Content and Product Recommendations That Delight
Offering your Target Audiences Dynamic and Relevant Product & Content Recommendations Based on Their Current Interests for Enhanced Sales and Improved Engagement!
With Personyze you get to take your product and content recommendation practice up several notches by introducing the element of automatic or manual personalization that stays updated of the changing interests of your target audiences. It is everyday recommendations on steroids and gives you the power that uber-sophisticated engines like Amazon have used for years to increase sales by more than 29%!
DO RECOMMENDATIONS WORK?
The simple answer to the question is ‘Yes’. E-commerce platforms have built their business on the practice of recommending products. And the trend has slowly spread to general content and news story sites as well.
WELCOME THE RECOMMENDATION ENGINE FROM PERSONYZE:
The Personyze content and product recommendation engine is one of its kind:
It is a recommendation system that is capable of suggesting products and content pieces automatically but has room for manual fine tuning as well. A platform that upgrades itself and keeps up pace with the changing interest profile of audiences. A platform that can gather data from past and present interactions and create wonderfully unique styles of recommendations that play with various factors like crowd wisdom, scarcity, products in cart and other custom metrics. A platform with the best of multiple recommendation logic systems.
- It is capable of automatically displaying recommendations and send email recommendation but allows business owners to specify rules or fine tune underlying logic if needed.
- It is non-labour intensive and intelligent. Upload your products list or article directory and set the interests. The engine is intelligent and it keeps learning, evolving the way it suggests items and actually becomes more accurate with use! So the ROI keeps increasing with time.
- It is not boring or cliché! Personyze allows the framing of various types of content and product recommendations that might make use of the scarcity factor (like in the scenario, ‘You like iPods. Now grab these Skull Candy Earphones Because we Have only 5 in Stock’) or the novelty quotient (like in the scenario of a blog that instantly displays the message ‘Your favourite author just published a new post. Read it Here’) to keep the recommendations interesting. In fact marketers can even specify (if needed) when and how the recommendations are displayed.
- It assesses many data points of which past browsing tendencies, products purchased, social media postings and friend preferences, unprocessed products in cart, the content of the wish list, items liked or reviewed, seasonal trends, existing Personyze profile data (if present) and CRM data (if uploaded) to recommend products and content. And it is always open to tweaks by the business owner. Which happen without any IT help through the Simulator.
-It give you the freedom to design the look and feel of your recommendations – You can use pre made template for the recommendations you make. You can choose to embed them in specific areas of pages and give it a ‘native’ feel for dynamic and personalized home pages or you can have recommendations as floating divs (popup) or exit intention messages .
THE PERSONYZE RECOMMENDATION ENGINE IN THREE DIFFERENT SETTINGS:
On a news website, if a visitor’s interest patterns show that he is an avid football fan and peruses only items and stories that have to do with a specific country – say United Kingdoms, then upon his subsequent visits, the system will load to the home page news pertaining to the UK and especially focused on football matches, leagues, events and celebrities instead of showing him a jumble of different links and stories from all over the world.
When there is new article about his team that he hasn’t read yet, an email can be sent out alerting him of the addition!
On an e-commerce website, the system can keep track of items viewed by a visitor, goods that are added to the shopping cart and abandoned, most bought/viewed and clicked items, seasonal favourites, new arrivals and so on. This data then guides Personyze to display site messages about price drops for products that the visitor has shown prior interest in. You can even send out re-marketing emails based on unprocessed items in the cart or new products that visitors are likely to buy.
On more generic content based sites like lifestyle and productivity blogs, interest widgets take note of categories that account for the maximum interactions (shares, likes, comments), bookmarked and unfinished articles and most read sections to dynamically recommend blogs and articles that make sense given the past history of the visitor.
THE PERSONYZE RECOMMENDATION ENGINE BACK-END:
The process of setting up Personyze to take over your product and content recommendation is extremely simple. And doesn’t involve IT hassles.
1. The admin may upload his product/article catalog using API or a .csv file upload to give the engine something to work with. Further he may also add the product “family id” and quantity in stock which can be used to frame interesting recommendations as discussed above. These recommendations are then dynamically interested into the pages based on visitor interactions and other data points.
2. The admin may also upload a list of “product interests” or “Content interests “. This defines loosely some of the possible interests associated with a product or content piece. For example the recommended product might be a high calibre DSLR camera if the visitor has searched for or otherwise expressed interest in ‘professional camera’ and ‘night vision camera’. If this isn’t set to begin with, the engine initially pulls the recommendations based on broader interests but learns quickly to become highly focused and specific. But the option to set manual interests is present! To avoid irrelevant recommendations under any circumstance.
3. The admin then sets the site up to report to the Personyze engine whenever a product or content piece is viewed/liked/commented/added to favourite/added to cart/checked out.
4. And finally he sets the recommendation widgets up specifying how the suggestions might be displayed to visitors (a footer ribbon, on page pop-up, email, instant alert).
Personyze then records each interaction with the site content or products and its nature (such as views/purchases/favourites) to set the user interest accordingly and display the recommendations that most appeal to his present set of mind, his temperament and requirements.