Tag: analytics


Amazon My Mix: Personalized e-commerce shop increases product views and conversion

Posted on October 16, 2017 by


Amazon, started a version of a personalized store called My Mix, in June 2017 to help visitors save time and find products quickly. The one common trend across every online store globally is that over 60% of their visitors, engage with the store via a mobile device. This changes the expectation of behavior as visitors on mobile devices have more urgency that visitors on desktop, they are typically on the go and get to see only 2 to 4 products per screen. So should you design a separate site for mobile visitors? No. Applying a personalization engine will address this challenge and a personalized store ensures visitors are more likely to stay longer at your store.

What is a personalized store?

Personalized stores from Tagalys, collects every signal left by visitors across your site to learn more about their interest and predict what products they might be interested in. This API collects data from online retailers via a Tagalys analytics file, requiring no additional work from clients to snd the data and applies real time 1:1 personalization to create a personalized store in real time.

Visitor benefits of a personalized store

While the benefits are applicable across devices, we feel the personalized store is more suited for mobile users, cause it allows them to engage with online store in a frictionless manner. By this we mean, if you have shown interest in midi skirts and maxi dresses for example, instead of having to navigate back to the categories and restart your browsing experience, the personalized store would already have that available for you, saving you clicks, time and the efforts to start your experience. It is like the store reorganizing its aisles for each visitor vs. having each visitor walk across the static store each time to restart their shopping journey.

Retailer benefits of a personalized store

Since the retailer now has the ability to dynamically reprioritize what products & categories to show each visitor based on their unique interest we have seen the following KPI’s improve at the store.

  • Engagement or reduction in bounce rate
  • Product views per visitor
  • Product views per session
  • Add to carts per visitor
  • Conversion rates that are 3X better that other product discovery channels

How do you signup for Personalized Stores from Tagalys?

Signup as a new client at Tagalys and install any of our extensions to your platform. Contact the Tech Support team from Tagalys and request them to enable Personalization for your store, as this API is not enabled by default. Our team will study your product catalog & configuration and work with you the determine the best personalized setup for your store. This process should not take more than 24-48 hours to be completed. Tagalys reports will also showcase the live data on the impact personalization brings to your store. This can help you determine if personalization from Tagalys is right for your business.

Posted in e-Commerce Personalization
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Algolia Search vs. Tagalys Search

Posted on October 11, 2017 by


Do you want the fastest Search (Algolia) or really fast Search (Tagalys) that increases your conversion & revenue?

At Tagalys, we obsess about improving product discovery at our retailers. How do we ensure visitors click on more products from search results or how do we ensure they find the most relevant products in the first 3 scrolls. To our customers (online retailers) Site Search under 100ms to ensure scores are calculated and products sorted by what is most likley to sell, to help with end conversion is an acceptable trade off. Read our testimonials to know more.

Algolia is a brilliant product, incredibly fast and ideal for database searches. But does solve the needs of e-commerce search? Which is to learn what products are more likely to sell and optimizing your search results by showcasing products that are more likely to increase conversion for that query.  Here is a quick comparison of the two with data researched on October 1st, 2017

Tagalys Algolia
Search Personalization Starts at $900/mo Starts at $2500/mo
Localized Search results Yes No
30 days Reports & Analytics Starts at $49.99/mo Starts at $2500/mo
Default sorting Trending Relevance
Merchandising results Yes No
Display Popular Searches Yes No
Modify Popular Searches Yes No
Pricing based on Usage tiers Records + Indexing
Reporting period 30 days @ $49.99/mo 30 days at $2500/mo
Speed < 150ms < 50ms
IT/Tech heavy No Yes

*mo – Month, ms – Milli Second

With inbuilt analytics, Tagalys by default sorts search results based on what is trending. Trending is the analysis of visitor engagement data across the entire site to predict the best selling products for any search query, based on historical engagement data. It collects data utilizing Tagays analytics for site wide engagement, understands engagement pattern and determines trending scores for all products. It continues to learn and improve these scores as visitors interacts with the site. We also analyze data patterns across segments or for each visitor, to personalize search.

Algolia on the other hand has gone on record and said “Elasticsearch is a wonderful tool for Big Data analytics, but it is very difficult to reach a good relevance with it on database search. You can try to add some logic on top of Elasticsearch or try to reorder manually results for some queries, but it’s tedious work that continuously needs to be tuned. Algolia on the other hand focuses on getting a very good relevance with minimal configuration. While not optimal for all use cases, it makes it particularly appropriate for database search”. The tedious work is what Tagalys has solved for online retailers giving them an out of the box solution, with simple installation.

Both Tagalys & Algolia also cover some of the basics of ecommerce search like showing suggestions with autocomplete, spell check & live product synching. Our core lies in the post click analysis to keep learning from patterns and update trending, personalization or localization scores, so the products displayed in search results are more likely to convert.  So if we can help you with improving your conversion at less than 100ms, happy to show you a demo anytime.

Check out Tagalys search and signup for a free 28 day trial.

 

Posted in eCommerce Site Search
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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 is one of the world leading e-Commerce platforms that help 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.

Watch our demo site to learn more or schedule a 1:1 demo with us at your convenience. What are you waiting for? Contact us now or start your 28-day free trial immediately.

 

 

Posted in e-Commerce General, e-Commerce Product Listing pages
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Extension for Magento 1 & 2 Catalog Search: Klevu Site Search vs. Tagalys

Posted on July 10, 2017 by


As we speak to new customers, we often hear questions on how we are different from Klevu Search and why customers should choose Tagalys over Klevu.

Product Discovery vs. Site Search

Klevu provides Search as a service only, whereas Tagalys provides powers Search, Recommendations & Intelligent Product listing pages. The Site Search funnel is a high value funnel, where Tagalys has recorded conversion rates 3X – 4X higher than other funnels within the same site and between 20%-30% of total revenue. We have been successful with these conversion rates because we collect data from the other funnels, to gather incremental insights thus accelerating conversion rates for customers using Tagalys search. In the world of Tagalys, each funnel has a different intent of shopping and understanding that intent, helps us better understand the customer.

Personalized Search Results vs. Self-learning search

Klevu has learning search and Tagalys provides trending/intelligent search, both showcase search results that change over time as we learn form engagement data. Trending search results from Klevu are based on product analytics & Tagalys relies on visitor engagement analytics. It will show the best results across the entire site and will increase conversion. Unless we do a A/B test, it is hard to quantify how much of an improvement in conversion with either solution have over the other.

But Tagalys also provides personalized search results, showcasing the best search results, based on each visitors individual shopping persona. This has proven to reduce the time to transact, increase engagement and increase conversions higher than trending results. Personalized search is a must if you have a high mobile traffic with return visitors, to ensure visitors quickly find products when engaging via mobile devices as they are more likely to get distracted and leave your retail site.

Pinned Searches

This gives the ability to retailers to modify the popular searches and showcases other searches, to promote business needs. This gives retailers more control on the search funnel. We do not offer this across all plans, because if done incorrectly it can reduce your conversion rate.

Pricing

While the pricing in each tier is similar across Tagalys & Klevu, Tagalys offers more features in each tier, thereby working towards increasing the conversion of our customers, thus providing higher value to the same price point. This leads to customers getting more value delivered for each pricing tier.

Reporting

Klevu reports are primarily tied to Search as that is the only product offered. Since Tagalys offers multiple product discovery solutions including search, we offer detailer reporting across all funnels that are powered by Tagalys, along with segmented product & attribute reporting.

URL redirect Or Category directed search

We do not redirect visitors for search queries to any Platform (Magento, Shopify etc.,) powered pages as we are confident the product sorting generated by Tagalys for any listing page, including search is far more likely to convert that the sort order on the platform listing pages. Typical sort orders in a platform like Magento is a manually updated and relies on the retailer to decide the product positions. This is also static, which relies on the retailer to update these sort order.

But since our integration, has the data of products across every category or promotion page, we show the products from the same page, but dynamically sorted by Tagalys. Thus if you search fro “Holiday” and there is a promotion for a “Holiday” page, that page will show in search suggestions and the search results will include only products from that page, sorted by what Tagalys does best, increase probability of conversion.

Content Search

Not available at Tagalys. Our core competency is engagement analytics to drive personalized or trending results across search, listing pages & recommendations to drive conversion.

Responsive & mobile first, Trending/Popular Searches, Search Merchandising, Synonyms, Instant faceted search layout, Rich autocomplete Suggestions, Dynamic filters, UIUX Customization

Available at Tagalys & Klevu

So Klevu or Tagalys?

Tagalys along with other product discovery channels, gives retailers complete control of managing visitor engagement & revenues across multiple product discovery channels to increase engagement. Tagalys also offers personalization of product discovery that is critical in todays world on mobile first engagement. While Tagalys search by itself can guarantee a 30%-100% minimum increase in search conversion, we do better when we utilize  engagement data across multiple discovery channels, as each channel serves a different intent of the visitor. Klevu no doubt is a good product, but improving Search by itself is not going to increase your topline manifold as your business expects.

If you want to checkout Tagalys, signup for a test account.

Posted in e-Commerce General, e-Commerce Personalization, eCommerce Site Search
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e-Commerce Site Search Review: Urban Ladder

Posted on April 25, 2017 by


Urbanladder.com is one of the leading venture funded curated online furniture sellers in India.

Yesterday I met with Ajay (Advertising professional) regarding a new Tagalys initiative, when he enquired if we powered Site Search for Urban Ladder. Unfortunately we do not, as their engineering budgets, allowed them to build, manage & continue innovating their site search needs. But the conversation with Ajay led to him sharing frustration in his search experience, especially cause he expected more as available in leading sites like Amazon.com. And add to this he was engaging with Urban Ladder via a mobile device, this frustration multiplies. Digging in deeper, he shared specifics of what was missing, so we thought we share this feedback with the engineers at Urban Ladder to help them continue working towards delighting their online visitors.

SEARCH BAR design (4/5)

Both mobile & desktop versions have an expanded search bar, that is visible to visitors on the home page & other product listing pages. What could improve is a more central location with a highlight and a clear call to action to nudge visitors to Search quickly, discover products and engage with Urban Ladder.

Search Suggestions (3/5)

Predictive suggestions are generated as the visitor continues typing the search query. UL either runs the partial query through historical searches, inbuilt suggestions or a combination of both to show a prioritized list of 8 completed suggestions. On the outside this checks the box, but the key metric is the CTR from suggestions to listing page views and lets assume this is also done by the team.

What could really improve is the way the suggestions are presented to the online visitor. It does not break down the suggestions into a category, subcategory or other attributes that matter to the visitor, thus preventing any chance of a search results drill down, thus shortening time to product discovery.

POPULAR SEARCHES (0/5)

Unavailable.

RECENT SEARCHES (1/5)

Only available on the mobile app.

SEARCH RESULTS (2.5/5)

80% of visitor are likely to engage with less than 3 actions (scroll, sort, filters) on any listing page pages. It is critical to utilize segment analytics of engagement data to understand and predict what visitors want. That prediction when applied to each search query, shows the products in the order of highest probability of conversion, thus increasing the products viewed from search results.

At Urban Ladder keyword relevance plays a big part as products are displayed in the search results if the keywords are available in the product table. There is no filter being applied at the Search or options to drill down the search results. The default sort is called “Recommended by” which we assume considers engagement data across the site for a period X.

This is not a great strategy for UL as most search results will lead to a large number of product listed, thus requiring users to use Filters quickly drill down, but unavailable hence leading to bounce. This visitor behavior will also be higher on mobile devices. A few examples

Search for “Coffee table” when sorted by Price.

UL Coffee table

A search for “Study Table” when sorted by New Arrivals

UL Study table

 

UL should allow visitors to drill down into a category or subcategory right from the Search Suggestions or from a filter in the search results page. This will improve the quality of search results displayed when Sorts or Filters are applied.

FILTERS (1/5)

Only available filter is price on desktop & mobile. While price is a critical filter, its value is eroded when the search results are no longer relevant on application of the price filter as seen below. In the category of Home decor / Furnishings, we recommend adding filters like Category, Sub Category, Fabric, Material, Color, Discount/Savings as a bare minimum, to help visitors quickly drill down and find something of interest to them.

UL Filters 1

UL Filters 2

SORT OPTIONS (4/5)

All obvious sort options expected by visitors are available although it is unknown why “Sort by Discount” is only available in the mobile app. The default sort option is “Recommended” which we assume is a product sorting based on some form of visitor engagement analytics over period X for the search query.

Stemming (5/5)

Visitors search the same products in the singular & plural forms and others.. Urban Ladder has enabled stemming to showcase the same search results for both.

Screenshot 2017-04-25 14.21.05Screenshot 2017-04-25 14.21.14

Screenshot 2017-04-25 14.24.13Screenshot 2017-04-25 14.24.24

Spell Check (0/5)

An easy to implement feature, helpful to visitors but not implemented.  Screenshot 2017-04-25 14.26.29Screenshot 2017-04-25 14.27.43

Overall Urban Ladder scored 2.3/5.0 in our review. This means while Urban Ladder is converting visitors to customers, but there is room for improvement to

  1. Convert more visitors to customers through the Search funnel
  2. Reduce Time to transact (
  3. Reduce bounce sessions in Search results

Have feedback or comments contact us! Find pricing and Site Search products from Tagalys here.

Posted in eCommerce Site Search, Site Reviews
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e-Commerce Site Search – Quantitative benefits

Posted on January 11, 2017 by


One of the services we offer at Tagalys is a Personalized search for e-commerce. Interested already – SIGNUP NOW, join a free plan or a free trial to know whats best for you. 

This is the ability to showcase unique, relevant and accurate search results for each visitor that are personally relevant. The reason we stress on personalized search is because of its impact on business metrics. All our clients ask us how much of an upliftment in buy conversion can be expected from Tagalys search. Truth is we do not control that directly, but we do control conversion to product discovery, that leads to uplift in buy conversion rate. So what metrics do we directly or indirectly influence?

  1. Bounce: Tagalys will reduce the bounce rate for visitors who search due to the quality of search results
  2. Product views: There will be an increase in product views as product thumbnails shown in results are personally relevant to each user and not standardized for the entire site.
  3. Listing page to View Conversion rate: Our goal is to ensure more visitors view products. There is a direct correlation to product views and conversion rate, especially for visitors who search. More the product views per visitor, higher the buy conversion rate. Our primary goal at Tagalys is to improve this conversion rate as that is what our engine has control over.
  4. Order value: We have noticed a 20% to 40% improvement in order value or basket value, with personalized search.
  5. Frequency of visits: As results are identifiable by visitors and user experience has improved, we have noticed a 10% to 20% rise in frequency of visits before purchase. Our hypothesis here is that this segment is probably undecided visitors who revisit the site and continue shopping. There is an increased probability that this segment could have been lost after the second visit.
  6. Reducing time to transact: While the frequency of visit has increased for a small subset of the search audience, there is overall decrease in the time to transact by over 40%. Our hypothesis here is that these visitors are more decided, who find products faster, convinced by details on the product page and trust the retailer. Improved product discovery, helped reduce time thus time to transact.
  7. Visitors who search: Across most of our clients, search volume is between 6% to 15% of overall traffic, there has been a 50% jump in the overall percentage of search. While returning visitors will continue to search, our hypothesis of this increase is due to increase in new visitors who search. This behavioral shift is driven by UI changes, the addition of a clear call to action and best practices of site search for ecommerce
  8. Revenue: We cannot directly take credit for all the increase in revenue. It has also be tied with the trust a retailer has created with its audience, the details (images, specs etc.,) in the product page to make the decision easy, the design of the product page, internet connectivity and payment gateway reliability. But, due to the increase in metrics 1-7, there is a direct correlation to increase in revenue.

SIGNUP NOW, speak with us and help us help you. 

Posted in eCommerce Site Search
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eCommerce site search best practises

Posted on December 31, 2016 by


In the last 5 years of building various companies and products in the eCommerce vertical, great Site Search experience has been a driving force to build engagement, conversion and revenues. But this is not always the case when done incorrectly. In a time when leading retailers push Site Search as a primary call to action on the home page, there are others who have hidden their search bar or made it almost invisible due to bad experience that negatively impacted engagement, conversion and revenues. So what factors help deliver great search experience? Here are some of the basic requirements you need to consider when building or buying a product aimed to deliver the best site search experience. Interested already – Sign up now.

If you intend to have a Search solution that does not rely of visitor engagement analytics, our recommendation is that you should probably use an open source solution like SOLR, Elastic search etc, and configure it properly. An intelligent search product is expected to not only show relevant results (SOLR can do that), but also use analytics to sort it based on what may convert best for the entire site, for a certain region or for a certain individual. If all you need is to show relevant results, please do not waste money on buying a product, Sign up now and ask for FREE search from Tagalys. You will love it.

THE SITE SEARCH JOURNEY

1. POPULAR SEARCHES

A great start to the site search experience is to load the popular searches that were conducted in period X for that retailer when a visitor clicks on the search bar. This is scheduled to load before the visitor starts to type something, cause predictive suggestions are expected to be generated from the point of query entry.

Popular searches are a good engagement feature as they are indicative of what searches have been trending in the site. This can be client configurable to decide the variables that decide what popular searches to show the visitors, as each client is unique from the other. It is imperative to ensure that clicking on a popular search does not lead to a page with no results, hence the assumptions that lead to determining that should be collected from the retailer. This requires search analytics to be installed and running and frequency Y to generate the searches. Interested – Sign up now.

popular searches

popular searches

2. RECENT SEARCHES

Recent searches is a simple feature that eases search experience for online visitors. Most visitors take more than one visit to complete their purchase. Search queries are repeated by visitors to continue finding what they seek to buy. Recent searches feature, saves visitors time and lowers friction as all they need to do is click on what was recently searched vs. having to type the same query each time they visit. This feature does not require analytics and is actively used in all leading ecommerce sites as its saves visitors a lot of effort especially in mobile devices. Interested – Sign up now.

recent searches

recent searches

3. SEARCH SUGGESTIONS

Search suggestions are expected to be predictive (fill as you type), to help visitors quickly find the relevant suggestion, without having the type the complete search query. Find more details on the requirements in another article we wrote targeting Magento, but the same principle works here.  Interested – Sign up now.

4.SEARCH RESULTS

The result of the Site Search journey is to find relevant, trending or personally relevant results to help visitors quickly find what they seek. Incorrect search results are blasphemy in the world of search experience, hence we will assume results shown are accurate.  Interested – Sign up now.

The disadvantage of using  open source search like SOLR is that analytics are not considered in results. Hence your visitors may not find the most relevant results in the top pages. Now how often do you visit page 2, 3 or 4 on search results in Google? Probably never. This coupled with the fact that mobile devices show 2 or 4 product per page scroll, make it important for the retailer to show what is relevant to each user right on top. If not, this will definitely lower your conversion rate. While you may cost by using open source, you will lose far more in revenue by not using intelligent or personalized search.  Interested – Sign up now.

5. FILTERS

Now that you have your visitors engaged with the right search results, you need to help them drill down. Most of them do not have time to keep scrolling and if they do, there is a high probability they will get distracted and stop the process. YOU LOSE A POTENTIAL CUSTOMER.

Make sure you have the right filters or facets for the search results displayed. Showing all available filters for all search queries is amateur and makes your business look very unprofessional. E.g., Having “Shoe type” shown as a filter when ‘Dresses” have been selected from the filters.

Make sure tags displayed within the filters are dynamic, to only show tags that have minimum results. You do not want your visitors running into dead ends, they will leave your site and shop elsewhere.  Interested – Sign up now.

6. SORT OPTIONS

Decide upfront what are the various sort options required for your visitor profile. Having too many might confuse them and too little gives them minimal options to choose from. Some of the options are sort by

  1. Relevance (Simple or personalized)
  2. Price – Ascending
  3. Price – Descending
  4. Name – Ascending
  5. Discount – Descending
  6. New Arrivals
  7. Popularity
  8. Best Sellers
  9. Most Viewed
  10. Highest rated
  11. Trending

7. SEARCH BAR

A well defined search bar is critical to get your visitors to want to search. You may have invested in search technology, but without a clear call to action for search, NONE of the above intended actions from your visitors will ever materialize. There is enough data to prove that search is a low cost high revenue driver. If search experience is not delivering a higher conversion rate vs. regular listing pages, it is not because your visitors do not want search, it is because the above features have NOT been configured properly in your search experience that is being delivered to your visitors.  Interested – Sign up now.

screenshot-2016-12-31-14-33-29jpg

 

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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Extracting insights from search data in your eCommerce store

Posted on December 22, 2016 by


We recently shared a detailed post on the need for Search in eCommerce where we refer to the Search function as speaking to a sales person in a brick & mortar store. So lets continue with the same analogy here.

As a retail store owner, your in-store sales person (search), is interacting with visitors and getting to know their interest in products, colors, fabrics, specifications etc., every minute detail of interest is explicitly shared by your visitors during the search process. This is further fine tuned by the selection of filters to drill down and find that product that will convert them to a customer. This treasure trove of data (search queries & filters) should be used by your marketing and planning teams to improve the topline and bottom line of your business.

Topline impact

With access to search data and resulting visitor engagement details for these queries – impressions delivered, what was clicked, position of clicks, time spent, visitor segments etc., your team has data to understand what your customers seek. 19660747568_02f3186094_b

For example, if the top search query in New York City, is for “Kohler Faucets” and in San Diego is for a “Moen”, for the same data period across segments, it would be wise to promote products that match that query or specific products within that query in your SEO and SEM campaigns, geofenced to New York city and San Diego. This process of creating focussed marketing campaigns to target each customer segment is bound to deliver higher ROI for your digital marketing budgets. The only caveat is that you need “enough” data to make sense out of it.

Search data can also be used to decide what banners within the website, to guide visitors to a landing page with relevant results. Our advise on home page banner strategy is to dedicate 2/5 banners to Search data (historical data) and 3/5 banners to external trend data. Hence if your top site wide searches are “Faucets” and ” Moen”, create a landing page dedicated to these two keywords, that include products which received the most engagement for these keyword searches and maybe throw in an offer or incentive, to accelerate engagement, conversion and revenue generation.

Bottom Line Impact

Analyzing search queries gives you access to customer demand data. This can be used to better plan stock to ensure supply meets demand. For example, if your customers are seeking “Ruby rings” and only 5% of your stock in rings contain the Ruby stone and over 50% is Diamond rings, there is a good chance, you are going to be subject to low inventory turns. This is not good for your cash flow and will drive the “Discounting” act if you need to move the stock. Stocking products that are in demand could increase the probability that customers are less inclined to discounts as they need it NOW.

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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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.

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