Big Data in eCommerce Personalization, Explained
Learn how businesses can use big data they collect to personalize eCommerce user experience for their customers and strengthen their brand.
Big data is everywhere. All the information that you ever provided online, all the saved cookies in your browser, and all the online banking apps that you ever used - all of these collect and store tons of information about you. That’s why when you search for something online, you’ll find ads for the same product following you everywhere you go. That’s why when the bank calls you, they know your name and personal details.
It’s not necessarily a bad thing. While there are arguments against collecting big data in general, there’s still plenty of room to theorize and practice big data for business purposes. One of the most promising domains is marketing, as user data is a major ingredient in a healthy marketing mix. Personalization is one of the most frequently exploited marketing domains that proves to have high ROI when it comes to real-life applications of big data.
And the reason is simple. Marketing and sales are all about understanding the customer’s pain points and desires. What used to take years of marketing research, now takes days or even hours with the data collected within personal profiles of online shoppers and enabled through ecommerce web development services. Let’s take a closer look at several prominent applications of big data for marketing, specifically where it drives product and service personalization.
If yours is not a sophisticated online business with wads of cash, then employing a sophisticated algorithm that will determine all of the patterns within your data is out of the question for you. Not to mention that you might not even have enough data. But what’s the point then? Where and how can you extract value out of the ‘big data’ that you might have?
Big data consultants say, don’t shun exploring your data. Learn what, where, when and how you collect: where the data is stored; how you can access it; and what kind of customer-specific information it contains.
Include advanced analytics into your mix to milk the maximum possible value out of the data. No matter how smart you are, machines can do it better. All of the tools below have SMB pricing plans, and most of them have trial versions:
- IBMs Watson (yes, it’s affordable for SMBs!)
Data Source: Statista
Map out areas where it can be applied. That’s how after you’ve learned the types of data that you have, you can start coming up with ideas for your personalization experiments.
Build personalization profiles. Lots of data points can serve as a personalization target. Location. Age. Shopping frequency. Preferred products. Used discounts. And so on. It’s all custom and depends on the types of data that you have. It all can potentially affect the results, because even though businesses think that all customers are the same or similar, many patterns don’t follow a clear business logic.
A personalization profile includes the types of data that you want to focus on when building your personalization tactics. For example, you have clients from Asia and America. Each one of these groups might be a personalization profile with unique features and characteristics that you can uncover using big data on your hands.
Don’t forget to use that pool of data analytics tools that we mentioned earlier. These tools are there for this particular reason – to help you build better personalization strategies and uncover dependencies in data that you couldn’t envision yourself. Now let’s take a closer look at specific personalization efforts that thrive on big data.
It’s probably one of the most commonly known ways of how big data can be applied in eCommerce. We’ve all dealt with personalized recommendations at one point or another when we received emails with item suggestions based on our previous purchases.
Data Source: bigcommerce.com
Big data helps in the process by uncovering product dependencies that aren’t that easy to notice. But recommendations shine when they’re applied to the most underserved group of potential customers - unknown users or visitors who haven’t purchased before.
By analyzing their behavior in real time, businesses can push them towards a purchase much easier. For example, by changing ‘Recommended’ items based on the items that have already been viewed during their session.
Providing a high level of customer support is sometimes overlooked as a great way to exploit big data and increase brand loyalty. However, it’s important to understand that your business has to be prepared to utilize big data in customer care. That’s why access to data has to be democratized so all your support representatives would get access to customer information, their purchasing history and advanced analytical capabilities that sometimes are only available for sales, marketing, and execs.
Data Source: eMarketer
This big data has to be presented in an already digestible format, as customer care representatives have a very limited time span to act upon the customer’s desires and issues. So how can customer care be used to personalize customer experience?
- Use their purchasing history to offer relevant discounts and promotions during interactions
- Use purchases by other customers in the same demographic or personalization profile to upsell related products during interactions
- Analyze purchasing behaviors and single out products or customer categories that experience the highest number of issues. Preemptively address such customers’ concerns or prepare your team for work with this particular group of customers.
Building Up Loyalty
How do you become friends with somebody? You create a relationship. They learn something about you. You learn something about them. Customer loyalty is similar. Friendly relationships with your eCommerce customers are important. New customers support business growth. Loyal customers keep businesses alive. Big data represents a fantastic pool of data about every one of your customers, which you can use to build relationships with them.
We’re talking about real loyalty. Not ‘buy more from us because we’re giving you this discount.’ This is the mistake that many businesses still make. Let’s say you sell tents for camping. Is it possible that a person might need another tent from you within the next couple of months? Highly unlikely. Then why do you keep spamming people with your tent discounts? This ‘promotion’ doesn’t reward their loyalty to your brand. It irritates them. So how can you build customer loyalty using big data that you have about them?
Personalize promotions by analyzing the types of products that they purchase, their type and item details. Include special promotions for people based on the size, color, and type of product that they buy.
Data Source: Annex Cloud
For example, if a person has bought blue t-shirts of specific size and type (let’s say, V-neck), then you could offer them a timely discount for this particular type of clothing, taking into account their common frequency of purchases in your store. These kinds of recommendations create a sense that you know the customer, and they’ll be more likely to purchase, as you’ve offered them exactly what they’re interested in.
Another great way of building up loyalty is to offer localized discounts or promotions that target their location, which could be extracted from their billing or delivery address. This localization could be anything from a promotion based on their local holiday getaway to a special delivery discount built around their locale.
Dive into Unstructured Data
Structured data is something that is stored in a database, categorized and includes all the information about customers that’s required for a business to operate. Unstructured data is something that’s not stored in a structured way. There’s no specific row or column that you can find in your database containing that information. Usually, when we talk about unstructured data, we talk about information that’s stored outside of your site. Social media publications about your brand are something that could be considered unstructured data.
You can analyze this information by collecting it in one place and extracting insights. For example, services like Twitonomy offer you the ability to download all your conversations and mentions on Twitter into a single file (like Excel). You can then use that information and create word clouds right in Excel. This data can help you analyze the sentiment around your brand, hint at what people are looking for when trying to interact with you and even point to specific products that might interest them. Of course, this method only works if you have a very active social media presence. How can you use this information to personalize shopping experiences?
It can be used to build content around specific products. It can help you understand the language that your buyers are using to communicate with your brand, which can also, in turn, be used for marketing materials and promotions to help you ‘get to their level’ and almost literally ‘speak the same language’ with your clients.
Time Is Essential
It’s important to realize that some big data initiatives have to be built around timely execution. Shoppers have a very short attention span, and that’s why it’s crucial to grab their attention as soon as possible and offer them the products that they need. That’s why big data is important for real-time personalization that many businesses still don’t practice. Its dynamics are different, but the principle remains the same - take all of the information that you might have about the user and build a personalized shopping experience based on that data.
Data Source: 1, 2, 3
These efforts have to be intertwined with a robust advanced analytical framework (data science and/or machine learning). These technologies allow you to adjust your tactics on the go - to make an impact within a single session.
A simple example would be a service that would personalize recommended products for a visitor based on their location. This service could see that people from a specific location tend to purchase specific products. It then would display those products in product recommendations or trigger your website to offer a special discount on those products. The same principle could be applied to site search, which people resort to when they don’t find what they’ve been looking for. Time and machine learning will make these recommendations more precise.
It’s possible to build a system like that specifically for your website, and in the end you’ll have more control over it. However, there are services that can help you test the concept of real-time personalization. Here’s a couple of them to get you started. This list is in no particular order.
A number of these services may be out of your league in terms of pricing, but just by scheduling a demo, you can learn a lot about the current landscape for personalization technologies.
Creating a workable personalization strategy for your business is easy. Even the smallest eCommerce companies already have a plethora of data beyond the usual analytics offered by Google and other well-known providers of business intelligence.
Take a direct approach to managing the influx of data. Learn more about the data that you have. Figure out ways that you can use the data to personalize. Test various analytics and personalization services, a lot of which are affordable even for smaller companies. It’s crucial to understand that even if your competitors aren’t using any fancy personalization techniques today, they’re thinking about implementing them tomorrow.
If you don’t act on the data that you already have, you might be fighting an uphill battle when your competitors will have that advantage. It’s not hard to start building personalized experiences for your clients once you understand your goals, objectives and have a basic understanding of the budget that you would need for these initiatives.
Looking for a custom personalization solution for your eCommerce business that won’t break your bank? Feel free to reach out to us and learn more about the benefits of our development experience for your business.
Have you tried personalizing eCommerce experiences for your customers? What were the issues that you had to tackle? How successful was your project? Share your thoughts and ideas below!
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