How harnessing customer data can help you optimise digital commerce performance
Do you know your customers? Do you really know them? Do you know each of them well enough to answer questions about a single shopper’s purchase history, demographics, and preferences? Probably not. But you can know them that well. And it’s likely you already have all the information you need to answer these questions.
The problem is, that information isn’t only in one place where it’s accessible and easy to analyse. It’s scattered throughout all of your data sets. Using data to get to know your customers is a key factor in determining what your customers want, and in aligning your company outlook regarding those customers.
Aggregate versus raw data
Before you can speak about your customers as individuals, you need to have the data to do so. When analysing customer data, there are two recurring types of data that you will run into: aggregate and raw. When data is aggregated, the results are summarised and from various sources, whereas raw data is the unpolished version of trackable metrics.
When looking at aggregate data, you can understand trends across your purchaser group, but you cannot analyse individuals. Additionally, if you export your aggregate data from any particular platform, it is unlikely that you will be able to import the data to any other platform. Summarised data is helpful in some ways – you can see how a product sold day to day or which products are selling most frequently – but no matter what, aggregate data will not give you the raw information you need to analyse individuals.
So why doesn’t everyone just use raw data? First of all, it’s more time consuming to analyse. Anyone with a website will have access to Google Analytics, and can learn to use it at a novice level to view data summaries. Raw data, on the other hand, has historically been very expensive because of the sheer amount of space required for mining and keeping raw data.
Nowadays we have tools that allow us to store and use this data for pennies on the gigabyte, but not everyone takes advantage of this. The greatest benefit of raw data is the fact that you have access to every line of data that you track, and that you can import it to almost any analytics tool.
The ethics of tracking customers
So you want to start gathering the right kind of data on your customers? First you need to determine how much data you can track and what kind.
This begins with deciding how much data you and your customers are comfortable with tracking. But beyond that, there may be laws and regulations surrounding what you can and cannot track, depending on your industry and audience. If you are dealing with stored credit card information, for example, it’s obvious that security regulations are much stricter than if you’re talking about gathering basic demographic info.
Making your data useful
So why do we track data in the first place? Some say it’s to optimise the sales process, but it’s really all about creating a better purchase process for the consumer. For consumers, the most important factor in the e-commerce cycle is to have access to the right information at the right time, but that need for information will vary customer by customer so e-commerce platforms must create personalised shopping experiences. Using raw data, it’s possible to find patterns in individuals and market specifically to them, thus giving those consumers the right information at the right time.
Understanding the distinction between raw and aggregate data, and knowing to use that data for a more targeted experience, is not quite enough for putting your data to the best use. You need to ask the right questions—to figure out how people use, think about, and purchase your products. Knowing these things enables you to target your sales plan particularly to these consumer desires.
You can’t answer these questions—or for that matter, define and understand your KPIs—unless you have your data under control.
For more information about harnessing customer data, watch Robert Brodie’s talk at Meet Magento NY, available here:
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