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Customer data management

3 Effective Tips to Improve Customer Data Management for Millennial B2B marketers

Today customer data is considered to be the most valuable resource for businesses across industries. By gaining insights from customer data, businesses can easily predict future customer behavior patterns, devise sales strategies, and estimate the success of their marketing campaigns. Therefore, customer data management can cement the success of any business that decides to dig deeper into customer behavior insights. Customer data management can further help businesses to analyze customer buying behaviors and any additional information that customers might have shared with any business across various touchpoints. With a better understanding of customers’ personalities and pain points, companies can cater to their needs on a far more personal level which is very essential in today’s highly competitive business landscape.

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Tips to Manage Customer Data Effectively

Tip #1: Monitor integrity and relevance of customer data

Data cleansing is one of the most important aspects of customer data management. For effective customer data management, it is important to keep up-to-date information about customers as customer behavior is dynamic, and the customer data reflects the movement of their actions. Cleansing processes can help to weed out contacts that aren’t engaging, or whose information is no longer correct. Also, monitoring the integrity and relevance of customer data can help you to devise more effective  marketing campaigns and customer segmentation strategies. A flow of incorrect data can lead to bad customer experience that can negatively impact the reputation of your brand.

Quantzig’s analytics experts help companies to deal with customer data privacy concerns. Get in touch with them now!

Tip #2: Gather all available customer data into a unified profile

Using multiple tools to collect data can result in the collection of disconnected data leading to fragmented customer experience. Also, creating a unified customer profile is difficult by using multiple channel-specific tools for data collection. For effective customer data management, businesses need to have a unified customer view that comprises all of the customer interactions with the business including email clicks, website browsing history, mobile interactions and so on. A 360-degree view of the customer is critical to offer seamless omnichannel and connected customer experience.

Our customer analytics dashboards can help you monitor  customer journeys and analyze the data at every touchpoint in real-time. Request a FREE demo below to know more.

Tip #3: Keep customer data secure

Data security is a crucial component of any customer data management system. So, businesses need to have data backed protocols in place to encrypt and protect their most valuable business asset- “customer data”. Therefore, businesses need to be very careful when it comes to safeguarding the confidential and private information such as credit card information, social security numbers and other financial information.

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Customer Analytics: A Step-by-Step Guide to Drive Customer Satisfaction and Revenue

With the rapid growth of companies in today’s technologically inclined world, it becomes very difficult and tricky to understand customer needs. Companies struggle to engage effectively with their customers due to the dramatic change in customer behaviors. As much as they would like to reach out to their customers, there’s no pragmatic way to it, as that is how the cookie crumbles. In fact, this is the biggest challenge that companies face today due to which delivering the best quality products and services along with the best customer experience have become a tedious task.

Failing to provide the best customer experience and analyzing customer data are prime factors behind the unsuccessful stories of businesses in this customer-centric era. So, companies need to recognize the patterns and trends of their customers’ behaviors while utilizing the massive amount of data they have. This is where customer analytics solutions help. Customer analytics results in better all-around decision making and paints a more precise picture of the different touch points in the customer journey. Companies need to unearth both opportunities and shortcomings to leverage into better strategies. Customer analytics solutions can dive into complex use cases with the use of machine learning and big data. Consequently, actionable roadmap to achieve desired outcomes and sharper predictions about the future falls into place.

At Quantzig, we understand the impact that customer analytics solutions can have on your business. And to help companies stay ahead of the curve, our team of experts has provided a detailed guide and useful tips on customer analytics that can assist businesses at any age.


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The Arduous Life of a Web Crawler – Challenges in Web Crawling

Ever wondered how Google can display a million search results in less than a second? Its because Google has indexed all the pages in their library so that it can return relevant results based on user’s search query. However, the web is dynamic, and thousands of content is uploaded each day. So they would have to continually refresh their indexes and go through all the millions of pages to find a relevant result. On the outlook, it seems like an impossible task, but thanks to web crawlers it is possible. The web crawler functions as an automated script, which browses the internet systematically. They look at the keyword in the page, the external and internal links, and kind of content before returning information to the search engine. It’s fascinating how these web crawlers do all the work in the background Free demoand make it look so simple. However, it is not as easy as it looks, as there are multiple challenges faced by programmers in web crawling.

Non-uniform structures

The web is a dynamic space which doesn’t have a set standard for data formats and structures. Collecting data in a format that can be understood by machines can be a challenge due to the lack of uniformity. For instance, a webpage can be created using HTML, CSS, Java, PHP, or XML. The process of data extraction becomes challenging when web crawlers need structured data on a massive scale. The problem gets amplified when the web crawlers have to extract data from thousands of web sources pertaining to a specific schema.

Maintain database freshness

Majority of the web publisher like bloggers and news agency update their content on a daily or hourly basis. The crawler has to download all these pages to provide updated information to the user. The problem arises when the crawler starts downloading all such pages as it puts unnecessary pressure on the internet traffic. Programmers can develop a strategy, where web crawling is done only on pages which update their content frequently.

Bandwidth and impact on web servers

One of the biggest challenges or limitations faced by web crawlers is the high consumption rate of network bandwidth. This can particularly happen when the web crawler downloads many irrelevant web pages. To maintain the freshness of the database, crawlers adopt a polling method or use multiple crawlers, which consumes a lot of bandwidth. If a web crawler is frequently visiting websites, then the performance of the web servers will be severely impacted.

Absence of context

Web crawling uses numerous strategies to download the content that is relevant to user’s query. The crawler focuses on a particular topic; however, in some cases, the crawler may not be able to find relevant content. As a result, the crawler starts downloading a large number of irrelevant pages. As a result, programmers need to find out crawling techniques that focus on content that closely resembles the search query.

The rise of anti-scraping tools

Today, web developers have tools such as ScrapeSheild and ScrapeSentry that can differentiate bots from humans. Using such tools, web developers can manipulate content shown to bots and humans, and also restrict bots from scraping the website. Although practiced on a small scale, if crawlers continue to disregard robots.txt file and keep hitting the target server, it can cause DDoS to the websites.

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Secret Tips to Get the Most out of A/B Testing

Traditionally, in roundtable discussions, many ideas are shared, and the two best ideas are compared and analyzed. People then voice out their opinion about a particular idea, and the company finally settles with the most-liked idea. Even then, so many ideas miserably fail in the market. The reason: those were the opinion of the decision maker and not the customer. Fast-forward a few years and we have technology that can implement both of the top two ideas in the real world and show you which works better.That is exactly what A B testing is all about. A B testing is extensively used in digital marketing to test everything from website layout, ad copy, to sales emails. A well-planned A B testing process can make a huge difference in determining the effectiveness of a company’s marketing campaigns. Although option A may prove to be 5% better than option B, when it comes to real-world implications,Free demo that 5% can amount to thousands of dollars or hundreds of customer conversions.

A B Testing best practices

Develop a hypothesis

It is crucial to have an idea of the desired outcome before setting out for A B testing. A hypothesis should be laid out before setting out to test. Otherwise, A B testing will merely be A/B guessing. Without a hypothesis, it may be impossible to measure the actual impact of the design or copy changes and may provide difficulty in further future testing. Before setting out for A B testing, it is thereby important that the marketer has at least a rough idea of what will happen. The goal of the A B testing may be to build brand awareness, or improve conversions, or just increase the click-through rate. For instance, before A B testing two ad copies, the marketer should have a goal in mind saying this ad copy should better entice the customer to click.

A/B TestingTest for the right duration with adequate sample

To adhere to the best practices of A B testing, one should start out testing at an earlier stage. Although the first test results may not provide real insights; over time, it can give the marketers a real understanding of what design or copy choices have a measurable impact on the conversions. One key thing to note is one shouldn’t end the testing too soon or too late. Completing the test too soon may provide results that are not statistically significant. Running it for too long means the test could be affected by multiple variables. Also, A B testing on the website with very low data may not yield a valid result, as the outcome may have occurred due to pure coincidence. You can quickly check the statistical significance of you’re A B test result by entering variables and outcomes such as number of test subjects, conversions or clicks achieved through each result, type of hypothesis, and level of confidence.

Be patient with multivariable test results

Apart from testing just A or B, multivariate tests could be used to measure how the combination of different aspect affects the outcome. For instance, A B testing can only measure if copy A performs better than copy B. However, multivariate testing can measure if copy A in design A is better or copy B in design B, or copy A in design B, and all such possible combinations. As discussed, for a test to show significant results, there should be enough data, but since multivariate testing uses multiple combinations, data for each combination may be limited. It requires a lot of time to gather such a large amount of data, even for websites that have millions of unique monthly visitors. So it is advisable to be patient when carrying out a multivariate A/B testing.

Always keep on testing

Once you start seeing patterns and know what works to increase the conversion, you might think that further A B testing may not be required. Marketers may think that by repeating what worked in the past will work in the future. However, that is far from the truth, as trends are ever-changing. Customer expectations are constantly evolving, and what worked yesterday may not work today. As a result, it is essential to always test ad copy, headlines, designs, and all other elements.

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Secret Tips to Reduce Customer Churn Rate in the Telecom Industry

The telecom industry is reaching a saturation point for their voice sales and wireless penetration. It can be inferred that there are hardly any new customers to be tapped. Everyone is using the basic telecom services, making it difficult for telecom companies to grow their revenues by recruiting new users. What this means is that the only way to gain new customers is to take away customers from competitors. With easy number porting services and lucrative welcome offers, customers are more willing than ever to leave their current service providers. This poses a significant challenge in the telecom industry, that of managing customer churn. It is evident that competitors are providingFree demo exciting bundled offers,   price cuts, and packs to lure in customers. So in the age of cut-throat competition how can telecom companies reduce customer churn rate?

Developing a comprehensive customer view

You can only develop activities to retain your customers if you know them very well. So the first step to reducing customer churn is having a clear understanding of your customers in terms of behavior, attitude, and customer journey. It is essential to know how the customer was acquired, on-boarded, and disconnected, if applicable. The knowledge of such parameters includes data like their complete profile, product usage, discounts offered, network experience, feedbacks, and promotions. Telecommunication companies can aggregate such data with external sources to learn about competitive pricing, media spend, promotions, and other factors to get a detailed customer view.

Using advanced analytics

Merely collecting customer data doesn’t amount to anything. Telecom service providers should use sophisticated algorithms to mine through the massive pile of customer data. These algorithms are smart enough to identify hidden traits to predict which customers are most likely to churn. Subsequently, companies can then analyze the reasons behind their churn before coming up with solutions to prevent such an event. Hundreds of variables could be examined and compared with customers who churned before. So decision makers could define a threshold level, which when triggered sends out warnings to the specific team to resolve the issue. For instance, if the customer is discontent about network quality then algorithms can be set to place to alert the customer support team after any user faces more than five call drops within a span of a week.

Customer segmentation

Although computer systems have gotten more powerful, it cannot be used to treat each customer in singularity. Such a method is time-consuming and requires an enormous amount of resources. An effective method is to break the customer base into scores of microsegment. Customers can be classified into different cohorts based on specific traits and behavior exhibited by them. Based on that, different classes of offers can be devised for each segment of the customer to avoid customer churn. For instance, customers who are frequently running out of their data packs can be offered higher data usage packs for a discount.

Test, learn, and collect feedback

Computers are only as good as the humans that handle them is an old adage that still holds true. Sure, predictive analytics can dwell deep into customer insights, but it all depends on the telecom operator who implements changes in order to tackle the churn issue. Identifying customer churn beforehand is only half battle, correctly segmenting customers and providing a customized solution is equally challenging. Additionally, it is also essential to measure the result of such action to enhance future retention strategies. Finally, telco companies should add a little human touch by asking for customer feedback, so that they gain not only an analytical perspective of the churn problem but also a human input.

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Top 5 Data Mining Techniques to Facilitate Big Data Analytics

For long, data scientists have been trying to find patterns and trends in data. To do so, they need to mine through a substantial amount of data. The more the data, the more accurate the insights and information. The recent explosion of big data has posed new problems to data scientists who are struggling to process such data sets. The data sets go way beyond the current storage capacity and computing power. So, it is essential to make the data mining techniques more efficient to gain relevant insights within seconds. These data mining techniques extend beyond just simple statistical analysis by analyzing millions and billions of data point to present in-depth insights. So what are the topFree demo data mining techniques used by companies to make sense out of their data?

Association rule learning

More like how humans develop knowledge, association rule implies the same principle of learning. A kid learns that fire is hot, and anything with flame will be hot as well. Similarly, interesting relationships can be uncovered between different variables in large datasets by using association rule learning. It is also the most straightforward data mining technique. The data scientist makes a simple correlation between two or more items to identify the same type of patterns. For instance, in a retail setting, a retailer may discover that a certain customer always buys eggs when they buy milk, and therefore they may suggest eggs the next time they put milk in their cart. Additionally, the technique can be used to determine product clustering, catalog design, shopping basket data analysis, and store layout.

Classification analysis

One of the easiest way to teach a machine is by classifying data into close groups. The data scientists assign the given data into a pre-defined category, and the machine can learn to accurately predict in the future what data belongs to which category. One of the most efficient form of this technique can be seen in Gmail, where the sorting algorithm can automatically identify if a mail is spam, promotional, update, or personal. This data mining techniques can also be used across other industries to classify customers based on age and social group.

Top Data Mining TechniquesClustering analysis

Clustering is more or less similar to classification analysis, with the only difference being that there is no pre-determined category. Cluster here refers to a collection of data objects that are close together when plotted on a graph. It indicates that two objects that are closer to each other exhibit more or less the same properties than those that are far away. It helps data scientists to identify customer profiles from scratch. However, it can be challenging to pinpoint a specific cluster, since one data set can be the same distance apart from two different clusters.

Anomaly or outlier identification

In statistical terms, data is dispersed consistently, with the majority of the data point clustering around the average. However, there can be few outliers which can be on the extreme end of the spectrum. Sometimes it can occur naturally, but usually, it presents the analyst with a concerning observation. Such data mining technique is used in fraud detection, intrusion detection, and system health monitoring. For instance, a customer who spends on average $50 per transaction, suddenly spends $10,000 in a single transaction can signal that a fraud has occurred.

Decision trees

Decision trees are closely related to other data mining techniques. It can be used as a part of selection criteria or to support the use of specific data within the overall structure. Each decision tree starts out with a simple question and based on the answers, further questions are asked which helps to be sorted to a particular category. With time, accumulating many answers can enable data scientists to make a prediction based on each type of answer. For instance, such data mining techniques can start out with a simple question like whether a customer is a male or a female; then based on the answer, further questions could be asked. Based on the answers obtained, accurate predictions can be made.

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How Data Analytics Can Find you ‘A Match Made in Heaven’?

The word online dating currently has quite a dodgy reputation. However, dating a complete stranger is not something new. It has been around in the form of meeting someone via friend’s circle, reference, blind dates, and arranged marriages. After all, we all were strangers to each other at some point in time. Nevertheless, the fact that online dating sites or apps match partners with similar interest so efficiently just amazes a lot of people. So one must wonder, how do online dating sites work? How can they so efficiently match partners and help people find ‘the one.’ This is because online dating sites don’t leave getting a perfect match to fate, they rely on data analytics and Free demoalgorithms to spread happiness across the world. So how can technology really build someone’s love life?

How do online dating sites work?

Using personality traits to match

One of the leading online dating site and app OkCupid learns whenever members answer questions that pertain to their personality and lifestyle. It determines how members would like their potential partner to respond and how significant the question is to them. For instance, the importance of race or religion may be crucial to some, but insignificant to others. With over 7 million active users in OkCupid, users have answered over 3000 questions, which assists predictive models to glean information from users’ profile and match them with their perfect mate. The data analytics tools that drive such online dating sites are so powerful that it can take 13 billion seeks relating to users profile in order to load a page of results.

Likeliness and popularity scores

The leading online dating app Tinder, uses likeliness and popularity score to show users the best match. Each profile or person will have a popularity score ranging from 1-10. The app thereby shows a profile that is rated eight other profiles that are similarly ranked. For instance, a new profile is shown to selected few users, if users who have higher likeliness score like the profile then their ratings increase. Otherwise, they are matched against people with a lower score to determine the actual rating.

Personal characteristics

In the world of dating and relationships, individual characteristics can matter a lot. For instance, for some people complexion, height, and age matters a lot, and thereby the matching algorithms show matches that comply with users’ preferred range. However, for others, race, religion, nationality, food preference, and work matters. So people lookout for partners in that specific category, and online dating sites and apps can easily present such matches before them.

Behavioral data

Similar to the movie recommendation engine of Netflix and product recommendation engine of Amazon, online dating sites know if you like a person, you might also like another that is similar. But of course, they must also like you back, so dating apps take the match from both sides before any communication can start. Dating companies are focusing on facial recognition as people are drawn to certain facial characteristics and features. This way they can better match people by learning what kind of facial feature users prefer and match them to people they would like.

Social media monitoring

A lot can be said about a particular person based on his Facebook profile and the kind of post they share. Online dating sites curate user data from different social sites and analyze profile pictures, page likes, and movie, books, and music preferences to make match predictions.

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The Power of Data to Drive Successful Product Personalization in the Food and Beverage Industry

In a world where there are more food & beverage brands than the kind of food itself, competitors are fighting against each other to gain consumer attention. Many brands have come up with innovative products, packaging, and marketing to grab more eyeballs from the customers.. But the truth be told, there is no better way to garner more customers than to make them feel that the product has been customized to suit their needs. Product personalization has been an effective tool which has been leveraged by the brands in order to gain customer attention, increase consumer loyalty, and thereby drive revenues. Owing to product personalization’s crucial  success, even small players are looking for economical ways to implement it to increase customer loyalty and satisfaction. However, manyFree demo brands have struggled to practice this in an efficient way, as with customization the cost also significantly increases.

Successful product personalization

The story of Share a Coke campaign needs no mention, as it was able to win hearts of consumers creating a big hype across the world. The primary objective of the company was to engage with the customers by talking to them, and what better way than to put their name across the label on the bottles. The beverage giant started with 150 names at the start of the campaign in Australia which would reach 42% of the population. The company compiled a list of most common Australian name and leveraged digital channels to effectively reach the masses. The second wave had names voted by the customers and analyzing both first and last name, Coke carefully chose the name which would reach most number of consumers. Pepsi also did similar personalization exercise by creating unique emoji designs for their bottles.

Big Data Driving Mass Personalization

Collecting a multitude of data of each individual customer and environment including customers, products, locations, events, and topics companies can create a social metadata. Such data offers the complete overview of the customers and segregates them into certain types who exhibit similar behaviour. With that information in hand, companies can provide a mass personalized products to the customers. The customization can be in terms of products, price, or packaging. For instance, such data can enable Amazon to employ dynamic pricing. Companies can modify products that are mass produced, to meet specific customer preferences based on existing data.

Leveraging social technologies to crowdsource ideas

There can be no better way to truly personalize a product than to create it using what customers suggest. Consumers are firing their suggestions on social media channels to brands hoping that someday they would listen and personalize the product as per their suggestion. Although, it is difficult to track thousands of comments flowing through the digital channel in the past, text crawling, natural language processing and social media listening has made it possible to aggregate the consumer voices to find out what they really want. Starbucks pioneered the crowdsourcing idea with which lets user build their own virtual frappuccino with ingredients such as protien powder and raspberry flavouring. The customers then rate each others frappuccino based on which Starbucks gauges the popularity and feasibiilty of the product before actually processing them. It also allows the company to discover popular combinations such as caramel and whipped cream which can help increase its sales.

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Top 5 Challenges in Customer Database Management

Advancements in digital technology have enabled businesses to keep track of the details of their customers and their behavior. As the modern marketing era calls for companies to satisfy needs for multiple market segments, the companies should know more about their customers to cater to their personalized needs. Big data technologies, ERP systems, cloud technology, and AI have allowed companies to store large amounts of customer data and make an accurate analysis of each customer. Such analysis, in turn, allows the company to efficiently manage the customer relationship and thereby increase the customer lifetime value. However, managing customer Free demodatabase is not an easy task. So, what are the challenges faced by businesses in customer database management?

Challenges in Customer Database Management

Volume of data

As businesses start keeping track of multiple facets of a single customer data, the volume of data grows at an exponential rate. Businesses have to process large volumes of data in order to make critical datasets available to stakeholders in the time of need. Such data needs to be made available to multiple stakeholders across various devices and platforms, which, in turn, rapidly increases the storage and processing costs. Such large volumes of customer database will have to be managed with state of the art ERP systems and software.

Data collection and storage

The modern-day business environment is getting more competitive with each passing day, and customer satisfaction seems to be prioritized in their agenda. The rapidly changing customer behavior is causing problems for decision makers to decide on what’s best for the customers. The rise of omnichannel shopping has increased the amount of customer data that a business generates. Businesses are having a hard time managing such vast amounts of data. On top of that, they are also struggling to decide whether to take into account single customer view or cohort view while making an analysis.

Tracing customer journey

The most popular method of inspecting sales performance is by using customer journey cycle. To do so, companies need to keep track of multiple real-time customer data including interaction touch points, consumer sentiment, behavioral stages, and cross-team resourcing. Such charts paint a picture for decision-makers to take strategic decisions, which can subsequently improve customer experience. However, the challenge arises on methods to track the process of how consumers move from the stage of brand awareness to conversion. In particular, it is hard to pinpoint where exactly in the customer journey map a given customer can be located.

Choosing the right technology

Another problem faced by businesses in terms of customer database management is to select the right technology for their set of requirements. From multiple software’s and ERP systems to cloud-technology or on-premise technology, each has its own benefits and costs associated with it. Additionally, the technology may also need to be customized to match their requirements. Also, the type and form of customer data also dictate what kind of technology would be right for the organization. For instance, two completely different technologies would be required for processing structured and non-structured databases.

Privacy of customer data

One of the biggest challenges in customer database management is ensuring the privacy of customer data. Some customer data can be sensitive in nature, which is why issues in data security could cost companies millions in terms of lost customers or legal battles. Companies are continually battling to safeguard customer database against cyber-attacks and hackers.

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