No matter what you sell, where you sell or how you sell, understanding your customers is key to understanding how to market your products and brand to them in the right place and at the right time. And that starts with predictive analytics.
Do you understand your customers?
It sounds like a simple question, but the reality is that many retailers don’t actually understand who their customers are when they’re likely to buy, and why they shop the way they do.
No matter what you sell, where you sell or how you sell, understanding your customers is key to understanding how to market your products and brand to them in the right place and at the right time. Essentially, knowing your customer means you can better determine what they’ll need, when they need it, and how you can deliver it to them.
It’s called customer targeting, and one of the most effective ways to do it is by using predictive buying analytics.
Let’s take a closer look at what this analytical approach is, and how retailers can use it in their business to drive sales and customer retention.
What is predictive analytics?
Predictive analytics is a branch of advanced analytics that implements statistical techniques such as predictive modeling, data mining, and machine learning. These techniques are combined to analyze current and historical facts about customer behavior.
In other words, it involves looking closely at customer buying history to predict what and when they’ll buy again (aka predicting future events).
By using that data, retailers can make informed decisions as to how they’ll market their products to their customers.
Imagine knowing when your customers are most likely to make a purchase based on their position in the buying cycle. That is exactly the kind of data that predictive analytics provides. By understanding your customer’s purchasing history and behaviour, you’ll be able to make data-driven marketing decisions to boost sales while spending less money on marketing campaigns that just don’t work.
You’ll find that all of this insight helps you optimize your marketing efforts – whether it’s emails, in-store discounts, social media campaigns and more.
How can you use predictive analytics?
The main goal or purpose of using predictive analytics is to spot repetitive buying patterns among your customers and use that insight to ensure you’re putting the right products in front of your customers at the right time.
However, for predictive analytics to work effectively, retailers have to collect essential data from their customers, whether that’s online or in-store. For example, you can collect information like cart abandon rates, past purchases, social media ad reponses, online sales, and more.
Model consumers’ behavior for future reference
Modeling previous behavior includes using data from past marketing and sales campaigns to determine what worked and what didn’t. When combined with demographic details, this information reveals your customers’ intentions at specific periods of time. Previous buying patterns will show the likelihood of customers buying specific products, as well as at what time they are most likely to make the purchase.
Let’s say, for example, that you own a travel accessory brand like Herschel Supply Co. You’ve looked at your customers’ purchasing history and behaviour and have collected the data; so you know that your customers are likely to purchase carry-on luggage and backpacks in the Springtime, because they travel more in the Summer than during other times of the year, and buy travel goods before they travel. By knowing this, and understanding when and what they’ll buy more of, you can market those products using discounts, product release announcements, social media campaigns and more, all near and in the Spring.
Many retailers already leverage predictive analytics without even knowing it. For instance, when brands offer ‘popular’ or suggested product options on the homepage of their website, or in-store, it’s typically based on what sells the most at that time of year.
Qualify and prioritize leads
If you use any form of digital marketing and outreach to target both existing and potential customers, predictive analytics is a great method to help you not only generate ‘leads’ but qualify and prioritize them.
In a nutshell, a ‘lead’ is anyone who shows interest in your product or intent to buy it. This makes them a potential customer, and once you determine if they’re a lead who is likely to follow through on a purchase, you have qualified them and can now prioritize them as someone you market your product to.
Most lead generation techniques are based on data, like need, interest, and urgency of a specific customer type. However, if you’re struggling with keeping potential customers engaged with your digital marketing, especially when using it to drive traffic to your store, predictive analytics can help you retarget those customers and turn them into loyal shoppers.
Perform customer targeting and segmentation
Predictive analytics can also help you group your customers into segments, where you put customers who are likely to purchase in one group, and customers who need help or nurturing to get them to that stage. There are three easy ways to group your customers:
- Group customers based on the traits they have in common
- Use data from previous digital marketing campaigns to determine which customers converted into buying customers, and those who didn’t
- Look at the number of customers who purchased more during certain periods of the year, like seasons, events or lifecycles
The benefits of understanding customers’ habits
While the main goal of using predictive analytics is to help you make more informed marketing decisions, the main benefit of using predictive analytics is the ability to determine your customer’s lifetime value (CLV).
By ‘customer lifetime value’, we’re referring to the total value that customer brings to your business during the entire time they’re a customer with you. Knowing this value helps you better understand whether that relationship is profitable for your business.
For example, if a customer purchases from your store over a 5 year period, and spends $1,000 each year, but you spent $2,000 acquiring that customer (through social media ads or in-store discounts, for instance), the total lifetime value of that customer would be $3,000. Here’s how to calculate that:
(annual profit from customer) X (number of years they’re a customer) – (acquisition cost) = customer lifetime value.
So, $1,000 x 5 years – $2,000 = $3,000
When you look at the lifetime value of your customers, you’ll quickly determine which customer groups (those segments we mentioned before) are the most profitable over time, but you’ll also see which type of products are the most profitable, too.
But that’s not all; understanding all of the above will allow you to also:
Boost sales without breaking the budget
Predictive buying patterns provide the information you need to reach the right customers at the right time. With this much accuracy, you won’t have to spend extra targeting budget on all those shots in the dark. Instead, your campaigns will hit the target every time.
You’ll find that you’ll spend less time trying to target random customer audiences and more time optimizing the engagement and relationships you build among the right customers – the ones ready and willing to buy.
Learn from past mistakes
Lastly, predictive analytics allows retailers to learn from their past marketing mistakes and put their knowledge to use in future customer campaigns. By finding trends and patterns to rely on, you can maximize sales and efficiency in your business without repeating the same old mistakes.
- Collecting customer data will help you better optimize your use of predictive analytics, and you can collect this data both online and in-store
- Your marketing team can use predictive analytics to model consumer behavior, qualify and prioritize leads, and target specific groups of customers
- The best way to boost sales with predictive buying patterns is to use it to make more effective and targeted marketing campaigns (on and offline)
ARE YOU PAIRING RETAIL DESIGN WITH PREDICTIVE ANALYTICS?
Predictive analytics can help you optimize your marketing initiatives and generate traffic from the right customers, but without an engaging retail environment designed with customer experience in mind, keeping customers coming back for more is a challenge most retailers can’t afford to face alone. At CBSF, we lend our decades of experience in retail design to help retailers elevate their physical stores and create environments customers can’t get enough of. Let’s chat to explore how we can help you take your next retail store to an entirely new level.