You’ve probably heard people talk about how big data and data analytics are the wave of the future in business, a real game changer for any industry. To truth is, big data analytics isn’t some new wave, futuristic technology that may one day transform your company — it’s very much happening right now. Name any aspect of business, and you’re bound to find a way in which big data is having an impact.
This especially holds true with the ecommerce industry. In fact, data within the ecommerce realm can be plentiful, especially since the business and interaction with customers is done online. It may come as a surprise, however, that businesses that engage in ecommerce aren’t always properly analyzing the data they’re collecting. This may come from the fact they have so much data, they don’t even know where to start. It’s time to narrow your focus and establish clear cut goals in order to effectively utilize ecommerce data analytics. It all comes down to finding a way to increase conversion rates.
Getting a Grasp On Ecommerce Analytics
So when we talk about ecommerce analytics, what exactly are we referring to? Broadly speaking, data analytics in the ecommerce sphere is basically a way to gain a deeper understanding of your current and potential customers. More specifically, ecommerce analytics looks at a number of important factors such as an ecommerce site’s bounce rate, the keywords that attract the most customers, what customers click on, where customers come from, and much more.
Needless to say, that’s a lot of information for ecommerce organizations to hold onto. The more customers, the more data they’ll need to collect and analyze. This type of data is extremely valuable since it’s one of the main ways that businesses can gain important insights into who their customers are and what makes them behave the way they do, especially when it comes to purchasing decisions. When it comes to increasing conversions, ecommerce analysis with big data is almost an essential strategy for ecommerce companies looking to remain competitive well into the future.
Different Data Analytics Strategies
Simply knowing about ecommerce data analytics isn’t enough; you have to implement the right strategy that will get your business the best results. In the broad scope of data analytics, that’s not always easy to do. With so many options, it’s best to narrow down and focus on just a few. Here’s a look at some of the ways you can use ecommerce data analytics in order to get that increase in conversions you’ve always wanted.
Product Page Optimization
The product page is where all the action happens. It’s where the customer can see the product, read up about it, peruse other customer reviews, obtain important information, and more. Do a product page well, and you’ll see your sales take off. Do it poorly and sales will struggle to get off the ground. But if your product pages aren’t performing well, how do you figure out what the problem is and how to fix it?
Product page optimization takes data gathered by customer actions and behavior on that page and analyzes it for patterns in order to obtain deeper insights. Sales data analysis can often determine where the problem lies in getting a customer to go from browsing to buying. Perhaps the page doesn’t flow well with a layout that does a poor job directing the visitor to where they want to go. Or maybe the call to action buttons aren’t prominently displayed or do a bad job communicating what will happen when the customer clicks on them. These are only a couple of factors that can affect conversion rates.
To boost the effectiveness of this type of optimization, one additional data source can be gathered through heat maps. Heat maps show where users are clicking on the page, which can be a clear indicator of how well put together the product page is. With all this in mind, you can design a product page that addresses any issues and leads to more conversions.
One key to better engaging your customers and responding to their needs is through predictive analytics. Analysis of the data you collect can help you determine what customers expect. Take recommendation engines for example. Based off of what customers have looked at and purchased in the past, it can be determined what a specific customer would likely buy in the future. Think of it like a hyper personalized marketing approach. Predictive analytics makes sure that what you recommend to customers is more likely to work.
Predictive analytics can also help you determine what prices to sell products and services at. Gone are the days of simply choosing the intersecting point of the supply and demand curves. In today’s ecommerce world, a customer’s willingness to pay a certain amount can vary depending on the time of day, week, month, or year along with any number of other factors. Price optimization can pinpoint the best prices for customers to pay that will lead to the most conversions, helping your business grow. Analytics of this type can also play a role in determining when businesses should respond to customers and what responses will yield the best results.
Choosing the Best Match
Sometimes selecting a single approach isn’t possible since customers can vary greatly in their behavior. That’s when utilizing best match algorithms can be extremely useful. This type of algorithm tests out two different versions of something — a landing page, call to action button, etc. — and recording the data on which is more effective at getting the desired reaction from a customer.
Based off of this information, you can get a better picture of what will lead to more conversions. Some analytical capabilities can change out the choices depending on times of the year, ensuring you are getting an accurate picture of what customers are doing.
Getting the Data
Of course, in order to perform ecommerce data analytics, you need to extract the data in the first place. Import.io solutions for retail and marketing helps you to do this. With Import.io, you can extract data from websites, like giving you information on marketplaces and product descriptions. You can also get data on product performance and price analysis. You can even get images for your own company’s marketplace. High quality images are a crucial piece for any sales strategy.
These data extraction solutions also give you the ability to monitor customer sentiment, helping you understand how customers are responding to your product as well as your competitors’ products. Data extraction from websites makes implementing strategies of ecommerce data analytics that much easier.
There’s no single solution that will lead to incredible conversion increases, but ecommerce data analytics can become a key component in your online sales strategy. It’s an effective way to understand your customers and optimize your online presence so that your business may continue to grow.