If you want to concentrate your business efforts somewhere where they will matter, then you need to know just which of your clients the most valuable buyers are. It's a tradition for businesses to use the MVP (most valuable people) label on the customers who spend more money with the business than anyone else. On the other hand, once you identify these customers, you might just discover that they're also among your most expensive clientele with the lowest amounts of long-term loyalty.

Big data is useful here because there's a number of ways it helps you to get to know your customers better. Once you know the players and cut through the clutter, get to know them better by using the following metrics:

Size of average purchase

When your customers do their usual shopping, how much do they spend? Don't just look at the aggregate data, but break it down by customer type. Also always remember that many individuals shop based on perceived value, not just by price alone. Are there ways you can sell more to your various kinds of clients by using sales promotions to spark interest or awareness into your other products or services?

Lifetime value

How much total sales do you generate from each buyer throughout their total lifetime business with you? Is it much? Or is not a lot? This particular metric tells you how significant a relationship you have (or not) with a customer.

Cost of acquisition

What was the average cost per customer in terms of sales and marketing in order to get each customer into your business? If it's a high number, it's better for your bottom line if customers are cheap to keep and spend a lot of money with you. If this is not your situation, then it's time to reconsider your current methods of acquisition.

Costs of retention

What must you do or spend for your buyers to stick around? Are large volumes of communication, support or training necessary? Client acquisition is usually more expensive than the costs of keeping one. Do your best to create relationships that make clients feel like you value them? One great way to do so is through using channel incentives. These loyalty programs create meaningful B2B loyalty and value and can really help keep others onside. 

Happiness of your clients

Do your services and products satisfy your clients? Does your client base break down into happy and unhappy groups? What separates them? Looking into this can show flaws in your business, illustrating needed changes and possibly even motivate you to make adjustments to the expectations of your customers.

Alignment of values

Do your target customers actually shop with you? If you're not netting your intended clients, then who is doing business with you? Knowing all this helps you make your customer personas more accurate, especially when you seem to be out of any kind of alignment.

Look over these metrics and information and check it all against your previous customer assumptions. If your early assumptions are still valid, that's terrific. We can still apply any big data calculations to put buyers into specific groups and then focus targeting even further than before.

Big analytics

This is when big data analytics enters the conversation. Ideally, you'll look hard at your preferred customers and isolate their behavioural and demographic trends (your preferred customers are those whose lifetime spending far exceeds the collective costs of their acquisition and retention). Also always be mindful of customers who are not greatly but still moderately valuable and could be harnessed further. Additionally, keep an eye out for outliers and customers that don't fit pre-existing moulds.

In the end, you should be able to define customers in terms of merit, behaviour, and demographics. Prioritize all these in terms of their individual value to your business.

One of the best results of squeezing behavioural trends out of the data is the power to isolate specific purchasing drivers and then creating touch-points to market to them. For example, consider a grocery store customer who intends to abandon his cart at the register because the bill is too high for him. Giving that customer 20 percent off his entire cart might change his mind, either on the spot or as a coupon in the mail later for future business. Likewise, socially conscious consumers might respond better to specific causes being supported more than a price reduction.

Put a human face on the cold hard facts

The sheer volumes of data you generate won't matter a bit in the end unless you personalize it somehow. Find connections between your data and analytics and actual human experiences, and then you'll have the ability to create client persona categories that make your optimal strategies and marketing targets obvious and easy.

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