Category: Optimization

Category: Optimization

Oracle Retail Category Management Planning and Optimization Consolidate multiple data sources into an easy-to-consume format, gaining actionable insights and. Design Optimization deals with finding the maximum and minimum of one or more objective functions by altering a set of design variables. When optimizing category pages, the natural starting point is its header tags. In the page's H1, utilize the primary keyword that is likely to.

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How do you prevent this from happening? Do your research, monitor your keywords and landing pages and make sure to write unique meta data & on-page content. Also, don’t be afraid to re-optimise and experiment with your existing meta data when opening new categories. Sometimes it will take you more than one attempt to get things right.

Crawl budget concerns

Google defines crawl budget as “the number of web pages or URLs Googlebot can or wants to crawl from your website”.

One of the arguments against opening new category pages might be crawl budget consumption. For large e-commerce sites with millions of pages, opening many new category pages might come as a risk in a way that could prevent some parts of your site not to be crawled anymore or not as often.

This is a concern only for (very) large e-commerce sites which are not necessarily well-maintained from an SEO point of view. Gary Illyes from Google seems to be on our side:

million pages

Source: https://www.searchenginejournal.com/gary-illyes-whats-new-in-google-search-pubcon-keynote-recap/274273/?ver=274273X3

In particular, a well-structured and optimised faceted navigation is vital not to run into crawl budget issues, so we recommend reading this MOZ post.

By following overall SEO guidelines and regularly checking Google Search Console and server logs, it is possible to determine if your site has a crawl budget issue.

If interested, learn more about server logs.

Internal linking equity

This is more of a real problem than crawl budget, and here is why: creating additional pages means that the internal linking equity across your site gets re-distributed. If not closely monitored, you might end up diluting it without a clear process in mind or, worse, wasting in across the wrong pages.

Here’s a great piece on how to optimise website internal linking structure.

When creating new pages, make sure to consider how your internal link equity gets impacted: needless to say that opening 10 pages is very different than opening 1000! Focus on creating more inlinks for important pages by exploring options such as navigation tabs (main and side navigations) and on-page content (remember the paragraph above?).

The rule of thumb here is simple: when approaching new category pages, don’t forget to think about your internal linking strategy.

Conclusion

Category pages are the backbone of e-commerce sites, hence they should be closely monitored by SEOs and webmasters. They are vital from an information architecture and internal (and external) linking point of view, and attract the most amount of traffic (beyond the homepage). By following the above tips, it becomes easier to identify opportunities where new category pages can be ‘opened’ in order to capitalise on additional organic traffic.

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Category pages target the keywords that consumers search for most frequently. But optimizing category pages for organic search rankings can be difficult, as default category templates on ecommerce platforms tend to hold less text than other pages.

What follows are seven category page elements that improve rankings on highly competitive keywords.

1. Start with Metadata

Title tags and meta descriptions are the basic form of content optimization. The title tag is the most influential on-page element that sets your page’s keyword theme and, combined with the meta description, influences the search terms the page ranks for.

Title tags and meta descriptions are embedded in a site’s HTML code. Both are typically accessible to optimize in content management systems. I’ve addressed title tags and meta descriptions in detail at “SEO How-to, Part 6: Optimizing On-page Elements.”

2. Make Headings Relevant

Next, start at the top of the visible page and optimize the heading tags — H1, H2, and so on — to help search engines understand the context of the content in a particular section. The primary heading at the top (usually an H1) usually reinforces the title tag’s theme for the entire page. Subheads such as H2 and H3 headings emphasize supporting themes.

For example, in the image below, Wayfair uses the full keyword phrase “Pendant Lighting” rather than just “pendants,” as many lighting stores do. The entire phrase is repeated throughout the site’s navigational links as well, sending the stronger “pendant lighting” signal to search engines. The result is that Wayfair outranks its competitors in a search for “pendant lighting.”

Wayfair’s "Pendant Lighting" heading reflects the primary keyword for the page.

Wayfair’s “Pendant Lighting” heading reflects the primary keyword for the page.

3. Include Text

Many designers and brand advocates despise body copy. But it’s critical for organic search performance. Text doesn’t need to dominate the page or even appear on the top view. Even a short phrase is better than nothing.

For example, Tiffany & Co.’s “Engagement Rings” category page (below) shows how even the most visually dominant products can use text.

Tiffany & Co. places well-spaced text on its engagement rings category page.

Tiffany & Co. places well-spaced text on its Engagement Rings category page.

Remember, body content doesn’t have to be multiple paragraphs. Focus on descriptive keywords that fit naturally without repeating them artificially.

4. Use Featured Content

Category pages can advertise sale items, loyalty programs, related products, or other messaging that you want shoppers to absorb. Use those features for organic search optimization, too.

First, make sure that a feature’s descriptions are coded as text rather than embedded in an image. Visual search has come a long way, but it’s not used in search-ranking algorithms. And make sure the text is optimized with descriptive language and keywords.

For example, the Tiffany & Co. Engagement Rings category page includes a feature promoting “The Guide to Diamonds.” The accompanying text reads, “Discover how Tiffany diamonds are crafted to be brighter and more vibrant.” Note that the text includes the famous branded term of “Tiffany diamonds.” The overall feature description speaks to the company’s key brand promise, accomplishing a marketing objective along with optimizing for search engines.

The Tiffany & Co. "Engagement Rings" category page includes a feature promoting "The Guide to Diamonds." The feature's description accomplishes a marketing objective along with optimizing for search engines.

The Tiffany & Co. “Engagement Rings” category page includes a feature promoting “The Guide to Diamonds.” The feature’s description accomplishes a marketing objective along with optimizing for search engines.

5. Make Link Text Relevant

Using vague language as link text misses a strong opportunity to increase the relevance signal of the linking page and the destination. “Learn More” and “Click Here” are meaningless to search engines, as are linked images with no text at all.

For example, the “Pendant Lighting” category page for retailer Shades of Light, below, invites shoppers to “Explore” with the link text. “Explore” sends no relevance signals to search engines. However, Shades of Light also includes an adjoining link using the subcategory name, such as “Glass Pendants.”

Shades of Light links from the "Pendant Lighting" category page to each subcategory using the word "Explore," which is meaningless to search engines. Importantly, however, the subcategory name, such as "Glass Pendants."

Shades of Light links from the “Pendant Lighting” category page to each subcategory using the word “Explore,” which is meaningless to search engines. Importantly, however, the subcategory name, such as “Glass Pendants.”

6. Emphasize Category Navigation

The primary purpose of category filters is to help shoppers find products. But category navigation also affects the indexation, authority, and relevance of each page in organic search. Thus including relevant words in the navigation and filters will benefit rankings.

For example, Wayfair’s faceted navigation, below, includes “Kitchen Island” text, which links to that subcategory page. Every page in the “Ceiling Lights” category uses that navigation module. Hence every page links to the Kitchen Island subcategory with keyword-relevance link text. As a result, Wayfair’s Kitchen Island lighting page is likely to rank for the 163,000 related monthly U.S. searches in Google, plus other engines.

Wayfair's faceted navigation for the Ceiling Lights category sends relevance signals to the filtered subcategory pages it links to, such as the Kitchen Island page.

Wayfair’s faceted navigation for the Ceiling Lights category sends relevance signals to the filtered subcategory pages it links to, such as the Kitchen Island page.

Make certain that search engines can crawl your faceted navigation before optimizing it. Otherwise, the effort will be futile.

7. Header and Footer Links

The same opportunities in category navigation are available in the sitewide header and footer. However, including links to every ecommerce subcategory and filtered page in the header and footer would be obnoxious for shoppers and over-optimized for search algorithms.

Use header and footer links judiciously to the most valuable category and subcategory pages. Include pages based on keyword research and keyword mapping, in addition to those with high business value.

For example, Fat Brain Toys, a manufacturer and retailer of educational toys and games, slips three links in its footer to pages that aren’t linked elsewhere: “Award Winning Toys,” “Christmas Toys,” and “Toy Catalog.” The link to the Christmas toys page is there to provide visibility during the holiday selling season. The other links are phrases with many searches.

However, links to SEO-related keywords should not outnumber links for other purposes, and long lists of links should never be placed in text fields or below the footer.

The footer navigation for Fat Brain Toys links to three pages that have organic search keyword value: Award Winning Toys, Christmas Toys, and Toy Catalog.

The footer navigation for Fat Brain Toys links to three pages that have organic search keyword value: Award Winning Toys, Christmas Toys, and Toy Catalog.

Jill Kocher Brown

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Sub-Category Optimization for Multi-view Multi-pose Object Detection

Abstract: Object category detection with large appearance variation is a fundamental problem in computer vision. The appearance of object categories can change due to intra-class variability, viewpoint, and illumination. For object categories with large appearance change a sub-categorization based approach is necessary. This paper proposes a sub-category optimization approach that automatically divides an object category into an appropriate number of sub-categories based on appearance variation. Instead of using a predefined intra-category sub-categorization based on domain knowledge or validation datasets, we divide the sample space by unsupervised clustering based on discriminative image features. Then the clustering performance is verified using a sub-category discriminant analysis. Based on the clustering performance of the unsupervised approach and sub-category discriminant analysis results we determine an optimal number of sub-categories per object category. Extensive experimental results are shown using two standard and the authors' own databases. The comparison results show that our approach outperforms the state-of-the-art methods.

Published in: 2010 20th International Conference on Pattern Recognition

Article #:

Date of Conference: 23-26 Aug. 2010

Date Added to IEEE Xplore: 07 October 2010

ISBN Information:

Electronic ISBN: 978-1-4244-7541-4

Print ISBN: 978-1-4244-7542-1

CD: 978-0-7695-4109-9

ISSN Information:

Print ISSN: 1051-4651

CD: 1051-4651

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The Age of Precision Category Management

The Age of Precision Category Management: Hyper-Localized Assortment Optimization Using Advanced Technologies

In order to meet customer demand and expectations, it is essential for CPG companies and retailers to carry the right product assortment. Effective localization of assortment and space planning can be a very challenging task, and most retailers often fall short of harnessing the true potential of technologies like Machine Learning (ML) , Data Science (DS) and Operations Research

In this report, Coresight Research analyze key industry trends and discuss how CPG companies and retailers can achieve the goal of customer-centricity through the hyper-localization of assortment. We consider challenges in using data and assortment planning, as well as the skills required to succeed in retail category management.

They also explore how HIVERY has combined ML and DS into "single learning engine", which harnesses store-level data for category management optimization, enabling precision in category management and localized assortment.

These technologies are now allowing teams in category management, shopper insight, marketing and sales the ability, for the first time, to conduct rapid retail strategy simulation & optimization.

By increasing accuracy and removing the manual process, these new methods and approaches offer a competitive advantage to both CPG companies and retailers; transforming Joint Business Planning (JBP) sessions between the retailer and CPG arming them both with win-win category strategies rapidly.

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Decision Analyst

Automotive Aftermarket Category Optimization
by Jerry W. Thomas

  • Category Optimization

    Category Management has been around in various forms since the 1950s and 1960s. The practice acquired its current name (Category Management) in the 1980s.
    Many marketing executives know quite a bit about category management, since it’s a core concept in retailing, or distribution through retail stores or online venues. The purpose of this article is to share some research and analytic ideas that might prove useful to stimulate your thinking about ways of improving the process of Category Management. The focus of this article is the automotive aftermarket.

The definition of category management varies across different industries, and it varies somewhat depending on whether one is a manufacturer or a retailer. For auto-parts retailers, the ultimate goals are to maximize long-term profits for a given category and simultaneously maximize long-term profits for the overall store or a given website. Any auto store could easily increase sales and profits of antifreeze, for example, by devoting half of its shelf space to antifreeze; but this would likely reduce overall store sales and profits. So retailers are greatly concerned with how much space to give to each category; but manufacturers, not so much.

On the other hand, manufacturers are keenly interested in maximizing sales and profits of their brands within a category, while the retailer is somewhat brand-agnostic. It’s important to keep this slightly different set of perspectives in mind as we think about Category Management for the automotive aftermarket. This article explores the optimization of a single category (Intra-Category Optimization) rather than optimizing all categories within the retail store to maximize overall store sales (Inter-Category Optimization).

Automotive Aftermarket Characteristics

Let’s start with some fundamental characteristics. Automotive aftermarket categories and the products that make up the categories tend to be complicated and complex, compared to many other consumer product categories. Most automotive products are technically complicated, and how those products get purchased and installed is often complicated—with retailers, consumers, and installers playing varying roles from one category to the next. Distribution channels are multiple and complex, ranging from online to retailers to automotive repair garages and to warehouse distributors, etc. The actual installation or use of an automotive part is often too complicated for the average do-it-yourself person. Competition in the automotive category is worldwide, so this adds another layer of complexity. And don’t forget complexity related to the large number of makes and models of vehicles. All of this complexity makes category management a daunting task.

Purchase frequency in most aftermarket categories is low; many products are purchased on cycles of many months to several years, compared to many consumer goods that are purchased weekly or monthly. There are marketing implications related to this extended aftermarket purchase cycle. Brand awareness tends to decay more quickly (no reinforcement from frequent shopping trips or frequent purchases). Vocabulary reinforcement is minimal since there are usually no reminders or stimulants from frequent purchases. The human memory fades over time, so the low purchase frequency often leads to loss of vocabulary related to the product category. If it’s been three or four years since a purchase, the consumer may not remember the brand name, how to install the product, or what the words in the “how to install” instructions mean.

Another characteristic of the aftermarket is that each automotive category tends to be unique. What works in one category will not work in the next category. As a point of contrast, the peanut butter category may share a lot of similarities with the ketchup category, but in the automotive aftermarket, categories tend to be very different. Automotive categories vary in complexity, in market size, in distribution channels, in who installs, and in advertising differences. The advertising differences are hugely important. Advertising is the long-range, heavy artillery of the marketing arsenal, and its presence or absence by brand is a major consideration in optimizing a category. Despite all these category differences and auto aftermarket complexities, category optimization is typically based on three types of research: qualitative, choice modeling, and sales analysis.

Qualitative Research

The starting point is always qualitative research (open-ended, non-directive interviews). The ultimate purpose of qualitative research is to learn what we do not know, the so-called “unknown unknowns.” The unknown unknowns bedevil marketers at every turn and defeat attempts to maximize sales and profits in a category. Most companies in the U.S. do far less marketing research now than they did 30 or 40 years ago, so the number of unknown unknowns continues to grow. Add to this the flood of data available on the web and through social media monitoring, where the sources, reliability, and validity of information are largely unknown. Besides these unknowns there is the human tendency to think that we know more than we actually do. That’s why we start with qualitative research. We don’t know what we don’t know.

There are many ways to do qualitative research, from focus groups to depth interviews and from webcams to bulletin boards, mobile ethnography, etc. For category-optimization work, depth interviews are recommended; that is, one-on-one, in-person interviews that last from 45 minutes to an hour or more among target consumers, installers, and retail store employees. It might also include mobile ethnography, where consumers visit stores, record their experiences, and take pictures with their smartphones. It might include placing cameras at category displays to monitor shopper behavior. It often includes shop-alongs, where consumers and/or installers might be accompanied by a depth interviewer as they shop a category in a retail store.

Qualitative research is exploratory and experimental. Qualitative research is a search for hypotheses about an individual product category, from the perspectives of consumers, installers, and retailers. How much does each group know about the category and products in the category? What are awareness and knowledge levels? What words, phrases, and terms are used, and what do those words and terms mean? What basic human emotions and motivations are involved in purchase decisions and brand choice in the category? Where do consumers go for information about products in the category, and who and what do they rely upon for accurate information? Whom do they trust?

The other types of basic information sought at this stage are brand awareness and brand perceptions. How loyal are consumers and installers to various brands? What attitudes and perceptions influence brand choice, and how does the presence of various brands influence category sales? If a high-loyalty brand is missing, for example, it could lead to reduced category sales and profits. The whole category and brand purchase-decision processes are explored in detail (the so-called “purchase journey” or “path to purchase”). Packaging and packages in the category are an important part of the qualitative research, and participants are asked to handle the packages, examine package designs, and review package information. Poor packages or bad labeling can often reduce category sales, as well as affect the sales of individual brands. Advertising awareness, message recall, and ad recognition are explored during the depth interviews, and no research would be complete without examining the role of pricing and the nature of demand within the category (inelastic versus elastic).

The analysis of qualitative data is involved and time-consuming. It helps if you can pretend you just landed on planet Earth from a distant galaxy, so that you can see and listen with the fresh eyes and ears of a 4-year old child. Your analytic job is to disentangle ideas and concepts and see each idea as a stand-alone clue. You are the detective searching for evidence at the scene of the crime. And, like the detective, your goal is to piece together all of the different bits of evidence to tell the full story. Typically, 20 to 30 depth interviews would be conducted to gather the background information and understanding necessary to design the next phase of the research: the choice modeling.

Choice Modeling

Based on the qualitative research, you now have hypotheses about which variables are important, and you have an understanding of the range of each variable. The number of possible category solutions is usually overwhelming. For example, let’s suppose the qualitative research led you to identify seven important brands in the category, and each brand could have zero, one, two, three, or four facings; four different price levels for each brand; five different configurations to fit all cars; four shelf positions; three left to right positions; and six, eight, or ten feet of linear shelf space. Just these limited variables create the possibility of 75,600 unique solutions. You could do A/B testing, sales analyses, and in-store experiments for the rest of your life and never cover even a small fraction of the 75,600 possibilities.

Now, try to visualize a giant Excel spreadsheet, with 75,600 cells. If each cell was one inch high and two inches long, this matrix would be roughly 17 feet tall and 34 feet long—a pretty big spreadsheet, to be sure, and each cell in the matrix is a unique combination of the variables (or possible solutions). A few of these cells represent optimal solutions, or near-optimal solutions. But how in the world do we determine the cells that represent optimal solutions? It seems like an insurmountable problem. Here’s where choice modeling comes to the rescue. Choice modeling is not one technique, but a whole family of similar techniques. You have probably heard of conjoint or tradeoff analysis, and these techniques are part of the choice modeling family. First, an experimental design is chosen that fits the total number of variables and possibilities. The experimental design identifies a subset of the matrix cells that will be measured during the choice modeling survey. If we precisely measure the reactions of consumers and/or installers to the combinations of variables represented by the experimental cells (a subset of the total possibilities), it’s possible to derive equations to predict how consumers or installers would react to all of the cells in the giant matrix. So it’s possible to create mathematical models that predict total category sales (and profits) for each of the 75,600 unique possibilities.

Measuring reactions to each of the experimental cells (a subset of the total) would be accomplished via computer simulation within a survey of target-audience consumers. If the category were familiar and simple, it perhaps could be conducted online. If the category were more complicated, participants may need to come to a location where product samples and packages could be shown, and then they would sit down at a computer terminal and go through the survey.

Typically, each person would go through eight to twelve scenarios (think of each scenario as a shopping trip), but on each shopping trip the person would see a different display (different brands, different arrangement of the display, varying numbers of facings, shelf positions, and prices, etc.). In each scenario the respondent would have the choice of making a purchase or not, and of purchasing one package or multiple packages. Let’s suppose that the sample size is 1,500 people nationwide, and that each person goes through eight scenarios. That yields 12,000 scenarios (or measurements) from which to derive predictive equations to determine the sales and profit potential represented by each of the 75,600 possibilities.

A mathematical simulator is put together from the choice modeling equations so that total category sales and brand shares can be predicted for each of the 75,600 combinations of variables. Often the accuracy of the simulator can be enhanced by calibrating the model based on actual sales data and brand shares, if such data exists. It’s also a good idea to include an “awareness” variable in the simulator. In the choice modeling experiment awareness of the choices is close to 100%. But in the real world, actual awareness could be relatively low, so the awareness variable helps desensitize the model (i.e., make it more similar to what would happen in the real world). A simulator lets you play “what if” games and explore hundreds of different combinations of category variables to see how total category sales and profits perform under different circumstances—and also explore how each brand’s performance can be improved.

Here’s an illustration of what a simulator might look like, given the hypothetical example. This is the input screen, where the user can enter and change settings brand by brand. Brands are represented by capital letters A through G (i.e., seven brands). This screen allows the user to choose which brands to include in the category, to set prices, to choose number of facings, awareness level, and so on. Once all the variables are set, then the user hits the run button.

Example Category Management Simulator

Here’s an example of how the output screen might look. For each combination of variables and settings on the input screen, the simulator (and the math it’s based on) calculates gross profits and sales per store for a given category, as well as each brand’s market share. It’s easy to see how valuable a tool like this could be. It’s also useful as a tool to determine the best way to respond to competitive challenges or pricing changes. Simulators can be used for at least two years, sometimes three, before market changes decay their accuracy.

Example Category Management Simulator

The choice modeling equations and the simulator provide one set of solutions for one mythical retail store, but what if you have a chain of stores across the U.S. in different markets and different types of neighborhoods? The goal might be to optimize a category for each individual store throughout the chain. It’s possible to link the choice modeling variables to demographic, economic, and automotive data for the trade areas surrounding each store so that you can calculate an optimal solution for each retail store in the chain, or for subsets of stores with common demographic characteristics.

To map simulator results down to individual stores, you’ll need to survey a larger-sized sample, perhaps 3,000 or 4,000 target consumers rather than 1,500. The larger sample makes it possible to segment or cluster the consumers into similar groups, based on demographics, geography, and economics. You might end up with five to eight unique store segments, and optimal solutions would be run for each segment. Then (based on demographics, geography, and economics of the trade area around each store) the optimal solution for that type of neighborhood could be determined. This is a very powerful concept, in that it acknowledges that major target-audience differences exist across the units in a chain, and it strives to optimize long-term sales and profits on a store-by-store basis. It’s also extremely powerful for a specific brand to be able to go to a retailer and say, “Here are the 156 stores in which you should increase the shelf space devoted to my brand, because the demographics and economics around those stores are optimal for my brand.” In summary, choice modeling allows you to look at an extremely large number of possible solutions and to identify optimal or near-optimal solutions. Now you are ready for the final step: sales analysis.

Sales Analysis

Sales analysis works best after a category is already optimized with qualitative research and choice modeling. The big important improvements in the category have been identified. The optimal mix of brands is determined, and you have a good first approximation of optimal prices and display organization, perhaps down to the individual store. You probably already have good analytic tools in place for routine, day-to-day sales analysis. The goal of sales analysis is fine-tuning and tweaking; it’s generally not adequate for making the kinds of major changes we have discussed. You can’t optimize a category with sales-analysis tools; you can only make minor improvements. But sales analyses can be taken to a higher level.

If you really want to get serious about sales analysis, then building an analytic database is an essential first step. You already have your own sales data organized by store or geographic area and time period. To this you would add a wide range of government data, demographic and economic, down to the Census Block Group level. The Consumer Expenditures Survey (CES) and American Community Survey (ACS) are especially rich sources of high-quality data. There are many sources of commercial secondary data you could use to bolster your analytic database. If advertising plays a significant role in your business, then add in media spending by geographic area and media type. The database needs to be pure and clean (i.e., not a lot of missing data or dirty data). Now you are ready for some serious analysis.

With an accurate and up-to-date analytic database, you can begin to address a number of big strategic questions. What forces are driving growth or decline of your category (or categories)? What’s the future potential of a given category in a specific location? What’s the role of advertising in driving growth of a category or brand? What’s the optimal advertising media-spending level? What’s the optimal mix of advertising media to maximize sales of a category or brand? Many strategic questions can be addressed through sophisticated sales analyses, using econometric and other modeling techniques.

Magic Formula

To maximize sales of an automotive aftermarket category or maximize sales of a brand within a category, always start with qualitative research to identify the important variables and levels. Once you know what the important variables are, you can employ choice modeling techniques to look at 50,000 or 100,000 possible solutions and determine the optimal solution for your category or brand. Use sales analysis for the final tweaking and fine-tuning. You can take sales analysis to a much higher level by combining sales data with demographic, geographic, and economic data. Presto, you now have the magic formula to optimize sales and profits for a category or for a brand in the category.

About the Author

Jerry W. Thomas ([email protected]) is President/CEO of Dallas-Fort Worth based Decision Analyst. He may be reached at 1-800-262-5974 or 1-817-640-6166.

Copyright © 2019 by Decision Analyst, Inc.
This article may not be copied, published, or used in any way without written permission of Decision Analyst.

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How to Optimize Category Pages for Ecommerce with Informational Copy

Category pages primarily serve a practical purpose, dividing an ecommerce store’s inventory into manageable subsections so online shoppers can quickly navigate to specific products.

However, like most website pages, category pages also serve a higher function; they can attract users from search engine results pages to the website.

Accomplishing this with a category page is easier said than done.

The process might involve creating unique layouts and hybrid copy that marry the functions of commercial and informational pages.

In this post, you’ll learn how you can optimize your category pages with informational copy.

Ecommerce Category Pages Can Have Competing Objectives

Ecommerce retailers use category pages to organize products into logical groupings that make the online shopping experience easier.

Just as many brick-and-mortar stores physically separate merchandise into departments and aisles, ecommerce sites separate products into categories with their own landing pages and sub-filters.

Category pages enable users to explore a virtual store in an intuitive way, and home in on the types of products they want to browse.

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In terms of visual design, category pages tend to have simple, highly-structured layouts.

More often than not, category pages feature images of individual products that link to their corresponding product pages organized in grids.

Take, for example, H&M’s category page for women’s tops:

H&M Category Page for women's tops

Excluding the markdown promotion, navigational links, and search filters, the only text on the page is a brief description of the product category and individual product names with prices.

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The product images speak for themselves, and the simplistic page design allows users to shop relevant products without distraction.

Once a shopper navigates to this category page, they can simply scroll through the product options until they find exactly what they’re looking for.

However, ecommerce marketers may be looking to increase sales for a specific product category, not just promote the brand at large.

How can you attract potential customers directly to category pages from search?

Adding keyword-optimized text is the go-to strategy for improving on-page SEO and page rank.

However, SEO pros have to tread carefully when adding text to category pages. Too much text can distract users from rich product images and take away from the functional simplicity of the page layout.

When it comes to category pages, SEO pros have to adhere to two seemingly oppositional SEO and UX strategies:

  • Including enough text to effectively target keywords.
  • Maintaining a simple, image-focused design.

Striking a balance between these two objectives has proven challenging for many.

To Optimize Category Pages or Not

Category page optimization requires a creative, informed strategy.

Ask yourself this essential question: What factors help category pages rank for specific keywords?

Google offers little insight into the matter.

Google’s Webmaster Guidelines resource does not include information specific to category page ranking, leaving SEO pros in the dark.

This lack of direction may lead optimization efforts for category pages astray, devaluing these important pages.

To avoid penalization for thin pages or ranking conflict between pages, some opt to noindex category pages rather than building them out for optimization.

But isn’t there something that can be done?

In September 2019, John Mueller, Google’s Senior Webmaster Trends Analyst, pulled back the curtain ever so slightly when answering a question about category page ranking during a Webmaster Hangout.

According to Search Engine Journal staff writer Roger Montti, Mueller offered the following advice on how to rank category pages:

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  • Optimize internal linking to category pages.
  • Avoid keyword stuffing within category pages.
  • Build external links to category pages.
  • As a short-term solution, make it easier for users to navigate to category pages from product pages.

Aside from the standardized warning against keyword stuffing, Mueller’s answers didn’t address category page content itself.

While internal linking, link building, and simplifying page navigation is good advice, these tactics aren’t specific to category pages.

Even with Mueller’s remarks, SEO pros were left none the wiser on how to craft high-ranking, functional category pages that simultaneously support both their UX and SEO objectives.

Keyword Selection for Ecommerce Category Pages

As with all other pages, category page optimization begins with identifying which keyword or keywords to target.

SEO pros should work through the standard keyword research process, evaluating search volume and ranking difficulty, considering the stops along the customer journey, and analyzing search intent.

Understanding keyword search intent is a top priority for optimizing ecommerce category pages.

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If you want to improve a category page’s ranking, you need to target search engine users who already know what product type they want and are ready to buy.

To identify the “right” keyword target for a category page, SEO pros may benefit from differentiating keyword types based on search intent.

You’ll find a useful classification of keywords on the SEMrush blog, which breaks keywords down into the following categories:

  • Commercial/Transactional Keywords: Commercial keywords direct consumers to sites that sell the products they’re looking to buy. Transactional keywords specifically target consumers who are eager to buy immediately and can be easily converted by adding terms like “buy,” “purchase,” and “for sale.”
  • Informational Keywords: Users looking for educational information on a certain topic or product often search keyword terms with question words such as “how,” “what,” “where,” and “why.”
  • Navigational Keywords: Search engine users who want to find the webpage for a specific company often search the brand by name.

Select keywords with strong purchasing intent, not broad terms a user would search to find informational resources.

Remember, category pages best serve consumers who already know what type of product they’re looking for and have serious or immediate intention to make a purchase.

Transactional keywords target consumers at this point in their customer journey, making them ideal for product pages.

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Optimizing Ecommerce Category Page Copy

With a keyword target identified, you can start writing copy for the category page.

However, in order to maintain the functional layout of the page, you cannot allow the text to take up too much space or add clutter to the design.

And, as Mueller pointed out, you must be mindful of keyword stuffing.

Remember, category pages rely on simplicity and organization to provide a seamless user experience.

They also need to prioritize rich images that show off individual products.

How can you maintain these important functions while incorporating enough text to rank for the keyword target without over-stuffing?

Here are a few tips for writing compelling, optimized ecommerce page copy:

Add Text in Small Snippets

Ranking for a keyword with minimal copy requires that you make the most of every text asset on a category page.

Fortunately, there are ways to include a substantial amount of text on a category page without writing lengthy paragraphs that are visually distracting.

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Optimize category page copy to incorporate short commercial text details by:

  • Including a category description of the product category creates an opportunity to use the target keyword contextually and to share information and details that set your brand apart from competitors.
  • Using headings & subheadings so that the category title is not the only header on a category page. Make use of subheadings to optimize the page copy, add visual organization, and to leverage product subcategories.
  • Leading users to informational pages. Category pages can link to internal informational pages that are relevant to the products promoted on the page. Writing a brief description of the linked page not only adds to the overall volume of text on the category page but also gives you a chance to use optimized anchor text.
  • Make product descriptions matter by making sure that every product featured on a category page includes a product name, price, and short description.

Using the strategies above, you can add optimized text to a category page without intruding on the page’s product-focused design.

Can Successful Category Pages Contain Informational Content?

Depending on the target keyword and the competition within your niche, this limited amount of copy may be enough to earn a first-page ranking.

However, ecommerce businesses in more saturated markets may struggle to set themselves apart with so little copy.

To further optimize a page while still avoiding keyword stuffing, your natural inclination may be to build out the page with useful text and graphics.

Doing so can present a conflict of interest, though, as additional imagery can not only confuse users visually but can also misdirect crawlers.

Once you’ve written the page’s transactional text (like the product descriptions mentioned above), the copy you add will likely be lengthier and informational by default.

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If this informational copy outweighs the commercial aspects of the page, Google may misidentify a category page as an informational page.

This makes it difficult to rank for transactional keywords and compete with other strictly informational pages.

This tug-of-war between category page function and optimization raises an important question.

How much informational copy can a category page feature without sacrificing its function as a commercial page – both in the eyes of consumers and crawlers?

Analyzing Top-Ranking Category Pages With Informational Copy

While there’s no magic formula to determine how much informational copy a category page can have without hurting its ranking, much can be learned from existing category pages that rank well for transactional keywords.

To gauge the performance of category pages with varying levels of informational text, compare all category pages that rank within the top 10 for a certain term.

In the examples below, the companies listed had a first-page ranking category page corresponding to the given transactional search term.

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The approximate informational content word counts include only content that doesn’t pertain to a specific product featured on the page.

To target purely informational copy, these word counts exclude individual product details, sale promotions, general company and shopping information, other category and subcategory names, filters, related products, recommended products, navigational text, reviews, sidebars, footers, etc.

For each example, the ecommerce brand rank and approximate volume of informational content are given.

1) Keyword: “shop throw pillows”

  • Pottery Barn – Ranked #1 with 440 words.
  • West Elm – #2 with 390 words.
  • Wayfair – #3 with 2,880 words.
  • At Home – #7 with 90 words.
  • World Market – #8 with 220 words.

2) Keyword: “buy mens glasses”

  • EyeBuyDirect – #1 with 290 words.
  • Eyeconic – #3 with 450 words.
  • LensCrafters – #4 with 110 words.
  • Coastal – #7 with 40 words.
  • GlassesUSA – #8 with 31 words.
  • GlassesShop – #9 with 100 words.
  • 39 Dollar Glasses – #10 with 580 words.

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3) Keyword: “order dining room table”

  • Wayfair – #3 with 1,050 words.
  • The Home Depot – #4 with 440 words.
  • Crate and Barrel – #5 with 490 words.
  • Pottery Barn – #6 with 730 words.
  • Ashley Furniture – #9 with 450 words.
  • Macy’s – #10 with 310 words.

These few examples are repeatable with all different types of products.

They reveal that category pages with a wide range of informational content volume can successfully rank for transactional search terms.

For the same search query (“shop throw pillows”), a page with around 3,000 words of informational content ranked higher than one with around 100.

While Wayfair’s practice of publishing text-heavy category pages could be somewhat of an outlier, it proves that optimizing a category page with large portions of informational content can be done.

Does that mean you should revisit category pages and start building them out with long passages of text? Not necessarily.

A safer strategy for optimizing category pages is to emulate your own competition—but do it better.

Once you’ve selected a keyword to target, go through the first SERP and read any informational content featured on ranking category pages.

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If you notice a trend in the length of non-transactional text, follow it.

Just make sure your copy is of better quality than the competition.

Informational Copy & Layout Strategies

Though the category pages analyzed above have wide-ranging informational text lengths, they present the information in similar ways.

First and foremost, it’s essential that SEO pros don’t allow informational content to transform a category page into something else altogether.

The page should retain the user-friendly, intuitive feel of a category page.

The page should focus on sales conversion, with rich product images and descriptions and useful navigation.

With these features in place, you can then create supplementary content to boost keyword optimization.

Many of the category pages listed above use the following strategies to incorporate larger sections of text:

  • Strategic Placement & Organization: To avoid interference with navigational tools and product visuals, SEO pros often place a brief introductory paragraph of informational copy at the top of the page and save the lengthier text for the bottom, after all product images.
  • Collapsible Content: Including collapsible copy with “see more” and “see less” options allows SEO pros to add substantial amounts of information without overwhelming the user or overcomplicating the layout.
  • Shopping Guides: To add relevant content to category pages, copywriters often write how-to shopping guides that explain all the considerations a shopper should make when purchasing a product on the page.
  • FAQs: Frequently asked question sections contain useful information, have keyword-rich text, and typically don’t require consecutive paragraphs of text, making them perfect for the bottom of category pages.
  • Quality Statements: A category page is an ideal place to make a value statement. For example, describing how the products are made and what materials are used allows SEO pros to add optimized text that’s specific to the product category.

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The Hybrid Approach to Category Pages

In the realm of search engine optimization, there’s rarely a singular correct way to complete an objective or reach the top spot on Google search for a target keyword.

Achieving (and maintaining) high rankings, traffic, and sales conversions require trial and error and ongoing evolution.

In turn, many category pages now employ both commercial and informational content strategies, resulting in unique hybrids with great variance between brands.

Fortunately, SEO pros and copywriters don’t have to find the exact balance and create perfect category pages – you just have to outpace their competition.

More Resources:


Image Credits

All screenshots taken by author, December 2020

Источник: [https://torrent-igruha.org/3551-portal.html]

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