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INCREASE GOOGLE SHOPPING/PLA PROFITS WITH KEYWORD SEGMENTATION HOW TO RADICALLY www.omnitail.ne t Over the past few years, retailers have seen steady growth in product listing ad (PLA) channels such as Google Shopping & Bing Shopping as a result of their capacity to qualify traffic prior to incurring cost. As a result, retailer investment in PLAs has steadily grown in recent years, and PLA channels have become increasingly crowded. Due to growing competition and rising CPCs, retailers who rely on PLAs as a significant source of their revenue need to adopt a more sophisticated approach to PLA optimization in order to remain both competitive and profitable. As an agency that specializes in advanced PLA/Google Shopping optimization, we have thoroughly tested many strategies over the past few years, however one has yielded significantly more value than all others. While the majority of retailers are optimizing their PLAs for product or product group performance, we have found that segmenting for combinations of products and queries yields far superior results. By adjusting the distribution of media spend by the intent and performance of the search used to trigger the ad, retailers who have adopted a query-driven approach to PLA management have seen dramatic increases in revenue combined with more precise control over media spend, allowing for radical increases in total profit. In this eBook, we will show you how valuable query segmentation can be and why it should become a component of your PLA channel strategy. We will also show you how you can incorporate query segmentation into your PLA channel yourself. HOW TO RADICALLY INCREASE GOOGLE SHOPPING/PLA PROFITS WITH KEYWORD SEGMENTATION 617-307-4969 | sales@omnitail.net | www.omnitail.net 2 Segmenting PLAs for the intent of the users’ query entails streamlining the way marketing investment is distributed, across PLA search queries, with the financial return that those queries are likely to yield. Akin to an investment portfolio, some investments yield greater returns than others. If you’re a search marketer, then you’re aware that some keywords or search queries yield greater returns than others. Different keywords and search queries often hold different values to a business. Since not all queries hold the same value to your business, it is critical to segment by query in order to maximize the return on your investment in PLAs. This principle is illustrated clearly by the consumer purchase decision process. Consumers generally follow the same steps on their path to purchase, progressing from need recognition, to seeking information, to evaluating alternatives and then finally making their purchase decision. WHAT DOES IT MEAN TO SEGMENT BY “QUERY INTENT” 3 PROBLEM RECOGNATION: PERCEIVING A NEED INFORMATION SEARCH: SEEKING VALUE EVALUATION OF ALTERNATIVES: ASSESSING VALUE PURCHASE DECISION: BUYING VALUE POSTPURCHASE BEHAVIOR: VALUE IN CONSUMPTION OR USE CONSUMER PURCHASE DECISION PROCESS 617-307-4969 | sales@omnitail.net | www.omnitail.net It’s no secret that consumer search behavior mirrors the consumer purchase decision process, with shoppers typically progressing from generic queries such as “Men’s Tennis Shoes” to brand-specific queries i.e. “Nike Men’s Tennis Shoes” and then onto product-specific queries such as “Nike Men’s Zoom Vapor Tennis Shoe” or “Nike Men’s Shoe 631458” before making a purchase. This behavior syncs perfectly with the purchase decision process – you can think of this as a funnel. The shoppers searching for a generic query are generally less qualified, less likely to convert, and not the best user segment for you to invest in if your objective is to generate an immediate return. Progressing down the funnel, brand-specific queries indicate a user has a brand of products in mind, and is a bit more qualified to make a purchase. Finally, you have users who search for an exact product, whether it be the name of the product, a product family, a style number, MPN or SKU id, UPC code, etc. While you will need to confirm this with your data, we generally observe these users convert at the highest rate, as they are highly qualified shoppers, though results vary across retail categories, such as apparel. On the next few pages we will illustrate, with anonymized client data, how the value of PLA queries can vary dramatically by their intent. For this example we evaluated 11 months of data prior to actively segmenting PLAs for query intent (before this retailer became our client). For this particular client, we determined intent based upon whether the search pertained to a product or product family (product-specific), a brand but not a product (brand-specific), or did not relate to a brand or product (generic). This data is only meant to serve as an example of one method for segmenting for query intent – for some retailers, it may be best to use a different form of query classification. 4 RESEARCH PHASE GENERIC QUERIES EVALUATION BRAND-SPECIFIC QUERIES PURCHASE PRODUCTSPECIFIC QUERIES 617-307-4969 | sales@omnitail.net | www.omnitail.net Here’s a sample of generic PLA query performance for this retailer: AN EXAMPLE OF GENERIC QUERY PERFORMANCE 5 For the client in this example, generic PLA queries in this sample converted at an average of 3%. That’s not necessarily a bad conversion rate, but the problem here is the average CPC, in this case at $0.93, which based upon the profit KPIs (contribution), is more than they are typically worth. Without isolating generic queries in their PLA structure, this retailer was unable to reduce average CPC to a level where they generated profit, without also reducing bids and sales from their product-specific and brand-specific query segments. 617-307-4969 | sales@omnitail.net | www.omnitail.net GENERIC QUERIES PLA Search Query Clicks Ad Cost Avg.CPC Transactions Revenue Conversion Rate COGS/VOH Contribution Contribution margin Tennis Shoes 1316 $1,276.54 $0.97 28 $1,800.00 2% $1,118.34 ($594.88) -33% Tennis Shoes for men 1330 $1,174.78 $0.88 40 $4,521.12 3% $2,808.97 $537.37 12% Tennis court shoes 200 $200.74 $1.00 0 $0.00 0% $0.00 ($200.74) - kids Tennis Shoes 214 $185.38 $0.87 12 $839.10 6% $521.33 $132.39 16% TOTAL 3,060 $2,837.44 $0.93 80 $7,160.22 3% $4,448.64 ($125.86) -2% Now take a look at the performance of brand-specific queries. These shoppers, who have a sense of what brand they are likely to purchase, convert at a slightly greater rate: 4%. Average CPC remains similar to the generic query segment, however the revenue generated per click (RPC), $3.87, is 66% greater than the RPC of the generic query sample. That’s enough to make brand-specific queries profitable for this retailer, despite the similar CPC. AN EXAMPLE OF BRAND-SPECIFIC QUERY PERFORMANCE 617-307-4969 | sales@omnitail.net | www.omnitail.net 6 BRAND-SPECIFIC QUERIES PLA Search Query Clicks Ad Cost Avg.CPC Transactions Revenue Conversion Rate COGS/VOH Contribution Contribution margin Babolat tennis shoes 1048 $929.68 $0.89 32 $3,179.82 3% $1,975.62 $274.52 9% Nike Tennis Shoes 1330 $1,174.78 $0.88 36 $3,699.08 3% $2,298.24 $226.06 6% Adidas Tennis shoes 200 $200.74 $1.00 20 $1,530.60 10% $950.96 $378.90 25% Prince Tennis Shoes 214 $185.38 $0.87 26 $2,390.14 12% $1,484.99 $719.77 30% TOTAL 2,792 $2,490.58 $0.89 114 $10,799.64 4% $6,709.82 $1,599.24 15% Finally, take a look at the performance of this sample of product-specific queries on the following page. These are all unique searches that pertain to a specific product or product family. These shoppers either have a very good idea which product they are going to buy, or know exactly which product they are going to buy, and are highly qualified shoppers. Consequently, these shoppers convert at a much higher rate – 23% for this retailer. Again, average CPC is similar, at $0.82, however revenue per click (RPC) averages $21.26 – 812% greater than the RPC of the generic queries. As a result, these product-specific PLA queries are highly profitable for this retailer. In this case, this retailer was actually underinvesting in product-specific queries. AN EXAMPLE OF PRODUCT-SPECIFIC QUERY PERFORMANCE 617-307-4969 | sales@omnitail.net | www.omnitail.net 7 617-307-4969 | sales@omnitail.net | www.omnitail.net 8 PRODUCT-SPECIFIC QUERIES PLA Search Query Clicks Ad Cost Avg.CPC Transactions Revenue Conversion Rate Contribution Contribution COGS/VOH margin Adidas adizero feather 3 tennis shoes 4 $3.34 $0.84 4 $411.76 100% $255.83 $152.59 37% Adidas adituff tennis shoes 4 $3.85 $0.97 4 $35.98 100% $22.35 $9.77 27% Adidas adizero cc tempaia iii orange purple women’s shoe 4 $0.98 $0.25 4 $319.80 100% $198.35 $120.13 38% Adidas adizero tempaia ii women’s shoe 4 $3.64 $0.91 2 $199.78 50% $124.12 $72.02 36% Adidas adizero tennis shoe 4 $2.64 $0.66 4 $390.70 100% $242.74 $145.32 37% Adidas adizero tennis shoes 28 $26.52 $0.95 4 $319.80 14% $198.69 $94.59 30% Adidas adizero tennis shoes womens 4 $0.04 $0.01 4 $319.80 100% $198.69 $121.07 38% Adidas barricade 8 blue boys shoes 6 $3.24 $0.54 4 $258.32 67% $160.49 $94.59 37% Adidas barricade 7 tenis shoes 10 $9.98 $1.00 4 $399.80 40% $248.40 $141.42 35% Adidas barricade 7.0 novak mens tenis shoes 54 $37.62 $0.70 4 $1,295.32 7% $804.78 $452.92 35% Adidas barricade 8 naw silver men’s shoes Adidas barricade 8 blue silver red men’s shoes 8 $4.02 $0.50 4 $339.80 50% $211.12 $124.66 37% Adidas barricade 8 tenis shoes 6 $4.58 $0.76 4 $459.80 67% $285.67 $169.55 37% Adidas barricade stella mccartney womens tennis shoes orange 14 $12.86 $0.92 4 $359.80 29% $223.54 $123.40 34% Adidas barricade team 3 white silver men’s shoe 8 $2.44 $0.31 4 $319.80 50% $198.69 $118.67 37% Adidas barricade tennis shoes 12 $10.64 $0.89 4 $220.82 22% $137.20 $72.98 33% Adidas barricade v classic carbon mint women’s shoe 18 $15.68 $0.87 4 $319.80 33% $198.69 $105.43 33% Adidas barricade v classic mens tennis shoe 12 $1.22 $0.10 4 $247.50 9% $453.77 $92.51 37% Adidas barricade women’s tennis shoes 46 $39.46 $0.86 4 $311.76 10% $193.70 $78.60 25% Adidas barricade youth shoes 42 $41.08 $0.98 4 $319.80 18% $198.69 $80.03 25% Adidas bercuda 3 mens tennis shoes 22 $17.62 $0.80 4 $211.30 19% $131.28 $62.40 30% Adidas bercuda 3 white green men’s shoes 42 $37.20 $0.89 8 $533.22 100% $331.29 $164.73 31% Adidas bercuda 3 womens tennis shoes 4 $3.14 $131.28 $399.60 $0.79 4 $211.30 20% $76.88 36% Adidas bercuda tennis shoes stella mccartney 8.5 20 $16.12 $248.27 $359.80 $0.81 4 29% $135.21 34% Adidas cc adizero tempaia iii womens tennis shoe 14 $18.64 $223.54 $539.70 $1.33 4 30% $117.62 33% Adidas cc rally comp womens tennis shoes Adidas galaxy elite junior 3 tennis shoes 20 $16.90 $335.32 $680.80 $0.85 6 10% $187.48 35% Adidas junior barricade team 3 tennis shoes core white and bold pink 42 $39.44 $422.98 $348.04 $0.94 4 18% $218.38 32% Adidas junior response approach tennis shoe 22 $17.28 $216.24 $211.30 $0.79 4 67% $114.52 33% Adidas men’s adipower barricade 8 tennis shoes solar blue and night shade 6 $1.74 $131.28 $315.76 $0.29 4 $78.28 37% 67% 37% Adidas men’s bercuda 3 tennis shoes white and black red 6 $2.76 $196.18 $459.80 $0.46 4 $116.82 36% 36% 33% Adidas men’s adipower barricade team 3 tennis shoes 12 $8.20 $285.67 $567.70 $0.68 4 $165.93 30% 50% Adidas men’s bercuda 3 tennis shoes 8 $8.80 $352.71 $680.00 $1.10 4 $206.19 32% 6% Adidas men’s response tennis shoes 64 $53.22 $422.48 $590.92 $0.83 4 $204.30 33% 24% Adidas performance men’s cc rally comp tennis shoe 34 $35.26 $367.14 $155.88 $1.04 8 $188.52 35% 33% 6 $8.02 $96.85 $111.50 $1.34 2 17% $51.01 12 $2.68 $0.22 2 23% $69.27 $39.55 TOTAL 622 $510.86 $0.82 144 $13,226.56 23% $8,217.66 $4,498.04 34% If we aggregate the performance of each of these data sets, the picture is a bit clearer: In this example, this retailer was not accounting for query intent and was instead optimizing for the average of query performance. In this data set the average margin is 19%, average CPC is $0.90 and average RPC is $4.82 – leaving plenty of room for a nice margin. When optimizing for the average, performance appears strong. However, when splitting the average by query type, it becomes clear that generic queries were a source of inefficiency within the account while product-specific queries were overly efficient and merited increased investment. This retailer was later able to dramatically increase profits by scaling bids and traffic across product-specific queries while decreasing investment to generic queries. It’s evident that the range of search queries within our “PLA search query portfolio” yield radically different returns for this business. Generic queries tend to account for the majority of the ad spend yet the minority of revenue, while more refined queries – particularly product-specific queries – offer the greatest return. It is likely your business faces a similar scenario, though this can be confirmed by analyzing your historical search query data (we can do this for you). Now that you understand there are differences in value across different query types, we can show you how to apply this knowledge to your PLA channel. But first, we must address the problem with PLAs today. 9 AVERAGE OF PLA QUERY SAMPLES Query Type Clicks Ad Cost Avg.CPC Transactions Revenue Conversion Rate COGS/VOH Contribution Contribution margin Brand-specific 2,792 $2,490.58 $0.89 114 $10,799.64 4% $6,709.82 $1,599.24 15% Generic 3,060 $2,837.44 $0.93 80 $7,160.22 3% $4,448.64 $(125.86) -2% Product-specific 622 $ 510.86 $0.82 144 $13,226.56 23% $8,217.66 $4,498.04 34% TOTAL 6,474 $5,838.88 $0.90 338 $31,186.42 5% $19,376.12 $5,971.42 19% 617-307-4969 | sales@omnitail.net | www.omnitail.net Product Listing Ads, whether they run on Google, Bing or Yahoo, all currently suffer from the same problem – they do not allow the advertiser to easily divert their ad spend by search query in order to spend more on valuable queries, and spend less on inefficient queries. Because PLA ad placement is determined by the probability of a match between a user search query and product feed attributes, and do not utilize keywords, this results in the same product being served to many different types of queries. For example, when a user searches for a generic query such as “men’s shoes,” advertisers essentially serve product ads at random, as the user made no indication of what product, other than a top-level product category, that they are interested in. Even if the product served is one which is a top-performer, a product the shopper previously viewed on the website, etc. the advertiser has no idea what exact product(s) to serve this shopper now. 617-307-4969 | sales@omnitail.net | www.omnitail.net 10 THE PROBLEM WITH PRODUCT LISTING ADS/GOOGLE SHOPPING Worse yet, because of the way PLA campaigns are structured, advertisers are forced to determine bids and budget by product or groupings of products, rather than by the search term. This forces advertisers to judge PLA performance by the average of all queries used to trigger their ad. For example, if REI is bidding $1 on their Born Men’s Sandor Lace Oxford Shoe, shown on the prior page, then they are applying that same $1 bid regardless of whether the user searches for a generic query with little intent to purchase, such as “men’s shoes”, or a product-specific query with high commercial intent such as “Men’s Born Sandor Oxford Shoe Size 12.” Data will show that these queries perform differently from each other, and merit different bids that are relevant given their value. A screenshot of the Google Shopping interface, illustrating how bids are assigned to products rather than search terms. This is problematic, as we previously determined that the value which queries hold to a business often vary dramatically by their intent. Therefore, it is not possible to maximize PLA profits with the typical PLA setup, where each product is represented once within the account structure, as the advertiser is forced to invest around the average of performance across query segments, rather than breaking them into isolated groups. However, if we target each product more than once within the account structure, it is possible to utilize keyword targeting to re-route traffic for specific terms to different bids. 617-307-4969 | sales@omnitail.net | www.omnitail.net 11 With the introduction of priority settings in Google and Bing shopping campaigns, it is far easier to segment your PLAs by query intent, or any particular terms which you desire to isolate. While some retailers with brand-agnostic products are best suited for a slightly modified taxonomy for segmentation, we typically find isolating product-specific queries, brandspecific queries and generic queries results in the optimal segmentation of traffic. Essentially, every product lives in at least three separate places within the account – once in a high-priority campaign, once in a mid-priority campaign, and once more in a low-priority campaign. Terms are then negated in order to funnel traffic to the designated priority setting, where bid and budget conditions, even mobile bid adjustments, are determined based upon the isolated performance of the product and search query classification, rather than a blended average of all of them. While some may prefer to modify the structure slightly, we have found that generic and brand-specific queries are far more evergreen than product-specific queries, reducing the total count of negative terms required to accurately funnel the traffic. If you wanted to flip our method and target generic terms in the high-priority campaign and product-specific queries in the low-priority campaign, then you would need to identify all of those product-specific phrases ahead of time, which typically results in significantly more unique terms which need to be identified and negated, and is less likely to be successful long-term, especially for large retailers. 617-307-4969 | sales@omnitail.net | www.omnitail.net 12 HOW TO SEGMENT PLAS FOR QUERY INTENT High Born Men’s Sandor Lace... Born Men’s Sandor Lace... Born Men’s Sandor Lace... $1.00 $0.65 $0.40 Product-specific queries (targeted generic and brand-specific terms negated) Brand-specific queries (targeted generic terms negated) Generic Queries Mid Low PRODUCT PRIORITY SETTING TARGET SEARCH TERMS BID Implementing this strategy will require the advertiser to maintain a regularly updated list of generic and brand-specific terms which are used to funnel traffic. We also recommend keeping a cache of product-specific terms to reduce the time needed for scrubbing your data. For a small business with little to no budget for management, you can probably manually classify all your search queries yourself, though it will be a large amount of tedious work. If you’re a larger retailer, or simply don’t want to dedicate the labor hours required to scrubbing your search query data, then send me an email at matt.stover@omnitail.net and I will run your data through our query classification tool for you, producing an analysis that shows the aggregated performance of each query type for your business. REQUIREMENTS FOR IMPLEMENTATION: QUERY CLASSIFICATION/NEGATIVE KEYWORD OPTIMIZATION 617-307-4969 | sales@omnitail.net | www.omnitail.net 13 After segmenting PLAs by query intent, the retailer I used in my previous example increased PLA operating profit 6352% within the first 90 days vs the 90 day period prior to segmentation, and 1416% Y/Y. This particular retailer had been overspending on generic queries, and underinvesting in product-specific queries – a result of the limitations of the typical PLA structure. By isolating generic, brand-specific and product-specific query segments from each other, this retailer was able to bid more appropriately on generic queries, and bid more aggressively on product-specific queries, maximizing impression share and dominating the market for these terms. This level of segmentation changed the dynamic of their PLA search query portfolio, radically increasing revenue and profits with little change in total media spend. RESULTS: HOW ONE RETAILER INCREASED PROFITS 6352% 617-307-4969 | sales@omnitail.net | www.omnitail.net 14 Operating profit Month 1 PLA Sales and Operating Profit ($) 20% 15% 10% 5% 0% -5% Month 2 Month 3 Month 4 Month 5 Month 6 Revenue Contribution Margin (%) Launch with Omnitail Contribution Margin (%) Q: “We sell proprietary products and therefore only carry one brand. Should we still segment for brand-specific queries? Or should we try another strategy?” A: We work with several clients where brand-specific query segmentation would not be needed or impactful. In these cases, we evaluate other themes of search queries to determine the optimal classification. For example, a home improvement retailer selling flooring might instead want to segment for product-specific queries, category or sub-category-specific queries and generic queries. Or a custom printing business might want to segment for any queries that contain qualifying language such as “custom,” “promotional,” etc. instead of brand-specific queries. Or perhaps you use other qualifying language such as materials, patterns, colors, or other product attributes. Query segmentation is not a one-size fits all solution - you might want to segment search queries differently depending upon the categories of products you sell. Regardless of how you implement query segmentation, you will find it allows for incremental growth within your PLA program. FREQUENTLY ASKED QUESTIONS & OBJECTIONS: 617-307-4969 | sales@omnitail.net | www.omnitail.net 15 Q: “Even if generic queries are not as likely to convert for us today, are we hurting ourselves by decreasing our investment in them? Aren’t we missing a valuable opportunity to introduce our brand to prospective customers in the research phase?” A: Potentially, but this can be measured and acted upon. Segmenting for query intent does not by default mean decreasing investment in generic queries. We have many clients who see generic queries out-perform product-specific queries in both the short-term and long-term. Segmenting for query intent simply means that you are better able to react to variances in value by query type and adjust the way you distribute your media budget to suit. For example, let’s recalculate the table from our previous client example after accounting for assisted revenue. Assisted revenue here would be the revenue that was not attributed to the channel on our default attribution model, but was impacted by a PLA ad within 90 days of purchase. 617-307-4969 | sales@omnitail.net | www.omnitail.net 16 Query Type Clicks Ad Cost Avg.CPC Transactions Revenue Brand-specific 2,792 $2,490.58 $0.89 114 $10,799.64 Generic 3,060 $2,837.44 $0.93 80 $7,160.22 Product-specific 622 $510.86 $0.82 144 $13,226.56 TOTAL 6,474 $5,838.88 $31,186.42 $7,127.76 $9,308.29 $5,290.62 $0.90 338 $21,726.67 $4,298.52 24% $3,399.19 21% $6,501.60 35% Total Revenue Impacted Assisted Revenue (90 days) Assisted: Closed Ratio COGS/VOH Contribution Contribution margin $17,927.40 $16,468.51 $18,517.18 $52,913.09 $0.66 $1.30 $0.40 $0.70 $14,199.32 $11,138.30 $10,231.87 $11,504.72 $32,874.90 46% AVERAGE OF PLA QUERY SAMPLES In this case, we found that for every $1 of revenue attributed to brand-specific queries, they assist $0.66 in revenue post-click that they do not receive credit for on the default attribution model. Generic queries assisted $1.30 per $1 in closed/attributed revenue, and product-specific queries assist $0.40 per every $1 in sales they receive credit for. If we sum the assisted and closed revenue together, we get our total impacted revenue. If we recalculate profit on this revenue figure, we find that generic queries actually do contribute to sales in a profitable manner. If you want to be more aggressive in your customer acquisition efforts, you may choose to place more value on the total impacted revenue rather than simply attributable revenue. This is why measuring performance on more than one attribution model is critical. Regardless, without segmenting for query intent, you will not have the ability to act upon this data. 617-307-4969 | sales@omnitail.net | www.omnitail.net 17 WANT TO LEARN MORE? If you’re interested in learning more about our approach, or if you would like us to perform an analysis for you that forecasts the incremental revenue and profit this strategy will yield for your business, then please send me an email at matt.stover@omnitail.net 11260 Roger Bacon Drive Suite 504 | Reston, VA 20190 617-307-4969

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