Five trends in personalization that you should follow in 2023
Deep Learning in product recommendations, personas as new segments, advanced analytics, search personalization, and omnichannel communication.
As the world continues to evolve rapidly, businesses must stay ahead of the curve to succeed.
Anna Ambrozevich and Darya Myagkova, founders of CRO & Personalization Academy, have identified five key personalization trends for 2023 based on our practical experience and ongoing research of industry trends and insights from subject matter experts.
By keeping our knowledge and approach to personalization current and effective, we want to help people and businesses navigate the constantly-evolving landscape of personalization.
1. Advanced algorithms in product recommendations.
Nowadays, companies use a set of basic product recommendation algorithms such as "Popular," "Sale," "New," "Recently Viewed," as well as "Purchased together," and "Viewed together."
What will be the next steps in this area?
Algorithms with Deep Learning and Data Science models.
Product recommendations with machine- and deep-learning algorithms allow a business to use the next level of advanced personalization. Only some software can offer it now, but this trend will grow significantly.
In addition, internal or contracted data scientists can apply specific algorithm parameters crucial for a particular business (seasonality, marginality, warehouse priorities, etc.) to the algorithm learning process or create their model.
The synergy of business merchandising rules and personalized algorithms.
Sponsored items in product recommendation widgets, which are a part of business contracts for large companies, may negatively impact the personalization of the recommendations and customer experience. This is because all users will see the same products.
While this placement brings additional profit to the business and strengthens relationships with suppliers, a company that can find the right balance between manual merchandising from partners and personalization through machine work will have a competitive advantage.
2. Enriched audiences and buyer persona methodology.
It's always a good idea to start implementing personalization from primary audiences: new/current users, key traffic sources, device types, and others.
And then comes the fun part!
Using buyer personas instead of standard audiences and segments.
Buyer personas methodology helps to identify and tailor marketing efforts to specific customer segments by defining their goals, behaviors, and challenges throughout the customer journey.
Buyer personas are more comprehensive than audience profiles based on a single attribute, such as demographics or purchasing behavior, because they incorporate multiple characteristics.
For example, a DIY store may create two buyer personas: a professional builder and a DIY homeowner. The builder may be a frequent shopper with shorter site sessions, higher average order value, and less interest in promotions. In comparison, the DIY homeowner may be a new customer with longer browsing sessions, a lower average order value, and a higher response to promotions.
It is essential to create a range of personas representing the full range of website visitors, focusing on more targeted personas at the beginning.
Once the key buyer personas have been identified, the effectiveness of current personalization campaigns can be evaluated based on the results for each persona, and new campaigns can be designed for specific personas as needed.
Enriching user profiles with data from your CRM system.
A unified User ID allows you to enrich customer profiles with historically accumulated data from your CRM system — for deeper and more omnichannel personalization and loyalty increase. For example, add information about offline orders with a loyalty card.
Using enriched data, it is possible to send personalized messages to customers to congratulate them on their anniversary, highlight their accumulated bonus points, or segment them based on their personal preferences.
3. In-depth analytics.
Once you understand how personalization impacts your business through basic analytics, consider moving on to more in-depth analytics.
Such advanced analytics will help you answer the following questions:
- How do personalization mechanics affect each other within a session/one user? Is there any cannibalization? Do they work well separately but poorly together?
- Do the mechanics work with consistent effectiveness? Is there any dependence on the season, the quality of traffic, and the availability of new products?
- Do the product recommendation algorithms work the same way for different product categories and pages?
- How do your results correlate with market averages or benchmarks?
- What results does personalization produce in terms of custom metrics specifically for your business?
In-depth analytics requires as much raw data as possible, including all user actions, complete campaign information, and detailed transaction data. If required, this data will need to be converted and validated for uploading to the BI system. We recommend hiring an in-house analyst or engaging an external expert company.
4. Personalized search experience.
Currently, the topic of site and application search is rarely covered. However, in most online shops, search brings up to 400% conversion (McKinsey), and conversion from visitors who have used search is 1.8 times higher (eConsultancy).
By the way, Q4'22 news from personalization engines like Bloomreach, Nosto, and Sitecore showed that technological companies also focused on search and discovery experience, improving this functionality in their technology stack.
Site search personalization can be based on multiple users and business data: geo, device, purchase history, views, product margins, stock availability, and more. The relevant searches must run in different channels and formats: on the website, mobile app, and voice search.
You can personalize your search with several tools:
- Personalized search suggestions for different segments. New users will see a set of popular categories, while those who have already visited the site will see a personalized setting.
- Automatic synonym matching. Based on how users select, reformulate, and refine queries, new similar words can be added to the dictionary (search engines do this automatically).
- Personalization of search results, filters, and sorting. You can customize your search results by setting specific rules or selecting segments. Also, you can try more sophisticated algorithms based on the user's interaction with a maximum of parameters in the results: what he entered, how he behaved within the categories, and what filters he used. The entire user history helps train the algorithm to learn and bring the best-personalized search results.
5. Omnichannel personalization (yes, it's still a trend).
It has been almost impossible to avoid hearing about omnichannel in the past five years unless you've been lazily ignoring it.
However, the trend is still worth paying attention to, as fewer than 10% of companies have effectively implemented personalization across channels beyond the digital realm (according to McKinsey).
Furthermore, only a few companies have successfully implemented omnichannel personalization, which requires a cost-effective approach and the proper collection and analysis of data.
By omnichannel, we mean end-to-end personalization at all user interaction points, including digital advertising, emails, and other direct communication channels, websites, apps, call centers, and offline stores.
All offline channels should be connected to a single database containing personalized content, product recommendations, and offers to provide the most relevant and unique experience for each user.
It's worth noting that a staggering 85% of users start shopping on one device and end up using another (according to Google).
Five personalization trends 2023. Summary
1. Smart algorithms for product recommendations. DL- and DS-algorithms and the synergy of "manual" merchandising rules and machine learning.
2. Enriched audiences and buyer personas. Use additional data sources, focus on CRM data and work with personas.
3. In-depth analytics based on sizeable raw data sets. Tracking the interconnectedness of different mechanics, business-specific volatility of personalization effectiveness.
4. Personalization of site/app search. Relevant cues and synonyms, quality handling of search results, and navigation.
5. Omnichannel is still trending.
Don't worry if you are still in the early stages of implementing personalization in your company. Every company is different. Building experience and knowledge take time to implement more advanced solutions.
Focus on learning the basic mechanics and remember that trends may change quickly in personalization.
Keep moving forward and be prepared for the rapid pace of change in this area.
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Authors: Anna Ambrozevich and Daria Myagkova
