7 Steps to Building a Successful Customer Behavior Analytics Model

7 Steps to Building a Successful Customer Behavior Analytics Model

by | Dec 6, 2019 | Content Strategy, Ecommerce, Retail

Having a comprehensive customer behavior analytics model is vital to ecommerce companies. Studies show that companies that use customer behavior information to their advantage outperform competitors by 85 percent in sales growth. Additionally, estimates show those who use behavior analytics beating competitors by an extra 25 percent in gross margins. The current influx of data available to measure customer behavior patterns is making it easier for ecommerce businesses to use analytics in the following ways:

  • Support core sales and marketing goals
  • Increase customer satisfaction rates
  • Improve product/service criteria
  • Optimize marketing channels
  • Make insightful improvements to overall sales strategies
  • Increase customer loyalty

Developing a customer behavior analytics model is vital to retail success for a variety of reasons, and ecommerce companies shouldn’t be without one. Read on to discover seven key ways to build the right customer behavior analytics model for your ecommerce business.

1. Use Detailed Data

As previously mentioned, there is an influx of big data in ecommerce businesses that help monitor customer behavior. Present analytics platforms can delve even deeper into consumer patterns, and tracking tools have become far more accurate. Estimates for the 2020 fiscal year suggest that ecommerce sales will pass over 4 trillion, and companies with a pulse on consumer behavior will take a larger chunk of those sales. The following forms of data can help retailers build a broad customer behavior analytics model:

  • Order tracking: includes buying patterns, most purchased products, and reordering habits
  • Consumer engagement: helps gauge what features, products, and information matters most to consumers
  • Changes in order patterns: helps businesses anticipate possible upcoming ordering changes that an online consumer may go through
  • Conversion rates: how many consumers convert from visitation to purchasing from a brand
  • Retention rates: how many consumers remain with a brand long-term
  • User feedback: customer satisfaction ratings, reviews, comments, and likes

The ultimate goal of using customer behavior analytics in retail is to get actionable insight into what consumers are looking for. Criteria for that can vary by company, which means the data needed can also vary; the list above gives forms of data that are useful to every ecommerce business. This is just a short list of ideas, and there are many forms of data that help ecommerce businesses build relevant models for customer behavior analytics. When determining what data to add, keep all major business specific KPIs in mind to get the best possible analysis of consumer behavior.

2. Throw Out the RFM Model

Use a life-time value (LTV) model instead of a recency, frequency, and monetary (RFM) model. Ecommerce businesses in particular benefit from using LTV over RFM. Digital tracking and an increase in the accuracy of analytics platforms has made RFM models a poor choice for customer behavior analytics. The reason they aren’t a good fit is because the majority of the key indicators for that model are based on immediate sales. They also primarily focus on those who spend the most money, and they lack any tangible insight into retaining new customers long-term. LTV models can provide actionable insight to take for individual consumers, target their long-term value to the ecommerce business, and select appropriate marketing for them.
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3. Stop Making Assumptions Based on Age, Demographics, or Finances

There are 35-year-old people who watch cartoons, and believe it or not, that’s an excellent point of reference for this segment. Customer behavior isn’t measured by simply lumping age groups, demographics, or income levels into tidy piles and assuming what they will or will not respond to. Making assumptions about customer behavior patterns is not conducive to developing a successful or accurate customer behavior analytics model. The goal is to start thinking of customers as individuals, not groups. A 70-year-old can buy hiking gear and a 20-year-old may purchase knitting needles. To get the best possible results, find out what individual consumers want by basing analytics on real data and not assumptions. In short, don’t stereotype.

4. Use Predictive Analysis

Predictive analysis is an excellent way to draw in potential customers, keep existing customers, and anticipate future needs in order to suggest relevant products. Predictive analytics drive the following ecommerce metrics:

  • Customer acquisition: helps by tracking the consumer’s journey from the initial site visit to checkout to personalize their experience
  • Customer retention: helps by anticipating problem areas to repair based on data such as customer feedback or drop rates
  • Customer growth: helps retailers create calls to action based on ordering patterns during specific periods

Personalized advertising, suggestions, and promotions can all be tailored to customers using data obtained from predictive analysis. Using predictive analytics to understand the behavior of customers is another way to use big data in ecommerce to make changes that improve retention.

5. Know Your Goal and Create Steps to Reach It

Building a comprehensive customer behavior analytics model means knowing what goals your ecommerce company wants to achieve. There are many steps to building customer behavior analytics that companies can take advantage of to reach goals, including :

  • Set analytics goals and KPI’s, and track them
  • Determine critical paths, then break them up to get the most data
  • Set user properties to receive data on customers using your site
  • Continually monitor and adapt analytics models based on consumer practices and new data
  • Measure success of new products to determine their impact on sales
  • Use funnel analysis methods

Having specific goals in mind while developing customer behavior analytics helps ecommerce businesses make marketing and advertising decisions and changes based on reliable data.

6. Incorporate Funnel Analysis

Funnel analysis is particularly useful for determining abandonment rates through each stage of the checkout process. They also help establish the set of steps consumers must go through to reach any specific outcome on a website. Funnel analysis helps ecommerce organizations visualize data by showing drop off points. For this reason, these types of analytics are ideal for verifying drop rates, tracking site abandonment, and showing weaknesses that exist in each stage of the process. Funneling is also an excellent tool for ascertaining why conversions were unsuccessful.

7. Search Out Customer Access Points

Are you getting the most customer engagement from external or internal links? Are they accessing the site through social media posts or click ads? It’s important to pinpoint consumer access points for your brand and track behavior across all points. This type of behavioral analysis helps businesses target areas that produce the most clicks and conversions. That way, retailers can position ads appropriately to maximize their potential draw. Being able to allocate resources properly is one of the key benefits of analyzing consumer behaviors. For customer behavior analytics models to be successful, they have to include data from all areas of the business that consumers have access to. Access areas to include during the process of developing a customer behavior analytics model include:

  • Primary website: main ecommerce site
  • Apps: downloads, uninstalls, in-app purchases, and feedback
  • Social media: Facebook®, Twitter®, Instagram®, etc.
  • Click ads: any advertisement that provides a direct link to primary site
  • External links: links on other sites that direct users to primary site or other company resource

Metrics measurements should span across any location that gives users access to your brand, or they are not truly accurate. Using data from websites alone can give a lopsided view of customer behavior, because not everyone will access your product through the main site. It all circles back to the importance of analyzing customer behavior on an individual level.

The insight gained into the personal habits of customers is crucial to recommendation searches, email campaigns, and product suggestions. Do you have any insights into creating a successful customer behavior analytics model? Comment so readers can add them to the information they gained from this post. If you still need guidance on how meet your ecommerce consumer goals, contact eZdia where our professionals will be more than happy to help you succeed.

Author: Kristin Ann Hassel

eCommerce Content Optimization

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Creating a Seller Management Platform for the E-Commerce Marketplace

Creating a Seller Management Platform for the E-Commerce Marketplace

by | Nov 24, 2019 | Ecommerce, SEO Strategy

In an ever-growing e-commerce sector, marketplaces are looking for ways to onboard quality sellers, improve the customer experience for various pages and increase the conversion rate for their sellers. And sellers, in their quest to sell better, are continually adding their own stores and products in these marketplaces. There’s one thing that both sellers and marketplaces need: good content. (more…)

3 Tips to Create the Best Walmart Rich Media Content Pages

3 Tips to Create the Best Walmart Rich Media Content Pages

by | Nov 18, 2019 | Content Tips, Ecommerce, Marketplaces

With the steady rise of mobile devices and online ordering systems, it isn’t surprising that Walmart began offering a platform for rich media content. E-commerce sales have become crucial to the success of small and large businesses, and Walmart’s Rich Media API tool offers users a way to build on preexisting content. The Rich Media API is available for content providers, drop-ship vendors, and warehouse suppliers. Using a media-rich solution allows sellers to include the following content on their page.

  • Videos
  • 360-degree views of products
  • PDFs
  • Interactive tours
  • Brand story layouts

Mobile responsive rich media pages are a must, as most purchases online are now made via smartphone or tablet.

Why Build a Walmart Rich Media Page?

While some sellers may not feel a rich media page is essential, there are tangible reasons why they are becoming the norm in e-commerce. Online sales have been shown to increase with the use of rich media on seller pages by 10% or more. Using videos, PDFs, and 360-degree views of products enhances content value and draws customers, which in turn accelerates sales growth, especially since roughly 84% of communications are visual.

It’s in the Numbers

A person is 85% more likely to buy products that have visual influencers like product videos. Rich media pages enrich the customer’s transaction and contribute to a more authentic human experience. SEO-driven marketing content is crucial to obtaining a top spot in search engine results. Staying on top of search engine results is crucial to drawing in customers, and SEO content marketing solutions like those at eZdia create the right content to ensure success. Professional writers develop unique content descriptions, using effective keywords that optimize online sales. We work tirelessly to understand our clients, their KPIs, and their target customers, then come up with a bespoke, client-centered SEO, and content strategy, then use our hand-picked teams to execute. On top of that, we can help you migrate rich media pages between platforms like Amazon and Walmart.

Combined with strong SEO content and marketing, a Walmart Rich Media page is a solid benefit for any seller. Providing enrichment to pre-existing SEO marketing content in the form of visual data draws customer attention. According to a recent article on Business Insider, an estimated engagement rate increase of 5.7% occurs across vertices when visual influencers are used. A Walmart Rich Media page truly makes the difference in forming a personal relationship with your customers.

How to Create a Walmart Rich Media Page

As previously discussed, Walmart offers Rich Media API tools for content providers. Sellers that want to create the page themselves would need to have a working knowledge of computer coding and content development. Alternatively, you’d need to use a compatible template and a specialist eCommerce content agency with an omnichannel experience like eZdia.

1. Get Image Specs Right

Walmart has strict guidelines for photos, and all product images need to meet the following specifications:

  • 1500×2000 pixels for zoom capability
  • 600×800 pixels for standard
  • 1 MB file size
  • RGB color value
  • 4:3 aspect ratio
  • Acceptable file formats: JPEG, JPG, BMP or PNG

2. Making Customer-Centered Videos and Virtual Tours

There are four key things to keep in mind when creating rich-media videos and virtual tours for e-commerce sites: engagement, education, quality, and call to action. Engagement requires using visual queues that keep the customers’ attention and hold their interest throughout. Use images that show personal belief in the quality of your product. If you build the items by hand, take the viewer through the process, or highlight the company’s quality standards and testimonials. Be careful of being too long-winded during this process, as the average person only has an eight-second attention span, so grab focus early in the video. Education encompasses product benefits, features, and uses, as well as the materials and the creation process. Keep the following questions in mind when determining how to educate a customer on the product:

  • Why should they choose your product?
  • What can you offer that other products don’t?
  • How can the product benefit them?

Quality videos don’t have to destroy a budget. High-resolution videos can be created using tools found pre-installed on most laptops. Virtual tours of product lines, the creation process, environmental practices, or how you live your mission statement work well to create a more personal relationship with the consumer, getting them to buy into you, not just your product. Finally, end with a call to action. Avoid flowery language like “you simply have to have this amazing product” or “be the envy of Kalamazoo, with the hottest product on the market”. It comes off as salesy and artificial. Active words like use, create, make, or enjoy are less pushy.

3. Using Templates and Tools

Generally, only skilled developers should use the Rich Media API tool, and a media-rich page will always include more complex coding than a standard product page. You can, however, use templates that may have one or all of the following beneficial features.

  • Rich media
  • Content tool that works across retailers
  • Item set up
  • Content health reports
  • Above-the-fold content
  • Tracking for miscellaneous items like pricing or availability (Webcollage Inc., 2018)(Salsify, 2018)

Above-the-fold content is the first content a user sees without having to scroll on the page or jump to another page. It is important that this content grabs the customer’s attention and makes them want to browse through the rest of the page. Content health reports will help the seller gain valuable insight into changes that can be made to increase traffic and sales.

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Mistakes to Avoid When Developing Rich Media

Creating Walmart Rich Media pages is time-consuming, and there are lots of elements to perfect. In their rush to get their pages published without utilizing the proper resources and expertise like those offered by eZdia. Here are the most common mistakes we see – and ones that can easily be avoided.

  1. Using non-unique copy. While it can be tempting, you should never just paste an identical copy, as Google does not like plagiarism – even if you’ve only plagiarized yourself.
  2. Creating unique but painfully fluffy descriptions that don’t tell the reader how your product benefits them and why they should buy it.
  3. Not conducting the right keyword research. Neglecting niche, relevant keywords, particularly long tails, in favor of only using head terms, or choosing keywords that aren’t relevant to the product is disastrous because you won’t be ranking for the right terms in search, so buyers can’t find you organically.
  4. Not including enough images. Because your customers can’t touch, pick up, or get close to your actual product, you need to include as many images as possible to show your product from all angles, to give the customer a sense of how the product will look, feel, or perform. 360-degree product tours are incredibly useful for this.
  5. Adding rich media modules before adding product information. This results in absorption failure. Get your product information in and successfully absorbed before you add rich media modules.

How to Get the Most Out of Walmart Rich Media Pages?

To get the best possible Walmart Rich Media page, utilize the information pages available on the Walmart Developer page to find out what can and cannot be included. Find a template option that you can trust to develop content that will attract and keep customers. Make sure products are displayed prominently, and descriptions are honest and informative. Content should be fresh, and imagery should be clear and include product branding when applicable.

eZdia’s CrewMachine Platform helps brands, sellers, and suppliers to update their product pages on Walmart.com with Rich Media within 24 hours.

Let’s get started!

  1. Just give us your rich media source URL and Walmart.com. URL where you want to publish the content.
  2. We prepare a proof of how your Walmart.com page(s) will look with the Rich Media. You can approve or request changes to be made.
  3. Your page(s) will go live on Walmart.com within hours of approving the proof. This all takes less than 24 hours.
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Why Every Ecommerce Business Needs a Predictive Customer Behavior Analytics Model

Why Every Ecommerce Business Needs a Predictive Customer Behavior Analytics Model

by | Oct 30, 2019 | Ecommerce, Retail, SEO Behavior & Demographics

The availability of consumer data has made it easier for retailers to direct ad campaigns and product recommendations to individual consumers and specific behavioral groups. Customer behavior data can help ecommerce businesses personalize promotional offers, drive conversions through behavioral pattern recognition, and find areas that are performing well and ones that need a tune-up. Every ecommerce business should have a personalized, predictive customer behavior analysis (CBA) model.

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Benefits of a Solid CBA Model for Ecommerce

Insights from CBA can help ecommerce businesses personalize advertising, replicate long-term customers, reduce acquisition costs, and increase leads and conversions. Targeting email promotions, recommendations, and campaigns to individual consumers offers personalized solutions that drive further sales. Satisfied customers are more likely to recommend the brand to friends and relatives, or share products on social media or through email. Using big data analysis, ecommerce businesses can find consumers with behavior similar to their best customers. eTailers can replicate the marketing efforts made to obtain and keep lifetime customers to create leads and potentially gain conversions. Knowing what consumers are looking for and predicting future behavior that may influence purchases means less guesswork when creating marketing campaigns. Determining like behaviors and gleaning valuable insights when creating successful campaigns the first time around helps reduce the amount of money spent trying to acquire customers. Having a predictive CBA model in place assists an ecommerce business through:

  • Better personalization: being able to create marketing directed at customers as individuals
  • Driving relevant content: add content based on popular content areas, topics, or consumer feedback from a variety of platforms
  • Design ICPs: big data analysis helps create specific profiles through the accumulation of digital information using CBA strategies
  • Predicting future behavior: tracking and analyzing individual data can help ecommerce businesses predict the next step in a customer’s purchase evolution
  • Finding best marketing practices: gathering customer behavior data to see what strategies are working and what practices need an overhaul
  • Tracking the consumer journey: tracking from discovery to ordering and beyond, then using data to drive recommendation engines and direct advertising campaigns
  • Adjusting analytics models and data mining strategies: help determine where data mining strategies are successful and what type of customer information gathering works but that still maintains brand trust

Customer behavioral analysis is becoming extremely popular in ecommerce. An estimated 69 percent of ecommerce businesses uses predictive CBA for acquisition, growth, and retention rates. By focusing on individuals or specific behavior groups, retailers can increase consumer satisfaction and turn visitors into repeat customers.

Types of Customer Behavior Analytics Models to Choose From

Customer behavior models are meant to answer set questions based on customer data analysis. Up until recently, the RFM (recency, frequency, monetary) model was considered the most effective method for CBA models. The primary goal of RFM is to focus on sales, and ecommerce specialists have realized this model of analyzing customer behavior has become obsolete. With the increase in data available and consumers’ need for a personal connection to brands, models have evolved to include those that focus more on consumer satisfaction than sales. The idea behind the shift is that sales will follow satisfaction, making the consumer the most important factor. There are three main CBA models that work better for ecommerce than RFM:

1. Customer Journey Analytics

Analyze data from all channels the customer interacts with throughout the entirety of their experience with your brand. This model helps an ecommerce business determine what drives consumer purchase behavior. Track four major steps in the purchase path to get a full picture of consumers’ journeys:

  • Awareness: influences and triggers
  • Consideration: product and ecommerce brand research
  • Conversion: where and when purchase decision was made, what step in the journey
  • Evaluation: experience, feedback left, satisfaction rating, review, any user-generated feedback

Gathering data from each step of an individual customer’s journey can aid ecommerce businesses in developing or adjusting personalized eCommerce marketing strategies.

2. Behavior Segmentation

The behavior segmentation model entails collecting data about the actions customer take, based on behavior patterns during the purchase-decision process. The data is used to help classify behavior groups and develop engagement strategies. Behavior classification is a better way to create groups of like-minded consumers without relying on age or geographic locations.

3. LTV

Modeling customer behavior analytics includes collecting customer’s data to determine their lifetime value (LTV) to an ecommerce business to obtain actionable insight on how to maintain customers with like behaviors long-term. Unlike RFM, which focuses primarily on immediate sales value gained by conversions, LTV is centered on the value of a consumer through their entire time with a brand.

Ways to Use Customer Behavior Analytics to Increase Conversions

The three most important attributes consumers consider when deciding where to shop online are best price, preferred website, and best delivery methods. An ecommerce site is more likely to be preferred if they are in tune with consumer behavior and display knowledge about consumers as individuals.

CBA_Purchase Decision Factors.png

One of the primary benefits of using predictive CBA is the opportunity to transform data insight into conversions for your ecommerce business. There are three key ways predictive analytics models can make the largest impact on ecommerce conversion rates:

  1. Increase customer satisfaction ratings: Predictive CB analysis obtains data from designated areas that can be used to increase customers’ personal experiences. The more satisfied consumers are, the more likely they are to recommend the brand to friends and relatives through email, social media, or word-of-mouth.
  2. Lead-to-marketing reconstruction: Predictive analytics can determine on which areas you need to focus the advertising budget, based on consumer purchase data and user-generated feedback. Consumer data can indicate how advertising campaigns have been successful and areas that need to be restructured or omitted to achieve better results. This provides an opportunity for ecommerce businesses to increase sales through personalized and behavioral group marketing campaigns.
  3. Attract potential partners: Positive statistics from data analysis methods can be leveraged to promote your brand on social media platforms, advertisements, and even turned into badges that can be placed on your website. A successful CBA model displays a knowledge of industry practices for optimizing success and makes your brand a trusted source of credible information on consumer behavior analytics methods.

Having partners increases brand visibility through recommendations for a product on their blogs, advertising, or recommendation engines. CBA is useful in the collection of data pertaining to what platforms draw the most user-generated feedback, which is vital for lead-to-marketing reconstruction. Knowing which one will work best for your company takes some research into current goals and possibly past data sets for customer satisfaction.

Customizing a Comprehensive CBA Model

Data points for each ecommerce business need to be customized to their end goals. The type of customer data they need depends on what type of goal they wish to accomplish by collecting customer behavioral data. For instance, if you’re trying to create a recommendation algorithm for an automatic feature based on consumer viewing history, you won’t need age or income information. One working example of this is Amazon’s recommendation algorithm, which uses the following data sets:

  • Purchase history
  • Shopping cart items
  • Rated and liked products
  • Views and purchases

Developing a list of areas vital to the goal they wish to accomplish helps ecommerce businesses develop a comprehensive CBA model that weeds out irrelevant data.

1. Include Unstructured Data

Using unstructured data allows etailers to gain insight from traditional forms of customer information, such as test data. These are available wherever user-generated feedback is found on social media, in comments, reviews, and other areas where consumers have direct interaction with a brand. Just because it isn’t necessarily big data, doesn’t mean it isn’t important data.

2. Know Your Goal

All the data in the world is useless if you don’t know what you’re looking for. Before developing an analytics strategy, ecommerce organizations should ask and answer the following questions:

  • What does the company plan to accomplish with customer behavioral data?
  • What metrics matter to the goal?
  • What are specific areas of concern?

Not having a clear view of what consumer data gathering is meant to accomplish but developing a strategy for behavioral data collection anyway is an exercise in futility. The influx of data alone just causes confusion. Determine motives. For example, is the company’s primary concern cart drop-off, conversion rates, or poor advertising campaign results? Obtaining clarity prior to the implementation of a CBA model saves time and supplies retailers with accurate data sets that apply to a specific goal.

3. Focus on Relevant Data Sources

Only use data that focuses directly on your end goal. Each data collection effort should center on resolving a very specific set of issues or accomplishing a business goal. Focusing on specific criteria provides ecommerce businesses with more reliable data results.

4. Sources of Data to Include

There are three primary ways to collect customer behavior data, and all three data areas must be considered while developing a comprehensive CBA model.

  1. Direct contact: phone, email, surveys, user-generated feedback, etc.
  2. Digital tracking: collecting data on behavior patterns such as purchase history, reordering, view habits, shares, etc.
  3. Competitor strategies: tracking competitor success with data collection methods, and adopting relevant methods.

With any form of digital data collection effort, and some direct efforts, software generally sorts results and breaks them into understandable pieces of data. This way, retailers can gain actionable insight faster and negate human error. Without data-sorting software applications, piecing through vast amounts of digital data could take months.

5. Build a Customized CBA Model

Customize your CBA model as much as possible to gather actionable insight into consumer behavior, what drives purchases, or why customers don’t follow through. Design a CBA model that uses analytics methods that are relevant to your brand. For a CBA model to be successful, it needs to fit your specific goal and provide accurate real-time data that applies to that goal. Using a cookie-cutter version of CBA isn’t going to produce tailored results, but there are some steps that should be included in every CBA build. In our 7 Steps to Building a Successful Customer Behavior Model, the primary steps to include during the build of a CBA model are broken down. The steps include tools like predictive and funnel analysis methods.

What Are Ecommerce Businesses Presently Using CBA For?

The level of actionable insight received from the end results of CBA helps retailers keep customers that generally drop off prior to conversion. In fact, 63 percent of ecommerce businesses use the data to increase customer satisfaction rates, while 46 percent use it to elevate loyalty in existing customers. One of the most popular ways to use predictive analytics rests in recommendation engines like the one created by Amazon. The majority of Netflix viewer activity is driven by their recommendation service, and it’s estimated it saves the company $1 billion per year by reducing turnover rates. A key benefit of CBA is customization to individual consumers, which makes it crucial to ecommerce success. One study shows that 52 percent of consumers often switch brands when advertisements are not personalized to their habits. The possibilities for data-driven success are endless using CBA, especially when combined with other customer behavior data analysis methods and AI technology.

What information on CBA did you find most helpful? We’d love to hear how CBA has changed your ecommerce company for the better and any tips you might have about creating a successful model. Want help reaching your goal? Please contact eZdia to learn how we can assist. We strive to offer services that help ecommerce businesses reach their full potential. This is done by developing engaging content, analyzing and developing data for algorithms, managing content, and providing other valuable ecommerce content solutions.

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Product Description Word Count: How Much Is Enough?

Product Description Word Count: How Much Is Enough?

by | Oct 20, 2019 | Content Strategy, Ecommerce, Featured

With 96% of Americans making online purchases in 2018, and 80% of those making at least one purchase in a month, you can see the importance of optimizing your product pages, and product descriptions are a key element. However hard you work on the rest of the page, if you get the product copy wrong, all the work you’ve put into the rest of the page is wasted. Bad product copy creates a bad user experience, and it doesn’t do well in the SERPs, either. How long a product description should be is just one of the many factors you need to consider when optimizing your product pages.

Just How Important Is Word Count for Product Descriptions?

Extremely. But it’s not as straightforward as “you must write 400 words for every product you carry”. It’s subtler. And there’s a number of things that contribute to the final decision regarding how long a product description should be. The key is to strike a balance between pleasing the Google Gods and creating the best user experience. There’s a marked difference between informative and engaging copy that gives the reader everything they need to know to make a buying decision, and padding a description with vague fluff and generic statements that add to the word count without adding value.

What to Consider When Determining How Long a Product Description Should Be

There’s no hard and fast rules for maximum or minimum word count for product descriptions, but here at eZdia, we’re experts in creating optimized product content that converts, so we’re sharing some of our key industry insights and guidance with you to help you win the content wars.

Type of Product

The type of product you’re selling is the main influencer that determines the length of your product descriptions. For example, a computer, large appliance, power tool, or electronic device requires a longer, more robust product description than apparel, simple tools, wires and connectors, kitchen accessories, or soft furnishings. When deciding how long your product descriptions should be, think about how many attributes, features, uses, benefits, and specifications your product has. If it doesn’t have many attributes or specifications outside of color and size, then you need a shorter word count. If your products are more complex, with lots of specs and features, then you need a longer description. You need enough words to convey all of the relevant information that the reader needs to make a purchase. If they have to leave your site to find more information on the product, they’ll buy from wherever it is they find the info they need. Depending on the client, their products, and their KPIs, we generally recommend 125-150 words for simple products like apparel, and 350-400 words for complex products like electronics and large appliances.

Using Bullets

Bullets are exceptionally effective when combined with a paragraph or more of product copy. Bulleted lists let you provide a rapidly scannable list of all the key features and specifications. They reduce overall wordcount, improve readability, and let your consumers quickly decide if the product might meet their needs, in which case, they can read your paragraph copy.

Using Feature/Benefit Structure

So many posts have been circulating the internet in recent years about only talking about benefits and ignoring the features. This is bad advice — and it just leads to vague, nonsensical waffle. It’s an over-simplified, distorted twist on the real best practices, to the extent that it’s moved beyond meaningless, into dangerous, because using the “benefits only” approach will harm your bottom line.

Product descriptions that sell seamlessly combine features and benefits. Yes, people want to know how a product is going to help them and therefore why they should buy it, but they need the hard facts, too. It’s true that you need to keep the focus on the reader rather than the product, but you don’t do that by eliminating features. You do it by relating how each product feature benefits the buyer.

There is no ecommerce niche where a fluffy paragraph of imagined benefits will outsell a well-crafted paragraph full of relatable features and their associated benefits. It doesn’t matter if you’re selling cuddly soft toys or cell phones. Features are equally as important as benefits.

While you should, of course, give product specifics, what people really want to know is how the product helps them, solves their problems, and what they can achieve with the product. The key is to combine the key features and the benefits each provide. Understanding the difference between a feature and a benefit is the first step.

  • A feature is a fact or characteristic of your product.
    • Resolution, size, weight, connectivity options, ports, included software, and similar all count as features.
  • A benefit tells the reader how the product or a feature of that product benefits them.
    • How it solves a pain point or problem, how it improves efficiency, saves time, money, and so on.

Let’s take a look at a bed. People don’t want to buy a bed — they want to get a good night’s sleep. However, they need to know the features and how they are of benefit to make an informed purchase.

Features only: “This bed has a four-drawer divan base and a memory foam mattress.”

Benefits only: “Enjoy a wonderful night’s sleep on this mattress and divan base in your uncluttered bedroom.”

Feature/benefit structure: “Keep your bedroom organized and uncluttered with this four-drawer divan base. The memory foam mattress cradles your body, eliminating painful pressure points and ensuring your body remains in the proper alignment, giving you a restful, comfortable night’s sleep.”

Therefore, when determining product description length, make sure you leave enough room to accommodate a proper feature/benefit structure.

Langauge

Keep it concise. Avoid cliches, jargon, and fluff at all costs. People don’t want waffle – they are busy and their time is limited, so get to the point. Don’t use 20 words when 11 will do.

And don’t be vague and ambiguous. Employ clarity in your product descriptions. Consumers don’t want a bamboozling intellectual challenge, they just want to know if the product they’re looking at meets their needs.

Avoid fluffy, salesy, promotional language. It’s a major turnoff. The modern consumer is smart and savvy and won’t be hoodwinked by exaggerated claims and over-promises. Be honest. And don’t try to sell to your reader – inform them.

In Summary

Product description word count depends on many factors, and it’s part art, part science — too little content, and you send potential customers away to find the information elsewhere, too much, and you lose potential customers who are intimidated by big walls of waffly text. Make it easy for your visitors to make a purchasing decision. Concern yourself with the clarity and quality of your content and how it improves the user experience. And use the experts here at eZdia as your ecommerce content solution provider – we’re an outstanding resource for seo analysis and improvement, and product copy.

Marketplace Competitive Analysis

eZdia is happy to offer brands and manufacturers a free Marketplace Competitive Analysis. Tell us about your marketplace strategy and we’ll prepare an analysis designed to help you outrank your competitors.

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The Future is Here: 5 Ways Virtual Reality is Revolutionizing Online Shopping

The Future is Here: 5 Ways Virtual Reality is Revolutionizing Online Shopping

by | Oct 15, 2019 | Business & Entrepreneurship, Ecommerce

Facebook made headlines when it made a $2 billion purchase of VR pioneer Oculus in 2014. The social media giant’s move signified a huge transformation for gaming and mobile devices. Its Focus 360 technology (thanks to in-house, proprietary Oculus tech) enables users to enjoy a 360-degree panoramic experience without using a headset. Now that we’re closer to VR experiences becoming a day-to-day reality, the question lingers about how virtual reality will improve the online shopping experience.

1. Hardware is Getting Cheaper

While not everyone is clamoring to buy a Samsung Gear VR headset, Google Cardboard is proving there is an appetite for a cheap, essentially disposable viewer. Amazon has been in the hardware business for some time, and it’s not unlikely that they could develop a proprietary headset that works exclusively with their store. Alibaba, the e-commerce giant, successfully launched a VR shopping headset in 2016 for Singles Day in China, the country’s largest online shopping day of the year. Other major e-tailers could easily rush to compete to give shoppers the most in-person like shopping experience from the convenience of their mobile devices.

2. Try it On

If you’ve never purchased clothing from a brand, and aren’t familiar with how its sizing fits you, buying clothing online presents some risk. Major retailers like Zappos and Nordstrom make their online shopping experiences more appealing by offering free shipping for returns, mitigating the risk that a new pair of boots or a coat doesn’t fit quite like you thought it would. Imagine, though, slipping on a pair of goggles and actually trying on a new garment or accessory.

It’s not out of the question to think that consumers would be motivated to use a similar easy-to-deploy tech for trying on everything from lingerie to a pair of jeans. That future isn’t as far away as you may think. In 2015, Underside, a Belgian app developer, created an app that enabled you to try on the Apple Watch. The technology wasn’t difficult to implement: cut out a piece of paper, affix it to your wrist and use the iPhone’s camera (and the AR Watch app) to view the watch on your wrist. The Gap is already letting shoppers play with AR dressing rooms.

3. Looking Around

Physical expansion is costly, and retailers can’t possibly open a location in small markets where a limited customer base doesn’t guarantee the volume they need. So, before Zara or luxury brands expand into a new city, they’re more likely to invest in virtual shopping experiences. Imagine touring a store, touching the merchandise, and enjoying the relaxing and fun experience of looking around. Virtual reality shopping won’t always replace the excitement of the in-person experience, but if the closest Chanel store is hundreds of miles away, this may be your only way to examine a $3,000 purse.

4. Using Virtual Reality Online for Large Purchases

It’s typically very difficult to experience a new sofa, dining table set, or other household goods when you’re looking online. It’s equally challenging to picture a sofa that you do see in person in your house. A future where you can virtually place that large piece of furniture in your home is not far away. VR and AR developers are working hard and the race is on between companies like Oculus and Magic Leap to solve these big problems for big purchases. For instance, the Ikea app already has AR features that shoppers can use on Android and iOS.

5. Booking a Vacation

Photos and videos already do a pretty good job of capturing the essence of sitting beach-side at a five-star resort. Travel agencies, hotels, and travel writers will soon deploy AR and VR to make it even easier for you to book your next vacation. This landscape is shifting constantly, but it’s probably only a matter of time before you can click onto a hotel website or the visitor’s bureau for a country or landmark and feel like you’re literally standing next to a volcano or sipping cocktails on the sand. There are a host of apps right now that are pushing the technology along, albeit with mixed results, but as the hardware improves and gets cheaper, we anticipate that the tourism industry will exploit augmented and virtual reality to improve online browsing for potential guests.

Is your content future proof? Is your team prepared to use virtual reality in your user’s online shopping experience? We’re here to help you plug up any holes in your content quality so you’re ready for anything.

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