How to determine your returning customers
Whether it is the weekly supermarket shop, or to snap up a couple of items of clothing that are on sale, or to choose a present, for many of us online shopping has become a regular activity. After all, it is hard not to do some online perusing when the email inbox teases new offers or latest discounts for us to take advantage of.
Personalisation makes our shopping experience easier, which in turn is more profitable for retailers since it drives more sales, more engagement and more loyalty.
Done well, it is like a magic trick or an illusion, because it seems so easy and seamless. We probably don’t even register how we went from clicking on an email from our favourite brand to clicking on the checkout basket just 10 minutes later.
But the challenge is getting it right behind the scenes. How do you identify the customers that will be repeat customers? Just as importantly, how do you avoid spending on discounts, advertising or other promotional activity on a shopper who won’t be returning?
How do you identify your customers that will tend to buy items at full price, without needing a minimum 10% discount or special offer?
You need an approach to optimise the level of effort you put into your customers based on what you think their total value to you is going to be.
The good news is you don’t need a crystal ball to achieve it.
Avoid scattergun prospecting
Remember, someone who shows signs that they are going to be a long-term customer is worth investing more in than someone you don’t think is going to be. A more scattergun prospecting approach will have you wasting valuable investment and resources on those customers that don’t intend to come back.
So, if you are looking to develop a strategy that identifies and helps you determine your returning customers and focus marketing spend on them, here is what is required:
- Access to historical data. Preferably, it needs to be a rich source of past data about all your customers as well as their purchasing history, such as what items, how much per transaction, frequency of transactions, what they actually bought, as well as returns.
- Next, you need to consolidate all that data into a single view and make it easy to query, with different fields and so on.
- Then you need to segment your user, or customer, base into cohorts, based on a hierarchy of facts, behaviours, attitudes and other factors.
- Now you can start to build some models, from very simple stats and analytics through to machine learning.
Even if you are in the early stages of better understanding your customers and determining those that are going to keep coming back, steps 1-3 are imperative, even just to produce some very simple stats to begin making those predictions.
Then, you can begin to kick things up a notch.
Cross category penetration
Once you have identified a returning customer you can look for the driver behind a purchasing decision and then manipulate that driver.
For instance, spending by a customer in more than one category is considered to be a driver of repeat purchasing. Big ticket purchases are, of course, important. If you are a clothing retailer and someone spends a few hundred pounds on a coat, while that’s a good sale that customer isn’t going to be buying a coat from you for the next four or five years.
Better still, incentivise the customer to buy an item, such as accessory, from another category, even by selling it to them at a heavily marked down price, or at cost. How about two shirts for the price of one? When customers make purchases in other categories they are more likely to keep coming back.
Upselling and cross selling both drive up repeat customers. But you can only begin incentivising and facilitating these kinds of buying behaviours, when you have the data to hand.
Get to know your best customers
How are you getting through to your loyal customers?
What kinds of offers you target at this group is an important question because when you optimise your spend by determining your repeat customers, you also need to consider what they respond to.
Do they like content, such as features that get them thinking and then looking to spend more? Do you send them new launches, because they see themselves as a trendsetter? Are they more likely to be a trend follower? Are they price-sensitive customers and only buy when merchandise is on sale? This latter category are still valuable customers because their frequency of spend might be high, even if the value of their individual purchases are low. Just don’t waste your marketing resources sending them new product launches at full price.
Does regular discounting devalue brand value? Every e-retailer or e-commerce brand sells items at sale, or discount, to some extent. Having the data to drill down to gain better insight into how each and every one of your customers spends can assist strategic decision-making about where you want your brand to be and who your core customer demographic is.
Optimising your spend for the outcome means you aren’t throwing money away on chasing customer sales that will never happen.
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