Archive for category: Blog

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Search and E-Commerce SEO

We often need to educate people on the strong link (no pun intended!) between eCommerce Site Search and SEO. And it is bit disheartening to see the lack of general education in the market on this subject.

Many companies spend tens or hundreds of thousands of dollars to optimize their SEO and adwords spend each year. Yet, if you are using the right e-commerce search engine, many of the answers you need to optimized SEO and adwords might be sitting right in front of you… for free!

The searches customers perform on your site, specifically, the long tail searches, can provide vital information about how customers want to find you on the internet search engines. These searches give you exact places where you can tune your SEO and Adwords to promote these products and give you a leg up on your competition – customers find you and these products faster.

Of course, a critical piece of this is an e-commerce site search engine that delivers accurate long tail searches and offering ways to tune links for optimal SEO. Natural language is the best at delivering long tail, descriptive searches. Also, the e-commerce search engine needs to offer fast, easy to use analytics (you don’t want to be trolling through logs!).

Make these two capabilities a distinctive part of your e-commerce strategy to deliver better SEO and a better customer experience.

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Intelligent Search: What Google’s New “Semantic Search” Means for Search on your e-Commerce Site

BY EasyAsk CEO Craig Bassin

Google has recently announced that it is adding more “semantic search” techniques into its otherwise largely keyword search. This means matching on the meaning of words, rather than just the occurrence of words. Since nearly all of your customers also use Google, their expectations for search are conditioned by Google. Over time, there is a trickle-down in the expectation that shoppers have of search, based largely on their experience on Google.

Therefore, it’s a reasonable question to ask: “What changes should I make in search at my commerce site to keep pace with customer expectations?” Beyond keeping pace with expectations, there is another even more important reason to invest in semantic search on your site — increased conversion rate. Analysis of Neilsen netRatings conversion rate studies across similar e-commerce sites has not only confirmed the impact of natural language semantic search, it has actually measured it!

What is Semantic Search?
The literal definition of semantic search is searching on meaning rather than searching on words. Google is now knocking at the door of semantic search by associating word groups as concepts. If some people search on “beach sandals” and other people search on “beach flip-flops”, while both groups click to show interest in the same item set, then the concept “sandal” and “flip-flop” may be related. The distillation of words into concepts is one part of the greater field of Natural Language Processing (NLP). Searching on concepts in their various forms delivers more complete results and is more tolerant of user search variations. As you have seen, semantic search is quite valuable – but there is more power available when you go deeper using more NLP techniques.

A semantic search with deeper NLP (let’s call this Natural Language Search, or NLS) support brings even more converting power to a commerce site. Lets look at these two commerce searches, “return policy” and “sweaters under $100”. Searching all your product descriptions for the words “return” and “policy” will clearly lead to ridiculous results. Clearly, the intent of this search is to display your policy on returns – treating this as a phrase and recognizing its special nature are important to the shopper, and easy with NLS.

Similarly, treating “under $100” as a keyword search will yield undesirable results. The intent of the user is to restrict the products based on price. Recognizing that “$100” is not a word, but rather a price requires something smarter than a keyword search. This occurs in other forms when the user wants to express a range restriction, not just on price, but any other numerical product attribute such as length, weight or wattage.

Units of measure commonly stump keyword search engines. For example, keyword searching for “12 volt 24 amp motor” will unfortunately return all motors with 12 or a 24 anywhere in the description. Thus, both 24 volt 12 amp motors as well as 24 watt .5 amp motors with a 12″ shaft will be shown! If your site gets lots of dimensional/size searches, the capabilities of NLS is absolutely critical. A semantic search with NLP is aware of units of measure, such as “volt”, “v” or “amp”, “A”. This unit of measure awareness automatically creates a phrase around “12 volt”, and to include searches on variations like “12V” or “12 V”. When a shopper searches for “Nike size 10”, NLS will recognize that “size” is an attribute with numeric values & therefore select the products with “size=10”. These capabilities impact countless unique searches that would otherwise stump almost all search engines.

These examples illustrate how easy it is for dumb keyword searches to yield embarrassing results. Have you ever searched a site only to see hundreds of irrelevant results? This not only reflects poorly on your brand, but can actually cause you to lose customers! Nearly all of us have had the experience of getting such poor results from a search on a commerce site. We get frustrated and leave the site altogether to buy from another site. This illustrates how improving search can improve conversion rate.

In order to measure the correlation between semantic search and conversion rate, we used Nielsen netRatings to compare the conversion rates of sites that were similar except for their use of semantic search. We compared sites for catalog companies and non-catalog companies separately. In both groups, the sites using semantic NLP search had about 20% higher conversion rate than the sites using keyword search. Of course, there are many other phenomena that impact conversion rate, but these would generally balance out across all the groups. Furthermore, the 20% improvement is consistent with the uplift we see when customers switch from keyword search to semantic search. Details of the Nielsen study are available on request.

Google is moving the world towards semantic search. Eventually user expectations will demand it from your commerce site as well. Switch sooner rather than later – you’re leaving money on the table every day until you make the switch!

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Benefits of Semantic Natural Language Search for E-Commerce

BY EasyAsk CEO Craig Bassin

How this paradigm shift will change Web and mobile e-commerce forever

Advancement in communication and technology over the last two decades has been dramatic, and the way people consume information has evolved in parallel. Not long ago, people turned to libraries, dictionaries, reference journals, books, phone books and printed newspapers for insight, but now they simply turn to “The Web.” Answering complex questions used to take hours or days – if we could figure out how to answer them at all. Now we are accustom to executing Internet searches in seconds.

ACCURACY, however, is the issue.

The next step is to provide the correct response on the very first page. To take this next step, we’ll consider some words and phrases that were once outside of mainstream vocabulary, more commonly used in academic and research circles at MIT and Stanford labs – things like Natural Language Processing (NLP) and Semantic Search (per Wikipedia: semantic search uses semantics, or the science of meaning in language, to produce highly relevant search results. In most cases, the goal is to deliver the information queried by a user rather than have a user sort through a list of loosely related keyword results.). Search will not evolve without these important concepts because even with all the great digital information available today, it still takes too long for people to find exactly what they’re searching for – whether on the Internet, on their phone, in an e-commerce store, or in a corporate applications like CRM and Business Intelligence.

It is interesting to think about where we started with search boxes – Yahoo, Excite, Netscape, to name but a few, and most recently Google, have all taught us to search using “keywords.” We know that search engines can’t understand the way we speak or think, so we had to adapt our behavior to make use of the services they provide. When we hit the search button, we hope that the algorithms, machines and logic in some distant server farm send us back a bunch of links that we can comb through to find what we are looking for. Search engines essentially provide us a starting point – lists of results – but we still have to manually navigate the final mile. We get streams of results in seconds, but it takes considerably longer to find the right thing, or often we get frustrated and stop looking. Google has learned from user interactions and are now developing semantic capabilities, and WolframAlpha takes it further by computing answers from a knowledge base of curated, structured data but still today ‘search results’ are simply a starting point to begin looking for answers.

Also, semantic search is a great step in the right direction, but it doesn’t have a full understanding of all possible responses. That’s where natural language processing completes the loop, understanding both the searcher’s intent and a deep understanding of the data to deliver the best possible response. Essentially, Semantic search provides understanding of the intent, or context, of the search. Natural Language provides knowledge both of intent AND content.

For the first time, you can have better technology than the search engine giants – who have certainly spotted this trend and are moving in the semantic direction. Recently Google shared its Knowledge Map plans. Jack Menzel, product management director at Google, in a very articulate video, questioned: “Wouldn’t it be amazing if Google could understand that the words that you use when you are doing a search, well they aren’t just words, they refer to real things in the world. That a building is a building, and an animal is an animal and that they are not just random strings of characters, and if we could understand that those words are talking about those real world things, than we could do a better job of getting you the content you want off the web…”

Google is obviously a large company and has the time and resources to integrate changes in stages, especially considering that their revenue model is still based on keyword advertising. You and the e-commerce industry do not have that luxury – we need to act now to improve the Web e-commerce search experience and to accommodate the growing number of mobile e-commerce shoppers.

Given where we are today, understanding the intent of what is being searched for has become a competitive advantage – especially when deployed in e-commerce environments. Understanding intent even helps when shoppers enter only a few keywords, because each single word carries so much value. Natural Language Processing (NLP) use techniques like relevancy, association, disambiguation and many more to understand what a shopper is actually looking for, and can deliver the most relevant options from your product catalog.

Again, semantic search can understand the searcher’s intent, but NLP understands their intent and all possible results, then processes requests and delivers the best possible results. This is an important distinction, especially for e-commerce sites, which need to present the most relevant items, even when search requests don’t match up nicely with what is in your product catalog.

Some general e-commerce industry statistics suggest that 20% of searches are now long-tail searches. A long-tail search is a more descriptive phrase that contains three or more words. It often contains a main concept, which are one or two words in length. For example, “London Olympic t-shirt under $20,” the main concept would be Olympic and the other terms can help us identify the most relevant item with the additional details. Now we can look at t-shirts from the 2012 Olympics in London and not t-shirts from 2008 in Beijing. Cost is yet another filter, but again intent is important. Keyword search will return items with ‘Olympic, t-shirt’, ‘under’ or ‘$20’ (potentially t-shirt underwear) while the searcher intent is to find any shirts under $20.

As an e-commerce retailer, you have to address long-tail searches, otherwise you will miss out on a key source of revenue and likely degrade existing traffic.

Hopefully you are beginning to see some of the benefits semantic natural language search can provide Web-based e-commerce, but more importantly you need to consider how this will support your growth into mobile e-commerce.

Since the iPhone was launched, that small screen has become an important window into the world for most users. Androids and others followed suit and smart phones have become a common entry point into e-commerce. Analysts from research firm Gartner Inc. say the shift from e-commerce to m-commerce will reach something of a tipping point by 2015. According to Gartner’s analysts, mobile applications and social media will account for 50 percent of Web sales by then. Additionally, Gartner said that e-commerce merchants will start offering “context-aware, mobile-based application capabilities that can be accessed via a browser or installed as an application on a phone” at that point. “E-commerce organizations will need to scale up their operations to handle the increased visitation loads resulting from customers not having to wait until they are in front of a PC to obtain answers to questions or place orders,” said Gene Alvarez, research vice president at Gartner, in a statement.
Additionally, because of Siri, Nuance Dragon, Google Voice Search and others, speech is now an integral way we interact with these little devices. As people become more conversational with these devices, the search terms will naturally become more descriptive. Again, with limited screen size and long-tail searches, natural language search functionality will not just be a nice feature; it will be mandatory if you want to provide the most relevant result quickly and efficiently on mobile devices. Imagine connecting to your favorite e-commerce site, hitting the microphone on your smartphone and SPEAKING, ‘ladies blue blouses under $35’ and immediately seeing your results. That’s taking e-commerce mobile.

Natural Language and Semantic Search are concepts you need to become familiar with in the next few months. If you learn how to integrate them properly, you’ll be able to provide your shoppers the right information at the right time to improve conversion rates and drive revenue. Regardless if you do or don’t, your competitors will. So… Where do YOU think your shoppers will turn the next time they pull out their iPhone?

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Take advantage of an internet niche

There’s value in having something unique, yet popular, to offer. In the world of commerce, it’s called a niche product. A niche product is something that is the goal of most any entrepreneur, especially in today’s world of online access. The internet allows people to get the word out on their product or service, not just in the community, but globally. That’s why almost all businesses need an online presence. And that rings true especially for the type of niche product that a company such as Cascade Coil produces. There are any number of practical and aesthetic uses for the type of wire fabric that Cascade manufactures and sells. Having a worldwide venue to boast those uses and advantages is crucial to their business.

 

The internet effect

The notion of promoting a unique product or service over the internet has been around for decades now. And the internet has revolutionized many traditional products by opening new doors to the niche product. Something as simple as the lottery is a good example of how the internet has transformed something and created a whole new segment of commerce at the same time. Of course the lottery has been around much longer than the internet. In fact, lotteries have been used for centuries as a way to raise money for things from building the Great Wall of China to financing the first colony at Jamestown. It lost favor with the law for a period of time and turned into a “numbers racket,” falling into the domain of organized crime. But now hundreds of states, provinces, nations and other government entities sponsor lotteries as a way to raise revenue. Plenty of government projects and programs are financed through lotteries.

Transformation

But playing the lottery is no longer just a matter of picking up a couple of tickets at the convenience store and crossing your fingers. The internet has changed all that. Nowadays millions of people go online for their lottery games, and that type of access has created a new type of player and a new type of website. The player is a high-volume seeker of odds, who browses lottery games worldwide in search of the best chances of hitting a winner. It may seem strange that so many people would devote good picket money to games of random numbers. But these players have a variety of systems and money-management strategies to help them parlay wins and taper losses. The internet has responded to their needs with centralized websites that provide the information the big-time players need for online lotteries worldwide. These websites provide the results of online lotteries across the globe. They also offer tips and guides on how to play each game, advice on the best payment/withdrawal systems, and eligibility rules for the games. They act as online hubs for lottery players, and they provide a good example of a niche product that gets big response because of the access millions of people have via the internet.

Online lotteries aren’t for everyone; the laws vary for each jurisdiction. That’s because online access has made for a competitive atmosphere for lottery games. Some online lotteries only offer access to players from particular places. And some jurisdictions don’t allow their residents to play online anywhere else. So playing online all depends on where you live – or your computer IP address. But these full-service lottery websites exemplify the transformation the internet has created in the most traditional things. They also demonstrate the advantages of having the world wide web to promote and offer a niche product.

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Top helpful devices for the kitchen

Every day in our kitchen we need to use different appliances and devices. It’s about electric devices that we normally use to prepare a toasted bread or to bake cakes and other kinds of food.

Often times we find it very normal to use all these electric devices and in most technology stores we can see even more of all kind.

It’s a matter of fact that modern cooking requires a little bit of technology. It helps us to prepare our foods in a quicker time or without to put too much effort.

List of top helpful appliances

We can even think to mention the most used and helpful appliances for the kitchen ever existed.

1. First off, appliances for toasting and grilling: these are very often used to toast bread and to grill meat or fish. Grills are also used to bake small cupcakes or handmade biscuits.

2. Blending and mixing: these are two of the most common methods to prepare recipes that are based on fruits and vegetables. In particular, mixed fruits and blended vegetables are important when having babies at home who are approaching new foods.

Blenders have a big role in the kitchen when it comes to prepare bread and big cakes. There are blenders of all sizes and all prices.

3. Have you ever thought what’s the first appliance you normally use every day in your kitchen? Well, of course it’s the coffee and tea maker! It’s a classic of all breakfasts in the world, just to start the day.

Where to learn to bake and cook?

If you are now thinking that it would be nice to use all these appliances, well you don’t have to think that baking a cake or making a bread is impossible. Actually, you can easily learn the basic techniques to prepare the most common and the most refined food if you visit the video culinary website.

Here you can see images and watch tutorial videos what show how easy and funny preparing food is. You can also discover foods and typical dishes from other places of the world, such as the tasty Italian macaroni, the Italian pizza in all its versions (from Naples, from Florence, from Genoa and from Rome) or the French fois-gras or even recipes from Kazakhstan or Sri Lanka.  

Taking on the table a new dish is the best way to find your personal hidden skills.

 

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Gartner E-Commerce Search Best Practices Part 2

In my last blog post, I discussed the recent Gartner report “Best Practices in Strategically Combining Search, Content Analytics and E-Commerce.” One of the most important e-commerce search best practices that analysts Whit Andrews and Gene Alvarez emphasize is the ability to “Offer effective definition-matching and handling of ambiguity in Query terms.” Let’s take a closer look at what this means, and how it applies to your search environment.

Effective Definition Matching

The Gartner reports talks about how a truly effective e-commerce search environment must understand the “language variations that are specific to what’s being sold and the audience to whom it’s being sold.” This really boils down to two items a search engine must be able to do:

  1. For each term in a search string, understand what that value represents – an attribute, product name, product category, etc. – and allow each column to have different relevancies.
  2. The ability to process search strings of different complexities as entire entities and understand how the individual terms relate in order to return the most accurate results.

This is the essence of natural language.  A natural language engine will process a complete search phrase, break it down linguistically and understand the full meaning of the request – NOT just what individual terms mean.  In this way, a natural language engine such as EasyAsk can fully support the specific “language of the site” and allow visitors to “speak” to the site in that language via the search engine.

With natural language processing, you can be assured that not only will simple searches – “blue shirts” – be processed effective, but so will complex ones – “women’s blue short sleeve shirts under $50.”  You can fulfill this e-commerce search best practice with the most effective definition matching possible.

Ambiguity

Ambiguity can come in many different forms.  It can come from mistakes or typos.  It can come from simple language variations such as different tenses.  Or it can come from a visitor’s lack of knowledge of the products – asking for “purple blouses” when none are available on the site.  To help you fulfill this aspect of the Gartner best practices, your search engine needs to give you the following:

  • Spell correction – your search engine needs to provide automatic spell correction.  Anticipating and pre-coding every potential misspelling of each term on your Website is a time consuming task. Who wants to do that?
  • Stemming – Your search engine needs to automatically support the different tenses, plurals and other variances of terms.  Once again, why should you need to have the time consuming task of entering every potential variance of each term?
  • Relaxation – this concept allows the search engine to drop part of a search term if no specific products exist in order to make sure some products are returned.  Seeing some products is always better than seeing NO products.  With relaxation, a search for “black levi jeans” will still return Levi jeans, even if there are none in black.  You search engine needs to have automatic support for this capability.

All of these characteristics will help you virtually eliminate the dreaded “no results” page and dramatically enhance the customer experience by always returning products to the visitor, even when there is some degree of ambiguity.

Further Flexibility

What if your “site language” is more complex than standard terms?  What if your site has a number of acronyms and industry terms?  What if you have cryptic model numbers that customers need to use to find parts or products?

To fulfill this last requirement, your search engine needs to make it easy to add synonyms, custom search terms and rules.  Once again this is where natural language engines help you implement best practices.

With natural language, you easily specify additional search terms and rules in – well, natural language.  You simply type in terms of any level of complexity and associate those with the existing terms or products in your catalog by simply pointing and clicking.

Learn More

To read more on these capabilities, please download our white paper, “The ABCs of E-Commerce Search: A Guide to Essential E-Commerce Search Features.”  In Part 3 of our blog post series, we’ll look at best practices in Search Analytics and Merchandising.

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Gartner Best Practices in E-Commerce Search – Part 1

July is “Best Practices” month here at EasyAsk – where we describe good search, navigation and merchandising techniques that can help you convert more customers.  As you and your teams ramp up for busy back-to-school and holiday seasons, we want to help you convert more visitors into sales.  Over the course of this month, our team will show different best practices in search, navigation and merchandising and how they can impact customer experience.

While EasyAsk has many lessons to share, we always like to recognize best practices from independent firms, especially when they align with our vision. Gartner, a preeminent research firm, recently released a report called “Best Practices in Strategically Combining Search, Content Analytics and E-Commerce“, written by Whit Andrews and Gene Alvarez – two of the brightest minds in e-commerce and search.

Among the findings in this report, the Gartner analysts clearly stated the value of search, navigation and merchandising to an e-commerce environment:

  • Search is the means by which shoppers most nakedly reveal their needs and wants (as they themselves perceive them) to sellers.
  • Search is, therefore, a particularly powerful way to promote, relate and reveal products in a shopping experience.

The analysts went on from there to lay out two very important best practices in e-commerce search:

  1. Offer Effective Definition-Matching and Handling of Ambiguity in Query Terms
  2. Use Search and Content Analytics to Fulfill Shoppers’ Desires Through Merchandise, Related and Suggested Offers, and Advertising

These two best practices highlight the unique advantages natural language technology delivers in an e-commerce search environment.  Since natural language understands both the intent of the search and the content being searched, visitor searches are more accurately matched and the search engine seamlessly deals with ambiguity – misspellings, tenses, stemming and when to relax terms.  Natural language also understands the relationship between terms in a search to derive contextual meaning and further eliminates ambiguity.

In addition, the actionable analytics and natural language business rules in EasyAsk make it easy for your business people to better merchandise your site with context-driven offers, promotions and ads.

In the next two blog posts of this series, I will drill down into each of the two Gartner best practices we discussed above.  I will examine the best practices, detail how natural language fulfills the promise of these best practices and show customer sites where these practices are applied.

The most valuable best practices typically come from experts that have visibility into the widest spectrum of implementations – learning how smart people across the industry approach problems differently.  We’re always happy to confirm when EasyAsk best practices match those of top-tier research firms, such as Gartner.

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EasyAsk Products – Excerpt from Sync-Up Interview on the Pulse Network

Sync-Up host Tyler Pyburn asks CEO Craig Bassin about EasyAsk’s products – EasyAsk eCommerce, Business Edition and Quiri.

 

 

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EasyAsk Quiri – Interview Excerpt on Pulse Network Sync-Up">Voice Recognition & EasyAsk Quiri – Interview Excerpt on Pulse Network Sync-Up

EasyAsk CEO Craig Bassin talks about the differences between “voice-recognition” and “natural language search” – without understanding intent, you can’t accurately answer questions. Craig also notes that even Google is evolving from traditional keyword search.

 

 

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Google joining IBM, Apple and EasyAsk? Pigs fly! News at 11…

 

(Message from the CEO of EasyAsk, Craig Bassin)

Looks like this is the beginning of the end for keyword search.  You’ve probably seen a number of articles discussing Google’s shift to ‘semantic search’.  Anyone understand what that REALLY means?  First, the definition of ‘semantic search’ is an understanding of the ‘intent’, or meaning, of the search, rather than just matching the keywords.

Now why would the undisputed 800-pound gorilla of keyword search, change course at this late date?  Conventional wisdom says they were forced to take a hard look after Apple launched Siri.  The timing sure seems to reinforce the fact that they’ve been playing with semantic search for some time, but needed to make a marketing splash now.

So, why change?  Well, obviously it’s a BETTER way to search and they had to, or they wouldn’t have!  I mean, really, Google acknowledging the limitations of keyword search?

Quoting from Paul Demery’s recent article (to read it, click here) about Google’s adoption of semantic search in Internet Retailer, ‘“Semantic search should allow Google as well as other search engines to better understand the true user intent of a search query,” says Kevin Lee, CEO of search marketing firm Didit.

Also, quoting from the same article: “Every day, we’re improving our ability to give you the best answers to your questions as quickly as possible,” Amit Singhal, Google’s head of search technology, said in a blog post. “In doing so, we convert raw data into knowledge for millions of users around the world. But our ability to deliver this experience is a function of our understanding your question and also truly understanding all the data that’s out there. And right now, our understanding is pretty darn limited. Ask us for ‘the 10 deepest lakes in the U.S,’ and we’ll give you decent results based on those keywords, but not necessarily because we understand what depth is or what a lake is.”

Now, understanding ‘intent’ AND ‘content’ is something that is at the very core of who EasyAsk is and how EasyAsk searches.  It’s the idea that, in an e-commerce setting, you can search for ‘men’s dress shirts under $30’ or ‘ladies red pumps size 6’ and get EXACTLY what you’re looking for.  Natural language understands the semantics involved in the search.  We understand the ‘intent’ of the question, we understand the ‘content’ of the data.  In adopting a new ‘semantic’ architecture Google will start to understand the ‘intent’ piece as well.

Now, who else searches this way?  How about Microsoft’s Bing, IBM’s Watson, obviously Apple’s Siri.

Now which of these companies can help you improve your e-commerce site?

None of them.

OK, but what about the other e-commerce search providers.  You probably know a few of them.  Endeca, SLI, Adobe, SOLR.

No, no, no and no.  Strictly keyword search.  Old news. Yesterday’s tech.

So we want to be the first to welcome Google.  We like them, use them all the time for internet search, along with Bing.  But when it comes to e-commerce search, folks, EasyAsk is leading the way.  Let us show you how.

It’s what we do.

Ready to see how EasyAsk's eCommerce solution can help you? Request a demo!
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