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Why Every Ecommerce Business Needs a Predictive Customer Behavior Analytics Model

by Admin | 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.

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.

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:

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.
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.
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.

Direct contact: phone, email, surveys, user-generated feedback, etc.
Digital tracking: collecting data on behavior patterns such as purchase history, reordering, view habits, shares, etc.
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?

by Admin | 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.

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

by Admin | 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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5 Steps to Build a Successful Customer Behavior Analytics Model

by Admin | Sep 15, 2019 | Business & Entrepreneurship, Ecommerce

There isn’t a marketer on earth who would turn down the ability to look into the future. What brand manager wouldn’t want to know exactly what and how much of a new technology or a hot new gadget customer will buy in droves next holiday season? Predictive analytics is a relatively new tactic and has been met with mixed reviews and success. Executing it properly requires a hefty amount of dedicated resources and a fair amount of experimentation. Going through these exercises, though, can certainly give you some interesting insights into how well you can leverage customer behavior analytics to facilitate business growth.

What is Predictive Analytics?

Predictive analytics has been around for a long time in several industries, including insurance. In that model, actuaries use historical data to predict certain outcomes and apply a cost to risk assessment. So, for automotive insurance, a driver’s history factors into the likely cost of potential accidents, driving individual policy premiums up. A policyholder with safe driving record typically receives an incentive (or discount) as a reward for good behavior.

Using customer behavior analytics to predict buying patterns, prevent fraud and make other business decisions is a relatively new development. With advancements in machine learning and more insightful data, companies are starting to develop methodologies to make educated, scientific guesses as to what their customers are likely to do in the next buying season. Developing your own analytical framework is resource-intensive and will garner mixed results, but the process, if deployed correctly, is informative and educational.

1. Use Regression Models

You can’t guess what someone might do if you don’t study what they’ve already done. There is a lot of experimentation that goes into content marketing. Start with buying history. Look at responses from your previous campaigns and organize them. Some items you should study include:

Number of items purchased
Total purchase amounts
Discount codes
Purchase dates

If you’re looking for more information about the general use of regression analysis, which is used across the spectrum from science to education, check out this piece from the Harvard Business Journal.

2. Segment Customers into Groups

Now that you’re on your way to building up your historical data, it’s time to merge that with some customer segmentation. The more refined you can make these groups, the more you can stop guessing what customers want and simply observe what they do. Customer observation is at the heart of all good customer experience marketing. If you have a strong statistics team, use them to integrate one of the many accepted statistical models for segmentation. If not, building those groups manually may take time, but it could also still be worthwhile especially if you segment by:

Demographics (age, region, income)
Buying history (x purchases within the last x months)
Responsive behavior (referrals from social media, newsletters, email campaigns, etc.)

Amazon Prime Customer Segmentation

Before you begin, understand that statistical modeling is complex and requires active maintenance. However, the better you and your team get at it, the more sophisticated your marketing becomes down the line.

3. Deploy a Smart Suggestion Engine

Amazon mastered the suggestion engine early on and owes a huge amount of its success to this mastery. If you don’t have the resources in-house to build your own, there are plenty of enterprise solutions that use actual machine learning to make smarter suggestions to your buyers using customer behavior analytics and buying history. The advantage of a third party engine is that you don’t have to maintain or update it. The disadvantage is that you don’t own it outright; you may stumble into some interesting and useful IP if you DIY an in-house engine. In any case, if you’re selling goods online, you need a recommendation driver. So do your homework and figure out which solution works best for you and your audience.

4. Build Slowly

The good news about customer behavior analysis is that the industry is getting better and better at data collection. The downside: there is an almost unlimited amount of data to sort through. So before you throw everything into one pot and overwhelm your marketing team, try adding one to two factors at a time, experiment, and then add another factor. Predictive analytics for marketing purposes is still a developing field. You’ll need plenty of flexibility to integrate real-time data as you go. Doing so will make the process more exciting and engaging for your team.

5. Stop Making Assumptions

Let this process educate you. If (or more likely, when) you learn the data you used to collect isn’t proving to be as useful as you assumed it was, stop collecting it. For example, data researchers used to bank all of their messaging predictions on bounce rates. Today, most marketers tend to ignore that statistic. The Predictive analysis should stop you from guessing, not encourage you to guess more. As soon as you start to see new patterns emerge, respect the data and let it push you into areas where you never thought you’d go.

The most important takeaway in all of the marketing is this: data never lies. Data and customer behavior analytics paradigms should become the drivers of all of your product development and marketing tactics. It’s never a perfect science, but it is a science. How well you use it can determine the difference between repeated successes or disappointment.

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Microblogging: how to effectively use Tumblr & other social microblogs for SEO, PR, content testing and beyond!

by Admin | Jul 24, 2019 | Content Strategy

Businesses of all types are using microblogging to promote their brand, products and services. From the owner of a local corner bakery to the independent clothing retailer located downtown, companies can now quickly, easily and affordably spread the word about their offerings.

Marketing blogs can be found on popular microblogging platforms like Tumblr, Google+, Heello, Instagram, Tout, Vine and so many others. These marketing tools allow companies to operate simple, yet effective, business blogs that get the brand’s message out to a large target audience in a few important sentences, images or quick (6 seconds!) videos.

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