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3 Ways Data Analysis Can Refine the Customer Experience

data analysis

3 Ways Data Analysis Can Refine the Customer Experience

Nowadays, you have to understand what the customers need if you want your business to succeed and grow. If you don’t put some effort into doing that, you might miss out on crucial details about what customers are looking for in businesses. It will force them to look for other alternatives. In this article, we’ll discuss three ways to use data analysis to help your business boost customer loyalty, among other things.

Customer Experience and Why It’s Vital in Business

Customer experience is the customer’s interaction with your business at different touchpoints. It refers to how customers will engage with your online ads, websites, mail, social media, commercials, visiting your store, phone calls, buying and using your products and services, etc.

So, customers always think about the kind of experience a company offers before checking on the quality of goods. It means that if your business offers great products but sucks at offering excellent customer experience, then people will start buying from your competitors. Therefore, you must always do some data analysis to ensure your business delivers exactly what the customers want.

Your customers will always observe a brand’s performance and compare it to their expectations. So, every interaction a customer has with your business and its products leaves a long-lasting impression, which then creates a certain opinion about your company. It explains why it is vital to keep a customer’s needs first to help get better results at the end of the day.

Many companies now agree that using data analysis helps boost customer experience hence more sales. However, measuring customer experience and applying it to your business to get results are two different things, and most businesses struggle with the former.

So, how do you measure customer experience effectively?

There is no one perfect way to measure the customer experience. However, here are the three most effective ones you can trust:

1. Customer satisfaction (CSAT) – It method involves reaching out to your customers after a certain sale, asking them to rate the service. You will get results ranging from not satisfied to very satisfied.

2. Net promoter score (NPS) – In this one, you ask a customer to rate your business on a numerical scale. Here is the most common one you might have come across, “How likely are you to recommend us?”

3. Customer effort score (CES) – It one helps to find out how much effort a customer needs to fulfill a certain task on, let’s say your website. Most businesses use a defined scale to do this, which also acts as a small survey. It contains options such as I agree, and I strongly disagree.

Redefining Customer Experience Through Data Analysis

Once you have mastered how to capture customer data and measure their experience, you can improve your services and delivery. These are some of the ways how data analysis refines customer experience.

1. It Helps You Understand How to Respond to Your Customer’s Needs Intelligently

Quality data is the biggest foundation of developing a great consumer experience. Customers and prospects usually generate a lot of data by engaging in different online activities, especially when engaging with your business.

Most leading businesses today tap their prospect or customer data from the activities generated through indirect or reseller channels.  You can gain some insight by combining the views of your data sources with detailed demographic information.

The first step you must take is developing a good strategy to help you access and integrate customer data. Choosing to focus on one touchpoint to get data can be a good idea, but it’s always best to try and use several sources. The main goal is to understand one common view that all customers have. It will give you a good view of your customers’ experiences and interactions across different channels. That way, you can easily respond intelligently to what your customers need.

With this information, you can easily determine the relevant goods and services your customers need from your business. A link building agency can also help to bring more targeted potential customers to your website, who you can then study to see what you can do to win their loyalty.

You can also work with your IT experts to help turn the customer data into an EDW (Enterprise Data Warehouse) that will serve different functions or servers. However, most businesses find this quite challenging, not just technically but also financially.  Luckily, there are pre-configured platforms you can use today to help make the data consolidation easier.

Therefore, you should evaluate some relevant approaches to determine which one is more relevant. Also, check for one that can provide the best data to better fulfill the customers’ needs.

2. It Helps You Determine Customer Behavior

Predictive analysis can allow you to anticipate your market and customer behavior and respond accordingly. Its analysis involves data mining or statistical tools and methods that help to develop predictive models. OLAP and BI are some of the most effective systems you can use as they enable you to assess past and current events.

So, doing data analysis can help you employ excellent tools and methods to explore patterns and trends in data to determine things that can happen if certain factors change. With that information, you can easily tell what most customers have in common, making it easier to fulfill their needs.

Some predictive analytics solutions can also use software computing power and automation to help you get insights faster. Using powerful algorithms can help mine plenty of data and rapidly analyze patterns, correlations, and affinities. Additionally, combining software automation with great data access can help your company evaluate performance more effectively.

You can see much success by implementing some predictive data analysis using data sources such as transactions or any other application data. Doing this can help your personnel in stores, contact centers, or those managing e-commerce sites to easily adjust marketing offers accordingly.

3. Helps Build Better Customer Relationships

A good company-clients relationship is a vital factor you must always consider if you want your business to thrive. Data analysis provides you with different ways to build a great one. If you want to be the go-to business for people looking for the services you offer, you must be a good listener. What does that mean?

You can use social media to help conduct some sentiment analysis, which can show you how customers regard your services. You can also check your help-tickets to see what your customers complain the most about. This information can show you where to improve hence allowing you to provide exactly what people need and better customer experience.

Some customers might also indicate in the help ticket the kind of services they expected to find from your company but didn’t. You can use this data to ensure your company has everything customers in your niche are looking for.

A good example is how a shoe store can observe the size of shoes that get more purchases or those that get many returns. That way, they can easily ensure the highly demanded product is always available hence boosting the customer experience because they never miss what they planned to purchase.

Summing it Up

We are living in a world where everything is analyzed. Your customers look at how you treat your clients and the quality of the goods you sell. You must always make data analysis a huge part of your business because it allows you to study crucial trends in the market and your customer’s behavior. Doing this helps to understand everything you need to do to ensure you meet people’s demands.

When customers visit your business to purchase a good or service and get what they need, you automatically become their favorite place because you have proven to be reliable. However, if potential clients come to you and don’t get something they wanted or your employees don’t act professionally, then you can easily lose them. Since you cannot provide customers with the great customer experience they expected, your competition will take over.

smart contracts

How to Save Time and Money with Blockchain Smart Contracts

Manufacturing processes are growing increasingly complex — especially as the coronavirus pandemic spreads — in today’s global marketplace. With so many moving parts, it’s becoming more difficult to reliably and efficiently track actions and data along the supply chain. Blockchain-enabled smart contracts are emerging as a solution — one that provides transparency and ensures everyone along the supply chain is following the same set of agreed-upon rules.

With everyone on the supply chain sharing the same logic and data, manufacturers can automate time-sensitive processes and avoid costly dispute resolutions. Blockchain is on the rise, and Gartner predicts that 30% of manufacturing companies making more than $5 billion in revenue will have invested in blockchain-powered projects by 2023.

Implementing the technology and data infrastructure to convert processes into smart contracts can seem daunting, and companies that don’t hit the $5 billion mark will be slower to catch up.

The fear of failing after the investment can be a serious deterrent. But smart contracts save enough time and money for manufacturers that the costs of waiting might be greater than the upfront investment needed to get started.

The Value of Smart Contracts

The core values of blockchain are transparency and trust, and smart contracts play a pivotal role in providing these benefits. Taken together in a business context, blockchain-based smart contracts make it possible to avoid disputes. A smart contract is software that automates a single trusted version of an agreement between parties. They might rely on one version of data about what’s happening (or has happened) and record the results of the contract, such as funds being transferred in exchange for using a piece of equipment.

Without smart contracts, businesses working together in manufacturing have to maintain separate systems that encode business rules with slight differences. The data they use might also vary from the data other companies use, making it difficult to reconcile any issues. These differences lead to disputes that require significant time and effort to resolve.

The automation and data standards that smart contracts provide allow manufacturers to consider different ways to work with partners along their supply chain. Their partnerships can be based on performance or quality in ways that would have been impossible to implement — much less trust — without the use of blockchain and smart contracts.

How Do Smart Contracts Work?

In a blockchain system, the word “contracts” doesn’t carry the same meaning as legal contracts. Instead, smart contracts are more broadly used to encode logic that often isn’t written explicitly in a contract. Unlike traditional software, they’re used to create business logic that multiple parties can rely on and trust.

Many of us are familiar with the concept of business rules in software systems. In the blockchain world, smart contracts are the business rules shared by the users of the blockchain. Think of blockchain like a shared database: Smart contracts are the rules that define how data can be entered or changed in the shared database. Within the supply chain, smart contracts are typically the rules shared by multiple businesses in the supply chain that are also users of the blockchain system.

For most applications, smart contracts can be executable versions of traditional business contracts, or they might be new logic that coordinates long-running processes and activities across different businesses. They’re trusted because they’re created and housed on a blockchain, which means the code is typically visible to system developers, business analysts, and auditors.

Although smart contracts are triggered by some external event, such as a user’s action or a change in external data (a commodity’s price, for example), the code they run is normally approved in advance by all businesses involved. Currently, businesses are already utilizing blockchain-secured smart contracts for a range of supply chain processes.

For example, some companies combine smart contracts with Internet of Things sensors to record the movement of supplies into a manufacturing facility. Then, they automate payment for those supplies. Others record the operating conditions of a machine to determine if maintenance is required or gauge the condition of manufactured products to ensure standards are met.

Such contracts produce equipment usage records and quality control checks in real-time, and parties on all sides of the contract can trust the data. How we handle everything — from securing supplies to monitoring equipment and manufacturing products — can be improved with the strategic use of blockchain-powered smart contracts.

Being Smart About Which Contracts to Convert

As companies convert more intrabusiness processes into smart contracts, the benefits of doing so grow easier to recognize. Shipments and payment approvals can be verified in real-time, and disputes are eliminated or resolved immediately with no intermediaries. The time and cost savings are substantial.

By using these strategies to determine where to use smart contracts, companies of all sizes have a better chance at reaping the benefits much sooner:

1. Break down costs before the converting starts. The first time a company implements a smart contract, the costs of establishing the blockchain system will be relatively high. These initial costs can often be the biggest deterrent, especially for smaller, less tech-driven companies. Over time, though, the incremental costs of automating smart contracts will go down. Account for this initial cost by taking time to identify the contracts that are currently the most costly to execute.

2. Prioritize external contracts over internal ones. Not every contract needs to be a smart one. In fact, the costs of executing some processes might not justify the investment in automating them. Focus on agreements, contracts, and other expectations that are between the company and another business (or better yet, where more than two businesses are involved), and rule out internal agreements between departments. Because trust is less of an issue, internal disputes can be reconciled relatively easily. Putting them on a blockchain would just be overkill.

3. Focus on contract difficulty — not frequency. Because the goal of automation is to create less work, it’s tempting to go straight for the contracts that are executed most often. Instead, focus on the amount of effort it takes to use each contract rather than how often it’s used. High-frequency contracts might be executed with few or no disputes, whereas low-frequency ones might be costly to manage due to complex and/or unclear terms. These are much better candidates.

4. Start with material sourcing for maximum impact. To know for sure which processes can benefit most from conversion into smart contracts, look for people throughout the organization who deal with reconciliation, quality control, and/or audit support. Also, consider the data used in each transaction. Between both parties, how important is trusting that data? Material sourcing is often ripe for improvement, and trust in data is critical to the relationship between manufacturer and supplier.

The ability to create smart contracts is becoming one of the best-known benefits of using blockchain technology in the manufacturing realm. Investing in the technology might be costly at first, but getting in on the ground floor will be easier if you use it to turn the right processes into irrefutable smart contracts.

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Alex Rosen is the vice president of business development at Chainyard, a blockchain consulting company focused on delivering production solutions that address financial services, supply chain, transportation, government, and healthcare pain points.

data science

10 Data Science Projects E-Commerce Businesses Are Using

Today e-commerce businesses are using data science in many different areas to stay ahead of the competition. For instance, e-commerce sites are investing funds into personalizing shopbots to enhance customer experience and recommending products to buyers based on browsing habits and previous purchases.

Selling the best products only works if e-commerce businesses can identify who wants to buy them and recommend them when these customers are ready to make a purchase. Here are some ways e-commerce businesses are utilizing data science to enhance the customer experience.

1. Retain customers

One concern for every e-commerce business is customers switching to other e-commerce websites. Customer retention is crucial if a business is to expand and grow. There are many benefits from having loyal customers, such as receiving real-time feedback from them and having them recommend products or services to others.

A churn model provides metrics such as the number and percentage of customers lost to the business as well as the value and percentage of this loss. When a company is able to identify customers who are most likely to switch to a different e-commerce site, it can take actions to try and keep them.

2. Give product recommendations

Using big data analytics offers a way to understand the shopping behavior of customers and predict patterns. For example, being able to establish which brands or products are most popular when spikes in demand for certain products occur or times of the year when customers shop more can help to determine the right strategies.

Recommendation filters for a particular user are based on past searches, purchase data, reviews read, etc. and allow a personalized view. This helps users with the selection of relevant products.

For example, if you’re looking for a mobile phone on an e-commerce site, there is a possibility that you might want to buy a phone cover too. Deciding whether this is a possibility might be based on analyzing previous purchases or data searches of customers.

3. Analyze customer sentiment

Gathering customer feedback is very important for e-commerce sites. Using social media analytics, data science and machine learning, companies can perform brand-customer sentiment analysis. Natural language processing, text analysis, data from online reviews and online surveys are just some ways to analyze customer sentiment.

If you’re running an e-commerce business and you’re studying at the same time, it’s possible to find writing services to help you, so you have more time to devote to the business and analyze all of this sensitive data.

If you need to deliver an essay consider Dissertation Today. Use the best paper writing service such as killer papers review or even resume services.

4. Predict the lifetime value of customers

E-commerce businesses can benefit from knowing what net profit a customer is likely to bring to the company. Being able to predict the lifetime value of a customer can help with factors such as defining objectives for expenditure, optimizing marketing strategies and deciding cross sell and up sell according to customer purchases.

By using data science models to collect and classify data, e-commerce businesses can predict future buying behavior and have more understanding when formulating business strategies. They know which customers are most loyal and can decide where spending money on advertising etc. will offer the most return on investment.

5. Manage Inventory

Proper management of inventory is essential for e-commerce businesses. When customers are unable to get what they want when they want it, it’s a major deterrent to retaining them. They will simply move on to the next company that can offer this. They want to receive the right goods at the right time and in perfect condition.

The maintenance of the supply chain has become complex today and using inventory data analytics enables businesses to manage inventory effectively. Using machine learning algorithms and predictive analytics enables patterns to be detected that can define inventory strategies.

6. Detect fraud

Living in a digital world where millions of transactions are taking place consistently makes fraud detection essential. Many different forms of fraud are possible and fraudsters are becoming smarter every day.

E-commerce businesses can detect suspicious behavior by using data science techniques. Signs of suspicious behavior could include a shipping address differing from a billing address, an unexpected international order or multiple orders of the same item.

Common data science techniques to detect such behavior include:

-Matching algorithms to estimate risks and avoid false alarms.

-Data mining to address missing or incorrect data and correct errors.

-Clustering and classification to help detect associated data groups and find anomalies.

A fraud detection system helps companies to decrease unidentified transactions and increase company revenue and brand value.

7. Improve Customer Service

A customer is central to any business, especially e-commerce. Personalizing services and giving customers what they really want and need is essential to keeping them happy. Big data analytics offers businesses the potential to enhance their processes so that customers enjoy transacting online.

Natural language processing allows customers to communicate with voice-based bots and data can be stored for future purposes. When businesses know more about their customers and what they want, they are able to devise the best strategies to improve their customer service.

8. Optimize prices

Data-optimized pricing is making some retailers plenty of money. Many online retailers, such as Amazon, Home Depot, Discover and Staples, vary their pricing based on secret formulas. Cost analysis, competitor analysis, and market segmentation are all critical when it comes to pricing.

Pricing of products can impact a business in many ways when it comes to market share, revenues and profits. A key for retailers is to be able to figure out the right price and with big data analytics, they are not only able to determine that number for the market in general but also calculate it with some precision for individual customers.

9. Make online payments easy

Many e-commerce sales are made via mobile platforms and online payments must be secure and safe for customers. Big data analytics helps to identify anything that threatens the process and helps to make online shopping safer.

Various payment options make the online payment process easy and convenient for customers.

10. Determine the quality and reliability of products

E-commerce stores usually provide warranties for products that allow customers to deal with any problems at no cost during the warranty period. Analytics relating to warranty claims can help to determine the quality and reliability of products.

If manufacturers are able to identify early warnings of possible problems, they may be able to address them in time to avoid serious damage to the business.

Text mining and data mining are two techniques that can be used to identify patterns relating to claims and problems with products. The data can be converted into real-time insights and recommendations.

The bottom line

We’ve taken a look at the ten ways that data science models can impact e-commerce. There are so many e-commerce websites and many of them sell similar types of products. Data science helps e-commerce businesses to understand and analyze customer behavior and provide ways to enhance customer service.

When companies understand what they do best and who their loyal customers are by using data science, they are able to improve product designs and customer service, formulate better pricing strategies, manage inventory effectively and provide secure online purchasing and payment options.

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This guest post is contributed by Kurt Walker who is a blogger and college paper writer. In the course of his studies he developed an interest in innovative technology and likes to keep business owners informed about the latest technology to use to transform their operations. He writes for companies such as Edu BirdieXpertWriters and uk.bestessays.com on various academic and business topics.