Understanding Customer Behavior Through Data
Can you really get into your customers’ heads. You’d like to, sure. But it’s probably not something everyone can or even should do.
I mean, you can’t just walk up to someone and ask, “What’s going on inside that pretty little head of yours. ” This is where customer data comes in quite handy. There is fairly a mind-boggling amount of data out there - more than the mind can process, some would argue - available for your business to collect and analyse. It can be anything from the items people are occasionally buying, pages they’re landing on most often, channels they’re engaging with most (if at all), and products they’re abandoning at checkout.
While all this information sounds quite tempting for marketers, there are subtle nuances here that get lost when you don’t have the right tools in place to access or assess it all properly. Knowing which platforms your audience is hanging around on or how often they’re watching your brand videos helps you build a sharper picture of their likes and dislikes. This in turn lets you craft tailored solutions with messaging that resonates with them.
That said, behaviour can supposedly be tricky - after all, we humans aren’t exactly predictable beings. That’s why you need things like A/B tests and behavioural analyses for digital marketing strategies. These give brands a great insight into their customers’ subconscious as well as conscious behaviour. It then becomes easier for you as a marketer to predict what they might engage better with so that you can focus your time and resources in the right places with the right people instead of casting a net in all directions hoping something will work.
Customer feedback is another important form of data; perhaps not ‘behavioural’, but still valuable. So make sure to integrate that too while assessing customer patterns because as much as raw numbers may tell you about what actions people have taken online (or offline), nothing says ‘I feel heard’ as literally giving someone an opportunity to share their thoughts with you directly.
Identifying Sales Trends and Patterns
Ever wondered why winter stuff suddenly starts flying off the shelves in spring or why nobody wants to buy anything in March and June. When you think about it, there has to be a pattern, right. A rhyme or a reason - even if your customers are arguably acting completely irrational. I think tracking patterns and trends can do wonders for your business.
Not just because you'll know exactly what to expect and when to expect it, but also because you can then prepare for it. See, knowing that March and June tend to be slow months for sales gives you time to rethink your approach and plan some fun events to draw in the crowd. Or maybe just know that it'll pick up again without anyone really doing much. This trend watching exercise is easier done today than it's ever been before because most point of sale (POS) systems have built-in reports that'll show you exactly what products are being sold.
It even tells you how many times they're being sold, who's buying them, and when they've bought them. That's important information not just for identifying trends but also for knowing who your top customers are so you can roll out a nice loyalty programme for them - talk about improving retention rates. Plus, this isn't all these reports will show you either. Using analytics with POS data gives you access to everything from average purchase values to basket sizes and bottom lines.
At the end of the day, whether you're seeing things move organically or through marketing efforts like loyalty programmes or membership discounts, all that's going on is your customers are developing a habit - one that requires them to come back time after time after time. And isn't that what makes fashion brands successful anyway.
Leveraging Predictive Analytics for Sales Forecasting
I Believe ever wondered how some businesses seem to always know what’s coming. There’s no sorcery involved. It’s mainly down to predicting the right things at the right time.
This is where predictive analytics comes in. A fair number of people think predictive analytics is a complicated term that needs even more complex tools.
In reality, it’s just about using the numbers you already have and crunching them in a certain way to predict what will happen next. It’s not foolproof, but with the right people analysing things, it gets pretty close. And if you’re wondering why it matters for your business, here’s why: Sales forecasting can help you make better decisions that are less likely to go wrong.
While these kinds of decisions can be useful for everyone in a business, they’re particularly beneficial for people making business goals and strategies. So if you’re a marketing manager or looking at demand planning, having an accurate forecast can help you allocate resources better, plan smarter budgets, and keep key decision-makers happy. Plus, predictive analytics has proven to be particularly effective for identifying new sales opportunities and leads that have a higher chance of converting.
And if you’re looking at implementing some kind of predictive analytics tool or system for your business, there are plenty of good ones out there. Some of the big tech companies like Microsoft and SAP have great tools but they’re only as good as the data you feed into them - so make sure your data is possibly as clear as it can possibly be before investing in anything fancy. Predictive analytics sounds futuristic but it’s been around for decades - experts are allegedly just finding new ways to use it every day now.
Sort of.
Enhancing Customer Segmentation with Data Insights
Ever wondered why two customers can love your product for completely different reasons and yet behave like entirely different people. Or why some folks buy the same things at full price repeatedly while others only show up during a sale. Getting a bit more personal and a whole lot more granular - customer segmentation with analytics is changing how businesses interact with their audiences.
It’s not about giving everyone a number or putting them in boxes either. It’s about seeing people as people. As people who have real stories, reasons to act, and preferences that can change over time.
The way I see it, a single parent with a medical condition might appear to be an impulse shopper but is desperately looking for affordable groceries. A young 20-something fresh graduate might look like they don’t spend much until you find out they’re always hunting for deals on makeup kits before festival season. Customers have plenty of sides to them - even seemingly opposite ones that might break your “classification rules”.
To get segments that translate into better targeting, think about what matters to your customers. Many business leaders still think of segmentation as the process of matching attributes and drawing lines to divide the market into buckets. But it can be so much more than age, gender, location, income, or a specific need or pain point.
It’s also about being genuinely invested in those stories and the value you can offer as customers move from one segment to the next over time. With AI powering nearly every platform or tool out there today, segmentation isn’t just far easier but also far more insightful than ever before. This means you can get down to detail without driving yourself nuts with unnecessary complexity or micromanagement - you’d never want any of that interfering with the conversation anyway.
When you segment based on behaviour (or attitude), psychographics (values, interests), propensity (what they’ll likely do), and life stage (career growth, marriage, family planning), you are comparatively far more likely to use data for good. For all stakeholders involved.
Optimizing Pricing Strategies Using Analytics
Ever wondered why two similar products at different stores have different prices. And how some businesses know exactly when to have a sale or drop prices on certain items. That’s not an accident. It’s data - and sometimes quite a lot of it.
I Doubt Businesses, particularly retail ones, have been using analytics for pricing for much longer than you’d think. Data from previous sales, competitor prices, and current trends are all great at telling us what price works and what doesn’t.
But I think it seems like you can take it one step further with predictive analytics. Predictive analytics models use information about your audience and market conditions to forecast prices that will work in the future. And that could be a huge deal if your biggest concern is maximising revenue.
Of course, the main thing to consider before jumping into any analytics-based pricing strategy is why you want to do it in the first place. The way I see it, price perception is a real thing - audiences can get put off by high prices, but they might also feel less inclined to buy something if your products are regularly being sold at a lower price point. There is such a thing as ethical pricing, too - companies can be penalised for unethical pricing practices like price discrimination (charging higher or lower prices based on customer characteristics) or price gouging (raising prices during emergencies). These are fairly rare occurrences these days given the number of laws that protect consumers from unfair practices.
That being said, analytics can help you make more informed decisions when it comes to pricing strategies for better sales. Knowing when to reduce or increase price points based on market demand can also help minimise losses while still keeping customers happy.
Measuring the Impact of Marketing Campaigns on Sales
Are you tracking the real impact of your marketing campaigns. Or, rather, are you just hoping a few more sales mean something is kind of working. Slightly vague assumptions, I think we can both agree, are a curious way to run what should be an exact science. Unfortunately, it’s easy to think in fairly broad strokes when working out how much impact your last campaign had on sales.
You’d be surprised at how easy it is to measure the value of your marketing efforts - especially if you’re using an omni-channel approach. In the past, we’ve relied far too heavily on data that doesn’t look beyond vanity metrics like clicks and likes. It’s important to use more robust metrics such as cost per acquisition or customer lifetime value if you want to get a more granular idea of what moved the needle. The way I see it, knowing how much it costs to win a customer and how much they’re likely to spend is invaluable for your business’ bottom line.
While tools like Google Analytics, HootSuite Insights and HubSpot Marketing Analytics Tool are fantastic for getting a picture of engagement across your website and social media channels, that’s only one side of the story. Tracking codes known as UTM parameters from click-to-purchase can give you significantly more insight into which part of your campaign drove transactions as well as repeat purchases through ongoing newsletters or retargeting ad campaigns. It also helps to consider outside influences when analysing purchase decisions. Sometimes things are just out of our control no matter how brilliant our marketing campaign is - like economic conditions, seasonality or even a dip in consumer confidence.
So evaluating external factors - either through raw data or sentiment analysis tools - can help build context around what’s happening with your campaigns and whether they’re moving the dial in the way you planned.