Personalization is a marketing term that I’m sure most people are now familiar with. The basic concept is easy to understand. It’s taking what you know about an individual customer or group of customers and using this information to engage them in a more relevant and personal way on your site. The goal, of course, is to enhance the customer experience in meaningful ways to gain trust, loyalty and promote business.
There are so many ways to achieve personalization. Some are quite simple while others involve big data. Some you can do yourself while others you need a service provider. This post aims to give you an overview of some of the types of personalization out there for ecommerce and to provide examples to get you thinking.
Individual and group based personalization
The first types of personalization I’ll talk about are those based on the individual customer or segmented groups of customers. Every interaction with your website is an opportunity to learn about your customers and to tailor the site experience to them.
Customer account tools
An easy way to provide a personal experience to a customer is through customer account tools such as wishlists and being able to bookmark or favorite products that they like. It’s simple, but being able to save products for later is a big deal. Not only does the customer have a quick and convenient way to return to the products that interest them, but these interactions increase the likelihood of repeat sales.
Example: Wishlists and save for later
Using my own experience as an example, there are products on Amazon that I have saved for later. Some of these products are low-cost items that I consider add-ons. I don’t need them now, but maybe later when it’s convenient. This is important because I don’t subscribe to Amazon Prime and so I have to spend a certain amount in order to get free shipping. When I do buy something, if I need to spend a little extra to get free shipping, this is where my saved add-on products come in.
Customer experience aside, the business operator also has an opportunity to learn through these tools what types of products their customers like the most. This data could be used in deciding what products to promote through sales, email newsletters, and by other means.
Interactive personalization tools
Like customer account tools, other interactive tools on the website can be used to help the customer find the products that are relevant to them. After all, whether you stock thousands of products that are widely different or a handful of products that are similar but not the same, understanding exactly what the customer actually wants is extremely important for getting the right products in front of them as soon as possible.
Example: Guided product filtering
A product catalog typically has a keyword search and/or list of categories and filters for narrowing down results. These tools are fine and dandy, but personalization lets us kick it up a notch with new ways of finding the product the customer wants. I was talking about this with Acro Media’s CEO a while back and he was raving about the Vitamix Blender Recommender. He’s big into fitness and eating healthy, and stumbled upon this page while looking for his next smoothie machine. The whole experience really stuck with him, partly because he’s already interested in personalization but also because it was done so well. You can view all of Vitamix’s blenders in a catalog, but the product recommender is an interactive tool that intelligently asks you every question that is relevant to finding the perfect blender for your needs. They ask if you have a blender currently and questions surrounding why it’s not working for you. They ask if you like new technology or prefer tried and true. Depending on the answers you give, they even ask how much space there is between your counter and cupboards (because you want a blender that will fit in the available space). In the end, the recommender comes back with a list of products that are personalized to your needs.
Example: Mimicking in-person buying habits
Another great example of interactive personalization can be found on many stores selling eyeglasses. If you shop in-person for these items, you’re most likely going to be trying a bunch of pairs on and looking in a mirror. After all, you need to make sure that the shape of the frames works with your own features. It’s a very personal shopping experience. So how do you achieve this experience shopping online? Online retailers in this space, Zenni Optical as an example, allow you to upload a front facing picture of yourself so that they can actually place the glasses you’re interested on you that way. It’s like looking in a mirror and it helps immensely for giving the customer the peace of mind that the frames they order are right for them.
Example: Product builders
One last example of an interactive personalization tool is a product builder. Instead of showing the customer a list of product variations, allow them to build the product the desire through an interactive builder tool. This gives the customer a feeling of uniqueness because they now get some direct control over the customization of the product.
I built an example of this type of personalization-enabled product on Acro Media’s Drupal Commerce demo site. It’s called the Urban Hipster Axe. It’s an example product builder that mixes in product-focused content while also allowing the customer to personalize the product to their liking. As you make your way through the page and select the options you like best, the selections are actually changing the product variation that you’re most likely to buy. When you finally reach the “add to cart” button at the bottom, all of your favorites are pre-selected your personalized axe is ready to buy.
Reactive and predictive personalization tools
Reactive and predictive personalization tools now start to dip into data learned from the customer’s actions. As the customer or group of customers interact with the website - makes purchases, saves products, views categories, etc. - tools can be created or subscribed to which act on the data gathered from these interactions. These tools, a good example being any list of product “recommended for you”, react to the interactions in order to predict what customers might be interested in next.
Example: Related product suggestions
A similar tool is one that predicts related products that the customer might also want to buy with their initial purchase. Related products can be assigned manually to a product by the store operators or data can be obtained from the habits of all customers to automatically predict what related products will more likely result in an add-on sale. For example, if you’re buying a game console online, many customers might also choose to buy an extra controller. Automation and data would confirm that this is true and show the add-on product. It would also show any other accessories or related products that customers are likely to buy.
Personalization from known customer information
When a customer signs up for an account or provides information about themselves through other actions on your ecommerce site, you have an opportunity to use this information to personalize their shopping experience. There are a number of ways that this can be done.
A personalization token is a simple way to recognize your customers based on the data they have provided to you. Here’s an example. Say a customer creates an account and enters her first name, Amanda, into a name field. You’ve now captured this data that is unique to the customer.
Now, on your homepage, you might have a large banner across the top of the page. Since you know the customer’s first name, a personalization token can be used to show the name within the message of the banner. The banner heading by default might say “Hi there, have a great day.” By using a personalization token and knowing the customer’s first name, the banner would change to say “Hi Amanda, have a great day.”
This same concept can be used throughout your website, email correspondence, live chat, and more.
Example: Social media sign-in
The personal information available isn’t necessarily restricted to what you’re able to capture on your own site. Many sites and services these days allow customers to create an account and sign in using social media services such as Google and Facebook. This method is convenient for many customers, but there is another side to it. These companies harbor your customer's personal data, and, by signing in with them, your customer’s may allow you to request information stored within the platform. The amount of information available depends on the social platform and the security settings configured by the customer, but typically you can get details like the customer’s name, email address, friend lists, employment information, photos, etc.
With the information provided through social media platforms, your ecommerce website can use the information potentially for use with tokens or other personalization methods mentioned in this article.
Example: Personalized promotions
What you know about your customer can also provide you with opportunities for some unique promotions.
If you know that the customer is a first-time visitor or hasn’t yet completed a purchase, promotions can be automated to encourage a purchase or some other worthwhile action (such as signing up to a newsletter).
Likewise, recognizing existing customers should be done as a way of promoting repeat business. Promotions personalized to segmented customer lists based on buying habits, age, gender, region, activity, lifetime customer value, and other metrics can all be used.
Finally, if you have data such as a customer’s birthday, you can really get personal and do things like sending a birthday card to make them feel special. It’s up to you to decide how to best use the information that your customers give you to personalize your promotions.
Big data personalization
All of the ideas and examples given so far are relatively straightforward to implement. Where personalization begins to get really wild and arguably more controversial is when you start to mix in services using big data.
What is big data?
I won’t get into too much detail of what “big data” means, but, to sum it up, big data is extremely large, complex sets of data that is getting increasingly larger and at a higher velocity. From Wikipedia:
Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that's not the most relevant characteristic of this new data ecosystem." Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."
You can see that big data is used in a lot of different industries. This is an evolving industry with many facets. There are companies who gather data sell that data or analytics to subscribers. There are other companies that help you capture your own massive data sets through AI and machine learning, and then provide tools for using and reporting on that data. I can’t profess to be an expert, but we’re at a point now where big data has become a big deal.
Big data and personalization
One property of what we typically think of as big data is that the size of the data exceeds the capabilities of software that would traditionally process it. Because of this, various specialized companies have formed which focus on processing and providing usable information from specific types of data.
Example: Predictive pricing
One area of big data personalization that has seen a lot of activity lately is in predictive pricing. This is a concept where pricing, demand and sales data for a particular product is taken from as many sources as possible. This data is then compared against the individual customer’s own information (such as browsing history, location, device used, etc.) and a dynamic generated price is displayed that has the highest-probability for conversion. The price shown can change from one customer to the next. Companies providing this service claim increased sales and revenue because more sales are likely to happen due to the right price being shown.
Be careful when using big data
Using predictive pricing again in another example, this model seems reasonable at first but there can be negative impacts on the trustworthiness of the business if your not careful. Everyone wants the best price and businesses want to make money, but no one wants to find out they’re paying more at regular price than someone else.
An investigation by CBC Marketplace back in November of 2017 revealed how popular hotel booking websites such as Hotels.com, Travelocity and Priceline were using predictive pricing (and presumably still are) for their hotel rates. In some cases there was up to a $70 difference between the price from one user to the next. That’s pretty significant and can create a lot of buyer confusion and stress once you know about it. A quote from Jesse Hirsh, a tech expert quoted within the article, had this to say which I found entertaining but so true.
“If we knew if gender or age or geography made a difference, like maybe my niece in Montreal should be booking my travel instead of me doing it from downtown Toronto.”
There you go. How would you feel if you discovered that you were paying more because of who you are or where you live than another person elsewhere. Think about that. That business strategy can be seen as both very clever and very shady.
The future of big data and personalization
I admit that big data is a topic that is quite new to me, but I do understand personalization and what goes into it well enough. The way I see it, online businesses in the future can expect to use big data for everything from stock and supply chain management to promotional opportunities, site layout and much, much more. Although, I expect businesses will be paying a lot of money to access these services.
From a personalization point of view, big data has the capacity to fine-tune many aspects of an online store to the customer automatically based on data. I’m not just talking about showing the most relevant products, but really the whole structure and layout of a page. This could be anything from showing the right products, price and promotions down to the colors, language and imagery used. A 45 year female business executive using a laptop will have a widely different perception of “what is right” than a 15 year old male student searching on a mobile phone. Big data personalization has the potential to reach these people differently in a way that is best suited to each. That said, the future of big data and personalization is still unfolding.
Personalization and your customer experience
The bottom line (and the big takeaway from this article) is that ecommerce businesses should be incorporating personalization strategies within their operations. Personalization leads to a better customer experience and customer experience is a real area of opportunity. Many organizations struggle with delivering the desired customer experience. You may feel this too, but don’t let personalization be one of the reasons. Start small and build from there.