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Predictive Analytics are your ticket to higher revenues

In case you haven’t spent much time with web analytics, I’ll start by giving you this definition of predictive analytics (PA), courtesy of Wikipedia:

‘Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns.’

Whereas with descriptive statistics you learn what data can and can’t describe your customers’ behavior, with predictive analytics you learn what to do next — that is, use data about past actions to make predictions about future actions.

Obviously, if you can discover and anticipate what your customers — and earlier-sales-stage prospects — really want from your brand, you’ll hold the ‘golden ticket’ to higher revenues and customer loyalty. As a result, you’ll become a rockstar analyst on your team.

This graphic below shows how three well-known brands use PA to serve up more customer-centered experiences.

In what follows I’ll first share the quantitative benefits of using PA platforms.I’ll then discuss the qualitative (and highly strategic) benefits of integrating these apps.

Quantitative benefits of PA platforms

Most e-commerce companies focus a lot on the Revenue Per Visitor (RPV) and Monthly Recurring Revenue (MRR) metrics. But boosting recurring monthly profits is even better. PA apps can help you do this by 1. driving up Average Order Value (AOV) and 2. offering optimal pricing for each visitor.

Higher order values from up-sells

First, let me define what I mean by ‘up-sell.’ An up-sell happens when a brand offers one or more complementary products (or accessories) together with the base product. If, after you’ve just decided to buy a new bike, the sales clerk says, ‘Here are the helmet and gloves I recommend to go with it,’ he’s doing an up-sell (actually two of them).

Up-sells are the ‘low hanging revenue fruit’ of e-commerce selling. Why? Because your prospect needs them to get a complete solution (for the previous example, to bike around the neighborhood safely). So, the better you can offer the right accessories at the best time and place, the more likely it is that a given visitor will add one or more of them to her order.

That’s where PA platforms come in. Based on the interaction and transactional data it collects over time, a PA algorithm can first determine in which segment a given visitor fits. During the user’s first visit, the algorithm can then show some ‘best fit’ accessories for the base product being shopped.

Higher profit margins with optimal pricing

In case you didn’t know, there’s a lot of price finessing going on in the world. For example, the person sitting next to you on your previous flight probably didn’t pay the same airfare as you did (perhaps even a lot less). Airlines were forerunners in the ‘dynamic pricing’ realm, and e-commerce PA platforms have now taken this technique to the next level.

For example, if you’ve visited an e-commerce site three times, signed up for its ‘special offer’ emails, viewed a particular product page three times, and hovered over the ‘add to cart’ button twice, you obviously have a higher engagement level than a first-time visitor who hasn’t yet hit a product page.

In such cases, a smart PA platform could push a message saying, ‘I see you’ve viewed [product X] a few times. How does a 15% discount sound?’, after which you could offer three choices:

‘Sounds good – add to cart and apply discount’

‘I have a question – let’s chat’

‘No thanks’

Some PA platforms will even adjust the pricing for you automatically. Let’s say, for example, that your visitor lives in a high-income Zip Code. You could offer this visitor the product with a default price of $XX. But the algorithm could offer a visitor from a middle-class Zip Code their the product at 0.85 * $XX. (Assumption: In both cases, the company would earn a good profit margin.)

Qualitative (and strategic) benefits of PA platforms

On the qualitative side, the benefits might not boost your tactical KPIs, but they can certainly inform your future marketing tactics and boost your team’s operational efficiency.

Gain business intelligence from usage reports

Since all of the leading PA platforms include reporting capability, you can easily run reports that show things like:

RPV based on user group.

RPV based on key visitor interactions (e.g. launched one or more chat sessions).

RPV based on correlations with other variables (how far down the Product page the visitor scrolled, and the pages viewed beforehand).

For example, let’s say you discover that (transacting) customers who view videos have a 20% higher RPV. You should then direct your designers to make the ‘play video’ interaction more enticing (for example by making the ‘play’ icon more visible, or the splash image more interesting).

Knowing these things — which amount to business intelligence — you can then hypothesize as to why these correlations are happening, and dream up new A/B tests ideas that capitalize on these findings.

Build a data-driven marketing strategy with the RACE Framework

As we know, working in a golden age of data-driven marketing, applying data and critical thinking to inform our marketing strategies can turbocharge marketing performance. However, misinformed strategies or decisions made based on misleading analysis can spell disaster later down the line.

Our RACE Framework is a simple strategic marketing structure designed to help marketers plan, manage, and optimize their marketing strategies using key insights from their customers’ journeys.

Book your free 1-2-1 consultation call with a member of the team today and take the chance to revisit your marketing strategy, and discuss your opportunities within the context of the RACE Framework. Get started today

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PA platforms increase your team’s efficiency

Without a ‘core’ PA platform, your marketing optimization team requires at least these people: an optimization analyst, a user experience designer, a software developer and a product owner (to negotiate with the business which tests to run). So you can best make sense of the reams of visitor and customer data flowing in daily, a data scientist would be great to have as well. These people would need to be proficient with a growing set of tools.

It’s a lot to manage. Besides that, the stakes are high. A failed split test or two could set your team back, both in terms of schedule and political clout.

That’s why these PA platforms are so valuable. By integrating your current analytics, predictive analytics, marketing campaigns, split tests and reporting into a single suite, they reduce the size of your team and keep everyone on the same page. Sure, PA apps do require a couple of weeks of learning ramp-up time, but once you’ve done that your team can focus on your core tasks of designing campaigns, running optimizations, and collecting insights.

Two case studies

I get it; what your CFO cares most about is financial results — in the form of revenue growth, improved operational efficiencies, and return on investment (ROI). So I’ll share some results, achieved through the use of two leading PA platforms.

Case Study: UGG Boots product page optimization

Here’s an example case study from HiConversion, a PA platform that’s been around for over 10 years.

For the parent company of UGG Australia, a seller of fashionable boots and footwear, HiConversion’s platform ran several variations of the ‘Heirloom Lace-up Boot’ product page. Their algorithm discovered a new version of the page that produced a 14.73% revenue lift within a six-week timeframe. In addition, the study found that the single biggest contributor to conversion lift came from a very small change in the look and feel of the checkout button. (In the chart below, ‘C-buttons’ refers to the checkout button colour.)

Something that’s important to reiterate: this revenue gain was for a single product page. UGG could run similar tests on other product pages, or pages leading up to them, over time, to produce additional revenue lifts. They could apply these learnings to other buttons on their visitors’ paths to this conversion.

Case Study: HelloFresh customer experience optimization

Here’s a case study for HelloFresh, a provider of ‘easy-to-cook meals in a box’ for a monthly subscription fee.

Partnering with HelloFresh, PA platform DynamicYield launched numerous campaigns aimed at increasing customer loyalty by serving up more personalized experiences at multiple customer touchpoints. Their stated goals were to:

Reduce the tendency of customers to cancel their subscriptions due to a lack of awareness of ‘hold my deliveries’ option.

Educate customers to plan their online-delivered meals weeks ahead of time.

The result of their optimizations: double-digit increases in both conversion rate and RPV. These results were achieved within the constraints of their existing technical ecosystem.

PA platforms could be your star player

Predictive analytics algorithms and the e-commerce optimization platforms they power have proven they can produce significant month-over-month and annualized revenue lifts for many top brands. Equally important, the reporting tools built into these platforms provide the insights e-commerce companies need to inform both UX optimizations and the rest of your digital marketing strategy.

Even if you have a talented team, they simply can’t make sense of the reams of data your e-commerce website and related pages collect every day. On the other hand, smart, data-rich PA platforms, if seeded with the right customer data and business rules, can become a star player on your team. And one that won’t demand a big raise next year.

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Predictive Analytics Vs Descriptive Analytics

Difference Between Predictive Analytics vs Descriptive Analytics

Predictive Analytics:

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Descriptive Analytics:

Descriptive Analytics will help an organization to know what has happened in the past; it will give you past analytics using stored data. For a company, it is necessary to know the past events that help them to make decisions based on the statistics using historical data. For example, you might want to know how much money you lost due to fraud.

Head to Head Comparison Between Predictive Analytics and Descriptive Analytics (Infographics)

Key Differences Between Predictive Analytics and Descriptive Analytics

Below is a detailed explanation of Predictive Analytics and Descriptive Analytics:

Descriptive Analytics will give you a vision of the past and tells you: what has happened? Whereas Predictive Analytics will recognize the future and tells you: What might happen in the future?

Descriptive Analytics uses Data Aggregation and Data Mining techniques to give you knowledge about the past, but Predictive Analytics uses Statistical analysis and Forecast techniques to know the future.

Descriptive Analytics is used when you need to analyze and explain different aspects of your organization, whereas Predictive Analytics is used when you need to know anything about the future and fill in the information that you do not know.

A descriptive model will exploit the past data that are stored in databases and provide you with an accurate report. A Predictive model, identifies patterns found in past and transactional data to find risks and future outcomes.

Descriptive analytics will help an organization to know where they stand in the market and present facts and figures. Whereas predictive analytics will help an organization to know how they will stand in the market in the future and forecasts the facts and figures about the company.

Reports generated by Descriptive analysis are accurate, but the reports generated by Predictive analysis are not 100% accurate it may or may not happen in the future.

Predictive Analytics and Descriptive Analytics Comparison Table

A king hired a data scientist to find animals in the forest for hunting. The data scientist has access to data warehouse, which has information about the forest, its habitat, and what is happening in the forest. On day one, the data scientist offered the king a report showing where he found the highest number of animals in the forest in the past year. This report helped the king to make a decision on where he could find more animals for hunting. This is an example of Descriptive Analysis.

Basis of Comparison Descriptive Analytics Predictive Analytics

Describes What happened in the past? By using the stored data. What might happen in the future? By using the past data and analyzing it.

Process Involved Involves Data Aggregation and Data Mining. Involves Statistics and forecast techniques.

Definition The process of finding useful and important information by analyzing huge amounts of data. This process involves forecasting the future of the company, which is very useful.

Data Volume It involves processing huge data that are stored in data warehouses. Limited to past data.

Examples Sales report, revenue of a company, performance analysis, etc. Sentimental analysis, credit score analysis, forecast reports for a company, etc.

Accuracy It provides accurate data in the reports using past data. Results are not accurate, they will not tell you exactly what will happen, but they will tell you what might happen in the future.

Approach It allows the reactive approach While this is a proactive approach


In this blog, I have specified only a few characteristics of the difference between Predictive Analytics and Descriptive Analytics; the result shows that there is an important and substantial difference between these two Analytical processes.

There is an increase in the demand for analytics in the market. Every organization is talking about Big Data these days, but it is just a starting point for creating valuable and actionable insights on the organization’s data. Therefore, analytical processes like Predictive Analytics and Descriptive Analytics will help an organization to identify how the company is performing, where it stands in the market, any flaws, any issues that need to be taken care and many more. By applying these analytical processes in business, you will know both the Insight and the Foresight of your business.

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Predictive Analytics Vs Data Mining

Difference Between Predictive Analytics vs Data Mining

Hadoop, Data Science, Statistics & others

Head-to-Head Comparison Between Predictive Analytics and Data Mining

Below are the top 5 comparisons  between Predictive Analytics and Data Mining:

Key Differences of Predictive Analytics vs Data Mining

Below is the difference between Predictive Analytics and Data Mining

Process – The process of Data Mining can be summarised into six phases-

Business/Research Understanding Phase – Enunciate the project objectives and requirements in terms of the business or research unit as a whole

Data Understanding Phase – Collect and use exploratory data analysis to familiarize yourself with the data and discover initial insights.

Data Preparation Phase – Clean and apply a transformation to raw data so that it is ready for the modeling tools.

Modeling Phase – Select and apply appropriate modeling techniques and calibrate model settings to optimize results.

Evaluation Phase – Models must be evaluated for quality and effectiveness before we deploy. Also, determine whether the model achieves its objectives in phase 1.

Deployment Phase – Using models in production might be a simple deployment, like generating a report, or a complex one, like Implementing a parallel data mining process in another department.

Define Business Goal – What business goal will be achieved, and how does data fit? For example, the business goal is more effective offers to new customers, and the data needed is the segmentation of customers with specific attributes.

Collect Additional Data – Additional data needed might be user profile data from online systems or data from third-party tools to understand data better. This helps to find a reason behind the pattern. Sometimes Marketing surveys are conducted to collect data.

Draft Predictive Model – Model created with newly collected data and business knowledge. A model can be a simple business rule like “There is a greater chance to get convert the users from age a to b from India if we give an offer like this” or a complex mathematical model.

Business Value – Data Ming itself adds value to business-like.

Deeply understand customer segments across different dimensions.

Get performance patterns specific to KPIs (Eg. Is subscription increasing with active users count?)

Identify Fraudulent activity attempts and prevent them.

System performance patterns (Eg -Page loading time across different devices – any pattern?)

Vision – Helps to see what is invisible to others. Predictive analytics can go through a lot of past customer data, associate it with other pieces, and assemble them in the right order.

Decision – A well-made predictive analytics model provides analytical results free of emotion and bias. It provides consistent and unbiased insights to support decisions.

Precision – Helps to use automated tools to do the reporting job for you — saving time and resources, reducing human error, and improving precision.

The performance of predictive analytics is measured on business impact. For example – How well the targeted ad campaign work compared to a general campaign? No matter how well data mining finds patterns, business insight is a must to work predictive models well.

Future – The data Mining field is evolving very fast. Trying to find patterns in data with lesser data points with a minimum number of features with the help of more sophisticated models like Deep Neural Networks. Many pioneers in this field, like Google, are also trying to make the process simple and accessible to everyone. One example is Cloud AutoML from Google.

Predictive Analytics and Data Mining Comparison Table

Below are the lists of points that describe the comparisons between Predictive Analytics and Data Mining.

Basis of Comparison Data Mining Predictive Analytics

Definition Data mining is discovering useful patterns and trends in large data sets. Predictive analytics is extracting information from large datasets to predict and estimate future outcomes.

Importance Help to understand collected data better. E.g.:

A better understanding of customer segments.

Purchase patterns across geography or time.

Stock price timeline analysis.

GPS street data analysis.

Predict on top of data mining results by applying domain knowledge –

What customer will buy next?

What will be the customer churn rate?

How many new subscriptions will be started if this offer is given?

What is the amount of stock of a product  needed for the coming month?

Scope Apply Machine Learning algorithms like Regression, Classification to collect data to find hidden patterns. Apply business knowledge on data-mine patterns with any additional data needed to get business-valid predictions.

Outcome The output of data mining will be a pattern in data in the form of a timeline with varying distributions or clusters. But it won’t answer why this pattern occurred. Predictive analytics tries to find answers to the pattern by applying business knowledge and thus making it a more actionable piece of information.

People Involved Done mainly by statisticians and Machine Learning engineers who have a strong mathematical background to do feature engineering and creating ML models. Business-specific knowledge and a clear business objective are a must here. Business analysts and other domain experts can analyze and interpret the patterns discovered by the machines, making useful meaning out of the data patterns and deriving actionable insights.


As Rick Whiting said in InformationWeek, What’s next is what’s next. Predictive analytics is where business intelligence is going. Data Mining helps organizations in many ways, and one of the most important is creating a good foundation for Predictive Analytics.

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Iot: The Key Benefits Of Predictive Maintenance With Iot

Statista reports that in 2023, $646 billion was spent on IoT globally. In 2023, it rose to $686 billion. It was $749 billion in 2023; by 2023, $1100 billion is expected. This staggering amount of money is being used by almost all segments of the economy, including industries.

We also have the manufacturing industry that uses IoT and related technologies to improve their work environment. This and other benefits have prompted more than 80% of manufacturing units to adopt IoT-based systems in their manufacturing and processing plants.

Predictive maintenance is one of the main benefits of IoT for manufacturing. Manufacturing can increase their productivity, decrease downtime and reduce costs by using predictive maintenance. We will be discussing how IoT-based, predictive maintenance can benefit manufacturing units in the sections that follow.

What’s Predictive Maintenance?

Predictive maintenance refers to a technique or system that uses data, special tools, and firmware to detect anomalies in machines or systems. We can fix faults that have yet to be discovered and avoid major obstacles.

This system uses sensors and actuators to continuously monitor the machine’s performance. It monitors the machine’s capabilities and refers to the threshold limit before it stops working or breaks down abruptly.

This information is sent to a computer interface and the managers and supervisors are notified about the anomaly. They can then initiate the corrective action. This reduces the downtime and ensures product quality and productivity doesn’t suffer.

IoT predictive maintenance uses condition monitoring to continuously evaluate the equipment’s performance. With the help of actuators and sensors, the IoT technology continuously records data about the equipment’s performance.

The computer firmware will suggest the timeframe and send warnings to start predictive maintenance.

Benefits of IoT Predictive Maintenance

Modern asset management is easier thanks to data analytics and other technologies such as IoT, Machine Learning, and others. They allow managers to move from manual and visible inspection to an automated monitoring platform. The latter makes decision-making easier and more efficient.

Management and Cost Reduction

There are two types of costs associated with every asset within an organization. One is the purchasing cost and the second is the operational cost. The second cost is the regular one. An unexpected failure can result in sudden expenses.

Companies can avoid sudden equipment failure costs by using a predictive maintenance program. This system is very useful in a manufacturing plant where many machines work day and night to produce the same product.

IoT systems can accurately predict an asset’s health and workability by taking into account its past performance, health, and potential for failure. This information can be used to schedule maintenance and inspections. Predictive maintenance can reduce costs by as much as 12%


Predicting asset failures can help you save time and money. Knowing the asset’s health beforehand can save time and prevent unplanned maintenance.

We can therefore correct potential problems before they occur during routine breaks or holidays.

Higher Productivity

To increase productivity, it is possible to forecast the machine’s and assets’ ability to perform and function. This is due to the fact we can start a timely maintenance program.

As long as the assets are maintained and issues are prevented from manifesting, productivity will not decrease. The same process can be used with all assets connected to the IoT network. This will allow for productivity to continue increasing over time.

Higher Customer Satisfaction

Customer satisfaction is a primary goal for any organization. Companies can achieve this goal through predictive maintenance. This involves setting realistic expectations and ensuring product quality.

Predictive maintenance ensures that assets, equipment, machines, and other machinery will always work well. This allows you to deliver the services promised.

Increases Asset Utilization

IoT allows for predictive maintenance that helps to maintain assets and machines. This can increase asset utilization if they are well maintained and there aren’t any sudden issues that could cause a breakdown.

IoT Predictive Maintenance can help identify the root cause of a failure. This will solve the problem at the root and ensure that your machine or asset performs at its best.

Assets last longer

We monitor, maintain, and correct assets in a timely manner to ensure they give their best performance. Effective action is possible when a supervisor or manager can monitor the performance in real-time and predict potential breakdowns.

This is in addition, to the fact the broken or underperforming parts can be replaced without affecting other parts of an asset.

Better Safety and Compliance

To ensure that employees are safe at work, every organization must be governed by law. This is particularly important in manufacturing industries, where workers are often working on large machines.

Reducing Downtime and Tackle Unplanned downtime

Predictive maintenance and IoT represent key performance-enhancing technology for the assets and machines in an organization. Predictive analytics is useful for identifying potential problems, as some assets are available 24 hours a day.

Supervisors and managers are able to initiate corrective actions promptly. This results in a reduction of planned and unplanned downtime.

Integration of IoT into Predictive Maintenance

Multi-technology and system integration is what allow us to accurately predict problems with machinery and assets. The Internet of Things, one of the most important technologies, allows companies to identify all issues and take necessary actions.

Many technologies, systems, and equipment can be used within the IoT network to create a predictive maintenance program. These include:

Data communications: As sensors gather data and actuators translate that data, it is transmitted to central storage with the necessary data communication tools. An organization can either set up an internal storage system or use a public cloud storage system.

Sensors: Sensors are available in many sizes and shapes. They can be used to collect data. They are placed at certain locations, points, and positions within assets.

Predictive maintenance software: These are the software that analyses the data. It is responsible for generating reports from the data provided. It can notify users about machine usage, including warning them if the asset exceeds the limits set by the user.

Data Storage: This can be either cloud storage or a server where all data is stored for further analysis. These data can be extracted by predictive analytics systems for interpretation and conversion into readable information.

Predictive Analytics: Powered by machine learning and predictive analytics systems, predictive analysis systems allow for actionable insights once the data has been understood.


IoT, predictive analytics, and maintenance systems are transforming organizations’ work practices. They provide the efficiency and accuracy that businesses require to achieve the highest performance in all areas. Predictive maintenance is a tool that can be used by manufacturers to increase their productivity using existing assets and workers.

Using The Power Of Quizzes To Enhance Your E

How to sell more products using quizzes and the power of personalization

What if you could be more like Amazon and offer just the right products at the right time to personalize your product recommendations in an almost creepy way? If you could do that, you’d experience an immediate 7.8% increase in sales, you’d also have a much better understanding of your customers, and your boss would definitely give you a thumbs-up.

This all sounds amazing, except for one problem – you are not Amazon, you don’t have thousands of engineers to work on perfecting every single aspect of your personalization program. Most likely you also don’t have tens or hundreds of thousands of dollars to build personalization into your product CMS, and you are a marketer, so doing any coding yourself is incredibly daunting.

Trust me, I’ve been there. Which is why what I’m going to show you today is so incredible. We’re going to look at the exact method used to turn a personality quiz into a powerful personalization tool. What we’re about to go through together is the ultimate growth hack for E-Commerce marketers. Let’s look at the precise way a quiz makes your site personal to every visitor.

The exact method for creating a product personalization quiz.

Part 1: Identify personalities (products) for your quiz

Before you start formulating the innards of your quiz, you have to figure out what the results will be. These results are always related to your products. The personalities of your personalization quiz can be formed in one of two ways.

1. Your actual products. If you are a specialty shop and only have a handful of products, you can just make individual products the results of your quiz. This tactic can also be used for larger brands by making category-specific quizzes. For example “Which Facemask is Right for You?” from a makeup brand.

2. A “style” related to categories of products. If you have a wide variety of products and they fall into general categories (such as Chic, Rugged, or Modern for clothing), then you can give people a personality based on the categories and then recommend products to the personality.

Part 2: Write a title to attract attention

The title of your quiz is incredibly important, and fortunately equally as simple to come up with for retail quizzes. It’s either “Which (product) are you?” or “What’s Your (blank) Style?” depending on which of the categories you chose in the previous section.

Part 3: Create Questions

The quiz questions are where some of the trickiness comes in. You have to walk a fine line between delivering an accurate quiz result and entertaining your audience. There are a few ways to do this well.

1. Use lots of pictures. Products are highly visual, all of your marketing is visual, don’t drop the ball on your quiz and ask questions with text answers! I did a study and found that all 100 of the top 100 quizzes created at my company have at least one image question.

2. Inject personality into the text. Remember that your quiz is a one-to-medium, an opportunity to speak directly with customers and ask them preferences. Since you are only talking to one person at a time, keep it very personal.

Part 4: Set up Lead Capture

1. Incentivize subscription. There is the obvious draw of being able to see your quiz results (they are “gated” by the lead capture form), but you also want to give people an added bonus for subscribing. For example, the quiz below promises to send out “Personalized messages designed with your style in mind,” which is pretty cool.

2. Be honest about what you are going to send. Tell people exactly how often you will send marketing communications and what you’ll be sending. It’s much better to say “We send one email each week” than “We’ll send you our newsletter.”

Part 5: Follow up with personalized recommendations

Whether or not someone chooses to opt in, you get an opportunity to make a sale in the results of your quiz by recommending personal products. There are a couple of tips that will help you get a higher conversion rate.

1. Connect the personality type to the product. Tie in the person’s personality traits (based on what they just told you), to the products you are recommending. This will seem like magic to the quiz taker, but it’s really simple.

2. Be positive. No one likes a downer, your results descriptions should be encouraging, not off-putting. It turns out that positivity is the most shared emotion, so being encouraging in your quiz results can actually increase the effectiveness of the quiz.

3. Include links. This might seem obvious, but make sure to include links or buttons to purchase the products that you recommend. Not everyone will purchase right away, but you can still pique their interest.

Part 6: Close more deals with pointed automation

Most people won’t buy immediately after taking your quiz. That’s not unexpected, it would be ridiculous to assume that everyone is immediately ready to buy. However, if someone takes your quiz and chooses to opt-in, there is at least a glimmering of interest in making a purchase down the line. There is a method for closing sales down the line using what you’ve learned about people from the quiz.

The first part of this method is to immediately send an auto-response email thanking people for taking your quiz and sending their result via email. You have to do this to introduce yourself and remind people why you have their email address. Otherwise they’ll forget and you’ll be accused os spamming.

After that you should continue to reference the person’s personality type when sending product recommendations and content. Don’t be overly pushy, remember that these people are interested, but they found your products through a personality quiz – don’t go for the hard close.

We can’t all be Amazon and constantly push the envelope of what’s possible with personalization, but we all can use simple personality quiz logic to provide a more tailored experience to web visitors and cash in on the benefits of recommending products to people based on their interests.

Hopefully this guide has sparked some ideas for you, and I encourage you to give a product personality quiz a try today!

10 Tips For Mastering E

According to a recent study conducted by Compass, the average conversion rate for e-commerce stores is just 1.4%, with top performers achieving around 3%.

While this may come as no surprise to anyone who manages or markets an e-commerce store, the reality is that the average marketer sees only one sale delivered from every 100 visits.

Do you want to be just average? Or do you want to be better than the rest? Better than your competitors? If so, you need to ensure that your conversion rate is better than average and converts as many visitors into purchasers as possible. You need to aim to achieve a conversion rate which exceeds 1.4% and comes in closer to 3%. Achieving higher may be possible in some industries, however for the most, 3%  should be seen as the upper target.

Luckily (or perhaps unluckily), most e-commerce stores suffer from some common issues. Issues which, in many cases, are relatively quick and easy to fix.

Here are 10 tips for mastering e-commerce conversion rate optimization:

1. Never Underestimate the Power of a Search Box

First, ensure that you clearly display a search box in a prominent position in your store. That means that it shouldn’t be hidden away in the footer of the site or even below the fold. Make it easy for those looking for it to find your search box and don’t try to reinvent the wheel.

The search box should, in most cases, sit within the header section of your site. However it’s not uncommon for this to now be the primary above-the-fold focus on some stores. Why? Because it’s disruptive and does a fantastic job of leading a consumer on their journey through your store. What could be clearer than offering the opportunity to search for products rather than trying to navigate menus?

This has become almost commonplace in the travel industry, with hotel and flight searches taking center stage.

It’s not only seen in the travel space, however, and there’s becoming a real justification to do something a little different and offer this sort of search option when selling everything from fashion to electronics.

Take a look at these eight great examples of great e-commerce site search on the Lemonstand blog.

Secondly, ensure that your site search returns relevant results. All too often, users search for a product only to be returned no results. This is a surefire way to pass your hard-earned traffic over to your competitors.

2. Offer Universal Free Shipping

Charging for shipping (especially when your competitors aren’t) can be a conversion killer for e-commerce stores. It’s absolutely vital that you regularly monitor what your competitors are offering in order to be able to at least match what they’re doing or, ideally, better it.

Not all stores will be able to offer completely free shipping as this, of course, depends on upon the products being sold and the costs associated with dispatching an order. Where possible, however, take the time to experiment with the effect which removing all shipping costs has upon both revenue and profit.

Offering free shipping allows consumers to make purchase decisions based solely upon the price of goods and makes it far easier to compare the total cost of an order between different retailers. This can be an especially useful tool if you offer the lowest total cost and are retaining low-value consumables.

In instances where you are unable to offer free shipping on all orders, test the effect of decreasing the ‘Free Shipping Over…’ threshold.

In short, keep a close eye on what your competitors offer on this front and explore ways to not only match but better their pricing.

3. Implement Independent Reviews

As a consumer, reviews collected by a retailer themselves often raise a slight concern as to their authenticity. At the end of the day, if you read a review directly on a merchant’s e-commerce store, what’s to say that it is genuine and hasn’t been left as a result of gifting or offering financial incentives? Trust is one of the reasons for a poor conversion rate of an e-commerce store.

Whilst many retailers have recently claimed that less than 2% of reviews are spurious, experts suspect that the actual percentage could be far higher.

It’s important that marketers give consumers a reason to trust the reviews and ratings left by previous customers and the easiest way to do this is to sign up an independent reviews platform. While Trustpilot is perhaps the most established and widely-used option available, both Feefo and Reviews offer comparable solutions, all of which work in the same way.

Reviews and ratings can still be embedded and shared on your e-commerce store and consumers love the fact that, in most cases, the platforms are automated while allowing independent, third party, reviews to be collected.

4. Offer Live Chat Support

Love it or hate it; live chat can work wonders for e-commerce stores looking for additional ways to engage with potential customers and clients and to be there to answer quick and easy questions from those who would prefer not to pick up the phone or wait for an email reply.

If a user has a quick question which they want answering before completing a purchase, they may not have the time available to pick up the phone or await an email response. Live chat, however, can usually deliver an answer in these cases within just a few minutes.

If you’re not offering answers to questions within product descriptions and fail to offer a simple solution to allow these to be asked, there’s a chance you’ll lose the sale.

The likes of Zopim or Olark offer completely free of charge plans which can be a great way to test the water and establish whether live chat works for your business and can help to drive an increase in conversion rate.

Be sure to allow your users to leave a message at times when your support team is offline.

5. Communicate Your Value Proposition

Perhaps the one question you need to ask yourself when considering ways to improve the conversion rate of your e-commerce store is, “What sets you apart from the competition?”

Too many stores fail to communicate their value proposition to potential clients and customers; that is, making it easy and straightforward to understand what makes them different and why someone should purchase from them and not their competitors.

Your value proposition could be your own products, your price point, your knowledge, and experience or even your ability to have purchases delivered faster than anyone else, but if users aren’t aware of it, how can they use it to make a purchase decision?

If your e-commerce store isn’t effectively showcasing the ways in which you add value to customers, take the time to make changes to ensure that it does so in a clear and concise manner.

6. Improve the Quality of Your Product Images

According to a recent infographic by NuBlue which looks at ‘The Anatomy Of A Perfect E-Commerce Store,’ customers no longer want to browse a website, they want to experience it.

This, in many ways, couldn’t be closer to the truth and is one of the reasons why we’ve seen a real transformation in the quality and creativity of product images in recent years.

To get straight to the point; if your product photos are low quality and unimaginative, you will lose sales.

Consumers want to see not only the very highest quality images alongside a product, but also those which present it in an in-use setting. You can no longer get away with product images which showcase your goods on a white background – you must take the time to show them being used.

Invest high-quality, creative images and you’ll undoubtedly get this investment back in a higher conversion rate.

See below a great example of pillows depicted primarily in a bedroom setting rather than using the product pack as the main image:

7. Take Time to Perfect Your Product Descriptions

Whilst it may be common sense to most; you’d be surprised at how many e-commerce stores simply use stock product descriptions supplied by the manufacturer of a product.

Not only does this present duplicate content issues, it also offers a less-than-optimal user experience.

There’s a good chance that a user has already touched upon a manufacturer’s stock description at some stage during their buying cycle and for that reason it pays to take the time to perfect your produce descriptions. Yes, a great copywriter won’t come cheap but the resultant increase in conversions from getting descriptions right will outweigh any costs.

Start by compiling a list of your ten most popular products and have a professional copywriter work with you and your team (especially your sales team) to refine and perfect the descriptions. Turn your product descriptions into great stories. Track conversion rates over a monthly period, both before and after rolling out the copy and, in the likely event that you see an increase, you’ll have the confidence to go ahead and invest in having each one of the site’s product descriptions rewritten.

8. Invest in Product Video Content

Video is the closest a consumer can get to a product without actually holding it in their hands, and it’s one of the main reasons why those retailers who make the investment in videos are showcasing significant levels of growth.

When a user is able to see a product up close, they are far more likely to convert; even if they’re not able to physically touch it.

Video goes a long way to bridging the gap between online and offline transactions.

It’s strongly recommend that you initially roll out video content across your top performing products in order to be able to justify the production costs associated with filming and producing.

Always ensure videos are prominent and clearly labelled, and work on a page layout which takes this into account.

9. Use Clear Progress Indicators on the Checkout

In 2023, the average basket abandonment rate was 68% – which means that two-thirds of online consumer who add something to their basket are failing to complete the purchase.

ConversionXL  has done a fantastic job of compiling ‘The Ultimate Guide To Increasing E-Commerce Conversion Rates” which highlights some of the key reasons why so many consumers are filling baskets only later to abandon them.

One of the tried-and-tested ways to reduce this, however, is to utilise clear progress indicators on the checkout for your site as this can help to show consumers how many steps are left in the purchase process or, in the case of simple checkout pages, how far through it you are.

10. Offer Multiple Payment Gateways

Last but not least; always ensure that you offer multiple payment gateways.

It may be simpler for you to only accept PayPal, not every consumer wants to use this payment platform and, as such, it’s important that you offer more than one gateway. Look into options to accept credit and debit cards directly as well as considering newer gateways such as Amazon.

By offering more than one payment gateway, you’re effectively covering all your bases and can ensure that basket and checkout drop-offs are not caused by issues relating to accepted payment methods.

The bottom line is that if your e-commerce store doesn’t tick all of the above boxes, there’s more than likely room for improvement and a real chance to generate additional conversions without growing traffic. Conversion rate optimization is not a one-off project, rather something which needs to be baked into your wider digital marketing strategy. Ultimately, increasing your conversion rate means a better ROI from marketing spend and for that reason alone is why it’s something which no marketer should ignore.

If you’re responsible for the marketing of an e-commerce store, do yourself a favour and put in place a plan to action the above tips in the coming weeks. For easy reference, here’s a summary in the form of an infographic:

Image Credits

All screenshots by James Brockbank. Taken September 2023.

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