Digital growth hacker and founder at Elogic Commerce. Ecommerce development & consulting is my passion🔥
Personalization was, is, and will be among the top ecommerce trends for the upcoming years.
Let’s be honest: it is incredibly difficult to choose from a pile of products in a catalog, especially if you know the majority of them won’t be the right fit for you. That’s why classic offline retailers specializing in a particular range of products became more popular than huge jumbled department stores. Yet, even one-product shops can get confusing: consumers face difficulties choosing between different brands, let alone product categories!
Now imagine coming into a store and seeing the product you’re searching for. While impossible in a brick-and-mortar store, this scenario is quite realistic in ecommerce. The key to customers’ hearts is…PERSONALIZATION.
Except for one problem: even though over 84% of retailers recognize the importance of personalization, 63% still struggle with technologies to back it up. The reasons are vast and mostly associated with the technological gap whereby retail marketers are held back by their technology (or a lack thereof).
But what if I told you that the game is worth the candle? All you need is a clear understanding of how personalization works in ecommerce coupled with realistic objectives and the right technology.
Personalization primarily refers to a set of practices in which an online store displays dynamic content based on the customer demographic, geographic, and behavioral data.
For example, when shoppers first come across your ecommerce store, they’re offered “trending now” or “bestseller” content to inspire them to keep browsing your store. Similarly, those users who have already made a purchase at your store will receive different product recommendations from those of new shoppers.
In ecommerce, personalization works in the following dimensions:
1) Personalized product search. The store displays search results based on the user prior queries in the same store.
2) Product selection and categories. Product categories are re-ordered according to the affinities of your shoppers.
3) Product bundles. After completing a purchase, a user received personalized recommendations based on the algorithm “people who bought X also bought Y”.
4) Dynamic content. The store tailors UI, landing pages, calls-to-action, pop-ups, etc. to different user categories.
I guess I don’t need to convince you of the importance of personalization in ecommerce: companies drive 40% more of their revenue from personalization than their slower-growing counterparts.
The frontline marketers placed it on their ecommerce radar a long time ago. But the problem isn’t the why; it’s the implementation.
The first step to personalization is complete and comprehensive data collection that reflects the entire customer lifecycle and interactions across digital touchpoints. More specifically, a retailer might track
Once gathered, the data is grouped to create buyer personas and form their purchase flow. You can identify customer shopping habits and make assumptions as in e.g. “those interested in X don’t care about the product Y” or “people who bought X also bought Y”.
Indeed, with the arrival of certain consumer data protection laws like GDPR in the EU or CCPA in the US, data collection has become more regulated. Google, Apple, and Facebook hit another nail in the coffin last year with their data protection policies. But retailers still have access to a plethora of data sources, including first-, second-, and third-party unstructured data that can be used in their personalization efforts.
Personalization is a journey. Even if you have it written on your general business roadmap, you should never stop gathering detailed transactional and behavioral data. The reason is obvious: a more in-depth analysis of your audience will allow you to personalize not only at the level of products but also at the level of product categories, bundles, emails, and web pages, among others.
There’s an amazing case of the Panama-based retailer of home goods Do it Center. After analyzing data, they noticed that visitors interested in DeWalt drills drop if they are shown the same item of the brand’s competitor — Makita. They used the user’s browsing history to sort the Category page accordingly and guess what: the brand now has an 85% higher conversion rate.
The case of Do it Center has been successful for two reasons. First, marketers had a clear roadmap of the kind of data they needed. Second (and most important), they had a rich sample.
Remember: the number of users you analyze is fundamental to your personalization efforts. The more customers and orders you analyze, the more accurate your rules and assumptions will be. For this reason, retailers with 1000 user traffic/month rarely make it to the accurate consumer segmentation.
Now, what do you do with all the data you’ve gathered? The worst thing (and here’s where most marketers make mistakes) is to interpret it yourself.
Your second step should be to pull all data into a digital personalization engine. The software analyzes, measures, and systematizes customer data for the purposes of segmentation and shopping trends prediction. This automated tool allows you to tailor customer experiences, including website layout, product recommendations, etc.
Now you may ask: how do I build a recommendation engine?
You don’t. The market is so vast and versatile that it doesn’t make sense to build a recommendation engine from scratch. They’re all using ML/AI algorithms and can be integrated via API or as an extension to your ecommerce website.
There’s good news for the retailers running on such popular ecommerce platforms as Adobe or Salesforce. These big guys offer their own recommendation engines, like Adobe Sensei or Salesforce Einstein, with built-in business logic and actionable algorithms.
The engine analyzes your stored customer data, applies complex mathematical formulas to filter it, and creates rules to recommend the most relevant items to users. And voilà! You’re all set to send personalized emails, toggle product/category pages, and implement any other marketing strategy you have a vision for.
There are many recommendation engines you can choose from, but Nosto is my personal favorite. You integrate it via API and have access to a rich feature set, including dynamic product bundles, personalized content, pop-ups, etc. to enhance the user shopping experience.
Gartner says only 17% of digital marketing leaders are using AI/ML broadly for a comprehensive personalization strategy and roadmap. Yet, 84% of them believe using AI/ML enhances the marketing function. What’s the cause behind marketers’ frustration to adopt personalization technology?
The fear. Of misinterpreting the customers’ data. Of incompliance with privacy regulations. Of increasing the business TCO while decreasing the ROI.
It’s understandable. All you need is to develop a consistent, comprehensive framework that will marry tech to your business objectives and your customers’ preferences. In addition to a strong marketing team, a skilled business analyst will be an asset.
To present an advanced personalization solution, you’ll need to think beyond data. For example, your customer has purchased a laptop. You’ve recorded this action and set rules in your personalization engine to send tailored emails to this customer about other laptop offers in your store.
But that’s how your worst fear comes true.
Let’s be honest, no one buys two laptops in a row in B2C business. It would be much smarter to set a “People who bought X also bought Y” rule and recommend portable accessories, like a wireless mouse or a set of headphones.
Prepare a roadmap to advanced personalization, make sure to tailor customer experiences at every stage of their lifecycle, and — most importantly — train your marketers on the digital recommendation engines and make them act on the obtained insights. You can increase your ROI with personalization even on some most unexpected website pages.
The best time to start is now, but you always have to consider the digital maturity and size of your business.
Personally, I always recommend personalization engines to mid-sized and enterprise ecommerce stores. They have the necessary traffic, customer base, and a decent number of orders to segment and sub-segment different buyers within a larger audience. To dive deeper into 1:1 personalization, they can further track a user's behavior (such as site visit history, browsing activity, geographic location, type of device, etc.) and leverage user-generated data (like account settings or completed surveys).
But if you’re a small-scale low-margin shop specializing in a single type of product, personalization won’t be the right fit for you. Simply because you won’t have sufficient data to segment your audience, let alone make personalization assumptions. Hopping on an augmented reality (AR) trend might bring many more benefits for you, but that’s a completely different story.
Personalization can’t be solved overnight. You can sell like Amazon once you stop being scared of technology. After all, it’s here to serve you.
It is a heavy lift, no doubt. Think through your personalization roadmap and needs. Lose your fear of adopting technology. Start collecting customer data — the more, the better. Smart strategy and the right tech solution will bridge your personalization gap and cement your market position for years to come.