3 Killer Abandoned Cart Strategies

Marketers have found that on average, 67.45% of online shopping carts are abandoned before customers check out. That’s a huge number of missed sales, and that’s why abandoned cart remarketing was developed.

Traditional abandoned cart platforms operate on a simple logic: Set a trigger when customers leave your website without finishing their purchase. Trigger an email with product info. Rinse, repeat ad nauseum. This basic trigger is pretty much a ground floor requirement for eCommerce websites, but most of them are highly limited in their logic and don’t don’t utilize data from CRMs or Customer Data Management Platforms. They’re missing out on valuable opportunities to reach customers with compelling reasons to revisit their abandoned carts.

Here are three ways you can reconnect your customers with their carts by tapping into your customer data sources:

1. Trigger messages on previously abandoned items that go on sale.

abandoned cart0

Surveys report that the top 3 most common reasons for shoppers to abandon their cart are related to the price of their items. When you let your customers see an item that they’ve previously considered has gone on sale, it’s just another reason for them to reconsider their purchase.

Example: Trisha abandoned a pair of blue suede shoes 3 months ago, but now these shoes are on sale. We’ll send her a triggered message alerting Trisha that her shoes are on sale.

Requirements: customer purchase/abandon history integration, sales category for products for trigger

2. Product recommendations in abandoned cart emails

abandoned cart1

Example: Jasmine purchased a grey backless dress and abandoned her cart before checking out. Our software will send her a triggered message with her abandoned item along with additional products that may interest her, just in case she’s decided that the dress didn’t match her needs.

Requirements: depends on the complexity of recommendations…simple recommendations can simply be built from product hierarchy modeling (grey dress is in same category as several other dresses), more complex variations will need software capable of predicting customer needs by combining lifecycle, purchase, lifestyle, clicks, etc

3. Trigger abandoned cart emails to users that are anonymous

abandoned cart3

Example: Sarah has made an account before, but is browsing the website anonymously. She puts an item in the basket and abandons the cart. The system recognizes her unique ID and triggers an email.

Requirements: 1:1 digital tracking service required to attach unique id to known profiles, automation system to connect the dots, validate confidence, and fire message.

With a powerful enough system, you could probably pull off all 3 mentioned abandoned cart strategies for increased ROI. There’s still time for brands to utilize abandoned cart remarketing to the fullest, and newer, tested technology enables companies like NectarOM to build the capabilities needed for marketing personalization across the omnichannel frontier.

Don’t Get Fooled By Data

April Fool’s is around the corner…and nectarOM has a few suggestions to reduce your risk of getting fooled by data analysis.

In a time when data is abundant and necessary for a strong personalized marketing strategy, marketers should be on the look out for these most common ways that data is misinterpreted. The following are some mistakes commonly made in data analysis.

Causation and Correlation

Understanding the difference between causation and correlation is important to interpreting data. Because both concepts sound relatively similar and are related to statistics, they are easily confused for one another.

Causation occurs when one event causes another. For example, as summer approaches, a swimwear retailer may see an increase in sales as more people buy swimsuits.

Correlation occurs when there is a mutual relation between two events. However, one of these events does not necessarily need to cause the other. For example, ice cream sales may increase and a swimwear retailer’s sales may increase, however, this does not mean that the increase in ice cream sales causes people to buy more swimwear. In this case, the rise in temperature is the cause of both of these events.

Understanding the difference between causation and correlation is important to avoid an incorrect data analysis. If the aforementioned swimwear retailer confuses its correlation and causation with ice cream sales, the retailer may see problems arise if it adjusts its marketing campaign to reflect the success of ice cream sales. For example, if summer ice cream sales increase because a neighboring frozen yogurt shop shuts down, the swimwear retailer may wrongfully assume their sales will increase as well. This assumption, which is not necessarily correct, could contribute to an ineffective marketing campaign.

Using Old Data

When a company is stuck with outdated customer information, its data may become useless. For example, a company may be sending emails to a customer’s old email address. If this customer no longer checks this email address, he or she will not have the opportunity to open emails from companies they might have registered with. This could alter the company’s email open rates. Several cases of this could lead to incorrect assumptions of ineffective subject lines or poor sending times based off faulty data collected in the scenario. To prevent these types of inaccurate assumptions, analysts should ensure they are using current customer information and use business rules to exclude customers who have not opened within a certain period of time.

Assuming the Data Will Do it All

One of the attractive selling points of using a Data Management Platform is that it reduces work for marketers and analysts. However, this mindset is a slippery slope. Companies should make sure their staff knows that a DMP doesn’t mean no more work in personalizing and customer care. Marketers can simply sit back and let their DMP run their data analysis and marketing campaigns. Marketers must remain attentive and responsive to consumer behavior, ensuring that marketing does not take on a robotic, impersonal feel.

Measuring the Average

When determining metrics in a data set, marketers must determine how to measure an accurate average. In some data sets, using mean versus median can present some vastly different results.

Mean accounts a total of all values added, then divided by the amount of data points. Median is the exact middle of the data set in numerical order. In cases where there are extreme outliers, using the median can give analysts a better picture of an average.

Oftentimes, the median gives marketers a more accurate look at its average. For example, consider a retailer’s data that tracks how long visitors stay on their eCommerce site. Imagine a retailer’s data shows that nine users spend 3 minutes on its site, while one user spends 45 minutes on the site. In this scenario, the mean average is 7.2 minutes spent on site, while the median is 3 minutes on site.

The median is a better value for the retailer’s average because it shows reflects a value that is close to what most site visitors showed. In contrast, the mean average reflects a value significantly higher than what 9 out of the 10 visitors generated. The mean’s higher value is skewed by one user’s unusually high value of 45 minutes. This lone value seriously alters the average time spent on the site.

Acknowledging outside factors

Oftentimes, marketers are so focused on the numbers that they forget to account for outside factors that might influence their customer data. For example, when looking at open rates in an automated email campaign, marketers should be sure to consider a customer’s geographical location.

While geographical location might seem irrelevant when sending emails, time zones and time of distribution can significantly impact open rates. Studies show that most consumers open emails from retailers between 2 p.m. and 5 p.m.

If all emails are distributed at the same time, a person in California might receive the message at an optimal time of 4 p.m., while a recipient in New York would receive the same exact message at their time of 7 p.m. While this delivery time is great for the Californian, the New Yorker may be in the middle of dinner and too distracted to open an email. Content should be delivered with the recipient’s location – and time zone – in mind.

Consider Kate Spade’s automated email campaign, which always considers the shopper’s time zone when delivering emails. The women’s clothing brand asks its registrants for two items of information upon signing up for an account: their email and zip code. With this information, Kate Spade emails customers according to their different time zones.

The email to the left is registered with under my California zip code, while the email to the right was registered under my Texas zip code. I received both emails two hours apart – a perfect example of a company accounting for time zone differences.

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While data analysis mistakes are bad for marketers, poor data management can be detrimental to a company’s growth and sales as well. Make sure your company’s data analysis and data management are up-to-date and set up for success when implementing data into your marketing campaign.

Top Trends in Data

It’s 2015, and companies are finally getting the hang of data.

Big data has been around for years so it’s about time! More and more companies are using data to profile their customers to generate relevant products and marketing strategies. A study by Forbes and Teradata found that 78% of marketers are incorporating data into their marketing.

As companies explore the possibilities behind data analytics, we have noticed a few trends in the datasphere. Companies that choose to incorporate these trends in their marketing strategies may notice an increase in ROI and an edge over their competitors.

Unsiloing data

Companies are beginning to break down barriers in data sharing. Un-siloing data allows different departments to combine different data sets. A company’s entire stock of customer information may be stored in one simple Data Management Platform. This storage system makes finding, organizing and sharing information an easier, more efficient process.

For example, a company’s IT department may have an impressive set of internal customer information. That same company’s marketing department may have a large collection of CRM data. Companies that unsilo data pool together both sets of information, creating a single in-depth consolidation of data.

The larger data pool is beneficial to both IT and marketers, as the departments will have access to a more complete profile of their customers. This will help both departments provide a more accurate understanding of their customers, generating a more personalized, relevant shopping experience.

Privacy

Over the past year, information security and privacy has been a growing concern amongst the public. With big security breaches like the Sony hack and the iCloud celebrity photo scandal, privacy is a growing concern for many.

In response to public concern about security, companies must employ data protection and safeguarding into their data management. Companies that cannot confidently ensure that a person’s private information will stay private, the likelihood of data sharing will decrease.

Nordstrom clearly outlines its privacy policy to build trust between buyers and itself.
Nordstrom clearly outlines its privacy policy to build trust between buyers and itself.

Additionally, easy-to-access privacy policies can increase trust between customers and a company. Provide an in-depth statement informing customers of their privacy rights can increase likelihood of sharing personal information.

Other than putting the public at ease, companies with protective measures in data management will have less risk at security breaches. Data encryption, multiple passwords and security audits may take a little extra and effort. But investing time into safeguarding customer information is vital to prevent big scandals and data breaches.

Data in Real-Time

Companies are beginning to use data in real-time to engage and connect with consumers. In the past, data has been used to create content for consumers, and is set to be viewed at a certain point in time.

Today’s marketers are integrating data and marketing strategies in real-time, to offer content that is even more current and relevant. This real-time marketing is commonly seen across social media platforms.

Nissan UK creates relevant messages by integrating real-time events into its social media marketing
Nissan UK also creates relevant messages by integrating real-time events into its social media marketing.

Google’s Fifa World Cup campaign is a prime example of real-time data integration. By analyzing Google’s search engine, data experts curated sharable images and facts for its users. This later fueled more discussion about relevant topics for Google users. This real-time analysis helps businesses understand why their target audiences share, engage and spread content.

As data analytics becomes increasingly popular, we expect that companies will employ a real-time data/marketing strategy similar to Google’s. Companies will begin to utilize social media more often, allowing their customers to share, engage and spread content easier.

More Personalization

Personalization has been a hot topic since data was first introduced to the marketing world. And, as more companies grow friendlier with data, feelings toward personalization are sure to follow suit.

Personalization increase also directly correlates with the shift of millennials as the largest purchasing power. As millennials are generally more open to sharing personal information, companies can adjust marketing to incorporate a more personalized, 1:1 marketing feel. A global study by SDL found that 46% of millennials are willing to provide personal information to businesses, in order to get rid of irrelevant marketing.

Netflix personalizes content by providing recommendations based off past interests.
Netflix – a millennial favorite – personalizes content by providing recommendations based off past interests.

As data continues to evolve, understanding where data is headed can be a big asset to companies. Anticipating changes in data and adjusting strategies accordingly can help your company stand out amongst competitors and remain a consumer favorite.

Defining the Loyal Customer with Data

What is a “loyal” customer?

The loyal customer is a person who keeps coming back for more. This customer frequently browses, shops and supports your company. A loyal customer may consider other competitors, but often chooses your company when making purchases.

In other words, a loyal customer is your favorite kind of customer.

Why is defining the loyal customer with data helpful?

Companies should market to all types of consumers. However, marketing to loyal customers can give a business an edge when making sales.

The loyal customer also tends to be a more valuable customer. Selling to existing customers is about 50% easier than selling to new customers, according to Marketing Metrics. Loyal customers also churn a greater profit, according to Gartner Group. In 2012, the group found that 80% of a company’s future revenue comes from 20% of existing customers.

Brick-and-mortar loyalists

In small mom-and-pop shops or some brick-and-mortars, labeling the loyal customer can be easy. For example, I frequent my local brick-and-mortar Starbucks so often that the baristas have my order memorized. When I walk through the franchise’s doors, I am greeted with a friendly “Hello Savannah!” followed by, “Grande vanilla soy latte, right?”

Because I am face-to-face with my Starbucks baristas every day, my baristas can easily discern that I am a loyal Starbucks consumer. Because of this, they offer me a personal greeting and predict my order. This makes my experience more enjoyable and convenient.

But what about entities that aren’t privy to daily face-to-face interactions?

Digital loyalists

What is a digital loyalist?Determining loyal customers across digital entities can be a little more dynamic. With a significant portion of sales happening over the Internet, discerning which customers are loyal is important. However, it is not always as basic as face recognition.

Companies can analyze big data from their online sales to determine which customers are loyal. Integrating data and marketing can be more effective than remembering names and faces.

In the aforementioned Starbucks example, defining loyal customers can help a company personalize shopping experiences. This personalization is not limited to in-store interactions. Marketers can offer the same personalized greeting and predictive product suggestions digitally via email, SMS or website.

Marketers can use data several different ways to determine which customers are loyal. Special membership programs are the most obvious way to discern which customers are loyal. These CRM systems generally require customers to share personal information in exchange for special offers or discounts. Personal data is compiled to create a personalized customer profile. Companies can also use specific metrics to determine which customers are loyal. Metrics can track how many purchases a customer makes, and determine whether they are a one-time buyer or a loyal customer. Marketers can also use metrics to see which customers are opening, sharing, or unsubscribing to online content.

Adjusting personalization for one-time shoppers vs. loyalists

Distinguishing loyal customers can also help a company shape their marketing strategy. Businesses may change the way they market based off how likely a customer is to be a return customer.

Starbucks is one of those companies that adjusts its marketing strategy according to a customer’s loyalty. According to Business Insider, the coffee company offers most of its special offers to consumers who are not as likely to be return customers. Starbucks tries to attract these irregular buyers with these special offers, whereas it doesn’t feel the need to woo loyal customers because these loyalists do not require special offers to buy from Starbucks.

However, Starbucks does not ignore its loyal customers. Through its rewards program, Starbucks offers different types of offers for customers that are more loyal. Offers for Starbucks Gold members  – the most frequent Starbucks customers – encourage further purchases from a sect of consumers who are already frequent buyers. For example, customers at the Gold level have access to free in-store refills – a privilege reserved only for Gold members.

Through this unique type of personalization, Starbucks plays the marketing game based on the strength of its consumer relationships. If you’re intrigued about incorporating customer data in marketing, learn about clickstream data in an automated world.

Measuring Automation Success With Data

Evaluating Digital Marketing Metrics Like A Pro

In every type of industry, data is used to evaluate the status of a concept, product, or idea. From politics to the red carpet, data is used around the world to measure what works, and what doesn’t.

Data is particularly important for those in the marketing world. Data can show whether customers like a company’s marketing, or show companies that their marketing is ineffective.

Data analysis is particularly important for marketers using marketing automation. Because experts believe that the future of marketing automation depends on data, marketers should implement data into their marketing strategy.

Drawing relevant, helpful conclusions from data sets can be difficult. With so many numbers and digits, its easy to get overwhelmed with different rates and percentages. But never fear – NectarOM is ready to make metric evaluation simple. There are certain metrics one should consider when measuring the success of marketing automation.

When evaluating automated email success, marketers should look at several different rates. First, marketers should use a bounce rate to determine how many emails actually get to the intended recipients. Inaccurate email addresses, poor server connection, or full inbox may prevent email subscribers from receiving emails. Establishing why a consumer might not receive emails can be helpful, as marketers can work to fix the bug in their email automation system. Bounce rates distinguish ineffective marketing from inaccessible marketing.

Using a calculated bounce rate, marketers can find the open rate. An open rate is the amount of emails opened as out of the total emails that were delivered to inboxes. Open rates can be useful in determining the attractiveness of a subject line, or the accessibility of a customer through the time the emails were sent.

To determine how successful email content is, marketers should consider click-through rates and conversion rates. Click-through rates reflect the amount of times a link inside of an email was clicked, directing the subscriber to the company website. Conversion rates measure the amount of subscribers who have made a purchased, registered for a new program, downloaded a file or attachment, or signed up for a contest via the email. Both of these rates can show marketers how effective their content is in creating customers or maintaining relationships.

Marketers should also examine their subscriber lists to gauge how effective their email marketing campaign is. Determining whether a list is generally growing or shrinking can indicate strong or weak email marketing. Campaigns that host high unsubscribe rates are obviously not doing email marketing the right way.

Websites are another platform that can use data metrics to evaluate marketing success. Like email automation, certain metrics (e.g. bounce rates and conversion rates) can be useful in measuring a website’s success. However, websites also can consider other metrics as indicators for success.

Shopping cart abandonment rate is a website-specific metrics. Ecommerce companies should be aware of the amount of abandoned shopping carts compared to actual purchases. Knowing this can help a company make necessary adjustments to keep abandonment rates low. For example, an ecommerce company might use its abandonment rate to determine whether it should implement an automated abandoned shopping cart email into its marketing strategy.

Marketers should also look at their site’s churn. Churn measures the amount of customers that come back or leave the company each month. Companies with a high churn rate may want to reexamine their marketing strategy, and make necessary improvements to their marketing strategy.

Regarding both email and website automation, marketers should evaluate their ROI rate. ROI (return on investment) is another key way to measure the success of marketing. This measurement shows the company’s net profit compared to the company’s investment.

As marketing digitally continues to change, marketers should keep a steady eye on their ROI rates. A dramatic increase or decrease in ROI signals that marketers are doing something right or wrong.

While these are a lot of numbers and metrics to consider, taking the time to evaluate each data set can be a huge marketing asset. If number crunching isn’t your strong suit? Learning more about data management platforms is a must!

Four Reasons Why Bad Data Management Hurts

For many businesses, data is a great addition to a marketer’s toolbox. Equipped with data, businesses can collect, store, analyze and interpret their customers’ information. This helps marketers develop the best sales messages and strategies for each individual customer.

However, if used incorrectly, data can be more of a hindrance than an asset for a company’s performance. Bad data management hurts businesses that base their marketing strategies off of data. Bad data management can include disorganized, underdeveloped, or irrelevant data. These problems are responsible for time wasted, unsuccessful marketing, and a weak ROI.

Time Concerns

When data is not managed well, retrieving relevant customer data can be a time-consuming pain. Businesses using data to drive sales may find themselves spending hours sifting through data.

This puts businesses at a disadvantage against competitors with good data management. By the time a business finds meaningful information with poorly managed data, its competitor that properly manages data will have already developed a marketing strategy that has generated dozens of sales.

Reporting and Forecasting Limitations

Data disorganization can also affect a business’s ability to report conclusions from the data. Data analysis may be construed with flawed, incomplete or inaccurate data. This tainted analysis can ruin marketing strategies.

Predicting customer behavior is impossible without an accurate customer data analysis. In a worst-case scenario, a poor analysis may actually prevent a business from developing any marketing strategy. A bad analysis does not give businesses the necessary material to create an accurate forecast for consumer preferences. This accurate forecast is key in marketing effectively.

Unable to market effectively

The ability to personalize marketing is extremely important to the successful modern-day marketer. Marketing personalization is the premier tool for marketers because it can be used in a variety of ways. Personalization reduces stress for businesses, brings back the disconnected customer, and improves email metrics. Personalization is no longer a choice for businesses, as marketers say it is the most important capability to marketing in the future.

When data clouds a business’s ability to personalize marketing, sales are sure to suffer. Businesses currently personalizing web experiences see a 19% increase in sales on average, according to Econsultancy. The reason for sales increase? Customers like  the custom, tailored messages they receive from businesses. If unable to offer personalization, businesses will lose customers to their competitors providing a tailored, 1:1 marketing approach.

The snowball effect

Bad data management is one of those problems that are susceptible to the snowball effect. If not fixed quickly, poor data management can grow into a larger problem and, eventually, spiral out of control.

Unorganized data management system is much easier to fix in its early stages of development. For example, a data management system that catches a problem early on should not require much effort to make a quick fix. In contrast, businesses that have ignored a necessary data cleanup for years would need to take several steps back to fix problems. In a worst-case scenario, the business might need to create a new customer database from scratch.

If you’re curious or concerned about the state of your data management, take a close look your material. Reexamine any data already collected, and extract any dirty data from the data pool. Review your data management platform and ensure that it is reliable, working as an effective, helpful tool. Reevaluate any good data you have, and use that to create tailored messages for your consumers. Lastly, remind your marketers and data analysts the importance of good data, and encourage proper data management practices across your company.

Four Things to Look for in a Data Management Platform

A customer data management platform acts as the backbone for any company using data to understand customers and generate sales. Data management platforms are multi-functional entities, as they collect, manage, process, analyze, organize, and activate data. And, as data-driven marketing continues to drive revenue, data management platforms have become a common implementation for all types of businesses.

While there are a number of data management platforms available for businesses, not all data management platforms are created equal. Smart retailers should evaluate the following four components to determine which data management platform is most effective for their marketing campaign.

Integration of 1st and 3rd party data

Cross-referencing 1st and 3rd party data yields gratifying results for marketers. Examining both types of data creates opportunities to hyper-target and hyper-personalize marketing toward customers.

However, it is important to note that data management platforms integrate 1st and 3rd party data differently from one another. For example, consider the practices of the platforms Blue Kai and Knotice. In a Forrester Study, Blue Kai was recognized for its “established leadership position in the third-party audience data space.” This contrasted against Knotice, which placed “strong focus on first-party data” with “little third-party data integration.”

Integrating 1st and 3rd party data has become a priority for businesses. A 2012 Winterberry Group study of marketers, publishers, technology developers and solution providers explored the importance of assimilating data, finding that 85% of the respondents considered integration of 1st and 3rd party data a “core competency” in desirable data management platforms.

A respondent from the study expanded on this idea even further, saying that data management should “allow us to harness first-party data, overlay the right third-party sources and create rich profiles of our customers that extend beyond the data we may have on their specific interactions with us.”

The “Right” priorities

There are several uses for data management platforms. Customer insight development, targeted media buying, CRM program optimization, and site content optimization are just a few ways customer data can be used.

Because of this, businesses must make sure that its end goals coincide with what a specific DMP can offer. Since businesses have different reasons for managing data, they must make sure that their DMP organizes, analyzes and presents customer data in a way that best corresponds with their priorities.

For example, consider findings from the previously aforementioned Winterberry Group study regarding important priorities for Data management platforms. While almost all marketers (93%) agreed that customer insight development is an important priority for future DMP support, a mere 59% call advertising yield optimization an important priority.

These findings tell us that marketers have varying priorities and reasons for utilizing data. While most marketers are looking for a DMP that offers customer insight development, only a fraction of marketers are looking for a platform that presents advertising yield optimization.

Cross-Channel management

Your ideal DMP should incorporate data from multiple sources. Data from multiple channels provides a better understanding of customers’ experiences. Omnichannel practices in data management platforms produce a complete profile of a company’s customers. This 360-degree view of a customer is guaranteed to enhance marketing personalization campaigns.

Bridget Bidlack of the DMP [x+1] is an advocate of this cross-channel implementation. In an interview, she said that ideal data management platforms should “close the loop by ingesting campaign data from all channels and vendors, as well as offline activities like in-store sales and call center interaction.”

Bidlack further explains that this helps marketers use data to its fullest extent.

Ease of use

Marketers, advertisers and data management platforms themselves have recognized the importance of data management. Data management platforms have been called the “brain,” the “backbone” and a “must-have” for companies.

In other words, data management platforms are a central component for a company’s success in the marketing world. Because these platforms carry so much influence over marketing success, understanding the data gathered and analyzed is imperative to a company’s success.

Getting stuck with a data management platform that is difficult to use can present unnecessary complications and create stress for companies. Choosing a straightforward DMP that is easy to use is critical for businesses that want to optimize data. Data-driven marketing is too reliant on data management platforms to incorporate a complicated, time-consuming platform into business practices.

71% of respondents in a Forbes Insights and Turn study believe that reliance on data-driven marketing will only increase this upcoming year. With high expectations for data-driven marketing in 2015, hopping on the data management platform bandwagon (if you haven’t already) is optimal. Just be sure to choose the right data management platform for a successful future!

Stop Dirty Data: 5 Steps to Data Cleaning

One of the biggest issues we see our clients have is dirty data; that is, inaccurate, erroneous, or otherwise incomplete data. While this may seem like a tiny blip in the realm of big data and analytics, it is quite possibly the biggest barrier in enhancing a company’s overall data insight capabilities. Here are 5 steps to data cleaning and ensuring data integrity.

1.) Know Your Data

Before you even attempt to start cleaning up your data, you need to ask yourself: What is the data used for currently and how it will be used in the future? Without a direction of how the data will be used, it will be difficult to clean it up because you won’t know what fields to eliminate (if any) or understand what glaring gaps lie within the data. We were given a task from by a client to clean up their data and bring it all in one place. When we presented the pristine, cleaned up, data it to them, we asked them what it would be used for and their response was they had no idea!

Bottom line: if you don’t know what your data will be used for, you will have a much harder time knowing where to even start the clean-up process.

2.) Sanitize Your Inputs

Let’s talk about how your data ends up in a database. Jane registers for a website and enters her name, e-mail address, and zip code. Her e-mail address is stored within the database with an extra space due to the input of the form, so instead of “janedoe@mail.com”, it reads as “janedoe@mail.com “. While this may seem insignificant, having that extra space will make the email field unreliable and could cause problems with how the data is read in other programs. Cleaning up your currently stored data is all well and good, but if you haven’t purified your inputs, there will be a continuous loop of bad data.

3.) Identify a Unique Field

This may seem like a no brainer, but without a unique field, harvesting your dataset will be much more difficult. Databases such as SQL and MongoDB insist on storing a unique field, labeled Primary Key or Object ID respectively. This unique field is important in identifying links between two tables, so you are able to append data from one table source to another.

For example, let’s say you have two separate tables: your customer list and your e-mail campaign data. Each customer has a reference number which is located in both tables. In order to link the customer data to the e-mail campaign data, having the unique reference number is vital in connecting the two tables.

4.) Validate Your Fields

If you pay attention to only one step in this article, this should be it. Validating your fields is vital in cleaning up your data.

Remove Spaces

Having spaces in your data could directly affect the readability if another software or API needs to read the information. Be proactive in removing any unnecessary spaces in your fields. If there is truly a space in the cell, the =TRIM function in Excel eliminates the unnecessary spaces.

Remove Illegal Characters

Sometimes illegal characters make their way into your otherwise clean data. We all know naming file names on your computer with characters such as #$%^& will elicit an error response, and the same goes with cell data. For example, let’s say you have a “date” column, and for whatever reason the “#” symbol is being inserted in every row. The date column will not read as a date column but as a text or general column.

Do Not Store the Date Field as a Text String

The date column is crucial in determining when an event took place. Let’s say you want to view the revenue for the past year that were attributed to your e-mail campaigns. If the e-mail sent date field is reading as text and not an actual date, you won’t be able to filter the data to gather your revenue.

Store E-mail Address as Lowercase

For consistency purposes, it’s important to store your customers’ e-mail addresses as lowercase. If you store a customer’s e-mail address as uppercase and he/she then decides she wants to unsubscribe from communications, the e-mail address may not get unsubscribed because of case mismatch.

5.) Keep Tomorrow in Mind

Looking ahead when it comes to data organization and cleaning will save you valuable chunks of time in the future. Not all data fields may be useful immediately, so hiding and safekeeping seemingly useless information instead of deleting fields will help save time and money when business needs change. Process guidelines for data structure guidelines may be time consuming to make, but it will mean a world of difference when explaining data entry and manipulation to new hires and other team members.

Consistency is the key to a cleaner data future.