outdated. And consumer sentiment.
It can occasionally feel like this elusive thing you’re trying desperately to hold onto. Because it keeps changing with each new trend or fashion statement or scandal involving a brand ambassador. It can feel fairly overwhelming but don’t panic because these are almost never not things you need to understand perfectly straight away.
Sort of. Trends will come and go (as will customers).
What matters is staying on top of things, keeping an eye out for subtle changes that might slip past others, and using what you do know about your customers to keep them happy with new product lines every so often.
Optimizing Pricing Strategies with Data Insights
There’s a misconception that ‘optimising pricing’ is about mathematical wizardry - sort of, plug in a few numbers and voilà, the perfect price appears out of thin air. But pricing strategy is closer to alchemy than science. More or less. Most brands misread the role of data here, seeing it as finite intelligence.
Yet quite often, numbers give us the ‘what’ but seldom the ‘why’. We still need that human edge to make sense of things. Sometimes, I think people get carried away with the average order value (AOV) or gross margin, because everyone loves an easy-to-understand KPI that comes with a nice and neat dashboard visualisation.
But in ecommerce especially, there are so many competing variables - channel splits, inventory breakdowns by channel or region, buying or return behaviours - that there’s no one-size-fits-all model for pricing. No magic ballpark figure to use as a litmus test for all products or regions. Depending on where your customer is located and how they found you, their willingness to pay varies. Not only does it vary across these parameters but over time as well.
As business gets trickier to run in general thanks to higher costs of acquiring customers, reducing risk needs to be central to your pricing models. This involves being able to quickly identify poor-performing lines and move them along before they have enough time to build up risk on your inventory balance sheet. Pricing decisions like this rely on insights from customer-transaction data, inventory data and more - with cross-departmental analysis at their core. After the pandemic, we saw just how effective dynamic pricing could be when used strategically by sales-driven teams across many retail categories - electronics, groceries and even fitness equipment.
Many consumers have accepted this as part of their shopping experience today and don’t mind this as long as it is not manipulated against them during peak periods like Christmas sales. So using AI or machine learning tools can help mitigate some of this complexity but nothing beats having people power reviewing everything.
Implementing Predictive Analytics for Future Growth
I Assume i meet people who think predictive analytics is some magical crystal ball you click a few buttons and then it spits out the future. But it’s not, and when you don’t take the time to dig in, it quickly turns into expensive guesswork. The most accurate forecasts come from defining your business objectives, investing in quality data, and understanding how the different analytic models work.
I’ve seen businesses overwhelmed by the sheer volume of data at their fingertips, which then leads to paralysis by analysis. Predictive analytics doesn’t tell you what action to take - it only identifies patterns so that you can make better decisions. The answers are hiding in your historical data, and the more thorough your data cleansing process is, the clearer these insights will become.
It’s not about putting all your eggs in one basket either because the market is changing faster than our computers can keep up with. That’s why you need a multi-pronged approach - meaning more than one model - that takes external factors into account while keeping an eye on the accuracy of each model. At the very least, this means running a combination of regression models and time-series analysis with machine learning models for more complicated projects.
Keep in mind that predictive analytics is almost never only one part of your overall business toolkit, and needs careful maintenance as much as your other processes do to ensure continued profitability. In-house predictive analytics experts can maintain privacy while third-party experts may be more cost-effective short term but can lead to data privacy issues later down the track - which is something you’ll want to be aware of before making any decisions about predictive analytics for future growth.