Express Analytics

The solutions provided by Express Analytics stand at the intersection of marketing expertise, data integration, and data science.   

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Visualizations improve communication. This post highlights the power of data visualization to turn complex data into actionable insights.

In a world where data is growing faster than ever, raw numbers can be overwhelming. Data visualization changes it, turning complexity into clarity and insight. Instead of sifting through spreadsheets and tables, leaders can use visual representations like charts, graphs, maps, and dashboards to quickly spot trends, patterns, and outliers. This makes data not just easier to consume, but more intuitive to act on.

Humans process visuals far more effectively than text. Research shows that we interpret visual information thousands of times faster than we do text alone. That’s why visual data is more memorable, engaging, and immediately actionable. Whether you’re trying to understand customer behavior, monitor performance, or forecast future trends, visualizing your data empowers faster, smarter decisions.

At its core, data visualization is all about storytelling. The right visual can reveal insights that numbers alone might hide. For example, a line chart can clearly show trend changes over time, while a heatmap highlights patterns or hotspots that require attention. These graphical tools help teams see what’s important at a glance, speeding up interpretation and collaboration across departments.

Today’s visualization tools go beyond static graphics. Many platforms now offer interactive dashboards with real-time updates, drill-down capabilities, and AI-driven recommendations. These features allow users to explore data on their own terms, enabling deeper insights without burning hours on manual analysis.

In business settings, data visualization directly impacts performance. From marketing analytics and customer journey mapping to operational dashboards and financial forecasting, visual data helps teams uncover insights, communicate findings, and drive meaningful action. It’s no longer just a nice-to-have; it’s an essential element of an effective data strategy.

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AI for Data Cleaning: Save Time and Cut Costs

Data cleaning contributes to a very high level of data quality within your enterprise data management system. Contact us to clean your raw d

Data cleaning services that fix errors, remove duplicates, and prepare your data for analytics and AI. Learn how clean data improves accuracy and decision-making.

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Data Transformation in Machine Learning: Unlocking the Power of AI

Data transformation is a crucial step in machine learning, involving the conversion and restructuring of data from one format to another. This process enhances data quality, making it more suitable for analysis and modeling. Effective data transformation can significantly improve the performance of machine learning models by ensuring that the data is clean, consistent, and relevant.

Key Aspects of Data Transformation in Machine Learning:

Data Cleaning: Removing duplicates, handling missing values, and correcting errors to ensure data accuracy.

Data Integration: Combining data from different sources to provide a unified view.

Data Reduction: Simplifying data without losing essential information, often through dimensionality reduction techniques.

Data Discretization: Converting continuous data into discrete buckets or intervals.

Data Normalization: Scaling data to fit within a specific range, improving the performance of certain algorithms.

Implementing these transformation techniques helps in unlocking the full potential of AI by providing high-quality data for training machine learning models. For a more info click here: Data Transformation

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Visit Express Analytics at eTail West 2025 at Booth #313

Are you ready to harness the power of data-driven decision-making? Join Express Analytics at eTail West 2025, where retail and eCommerce leaders come together to shape the future of the industry!

📅 When: February 24-27, 2025 📍 Where: JW Marriott, Palm Springs, CA

At Express Analytics, we help businesses turn raw data into valuable insights. Stop by our booth to discover how our advanced solutions in customer analytics, RFM modeling, lookalike marketing, and supply chain optimization can revolutionize your retail strategy.

Why Visit Our Booth?

✅ Live Demos – Experience our analytics tools in action and see how they drive smarter decisions. ✅ Expert Consultations – Connect with our team of data scientists and industry specialists. ✅ Exclusive Insights – Learn how AI-powered predictive analytics can enhance customer retention and maximize ROI.

Don’t miss this chance to explore innovative solutions, network with top experts, and take your business to the next level.

🔗 Learn more: eTail West 2025

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Customer Acquisition vs Customer Retention: Which One is More Important?

Customer acquisition vs customer retention – which one is more important? This question is unjustifiable, as both are equally important to the growth and viability of an enterprise. However, many companies spend more time and resources acquiring customers and are unaware of the retention part. Retention provides a better ROI for certain businesses and is profitable.

What does customer acquisition mean?

Customer acquisition is how you promote your business, and design conversion experiences to compel prospective customers to purchase a product/service or download an application. This process can involve many ways that the prospective customer passes from being completely unaware of your product to choosing your product against your competitors to decide to buy.

What is the purpose of customer acquisition?

The main objective behind getting new customers to any business is to increase more sales, which in turn enhances the ROI.

What are the three parts of customer acquisition?

A successful customer acquisition strategy consists of three following parts:

Magnetize leads

Nurture those leads until they become sales-oriented

Convert nurtured leads into customers

SEMRush’s general statistics show that online display advertising is the most effective platform for acquiring new customers, yet only 4% of marketing professionals use it for customer retention.

What are the advantages of acquisition?

Businesses can expect the following benefits from customer acquisition services:

Grow your business: If your business gets new clients, it can develop fresh products based on their suggestions. It is because they compare your product with competitors, and come with important information that will reshape your business.

Expand your business: What will you do if you get multiple new clients at once? You’re growing your business, aren’t you? That’s one of the major advantages of acquisition. Once the company grows, there’s going to be an increase in sales and profits.

It helps in maintaining a business: Sometimes, businesses don’t retain their clients. In such cases, they end up making profits and start seeing a downfall. To overcome these situations, businesses must start looking for new clients and try to retain the present ones. That’s going to keep them in the marketplace.

The major drawback of acquisition is, it has a very short life span.

What are the top challenges to great customer acquisition?

Marketers face the following challenges in creating a great acquisition campaign.

Grabbing customer’s attention

Creating the first impression with their customers

Customer acquisition and GDPR

The increasing cost of CAQ

Why is CAC important?

CAC (Customer Acquisition Cost) is a very important metric to evaluate the effectiveness of your marketing campaign or customer acquisition strategy. It acts as a key metric for potential customers, helping them to measure the growth of your business.  

However, you need to understand that focusing too much on reducing CPA can be dangerous as Lifetime Value (LTV) also plays a major role in increasing efficiency.

In short, CAC is the money that a business invests in acquiring new clients.

The formula for CAC is complete sales and marketing investment over a specific time divided by the number of fresh customers over that same time.

The total CAC involves numerous components:

your marketing costs

the cost of your tools

Your sales costs

technical costs

Inventory, and creative costs

Reducing your customer acquisition cost indicates you earn enough from each transaction, enhancing both your profit margin and ROI.

So, listed below are some simple strategies you could implement if you want to lower your customer acquisition costs.

Prioritize niche/target audiences

Retarget customers

Increase customer retention

Create effective content to offer value-added information to customers

A/B testing and optimizing landing pages

Increase the sales funnel

What is meant by customer lifetime value (LTV)?

LTV is generally the revenue you generate from any given client over some period. LTV can be difficult to understand in a young business where there is no large quantity of historical data, but it is definitely a major element you can use to improve the effectiveness of your business decision-making.

How do you calculate customer LTV?

There are several techniques to calculate LTV. It involves measuring how long an individual client is expected to continue with your business, and how much revenue he/she will contribute over his subscription.

A very simple method to calculate LTV is to multiply the average revenue a client generates over a month/quarter by the average duration of the contract.

Once you have got the CAC and LTV, you can easily calculate the ratio. For this, you must divide the LTV by the CAC.

A 3:1 LTV/CAC ratio is a good target, which defines you should make 3 times more of what you would invest in acquiring clients.

LTV/CAC ratio of less than 3 indicates marketing expenses are high, and the focus should be on reducing expenses.

How to improve LTV/CAC ratio?

You should remember the following points to improve LTV: CAC ratio:

Concentrate on the appropriate channels

Experiment with your pricing

Set up the tight sales funnel

Why is there a greater emphasis on acquisition than on retention?

That’s because, without any customers, it’s difficult to imagine the existence of any company. To set up any business, it is necessary to get at least a few customers. This continuous process helps organizations increase their customer base and get more business opportunities. Companies spend a lot of money to acquire customers because they think it is a powerful option for increasing revenue.

What does customer retention mean?

Customer retention or client retention is an activity and action businesses take to minimize the count of customer defections. It is very important to understand that this process starts with a customer’s initial contact with the company, and maintains throughout the entire cycle of the relationship.

What are the major benefits of customer retention?

Customer retention is a faster process and costs up to 6 times less than acquisition. Selling your product to customers with whom you have a strong relationship is the best way to increase revenue because you can avoid the losing time and money required to attract, educate, and convert new customers. 

It is a far better process because here potential customers will get more knowledge, and practices required to improve the value they get from your product. People who visit your website for the first time will also be in the awareness path of the marketing funnel.

They spend some time visiting other websites, comparing prices, and looking at the reviews before making decisions. Whereas, your present customers are not required to go through all these stages, and know how perfect you are. In short, this activity will increase customer lifetime value (CLV).

What are some customer retention strategies?

Feedback collection and listening to customers: Conduct a survey to collect feedback from your customers about your brand. Do you need to find out whether your product was able to meet their expectations? Was it expensive? Ask, listen, and improve.

Provide offers: You can customize offers for your customers only if you know them better. Understand their behaviors, their interests, and see their purchase history.

Launch customer loyalty programs: Loyalty programs including free upgrades, special offers, and access to unreleased samples will encourage them to stay with your brand.

Original Source: https://www.expressanalytics.com/blog/customer-acquisition-vs-customer-retention/

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The Customer Data Platform (CDP) is the most useful tool for Chief Marketing Officers.

Many businesses use CDPs to understand their customers, solve many business problems, and get more customer insights. This in turn helps them generate more business results.

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Recommendation systems are also called data filtering tools, and they use machine learning algorithms and data analysis concepts to recommend the relevant product to the user.

The main objective of any recommendation engine is to encourage demand and engage users.

Read more: https://expressanalytics.com/blog/what-is-a-recommendation-engine-and-how-does-it-work/

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Why is Identity Resolution Important

Discover what identity resolution is and how has been employed in industries, including financial services, retail, and transportation.

Identity resolution or identity proofing is the advanced technique used in recognizing customers and helps for a more personalized customer experience.

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Dynamic Pricing – The Inevitable Future Of Traditional Retail

What is the biggest threat to retail today? Why are traditional retail businesses witnessing a steep downfall in their top and bottom lines? Forget about the numbers, why is the very presence of real-world retail suddenly endangered? Dynamic pricing is the inevitable future of traditional retail.

This may surprise you, but the answer is not the COVID-19 pandemic. It is retail’s overarching archenemy, e-commerce. In this blog post though, we shall explain how traditional retail can overcome the challenge posed by e-commerce, turning one of its most powerful weapons against it – that of dynamic pricing.

What Is Dynamic Pricing?

Dynamic pricing tools take into account a combination of historical and real-time market data to offer price suggestions. The data can range from past performance to geography to seasons or even local events. The algorithm makes frequent changes to rates based on such data inputs, thus helping companies stay competitive at all times. Such pricing can be based on a group of people or on the passage of time.

Dynamic pricing is most suited for highly competitive industries which enjoy elastic demand for their offerings. Airlines, hospitality, events, transportation, and most recently e-commerce have successfully implemented dynamic pricing. The latter has always been around, but the underlying technology for its implementation is what has changed and evolved over time.

So Why Should Traditional Retail Also Deploy Dynamic Pricing?

This is a very important question because tackling e-commerce is merely scratching the surface. Traditional retail has always operated on fixed pricing. Fixed pricing means that there is no flexibility for the retailers to attract more customers and build brand loyalty. Simply lowering product prices is also detrimental to their business eventually, since it brings a roundabout change in customer’s overall expectations. Also, fixed pricing holds businesses back on all the potential incremental profits that can be earned if dynamic pricing is implemented to take advantage of the shifts in customer loyalties, demand and supply, and the broader external environment. It also allows businesses to carry out a huge number of transactions without having to worry about reduced margins.

Various studies show that dynamic pricing has proven itself to grow in revenue by up to 30% and increase profit margins by about 11%. These numbers cannot be ignored by retailers fighting for every cent and dollar. The potential to grow is seemingly limitless. Here’s what a Forbes report had to say about the future of dynamic pricing:

“In the not-so-distant future, virtually nothing will have a fixed price. Say goodbye to the familiar $1.99 and hello to a price between, say, $1.39 and $2.17, based on changing supply and demand. This is the phenomenon of dynamic pricing, and it is already rapidly changing the way we buy goods and services and dramatically reshaping our economy”

“Companies are using artificial intelligence (AI), machine learning (ML), and automation to balance demand with supply in near real-time. For the first time, all the economic forces at work (the “hand”) are finally visible in the data. As a result, for the first time ever, businesses no longer have to use guesswork to set their prices: They can use data to understand what their prices should be and automatically change as supply and demand fluctuate.”

Dynamic pricing can help retailers to reduce waste by enabling them to sell “unsellable” products. For instance, electronic products being replaced by newer models, or grocery and consumer perishables nearing their expiration. Retail outlets discard such perishables worth thousands of dollars daily. Giving customers incentives by way of reduced prices to purchase such items will significantly increase their salvage value to the retailers. AI-based dynamic pricing engines can correctly identify the exact price needed to move products, protecting sales and profit margins.

Another major advantage of dynamic pricing is its ability to establish strong brand loyalty. For example, if a dynamic pricing model is effectively incorporated into a firm’s CDP (customer data platform), its AI, ML, and predictive modeling capabilities can provide retailers the opportunity to generate attractive and compelling offers for their customers. The dynamic pricing engine can be used to generate user-specific offers in the form of discounts, coupons & vouchers, and bundles for exactly those products on which the customers are most likely to spend on. It makes a very solid use case for retargeting customers who can be encouraged to revisit the stores and make follow-up purchases.

Now that we have covered the what and the why, it is time for the how.

How Can Retailers Implement Dynamic Pricing?

The first thing you need to know is that dynamic pricing does not require some over-the-top, fancy or complex software. Here’s what a McKinsey report says about it:

“Dynamic pricing isn’t just for travel companies or e-commerce giants, and it doesn’t necessarily require ultra-sophisticated software that changes every product’s price multiple times a day. Even traditional retailers can reap tremendous benefits from merchant-informed, data-driven algorithms that recommend price changes for selected products at some level of frequency.”

“Despite the competitive advantage that dynamic pricing can confer, few omnichannel retailers have developed this capability. Some are only now starting to explore the potential of dynamic pricing. Other retailers conducted half-hearted and poorly planned pilots that, unsurprisingly, had little impact and thus failed to get the organization’s buy-in.”

This suggests two crucial things: First, traditional retailers have the chance of gaining a first-mover advantage since their rival counterparts are either not doing it at all or perhaps not doing it right. Second, dynamic pricing is not as hard to implement as everybody thinks it is.

The process starts with the cleansing and the organizing of enterprise-wide data. Smaller businesses will find it easier to do so and probably do it in-house. As for the medium and large-scale retailers, the unstructured data can pose to be a challenge as there is lots of it.

Original Source: https://expressanalytics.com/blog/traditional-retail-dynamic-pricing/

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4 Reasons Why Social Media Sentiment Analysis is Important

Social media sentiment analysis can help you understand where your business is today, and the areas where improvement is needed.

Read More: https://expressanalytics.com/blog/sentiment-analysis/

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How Can Attribution Models Improve Your Online Marketing?

Attribution Model in digital marketing is the craft of understanding how marketing efforts attract and convert prospects into customers. This is a complex exercise and the best way available to do this today is through touchpoints, which are interactions with your customer.

Attribution models are important for marketers as it helps them understand their customer’s journey, and provide an analysis on the perfect time to invest in marketing campaigns. Hence, they should know which attribution model should be used to track data.

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Prediction using Neural Networks

Prediction using Neural Networks: In the first part of this post, we discussed what neural networks prediction are, what the “artificial” component in them is, and how they are used in data science.

Today we look at how they are used in predictive analytics. We will also answer why neural networks still are not being used by many businesses. Read more about prediction using neural networks.

The two big arguments against using artificial neural networks are:

They are resource-intensive

Their results are often hard to interpret

On the other hand, neural networks may be used for solving problems the human brain is very good at, such as recognizing sounds, pictures, or text. They can be used to extract features from algorithms for clustering and classification, essentially making them modules of larger Machine Learning apps.

As we said in our earlier post, an artificial neural network (ANN) is a predictive model designed to work the way a human brain does. In fact, ANNs are at the very heart of deep learning. Deep neural networks model (DNN model) can group unlabeled data based on similarities existing in the inputs, or classify data when they have a labeled dataset to train on.

What’s more, DNNs are also scalable, best suited for machine learning tasks. Using these, we can build very robust and accurate predictive models for predictive analytics.

So how does actually Neural Networks predict?

Each neuron takes into consideration a set of input values. Each of them gets linked to a “weight”, which is a numerical value that can be derived using either supervised or unsupervised training such as data clustering, and a value called “bias”. The network chooses from the answer produced by a neuron based on its’ weight and bias.

Where “Classification” is concerned, all such tasks are contingent on labeled datasets.  This means that you need supervised learning. Supervised Learning is where humans check to see if the answers the neural network gives are correct. This helps the neural network understand the relationship between labels and data.

Examples of this are face-detection, image recognition, and labeling, voice detection, speech transcription. With classification, deep learning can associate pixels in an image and the name of a person.

“Clustering” or grouping is the recognition of similarities. One must understand that deep learning model does not always require labels to find similarities. When there are no labels by helpful humans to learn from, it uses machine learning to learn on its own – which means unsupervised learning. This retains the potential of producing highly accurate models. Examples of clustering can be customer churn.

Original Source: https://expressanalytics.com/blog/neural-networks-prediction/

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Identity Resolution – How it Works

More and more businesses online are veering around to the fact that if they have to remain competitive, the one thing that will keep them ahead of the pack is a complete understanding of their customers. A customer’s identity resolution is like the Holy Grail for any business, especially in today’s competitive world. Know what is identity resolution and how it works. What are the key benefits of identity resolution?

A single unified customer profile is of paramount interest to businesses today. Identity resolution is the way of attributing customer interactions with your business across all touchpoints. Also, let’s not forget that in the last few years, the number of touchpoints customers can have with your business has simply exploded.

Want to know about the customer journey and customer identification? Fill up this short form and allow our experts to talk to you.

In a recent report, Forrester has described identity resolution as, “The process of integrating identifiers across available touchpoints and devices with behavior, transaction, and contextual information into a cohesive and addressable consumer profile for marketing analysis, orchestration, and delivery.”

So far, website cookies alone would do the trick where identity resolution was concerned but consumers are no longer shopping only on their desktops. In fact, more and more of them are now taking to buying from their mobile computing devices. That, and new data privacy laws have made the browser cookie passe.

What is “in” now instead is cross-device Identity resolution tracking. This is a process of collecting and combining data around a single customer in a way that links all his/her devices used back to him/her.

Why is cross-device tracking gaining importance? It is a fact that today, the average person owns multiple devices connected to the World Wide Web. Eg: laptops, smartphones, smart TVs, game consoles, etc.

Device tracking allows marketers to confirm which device and channel were used when a service or product was eventually purchased – whether it was a desktop, mobile, and whether it was from an e-commerce site or a social media channel.

With that, what has effectively happened is the e-commerce company or retailer has also managed to create a “unified” picture of its customer. The process is called “Identity Resolution”. Identity Resolution is the method in which certain unique “identifiers” are used to “connect” all the actions of a buyer to create a single, unified, real-time, customer identity.

So what are these identifiers? The location, the browser used, the device used, the platforms, and the channels, all of these are identifiers that help link the same person, irrespective of the device or channel used. Once a single customer’s identity is “resolved”, it becomes far easier for the marketer to serve up offers to him/her.

Because of Identity Resolution and customer data platform (CDP), each customer can be served up a personalized and unique brand experience.

All about cross-device tracking

So, for the reasons stated above, more than cookies, marketers these days prefer to use cross-device Identity Resolution tracking to maintain a complete profile of every client. Essentially, the latter allows the collection of data and its linking in a way that all devices used by one person are tracked back to him/ her.

The importance of cross-device tracking lies in the fact that it enables business teams to understand if a particular advertisement served on the desktop first resulted in a sale on a mobile; both devices are owned by the same person.

But here’s where things start to get a little tricky. There are two types of cross-device tracking – deterministic and probabilistic tracking – leading to two types of identity resolution – probabilistic and deterministic.

While both these methods involve complex matching across millions of data points as well as access to ALL the digital and device data, depending on the technology and data sets, they can deliver one of two types of matches.

Deterministic matching

Under deterministic matching, using a deterministic connector like perhaps a hashtag, email address, or username, the records of a particular customer are matched. This approach works best with first-party data.

Why first party? Because it involves the use of personally identifiable information of a customer like his/her email address, home or work address, telephone or credit card numbers, etc. At the end of this exercise, there remains no doubt about the identity of the customer.

Here’s an example: If John Doe uses his email address and logs into his email on his desktop, then uses the same on his Tablet, deterministic matching will show he is the same fellow. Deterministic matching is considered to be more perfect and takes the guesswork out of the tracking game.

But here’s the thing: although a more accurate matching method, it cannot be implemented by all businesses. One reason for that is because of the kind of resources it draws on, and because of the humungous amount of data, implementation is not possible for all the companies. So here’s where probabilistic matching comes in. The other factor that poses a hurdle in the implementation of deterministic matching is that it requires concrete identifiers like social security numbers or driver’s license numbers. But many a time, some companies do not need such information. Or, even if they do, consumers may refuse to submit such sensitive information.

Original Source: https://expressanalytics.com/blog/identity-resolution/

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What are the Benefits of Cohort Analysis?

A cohort analysis refers to the study of a group of people over a specific period of time. It is one of the powerful ways used in retaining customers and is helpful for business professionals who have websites. As many visitors visit the website and convert it into more business, it is necessary to retain such visitors and monitor their activities within a particular time period.

 In short, cohort analysis describes the time that is spent accessing the landing page or product. It indicates frequency and is necessary to calculate this level.

 How to Use Cohort Analysis?

 You need the best marketing platform to use a cohort analysis. The objective of this analysis is to break down your data into many campaigns – each one should have a specific objective so that sum of all these leads to customer retention.

 You can try the following strategies after breaking your data:

 1.   Improve User Journey: Most of the time, your website users could when their journey becomes difficult. It can identify the exact point in the user journey when they skipping out.

2.  Targeted offers: It identifies what kind of users buy the most and what kind of product they buy. Such information can be used to create coupons, offers, and free shipping to retain your existing customers.

3.  Introduce rewards: Introduce rewards, points, and gamification systems for retaining your customers. With cohort analysis, it is possible to narrow down the similar audiences who can be retained after introducing rewards.

 Benefits of Customer Cohort Analysis

 Accuracy: This analysis is beneficial to divide the audiences into cohorts. Thus, audiences who visited the site during a specific time period are combined together e.g. the April cohort, the May cohort, and so on. 

Like this, the analysis of their behavior and how they interacted over time is unaffected by the audiences in remaining groups, thus keeping the groups totally different than one another, and facilitating an accurate study.

Clear comparison of data between cohorts:

This analysis helps you compare the outcomes between two, three, or more groups. For example, if the May cohort is more engaged in the product than the April cohort, an analysis is needed on any changes that may have taken place between these two months.  

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With a customer behavior analysis, eCommerce companies can extract targeted customer information and predict how they will behave in the coming days.

Read more: https://expressanalytics.com/blog/how-to-analyze-and-predict-the-behavior-of-consumers/

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How to Analyze And Predict The Behavior Of Consumers

A customer behavior analysis uses actionable intelligence to improve retention, which helps you target the right audiences at the right time.

Customer behavior analysis is the process of identifying common behaviors among specific groups of customers to predict how similar consumers will behave under the same situations.

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Dynamic Pricing - The Inevitable Future Of Traditional Retail

As e-commerce firms like Amazon have increased the frequency of dynamic pricing, traditional retailers are left with no choice but to follow suit.

Dynamic pricing model refers to the way of selling the same product at various prices to various groups of users. It is also called price discrimination.