Retail store

Data is incredibly powerful. When utilised properly, data can inform business decisions and help you create impactful marketing campaigns that resonate with your customers. 

In the retail sector, this can lead to both an increase in sales and customer loyalty. 

But data on its own is not enough – it’s how you use it. Our guide covers everything you need to know about retail data analytics. From what it is, to how you can use it to bring about tangible results for your business. 

What is retail analytics?

Retail analytics is the process of using retail data to identify trends, patterns and consumer purchasing behaviours. Retail analytics can be used to predict outcomes with more accuracy and make better business decisions. Retail data includes consumer, sales and industry data. 

The goal of retail analytics is to transform data into quantitative insights that go on to drive high-level decision-making. Retail data analytics plays an important role in just about every aspect of retail, from sales and marketing, to operations and inventory.

For instance, retail analytics can be used to help companies deliver personalised promotions, optimise marketing spend, predict better supply and demand, and attract new customers.   

In short, retail data analytics is about utilising the power of your data, and using it to form strategic and sophisticated business decisions. 

The importance of retail analytics

A business decision that isn’t informed by data is essentially a shot in the dark. Your customers might surprise you. Retail analytics takes away the guesswork.

For example, you might think your target market are more likely to buy if you offer 10% off, when in fact they’re more receptive to free shipping. 

Additionally, the online retail space is growing at an incredible rate, and we have seen some major shifts in consumer behaviour in light of the pandemic. As retail data grows more and more rich, ignoring it would see you miss out on a huge opportunity. 

The benefits of retail analytics

The use of retail analytics brings with it a multitude of advantages that will put you on top of the competition.

Here are some of the main benefits of using retail data analytics, as well as some examples of how you might use them in the world of retail. 

1. Customer behaviour insights

Retail data analytics can give you a 360-degree view of your customers, which goes beyond age and gender. Find out your customers spending habits, likes, hobbies, behavioural patterns, and much more. This kind of single customer view knowledge is invaluable; it can help shape everything from your communication channels to the promotional offers you run. 

For example, looking into the spending habits of your customers can arm retail companies with a valuable insight into the type of products they prefer, how much they usually spend, and when they make purchases. 

As a result, you can make personalised recommendations that they’re more likely to purchase. 

2. Improve marketing campaigns 

Marketing efforts that attempt to appeal to everyone will appeal to no one. You need to find out exactly who your customers are, and speak to them in their language. How do you do this? By listening to the data. 

Retail analytics help develop impactful marketing campaigns, that are more likely to convert and show promising ROI. 

For instance, if the data is showing you that ethically sourced and sustainable products are popular within your target market, then you can emphasise and promote products that fit that criteria. 

3. Choose the right channel

Not only can retail analytics inform the tone and content of your marketing campaigns, but the media by which they are delivered on, too. 

For example, an analysis of your retail data could reveal that your desired customer is more receptive to Facebook ads than they are to direct mail marketing. As a result, you can choose your channel of communication accordingly. 

Your campaign could be perfect, but if it’s not run on the right platform, it will fail. Retail data analytics can ensure that you’re making wise, well-though-out decisions. And, whilst this analysis cannot always guarantee success, it does increase the likelihood of it. 

4. Optimise your operations

Retail analytics can help you better predict supply and demand. Analysing spending habits and sales will help you identify which products and services are in demand. 

Again, retail data analytics offers you the opportunity here to take away the guesswork. Your orders and inventory can be led by the data. In turn, you can maximise profits by not wasting money on stock that won’t sell. 

5. Increase customer satisfaction

If your customers consistently abandon their carts at certain points in the sales process, or sales consistently dip on a particular national holiday, this will be reflected in your data. As such, it means you have the power to do something about it. 

Perhaps there are too many steps at the check-out stage, or the free delivery on orders over a certain amount is causing customers who don’t meet that threshold to abandon the purchase. 

You can design a better customer experience by knowing what keeps your customers coming back and what drives them away.

You can significantly increase your store’s customer satisfaction by making these changes based on retail analytics. Since you’re likely to be rewarded for this with their loyalty, it’s a win-win situation. 

These advantages have the potential to bring about invaluable business results. Firstly, they present the opportunity to stop putting your time and resources into products, services, and marketing campaigns that won’t produce a strong ROI. Instead, you can focus your efforts on initiatives that are driving growth. 

Secondly, these benefits will drive customer loyalty and satisfaction. Not only can this push sales, but it will also increase brand awareness. Brand awareness can go a long way to securing future sales and expanding your customer base.  

As we can see, if you utilise data analytics correctly, you can be rewarded with some impressive competitive advantages. 

Retail analytics factors to consider

Though retail analytics can form the backbone of some incredibly smart business decisions, there are still some factors you will need to consider. These include: 

1. Be aware of the bigger picture

Make sure that you don’t focus too much on one single metric. Retail analytics can provide valuable insights, but even more so when you connect the dots. 

Comparing data sets can provide you with deeper insights, as mixing and matching will allow you to understand the context and see the bigger picture. So delve into your retail data and look for other reports or points that you can then combine.

2. Focus on the right metrics for your business

Retail data analytics can provide you with insights to almost every single aspect of your business. However, every business is different, and metrics that might matter to some, might not to others. 

Identify your business goals and priorities, and seek out the data that will edge you closer to achieving these. 

3. Combine data with expert opinion

There’s no denying that retail data analytics have the power to provide you with advanced knowledge, that can lead to strategic and successful decision-making. However, it’s still vital to marry the data with human insight. 

Combining the data with expert human opinion can take your findings from powerful to unstoppable – so don’t overlook it. 

Start your retail analytics journey today

At REaD Group, we are the experts in retail data. Our unmatched retail data services have been proven to drive impressive results for businesses. We can provide you with the tools you need to succeed. 

Our retail data services are tailored to your specific needs and variables, that will help you acquire, retain and re-engage with your customers. 

Start your retail analytics journey today!

enhance

Data enhancement is the process of combining raw data with another set of data to form a more comprehensive view of customer information. The additional dataset is usually provided by a trusted third party offering specialist data enrichment services.

For instance, if you wanted to enhance your customer data with consumer spending information, data enrichment can be used to update existing customer records with this data. This can be used to enrich customer data with a range of valuable records, helping businesses maximise the use of datasets.

Enhancing your data can yield some powerful results for your marketing efforts that aren’t to be underestimated. From increasing ROI, to establishing deeper relationships with customers, the benefits of data enrichment are invaluable. 

In this guide, we’ll cover everything you need to know about data enrichment, including how it works, the types of data you might combine, and how you can use it to your advantage. 

How does data enrichment work?

Data enrichment seeks to enhance data by combining existing customer records with additional information. Data enrichment is most effective when the additional data is provided by a reliable, external source. Though, businesses can also enrich databases with existing first-party data. For example: 

In the retail sector, you could combine demographic data with purchase history information. This would leave you with a holistic view of your customer, with meaningful insights that goes beyond the basics of age, gender, and location. In turn, this can help you understand what type of people are attracted to certain products and adjust your marketing strategy accordingly.

Combining data in this way can help you identify patterns and trends, and consequently create more targeted ads and communications. 

There’s no strict limit on how many datasets you can combine, either, it will depend on what you are looking to find out by combining them. For example: 

A retailer selling high-end wellies might want to find out their customers’ geographic location, age, gender and salary, or average spend, combining this information to tailor marketing efforts and directly target specific customer segments.

Online data enrichment

One of the most simplified ways that businesses are enriching their data is through the use of online data management platforms, such as REaDOnline

This offers a self-serving data enrichment solution, where organisations can upload their existing data and view insights into data accuracy and enhancement opportunities.

The advantages of data enrichment

Data enrichment brings with it an abundance of benefits for your business that shouldn’t be overlooked. Here’s an overview of the main benefits of data enrichment. 

1. Make informed business choices

Decisions without data are essentially a stab in the dark. Data enrichment allows you to utilise and strengthen data, and then use it to make informed, stronger business decisions. 

That’s not to say that every data-driven decision will soar, but it will increase the likelihood of success. 

2. Create impactful campaigns and increase ROI

Data enrichment gives you the opportunity to produce marketing campaigns that speak to your customers in their language, and promote products or services you know they are interested in. 

For example, through data enrichment, you might discover that millennial women are more likely to purchase a certain product over Gen Z. You can create targeted ads and promotions that appeal to millennial women. As a result, you’re likely to increase your sales and ROI. 

3. Forge meaningful connections with your customers

Securing sales and a strong return on your investment is desirable. But let’s not forget your customers are people, too. Data enrichment allows you to see your customers as just that, by providing you with a deeper insight into their behaviours, characteristics, and personal preferences. 

You can take this information and use it to forge meaningful relationships with your customer base. This is important in and of itself, but will also undoubtedly lead to increased customer loyalty and satisfaction.

4. Reliable, accurate data

We’ve talked about how data enrichment can inform smart business choices, but to be led by data, first you need to trust it. 

Enhancing data doesn’t just broaden your view of individual customers, but also ensures the data is up-to-date and accurate too in combination with data cleansing

Consequently, you’re left with datasets you can actually rely on to improve business performance.  

Let REaD Group take care of all your data enrichment needs!

We’re proud to offer the most comprehensive data enrichment service the UK has to offer. Our REaD Enhance service consists of: 

Core – A comprehensive individual level product containing over 50 million UK adults, with over 800 actual and modelled attributes across demographic and lifestyle variables.

Property – An address level product built using REaD’s trusted, comprehensive and relevant data assets.

Postcode Indicator – A postcode level product containing all PAF valid residential postcodes, with indicators across over 300 variables. This data supports location analysis, marketing strategies, targeting, and product development.

Geo – Compiled with a number of open-source data points with additional modelled attributes including GeoSociety; a rich source of data that gives valuable insight into what issues are concerning particular locations and sections of society.

Get in touch

Enhance is a one-stop shop for all your data enrichment needs. Get in touch with our team or explore our data enrichment services.

Person at computer

Data processing is an integral part of any modern day business. There can be many reasons why organisations need to process personal data, from acquiring new customers, to collecting cookies and running marketing campaigns. 

GDPR regulations outline six lawful bases by which businesses can process personal data. These 6 lawful bases include: consent, contractual requirements, vital interests, legal requirements, public interest, and legitimate interest

What is legitimate interest?

Legitimate Interest is one of the six conditions outlined by GDPR regulations that allow organisations to legally process personal data. Legitimate interests can include commercial, individual, or societal interests. The data processing must be necessary in order to be considered legitimate. 

Legitimate interest can be a confusing concept to grasp. Other GDPR conditions are more self-explanatory, such as ‘contractual requirements’, ‘legal obligations’ and ‘vital interests’. Although, legitimate interest is less definitive. 

This article covers everything you need to know about legitimate interest to help organisations unpack this broad area in more detail.

When is legitimate interest mostly used?

Legitimate interest is the most flexible condition of GDPR’s six lawful bases of processing data, and relates to processing data in the interest of legitimate business, individual, or third party needs.

Legitimate interest is mostly used to process personal data in ways that people would reasonably expect, in ways that have minimal impact on privacy. To process personal data on the basis of legitimate interests, organisations must have a compelling justification for processing the data.

For instance, it could be in the legitimate interest of a charity to increase its donor database. It could be the legitimate interest of a new business to obtain new customers via an acquisition-based marketing campaign.

The Information Commissioners Office (ICO) guidance states the following:: “the legitimate interests can be your own interests or the interests of third parties. They can include commercial interests, individual interests or broader societal benefits.

Defining legitimate interest sits at an organisational level. When processing personal data for direct marketing purposes, the organisation must complete a Legitimate Interest Assessment (LIA) and balancing test in order to document their legitimate interests.

Choosing to process personal data on the basis of legitimate interests can come with extra responsibility and obligation. Organisations must weigh up individuals’ rights and interests using a Legitimate Interest Assessment.

What counts as a ‘legitimate interest’?

Under GDPR, legitimate interest applies when organisations process personal data in ways that individuals would expect their data to be handled. Legitimate interests must be clearly specified and cannot apply against the law, ethical reasoning, or public policy.

This can make it difficult to determine whether legitimate interest can apply to your organisation’s data processing activities. To get a better understanding of what counts as legitimate interest, here are some examples of cases where this lawful basis is often applied.

Examples of legitimate interest

Examples of legitimate interests can include (but are not limited to):

  • Processing client and employee data
  • Prevention of fraud
  • Intra-group transfers
  • IT security

How to demonstrate legitimate interest

Demonstrating legitimate interest is essential if it is to be used as a lawful basis for data processing.

Article 6(1)(f) in the GDPR guidelines incorporates three key elements that can be used to test whether your activities demonstrate legitimate interests. 

These three elements of legitimate interest include:

  • Purpose test – is there a legitimate interest to process data?
  • Necessity test – is data processing required to fulfil that purpose?
  • Balancing test – are the legitimate interests outweighed by the individual’s rights, interests, and freedom?

Purpose, necessity and balancing tests can be used to ascertain whether your data processing activities are within the realm of legitimate interests.

What does Article 6(1)(f) state about legitimate interests?

Official GDPR regulations state that organisations must adhere to a lawful basis when processing personal data – this should follow principles of lawfulness, fairness and transparency.

Article 6(1)(f) in the EU GDPR regulations states: 

“1.Processing shall be lawful only if and to the extent that at least one of the following applies:

(f) processing is necessary for the purposes of the legitimate interests pursued by the controller or by a third party, except where such interests are overridden by the interests or fundamental rights and freedoms of the data subject which require protection of personal data, in particular where the data subject is a child.”

Does legitimate interest apply to marketing purposes?

Legitimate interest is one of the most lawful bases used to collect personal data for marketing purposes. There are many reasons why businesses may choose to store personal data for marketing, such as acquiring new customers. 

This lawful basis can apply to marketing purposes, but the legitimate interest must be justified and documented. 

This means that the GDPR basis of legitimate interest can depend on the circumstances, since interest can differ amongst businesses, sectors, markets and individuals. 

Recital 47 of GDPR

Recital 47 of GDPR states that “direct marketing purposes may be regarded as carried out for legitimate interest”. The key word here is ‘may’, meaning that the justification of legitimate interest for direct marketing can depend on the context.

The best way for businesses to demonstrate legitimate interest is to carry out a legitimate interest purpose test. The results from this will give a more definitive impression of whether legitimate interest can apply to your marketing activities.

Does legitimate interest apply to cookies?

Legitimate interest does not apply to cookies. Cookies that collect website visitors’ personal information cannot be processed lawfully under GDPR without consent. 

Following the introduction of GDPR laws in 2016, websites required pop-ups that ask users for consent to collect cookies. If users do not accept the website’s request to collect cookies, then the site cannot lawfully process cookies to collect personal visitor data.

Where the required consent is not obtained, organisations cannot choose to rely on legitimate interests as an alternative. 

What are the individual’s rights?

Under GDPR regulations, individuals have the following rights in regard to the processing of their personal data:

  • The right to be informed
  • The right of access
  • The right to erasure
  • The right to rectification
  • The right to restrict processing
  • The right to object
  • The right to data portability
  • Rights in relation to automated decision making and profiling.

Recital 75 of the GDPR regulations provide guidance on the individuals’ rights and freedoms when it comes to data processing and legitimate interests. Individuals have protective rights in cases where data processing has the potential to impact the individual in any way. This includes physical, financial, personal impacts and many other types, such as:

  • Prevention from exercising rights
  • Loss of control over personal data
  • Social, economic, or reputational disadvantage

Can individual rights override legitimate interests?

Individual rights can override legitimate interests if their personal data is processed in ways that they would not reasonably expect. If processing of personal data is unexpected in any way, the individual can exercise their rights to object and restrict processing, as well as other freedoms.

This is because the individual loses control over how their personal data is used, and that the processing does not align with their expectations and interests.

It is important to manage reasonable expectations from the outset of data processing under legitimate interests through clear transparency obligations that inform the individual of their ability to exercise rights.

When to avoid legitimate interest as a lawful basis

Avoid legitimate interests as a lawful basis of data processing if:

  • You believe individuals might have personal reservations about the way their data is processed
  • Data processing has the potential to cause harm to individuals or groups
  • If you are a public authority – public authorities cannot process data under legitimate interests unless there are clear commercial justifications.

Summary

To summarise, legitimate interest is one of the 6 lawful bases of data processing under GDPR. Legitimate interests can be a grey area that many businesses find confusing, since these are broadly defined as reasonable commercial, individual, or societal interests.

It is important for businesses to clearly define the legitimate interests they intend to rely on when processing data, and ensure that these are reasonable.

Is your data GDPR compliant?

Ensure your data is lawful and compliant with our GDPR compliance services. We help businesses process data lawfully and keep databases to a high quality for maximum results and fewer risks. 

Have any questions? Get in touch with us.

Person managing data on laptop

A DMP (Data Management Platform) is used to collect, organise and optimise data from different sources. Data Management Platforms help organisations understand more about customers by matching data against 3rd party lists, segmenting customers into marketable groups and ensuring data is accurate.

Data collected in the DMP is used for personalisation in marketing and advertising, as well as ensuring data quality.

Using a DMP, such as REaDOnline, is a great way to improve marketing efforts and engage with customers using more targeted communications. All whilst keeping data organised and centralised in one platform.

A lot of companies already consider using a data management platform, but need to learn more about it before going ahead. How does a DMP work? How does it integrate with current marketing activities?

This guide covers everything you need to know about Data Management Platforms. From how they work, the kinds of data they handle, and how they enhance your marketing.

How does a DMP work?

A Data Management Platform works by gathering your customer data and storing it in a centralised database. Stored data may include customer information, demographics, buying habits and contact details.

As an audience-focused solution, DMPs help marketers draw insights from the data, using first, second, and third party data to validate the data quality and create segmented audiences.

As well as improving the quality of data and creating segmented audiences, DMPs work by unifying datasets all in one place – making it simpler for organisations to manage data.

Data quality

DMPs can help improve data quality by auditing the dataset for inaccuracies, duplicates, and formatting errors.

For example, the platform can ensure the data is kept clean through data cleansing methods. This cleans data by measuring the details against 3rd party data lists. So, for example, if a customer had changed their address, the DMP could update the contact address details, using goneaway suppression to cross-reference this information against up-to-date databases. Similarly, a DMP can use deceased suppression to identify and remove deceased individuals from the database.

Additionally, Data Management Platforms help validate email addresses and check name formatting to ensure the data is of high quality.

This is a vital aspect of data management for any organisation, in any sector. If data is inaccurate, it is simply not useful to the business. In fact, 69% of organisations say that inaccurate data is the biggest challenge in data management, according to a study by Experian.

The DMP can also check for duplicate information, keeping data clean and accurate to ensure that the organisation is engaging with active customers, and is not sending duplicate communications.

Personalised experiences

As well as ensuring that information is accurate, Data Management Platforms can enhance your database using Data Enrichment and Data Segmentation methods.

This can help organisations develop more personalised experiences for customers and prospects. For instance, by using the DMP to segment audiences into meaningful groups. Based on shared qualities, these segments can then be targeted using tailored communications. 

As a result, businesses can expect to see better return on investment from their marketing campaigns, since communications are more targeted towards the intended audience. Personalised communications are also shown to increase trust between consumers and brands.

Unified data

DMPs help make data management more straightforward. Designed to bring all data into one place, Data Management Platforms are a dream come true for marketers. With a centralised view of customer data, organisations can optimise campaigns with more comprehensive data insights.

With unified data, all in one place, this makes day to day data management easy and simple. Not only does this make customer data easier to manage, it can also help piece together valuable information about customers to reveal key insights.

As a result, organisations can understand their customers better and develop more effective marketing campaigns.

Using a data management platform

Using a Data Management Platform is actually pretty straightforward. Data management is one of the most important, but time-consuming processes for any modern marketer. As a result, Data Management Platforms are designed to take the hard work out of handling data.

In a study from Experian, 83% of organisations said that data formed a central part of their business strategy – so it’s vital that DMPs offer a simple solution. 

Our own DMP, REaDOnline, offers an intuitive user interface, making it easier than ever to manage and optimise your data.

How DMPs use data

Data management platforms often use a variety of customer data sources to ensure the data is accurate, and also enhance it with relevant information. DMPs can combine first, second and third party data, allowing businesses to gather customer insights all in one place. 

This is especially useful for organisations that collect customer data through a variety of sources. For example, for companies who collect data from website visits, also buy prospect databases.

REaDOnline lets users upload data from a variety of sources, which is then audited 

Using this combination of data, DMPs can:

  • Collect data from different sources (online, offline, first-party, second party, third party)
  • Enhance data by creating targeted audiences
  • Clean and validate the data by cross-referencing databases

REaDOnline

REaDOnline is the Data Management Platform from REaD Group. Our platform helps businesses easily manage and enhance their data. Combining the most accurate and comprehensive data available in the UK, REaDOnline cleans and enhances data in a few simple steps. 

Simply register for an account and login to the REaDOnline portal. From there, upload your data to get started. 

Explore REaDOnline

Have a question?

Have a question before getting started? Get in touch with our team, who will be happy to help you with the right advice.

data cleaning

Data is an essential part of almost every key business decision. Effective marketing, analytics and customer engagement are only possible with quality data, making it essential to keep your data clean and up to date with an effective data management strategy.

Essentially, poor quality data almost certainly means bad results, which is where data cleansing can help. 

Data cleansing is a data management process that helps ensure your dataset contains accurate and compliant information. Without clean data, businesses and organisations are unable to make informed decisions based on reliable data, and are at risk of facing GDPR fines.

For instance, data cleansing helps keep vital customer data up to date, such as if a customer has changed address, phone number, or other contact details.

Additionally, data cleansing ensures that business datasets remain GDPR-compliant. This is essential for any business that acquires and holds customer data, as non-GDPR-compliant organisations run the risk of legal intervention and brand damage, as well as substantial fines.

Besides keeping your data accurate and legally compliant, there are many benefits to data cleansing. Not only does clean data ensure that you are targeting active customers with relevant communications, but clean data can also help businesses save costs, run more efficiently, and avoid brand damage.

What is data cleansing?

Data cleansing is the process of reviewing and removing inaccurate, incomplete, or irrelevant data from your dataset. Data can become inaccurate in several ways. Over time, businesses acquire large amounts of customer and prospect data, and no matter how good the data capture and management systems are, there will be errors, duplication, or incomplete information in the dataset.

Data inaccuracies can arise because of input errors, formatting errors, processing errors as well as day to day changes such as people who have moved homes, died, changed contact details and more.

Data cleansing allows businesses and organisations to keep datasets clean and accurate by ensuring it only contains information that is meaningful, accurate and complete. This data can include customer names, phone numbers, email addresses and physical addresses, and can also include more specific customer details such as buying habits.

If data is incorrect, this can result in unreliable outcomes that can consequently cost businesses money and cause brand damage. For instance, if you were to plan a direct mail campaign, then you’d need to make sure that your customer address information was correct in order to ensure the most reliable results.

Why is data cleansing important?

As data becomes more and more central to the ways businesses operate, data cleansing plays a more important role than ever in ensuring that data is clean and accurate.

Businesses and organisations rely heavily on the quality of the data they collect and hold, particularly in the current digital age of marketing where businesses primarily engage with customers using digital communications. 

Additionally, customer data must remain GDPR-compliant after the introduction of new legislation in 2018. This introduced strict new data protection laws relating to business data collection and retention processes.

There are many benefits to data cleansing that make it such an important business process. These benefits include the following.

1. Save costs

Data cleansing helps save costs that arise as a result of errors. It costs money to hold data, and if portions of that data are incorrect or irrelevant, this wastes money and budget that could be spent on retaining better quality data. It also costs to pay staff who are responsible for processing and troubleshooting data errors and inconsistencies, all of which can be avoided with a clean dataset.

2. Use your data for multichannel purposes

Data can be reused for a variety of marketing purposes, from email marketing, direct mail, customer engagement strategies and more. With a complete dataset of phone numbers, email addresses, physical addresses and additional variables, your marketing efforts can be easily widespread across different marketing channels. This makes data cleansing a useful tool for improving customer engagement.

3. Remain GDPR-compliant

As of 2018, GDPR laws have affected the way that businesses acquire and handle customer data. Data cleansing helps businesses stay compliant with the law, which not only helps prevent the possibility of lengthy and costly legal battles, but also helps protect against potential brand damage. 

4. Identify gone away and deceased individuals 

One of the most common mistakes businesses make when using data is to assume that every contact is active, and that individual details are up to date. Data cleansing helps businesses identify individuals who have either moved home, or individuals who have passed away. This is identified through goneaway suppression and deceased suppression services.

It is important for businesses to stay respectful of such life events. For example, sending communications to deceased contacts can be highly distressing for family and friends of the individual.

5. Make quicker business decisions

A major benefit of data cleansing is that clean data can help support better decision-making processes. Having a clean dataset gives a more accurate overview of customer information and analytics, which allows businesses to make decisions more confidently and strategically.

6. Improve work productivity

Data cleansing can help improve team productivity by keeping the datasets they work with clean and accurate. This avoids the need for workers to sift through large and potentially irrelevant datasets by ensuring it only contains high-quality information.

For larger datasets, teams may not have the appropriate resources or time to manually review customer information, making it beneficial to maintain a clean dataset from the outset.

7. Improve customer acquisition and retention

Simply put, customers can only be acquired successfully if the data you hold on them is correct. Clean prospect data ensures that you are reaching out to valid contacts and ensures that you are communicating with those customers appropriately. A consistent and clean approach to data will also help speed up the onboarding process and give the customer a better experience. 

How does data cleansing work?

There is no one set way to clean data, and the data cleansing process can be different depending on the kind of dataset.

For most customer datasets, the data cleansing process involves a full review of the dataset, followed by detailed techniques to remove incorrect or duplicate data. Data cleansing also involves resolving structural errors, whereby there are inconsistencies in the formatting of data.

The data cleansing process can be quite complicated when there are many data variables involved, and many types of irrelevant data. That’s why our data management experts help businesses keep their data clean with our market-leading REaD Cleanse service.

1. Removing unwanted data

Removing irrelevant data is the most important step in the data cleansing process. This includes removing errors, unwanted and duplicate data. 

Duplicate data

It is very common for duplicate data to occur in datasets, often as a result of many datasets being combined across different platforms or departments. Not only does removing duplicate data help save businesses costs, but it can also prevent customers from receiving the same communications twice. 

Irrelevant data

Irrelevant data can present itself in any kind of dataset. Data can become irrelevant if it is outdated, or contains errors such as incorrect addresses, names or numbers. Our goneaway suppression services validate data across multiple sources to keep contact addresses up to date. Data can also be irrelevant if it does not suit the needs of the business or campaigns.

For instance, if customer data exists for a demographic that does not apply to your business, consider having this removed to maintain more targeted communications. 

2. Correcting formatting errors

Data errors do not occur only as a result of incorrect information, they also occur as a result of incorrect formatting. The naming conventions used in your dataset must be consistent for the information to be useful. Naming conventions can sometimes differ across platforms, causing inconsistencies when datasets are consolidated.

For instance, you may find that there are discrepancies between terms such as ‘surname’ and ‘last name’, or ‘N/A’ and ‘not applicable’. Inconsistent data formatting can mean that this data is not categorised properly.

3. Validating data

Data errors can be difficult to spot when cleansing your own data. For instance, it is hard to know whether customer contact information is still correct, or if an individual is still active. 

Data validation often requires the support of professional data cleansing services to accurately validate information. Data professionals, such as REaD Group, have access to comprehensive databases in order to cross-check data and handle missing data. This includes:

How clean is your data?

Clean data is essential to business success. It is important to examine your data and evaluate the overall quality. If you suspect that performance is not reaching its potential because of low-quality data, here are some quick ways to check whether your data requires a clean-up.

Quality data check

  1. Is your data accurate? Check whether your data reflects the values that they should. 
  2. Is your data complete? Check whether your data contains any missing values. 
  3. Is your data consistent? Check whether your data has any inconsistent categories or naming conventions.
  4. Is your data formatted correctly? Check whether your data contains any spelling errors and uses the same units of measure throughout. 

Start cleansing your data

At REaD Group, we are experts in data cleansing. We take a tailored approach to your data in order to cleanse it in a way that suits your business goals and ensures your data is high quality.

We have access to the UK’s largest customer database, with variables including homemover data and deceased suppression data in order to validate accurate and active customer information.

Get in touch

Get started with data cleansing services today, or speak to our data experts to find out more about how we can help you.

data segmentation

Data Segmentation involves dividing up and grouping data into relevant segments, allowing an organisation to make better marketing decisions based on customer personalisation and prospect insights. 

For instance, customer data can be segmented by lifestyle choices, location, personal identifiers, and other parameters that help a brand identify and market to its best customers. 

The benefit of Data Segmentation is that it gives organisations more personalised datasets. With a strong understanding of who customers are, and the experiences they value comes a stronger understanding of how to best communicate and connect with them. 

This article covers everything you need to know about Data Segmentation, and how to get started. 

The importance of data segmentation

Data Segmentation, also known plainly as Segmentation, is important for several reasons. By segmenting customer data into relevant subgroups, organisations can identify opportunities more effectively, as well as deliver more targeted communications and improve revenue streams.

Data Segmentation can help businesses achieve the following: 

 

1. Identifying opportunities

When it comes to customer data, there is often a lot of information to take in. Data segmentation helps break up the data in meaningful ways, providing a more detailed insight into the types of customers in the database.

Once segmented, organisations can identify opportunities and patterns amongst subgroups of customers. For instance, older demographics might be more responsive to one form of communication than younger demographics.

2. Targeting communications 

With segmented data, organisations are able to tailor communications to relevant audiences more effectively. This can help deliver marketing messages in ways that each individual customer or prospect is more likely to resonate with.

By understanding what customers are interested in, and more about who they are, your communications team can engage with customers in ways that they are more receptive to. 

Data segmentation can also be applied to qualify prospective customers in your pipeline.

3. Improve revenue

The main benefit of data segmentation from a business perspective is the potential to increase revenue. 

By understanding customers in more detail and targeting communications in a way that they are more receptive to, businesses can in turn increase revenue opportunities. 

This can also save your sales team a lot of time since they’ll have access to more detailed overviews of customers through segmentation.

How does data segmentation help businesses?

Data segmentation can be used to divide customers into almost any category that provides value to your business and marketing efforts. Segmentation is a powerful data strategy for businesses in any sector.

Customers can be segmented based on a number of parameters, including:

This information can be used to make better business decisions, by giving you a stronger understanding of who your customers are. Segmented data can also help businesses build trust with customers, which in turn helps improve revenue. 

For example, this Harvard study found that top customers spent an average of 40% more following the segmentation of data. Data was segmented based on key criteria to identify opportunities that would provide value to the customer.

Challenges in data segmentation

Data segmentation is one of the most valuable data management tools for any business holding customer or prospect data. Though, many businesses find it difficult to segment data effectively.

Some of the most common challenges businesses face with data segmentation include:

  • Not having enough data
  • Having too much data
  • Inaccurate data
  • Lack of internal resources

However, many of these challenges are often easier to overcome than people think. 

Size of a dataset.

In most cases, it is unlikely that there is not enough data to segment. Even with smaller datasets, data segmentation can provide hyper-personalised insights into customer groups. 

Smaller businesses that don’t hold much customer data can also ensure that sales communications are highly personalised by segmenting B2B or B2C prospect lists.

Equally, having too much data does not hinder the ability to segment data. If you have a dataset of any size, it is likely that there are key insights to uncover with data segmentation. 

As an experienced data insight agency, our experts help businesses of all sizes maximise their data with segmentation. 

Inaccurate data

Another challenge businesses face in data segmentation is inaccurate data. Where there are inaccuracies in the dataset, it becomes more difficult to make reliable business decisions based on the segmentation. 

Though, this issue can easily be overcome by validating and cleaning the data beforehand. For instance, at REaD Group, our data experts can ensure data is validated and cleansed before segmenting data. 

This way, businesses can rest assured that the insights offered from segmented data are accurate and reliable. 

Lack of internal resources

Many businesses also struggle due to a lack of internal resources. This is especially true for businesses with larger datasets, or smaller teams.

This can make it difficult to manage the data effectively, particularly when datasets contain inaccuracies that need addressing before segmenting the data.

Creating well-defined segments provides benefits across every area of the business, which is why our data experts help organisations with professional data segmentation services

Why we help businesses with data segmentation

At REaD Group, our data segmentation services help brands connect with customers and make better decisions with tailored customer analytics solutions. Our team help uncover new customer insights to optimise customer acquisition and retention strategies.

Our data segmentation services provide targeted customer segments, helping brands identify relevant customer groups based on specific variables.

Target customers in the right way with segmented communications and more meaningful marketing. With the help of our data experts, transform your data into knowledge, and knowledge into profitable action.

Get in touch

To get started, explore our data segmentation services, or get in touch with our team.