Author: Antony Kattukaran


Magento 1 & 2 Catalog Search vs. Tagalys search for Magento

Posted on October 11, 2017 by


Relevant Search Results vs. Trending Search Results

Site Search is one of the highest converting funnels at any ecommerce store. Platforms like Magento rely on open source search like SOLR, that can be configured with simple functions to help you get started with search. But does it really work in improving conversion?

If your goal of the search conversion is to show products, you should stick with SOLR search from Magento. But if your goal is convert more visitors from search to customers, get detailed reports to understand how they interact in each search segment, you should use Tagalys Search. Here are key functionalities that Magento will NOT offer as part of the SOLR search it has inbuilt.

  1. Personalized search results – Best used if high mobile traffic, to ensure each visitor engages with search results catered to his/her interest
  2. Search results sorted by location based analytics – Best used if buying patterns varied by location
  3. Search results sorted by site level analytics – Best used if buying patterns were the same across the entire site
  4. Search Merchandising – Ability for the retailer to modify search results and promote products based on internal needs
  5. Popular Searches – Grouping high volume search queries (ignoring spam inputs), to show visitors what is trending at the store
  6. Pinned Searched – Ability for retailers to modify the popular searches and pin search queries to direct traffic to a search results page of business interest
  7. Prioritized product and text suggestions with auto complete, to help visitors save time time and quickly land on a search results page

Here is a quick video that gives an over of Tagalys search.

Other basic features that are also available are Synonyms, Stemming, dynamic facets, live catalog updates, spell check, multi currency, multi language and multi store support. Check out Tagalys search and signup for a free 28 day trial.

Posted in eCommerce General, eCommerce Site Search
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Personalized e-commerce recommendations: Home page Product recommendations

Posted on October 4, 2017 by


When you think of Personalization in ecommerce, you should think of Amazon. No online retailer has experimented across varies application of personalization with the goal to help visitors find a product of interest.

Personalization should not be measured by improvement in conversion rate, cause factors like marketing, promotions, product content, payment gateway failure rates, discounts influence the purchase decision of a visitor. What personalization should do instead, is to allow a online shopper discover as many products thus improving the unique product views per unique visitor metric, eventually improving the end conversion rate. Here are a few funnels where Amazon experiments with Personalization and Tagalys provides the same for online retailers

Personalized home page Product recommendations

When you engage with products at Amazon, you start sending signals of intent helping them understand what you might be interested to buy. If you return to the home page, almost instantly you will discover many personalized product recommendations by category to ensure you do not get lost in the store and remain to engage with the products or categories of your interest.

Personalized home page recommendations

Search is usually the best performing funnel at any online retailer. At one of our clients candere.com, personalized home page recommendations led to 10x higher revenue per visitor vs. the search funnel, with a 4x higher conversion rate

Personalized Site Search

Once you have created a persona for each visitor, its easy to curate the catalog based on their intent and show them exactly what they want. For example, if I am in the mood for running shoes, then when I ask/search for shoes, the retailer would show me running shoes first besides showing the generic product sorting that is defined site wide.

Personalized Store or Product feed

Amazon has been experimenting with this feature with a small subset of products in what is called “my mix”. At Tagalys we call this feature my-store. Essentially, what this allows a retailer to do it dynamically prioritize categories and products for each visitor based on exactly what they want.

More on My Store from Tagalys in the next article. If you want to signup, please visit https://next.tagalys.com/signup

 

Posted in eCommerce Personalization, Product Recommendations
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Amazon my mix – Personalized shopping feeds for ecommerce

Posted on July 19, 2017 by


In June 2017, Amazon.com made a quite launch of “My Mix”, a personalized shopping feed that curates “Interesting” products based on visitor interest.

My mix from amazon

Here is a company, that rules the market, commands your attention, for all purposes knows you will come back to Amazon tomorrow to check on on your next purchase. So why a personalized shopping feed?

Amazon, knows that tomorrow it will be fighting for visitor attention. Attention, not just from Walmart, Target, Alibaba etc., but from the millions of mobile applications that distract you in your shopping experience. Amazon, is fighting to keep you engaged at Amazon. Only then can the billions of dollars invested in R&D, supply chain, etc have any likelihood of influencing your purchasing decision.

What will start as a curation of “interesting products”, will tomorrow most likely be a curation of the entire store, to be delivered as a feed to you. Amazon understands, you the visitor are a busy person, engaging with the store usually on a mobile device.  The retailer knows what you want today or may want tomorrow. Given the data is available, Amazon is making the whole shopping process personalized. By this we mean, instead of you walking to the aisles or categories to find products, the aisles that are of personal interest, will come to you. All you have to do is walk into the store and the entire store will reorganize itself, to start showing you products of personal interest. This is personalization of e-commerce, where engagement data is used to predict shopper persona of what visitors are most likely going to buy.

Real time 1-1 visitor personalization always existed and with the lowered costs of computing power, it will be applied to across the product discovery journey in an e-commerce store. In the future, Amazon will ensure each shoppers product discovery channels will be 100% personalized, to ensure they get 100% of that shoppers attention. Lose attention, lose engagement, there will be no sale.

Why are we at Tagalys excited about My Mix? You will soon find out in a few more weeks. You can signup for Tagalys and get access to our full suite of product discovery solutions.

 

Posted in eCommerce Personalization
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Intelligent Product Listing pages for Magento 1 & 2: Data driven dynamic product sorting to boost conversions

Posted on July 12, 2017 by


Magento in one of the world leading e-Commerce platforms that helps retailers sell online. It is easy to use and has features to help setup complex product & store configurations. But it also has some disadvantages, one of which is the intuitive ability to create product listing pages.

Magento allows you to create categories and product listing pages, but assigning products is a manual time consuming process. Tagalys Merchandising extensions for Magento brings two benefits to retailers using Magento

  1. Create product listing pages in less than 2 minutes in a visual easy to use interface – Save time
  2. Utilize trending logic from Tagalys that works on visitor engagement analytics to dynamically determine product sort order for each merchandising page – Increase revenue

Visual Merchandising from Tagalys allows teams to create product listing pages, with minimal assistance from your development or tech team. It was designed with creative/non tech users in mind, with a visual interface allowing you to easily create, curate & modify product listing pages.

Pin products: Paste SKU’s, drag & drop products, select multiple products and choose a position to pin them. There are so many visual ways you can create a product listing page to suit what is best for you.

Choose conditions: Filter your entire catalog instantly, to curate products that best need the needs of the listing page. Go ahead and again use the PIN products functionality on the filtered list to best suit your style of merchandising

Page heading: Help your visitors engage better, by giving a heading to the product listing page.

SEO benefits: Help with SEO by giving your pages titles, meta key words, meta description in an easy to use interface

Catalog updates: Pages created by Tagalys, are updated between 24 hours to 10 minutes, depending on the plan you are in.

View our pricing plans and sign up for a FREE 28 day trial to help you save over 50% of time spent on creating product listing pages & make more revenue with data driven product sorting.

 

 

Posted in e-Commerce Merchandising, eCommerce General
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Free eCommerce Site Search

Posted on March 21, 2017 by


Date: November 5th, 2017. This offer is no longer in effect as we reached the free quota.

In 2015, when Tagalys won the TechinAsia National Finals, one of the judges suggested: “Why not offer e-commerce Search for FREE? “. Back then, Tagalys was fully loaded, meaning we offered 1 packaged solution for all clients without the ability to break down our algorithm & features in parts. But each client’s needs are different and today the team has re-architectured Tagalys to offer Site Search free for any retailer with less than 5,000 monthly searches & 20,000 active products along with a few value-added features. Google used to offer free Site Search (not tailored for e-commerce), but they pulled the shutters. Interested – SIGNUP NOW

Value Additions in the PAID e-commerce site search plan

If your business has the budget to configure & continuously maintain indexes across SOLR, LUCENE, Elastic Search etc., for your e-commerce store. Save time & money. Get Tagalys. Listed below are the value-added features we offer you in our PAID plan with availability/SLAs that will meet, maybe exceed your internal requirements.

Trending & Localized results: Do not show only relevant results (Free plan). Show the products that have the highest probability of sale based on engagement data. 80% of your visitors will not scroll more than 3 pages of Search. Allow Tagalys to determine what products to display for each search query

Search Suggestions: Auto generate text suggestions and display the top X to your visitors that predictively change as they type. Interested – SIGN UP NOW

Suggestions - dresses

 

Multi-Store & Currency: No problem. Easily configurable with our integration kits.

Suggestions - Currency

Spell Check: Visitors make mistakes when they type. With fuzzy logic, these mistakes can be corrected on the go, providing frictionless site search experiences. Interested – SIGNUP NOW

auto correct

Synonyms: Unlimited Synonyms. Show your visitors the right products no matter what they call them. Cell phone vs. Mobile phone, Trouser vs. Pant.

Catalog updates: Update your search indexes LIVE via push integration mechanisms or choose the easy way out to synch your catalog once a day.

View comparison table

Magento search pricing compare

If Site Search is not configured in your e-commerce store, help us, help you. Interested – SIGNUP NOW

Posted in eCommerce Site Search
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Personalization for ecommerce – How did Tagalys start its journey?

Posted on December 26, 2016 by


Tagalys provides personalization of a service for ecommerce and our apis (using plugins) can be applied to personalize product discovery across channels like site search, recommendations and listing pages. Our founders did not wake up one morning and decide to build personalization as a service, it was built by accident.

In their previous avatar, Antony and Palani had conceptualized and built a social commerce platform as they saw a growing consumer need driven by the growth of eCommerce. The supply side of the platform consisted of a complex web of spiders crawling ecommerce sites for products and all public information about them. At its peak the platform covered over 3000 unique brands and over 8 million unique sku’s. The demand side consisted of consumers who were interested in online fashion and home décor shopping.

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Like most platforms, Monthly active users (MAU) was the primary metric of focus as that tied directly to their current and future revenue model. But how do you keep consumers engaged to ensure churn is minimal? How do you build an engaged community that will refer others to this platform?

Early metrics showed users spent over 9 minutes/session and the team was elated, but we also noticed the percentage of return visitors was not as expected. Post feedback with many users, we realized high session time is also an indicator that users being unable to find what they seek. Solving this problem was the advent of Tagalys. The founders first started by building their own analytics engine that collected visitor engagement data and also extracted meta data from products to  start extracting insights on why visitors might engage with certain products. The meta data also allowed to create data model or trend analysis to better understand the shopping persona of users. The first step was to start making sense of the data and apply the insights across search and listing pages, so products were sorted by what was most engaging site wide and not simply sorted by new arrivals etc., This logic helped increase engagement but still did not appeal to all users, as what maybe most engaging for user X might have no relation to user Y. The team then went onto analyze data at a user level and found out the high variances users have in preferences for the same category or subcategory of products. Thus the next steps was to  build a personalized view for each user for any page they engaged – Search results, Category pages, Recommendations. The end result was to showcase a unique set of products that match a user’s shopping persona and based on interaction with the assumed persona, to learn and continue improving the shopping persona to improve engagement.

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While founders were successful in delivery personalization at scale, as a business it failed. Failed due to unavailability of funds or maybe they were too early, but the learnings of this journey helped build the foundation of Personalization as a Service for eCommerce. When many of the users shared feedback on how such an experience was unavailable at regular eCommerce store, it got them thinking and made them pivot as they recognized the same need for eCommerce to help increase engagement, reduce their CAC and improve revenues.

Do share your feedback with us here or email us at cs at tagalys dot com

 

Posted in eCommerce General, eCommerce Personalization
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How Amazon Site Search algorithms affected Flintobox in SEM

Posted on December 21, 2016 by


Want Amazon like Search experience in your online store?  Signup now and see your visitor engagement improve or read from one of our data analysts on the inside workings of Amazon Search.

Recently a fellow entrepreneur raised an issue on unfair acts by Amazon.in infringing on their trademark name “Flintobox”. The issue came alive because Amazon.in had bid for the keyword “Flintobox” on Google and given that it’s Amazon, there is no way a startup can outbid them. Also, legally no company is allowed to bid on a name that is trademarked by another entity. Below a screenshot of the dispute in question.

amazon-trademark-violation-2016-10-18

This has been rectified by Amazon, but it sure caused some heartburn for the Flintobox founders, a few folks at Amazon and many small businesses that feel threatened by such incidents.

amazon-response

It always confuses me when large, funded players are willing to outbid, underbid or even lose money to acquire your customer. This probably led to the founder sharing his woes across the social media where it was retweeted, shared and hence reached my timeline. My friends at Amazon, who speak highly of the company, insist that something like this must have been a mistake and not some personal agenda of any category manager to acquire customers by breaking the law. So, I personally spent some time trying to understand what could have happened, given that Amazon has been a very big influence in my life.

At Tagalys, one of the products we sell to our customers is personalized search as a service and we think the internal Amazon search tool, something that most visitors engage with, might be the cause to the above dispute. Here is our take on what could happened. We hope to hear your feedback as it will also help us improve what we live to build everyday.

Today (Dec 21st, 2016, 1358 hours) if you visit amazon.in and search for “Flintobox” you will still find search suggestions, even though Amazon.in does not carry any stock of the Flintobox products.

flintobox-search-amazon

Now everyone knows “Search” at Amazon is best in class, hence there is no way Amazon will show you Flintobox in search suggestions, if the engine knows products are – OUT OF STOCK. But if you click on any of the suggestions, it leads to a Search results page, that as expected, does not display any Flintobox products instead it lists Einstein box or Xplora box.

explorabox-jpg einsteinbox-results

This leads to two possible reasons, when sellers upload products onto Amazon.in, they upload details about the products including tags, search keywords, potentially even synonyms. We assume, that these products had the “Flintobox” keyword somewhere in the seller upload sheet.

Another reason, though highly unlikely, is that Amazon dynamically detects that products sold by FlintoBox, Einstein box, Xplora box are targeting the same customer segment. Hence even before a customer visits a “No results” page as Flintobox is not in stock, it redirects them to a page with products that will appeal to the same customer segment. Thereby ensuring engagement and potentially conversion.

But what does all this have to do with an ad created for “Flintobox” by Amazon on Google?

Every eCommerce Search or Site Search engine worth its salt, collects, groups and analyzes “Search” data and how visitors engage with the results across segments. Search is demand data directly from customers and visitors telling you what they want. There is also a high probability if X volume of visitors ask for something within your store, there are many more out there (Search engines like Google) who may want the same.

“Flintobox” as a keyword led to Search results and had engagement. It still continues to do so. For the category/subcategory, their search volume must have met certain minimum criteria and also led to engagement – views, add to carts, buys etc.,. We assume, Amazon has an inbuilt or uses a 3rd party keyword bidding tool for Search engines like Google etc.,, that studies keyword data, with engagement and dynamically creates a landing page (at a category or subcategory level) provided the real time bid requested by Google falls within the budget of Amazon. And since the dynamic page created for the advertisement is powered by Search keyword data, the keyword bidding tool has an active keyword, products to display and bid within budget and voila the landing page is created (This is a screenshot of the landing page reproduced from the blog posted by the Flintobox founder, hence products displayed may not not current)

flintobox_search_listing_on_amazon1

So what could have been done to have avoided this incident?

If the above assumptions are correct, then the Amazon seller services team should have more checks in place when sellers upload data. Google today will not allow you to bid for a search keyword that is trademarked by another entity and something similar within Amazon Seller services could have voided this incident from happening. Another way, although rudimentary is the compare the seller upload tags/keywords data with the Brand names stored within the Amazon database. This may not cover trademark infringements, but will definitely avoid Brand X showing in suggestions of results when Brand Y is what a visitor is searching for.

I feel this was not done on purpose, but sometimes a small mistake by a large company is enough to make a big impact in the ecosystem around them. While an apology may have been issued, it does not help with the loss in cash flow that many of the smaller companies rely to grow. Fortunately Flintobox survived to tell a story, many others may not.

SIGNUP NOW, join a free plan or a free trial to know whats best for you. 

Posted in eCommerce Site Search
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