5 Ways To Use A CDP To Optimize Data Quality

Melissa IN Team | Customer Data Platform, Data Quality | , , , ,

In today’s customer-centric environment, businesses must be able to predict customer needs and offer personalized service. Your ability to do this is determined by the quality of customer data held. Poor data quality can keep even the best marketing campaigns from succeeding. According to a study, sales teams lose approximately $32,000 per sales rep and 550 hours just because of bad data. Read more “5 Ways To Use A CDP To Optimize Data Quality”

Understanding The Cost Of Missed Deliveries To Shippers

Melissa IN Team | Retail | , , ,

Today, anything from a set of spoons and forks to a car can be bought and sold online. Though the products may vary, one fact remains constant – the responsibility of having the order delivered to the customer on time rests squarely on the shoulders of the retailer. Irrespective of whether there’s a delay in delivery because of traffic or because the customer forgot to mention the street name, missed deliveries are a frustrating and expensive affair.

How Do Missed Deliveries Impact Customers?

Almost all of us have been at the receiving end of a missed delivery at some point of time or the other. You’ve waited all day for an important delivery only to be told that it will reach you tomorrow or you’ve been tracking an order and suddenly it seems to be going in the opposite direction! Missed deliveries are frustrating and inconvenient. When it comes to high-value orders, many people actually take the day off on the delivery date so that they can be home to receive the package.

So, when a delivery doesn’t arrive as scheduled, the frustration typically turns into complaints and a loss of goodwill towards the retailer. The customer may leave a negative review and may turn to competitors for future purchases.

How Do Missed Deliveries Impact Businesses?

Understanding the impact of missed deliveries on businesses is slightly more complicated. There are immediate and long-term effects to be considered.

• Increased shipping costs

When a delivery doesn’t go as scheduled, the shipper must instantly reschedule it. This means additional expenses such as fuel. Last-mile delivery within a supply chain costs around 28% of the total transportation costs! Even if the address entered by the customer is wrong, 32% of them expect the retailer to re-route the shipment on their behalf.

This may also have a knock-on effect on other deliveries. Delivery vehicles have a limited capacity and in the case of large, bulky items, the delivery of some other order may need to be rescheduled. This can have a domino effect.

• Lost revenue

According to the Indian Motorcycle Riders Group (IMRG), missed deliveries cost retailers, shippers, and consumers a collective $2 billion a year. In a bid to ease the customer’s frustration and apologize for the inconvenience, many retailers offer a discount on future purchases. This eats into their revenue.

There’s also the risk that the customer will not place an order with the seller again. According to a study, 69% of consumers will not make a second purchase with a retailer if their order is not delivered within 2 days of the promised date. 16% of respondents said that they wouldn’t shop with the retailer again after just a single incorrect delivery. Estimating this loss of revenue is nearly impossible.

• Fall in brand perception

E-commerce has risen to its current popular position because of the convenience it offers in terms of saved time and effort. It has led to a demand for instant gratification and lowered a willingness to compromise.

In an era where product differentiation is extremely difficult, customer experience is what builds a brand. That said, even the most loyal shopper will start looking for alternative service providers if they face missed deliveries on more than one occasion. Nobody wants to spend their time calling customer services.

Thus, missed deliveries affect the way your brand is perceived. Customers don’t really care about whose fault it is and cannot differentiate between the service offered by the retailer and the shipper. A survey reported that 73.6% of customers felt that delivery is the most important part of their overall shopping experience. They will be quick to leave a negative review and even if the product is excellent, when they talk of it, they will mention how the delivery was delayed. This drop in perception can keep other potential customers from shopping with your brand.

• Increased carbon footprint

Environmental concerns are an indirect effect of missed deliveries. Getting your delivery agents to drive down to a customer’s address again means extra fuel usage and higher greenhouse emissions. Estimating around 1 billion missed deliveries annually puts the amount of carbon released due to this at around 3,742 metric tons. This is the amount of carbon it would have taken 9000 trees 58-years to sequester.

Today, all brands big and small are talking about the steps they’re taking to reduce their carbon footprint. However, these greenhouse gas emissions negate their hard work and can once again affect how the brand is perceived.

How Can Shippers Minimize Missed Deliveries

It would be impractical to suggest that a brand can achieve 100% delivery success. Many causes of missed deliveries are out of your control. For example, you can’t fight storms and natural calamities. With advancements in GPS technology, traffic can be navigated and delivery routes can be optimized but only to an extent. That said, there’s still plenty that can be done to improve last-mile shipping.

One of the most important steps towards minimizing missed deliveries is to validate customer addresses and contact details when they are being collected. There are a number of data cleaning and enrichment software that can help do this.

When the customer enters his/her address, the address must be compared to data from reliable third-party databases to ensure that it is correct. Missed details such as pin codes can be added on to enrich the data and make last-mile delivery easier. There may be instances where the address entered is correct but elements are outdated. For example, the city may have changed a street name but the customer may have still entered the old street name. In such cases, you need to enrich the data with updated details.

The Bottom Line

Having a reliable database with correct, current addresses will show you instant results in terms of fewer missed deliveries. That’s not all, having reliable data also helps with your marketing ventures and helps make intelligent business decisions. The bottom line- don’t let missed deliveries become missed opportunities.

Schedule your demo today, to learn more about avoiding missed deliveries with Melissa.

Disparate, Dirty, Duplicated Data – Understanding the 3Ds of Bad Data

Melissa AU Team | Data Quality | , ,

Disparate, Dirty, Duplicated Data – Understanding the 3Ds of Bad Data

In 1999, NASA learned how expensive bad data can be the hard way when they lost the Mars Orbitor in space. Why did this happen – because the engineers made calculations based on the Imperial system of measurements while the NASA scientists used the Metric system.

A simple mistake of not ensuring that the data was measured in the same units cost NASA billions. Such is the impact of bad data.

When talking of bad data quality, there are three ‘D’s that come into play – dirty data, disparate data and duplicate data.

  1. Dirty Data

Entering an address as ‘Main Str’ instead of ‘Main Street’, typographic errors, using numbers in fields intended only for alphabets – these are some of the most common examples of dirty data. Such data issues can be categorized as:

  • Incorrect spellings
  • Negative spacing
  • Incomplete information
  • Incorrect use of upper/lower cases
  • Use of abbreviations and nicknames
  • Incorrect use of punctuations and symbols

It may seem like a small inconsequential error but data specialists and analysts spend a considerable amount of their time simply cleaning dirty data like this. Leaving it as is, is simply not an option. How can you expect delivery agents to reach customers on time if they do not have a complete address or if they cannot understand the street name?

And imagine a customer’s frustration if they were to receive a promotional email that addresses them by a misspelled name…

  1. Disparate Data

Companies collect data from various sources. In theory, this helps create a cohesive record. But, the issue with collecting data from multiple sources is that every source may use a different format to record and present data. A difference in date formats is the simplest example.

The sales team may records dates in the DD/MM/YYYY format while the accounts team may record it in the MM/DD/YYYY format. Thus, the latter may read 06/12/2020 as the 12th of June while the sales team may be referring to the 6th of December. It’s a small misunderstanding that can have dramatic impacts on sales projections, marketing plans, etc.

Disparate data refers to data extracted from different sources and stored in varied data formats. This type of bad data keeps analysts from getting a deeper insight and makes it difficult for them to derive anything of value from the data.

  1. Duplicate Data

Duplication is the third and, in many ways, the biggest data quality issue. There are many reasons why your databank may contain duplicate records.

  • A new record may be created every time information is updated. For example, a new record may be created every time a customer makes a purchase instead of updating the original record.
  • New records may be created every time a customer interacts with the brand through a different medium. For example, let’s say a customer places an order through a brand’s website. The next time, he places an order through the app. Instead of using a single account for both interactions, he may create different accounts – one with his first name and one with his last name.
  • New records may be created when re-registering with new phone numbers of email IDs.
  • System glitches

Duplicate records make a data bank very unreliable. Think of it this way – the marketing team looks at a data bank of 500 records. Of these 300 seem to be in a particular geographic area and hence they decide to open a new branch for easier accessibility.

However, 120 records are duplicates. Thus, the new branch will cater to only 180 customers in reality. If they had access to this information, they may not have decided to open a store in that particular location.

Eliminating all duplicate records manually is simply not possible. For example, a person may create accounts as ‘Aditya Chauhan’, ‘Adi Chauhan’, ‘A. Chauhan’, etc. While some records may share the same email address, others may have only the same phone number. Thus, to truly de-duplicate records, you need an algorithm that compares all the data rows and weeds out cases with even the lowest probability of duplication.

Dealing With The Three ‘D’s

Bad data becomes more expensive the longer it is kept. Thus, it needs to be dealt with as early as possible. Logically speaking, the first step is to put quality checks in place at the data collection source. There are a number of tools that can help with this.

For example, address verification tools ensure that complete addresses. Instead of relying on human input for the complete information, using an autocomplete feature can help minimize errors and capture information in standardized formats. Similar tools can also compare data in new records to the existing database and keep duplicates from being created.

Instead of blaming IT for bad data, data governance policies need to be created to standardize fields for records and minimize the issue of disparate data. This policy will outline how data is collected, processed and managed to ensure that it is accurate and consistent.  It should ideally be flexible so that it can be adapted to changing needs.

Data quality checks at only the collection source are not sufficient to keep bad data out of your system. Data often goes bad simply with time. For example, a city may choose to rename a street, thus, invalidating records that mention the old street name as part of the address. To counter this, data quality checks must be made a routine task.

All you need to do is find the right tools. For example, email verification tools can ping emails without human interaction by the company or customers to check whether the email ID is still in use. Those that have been discontinued can be highlighted and removed from the system.

Lastly, it is important to set realistic goals. Hoping to achieve 100% perfect data is a bad goal. Instead, your goal should be to make data credible and fit for intended use by ensuring that it is accurate, complete, valid, standardized and accessible.

 

MDM – Secure, Fast and Hassle-Free with Unison

Author | Data Quality, MDM, Unison | , , , , , , , , ,

Automated data quality routines, lightning fast processing (50 million records per hour), and no programming expertise required for master data management? Unison has you covered. It unifies all of Melissa’s data cleansing technologies through a straightforward, modern and powerful user interface without sacrificing speed or security. Explore what makes Unison so unique from other platforms and how it was designed with data stewards in mind. Turn to page 34 in Big Data Quarterly for a Melissa exclusive on Unison.

The Half-Life of Data

Author | Big Data, Data Cleansing, Data Quality, Global Data Quality | , , , , ,

Data is the new oil of the digital economy. Effectively used, data can help organizations to better understand customer needs and provide winning strategies to meet them. But the data you depend on to make these important decisions is always in flux.

In The Half-Life of Data, Bud Walker, Vice President of Enterprise Sales and Strategy for Melissa, breaks down the true nature of data and what it means for your business. From scientific fact to census info, to contact data and beyond, data will expire – and as it turns out, you can easily calculate when that will be.

For the full article, check out pages 19-20 in the latest addition of NoCOUG Journal. If you’re interested in joining NoCOUG Journal, click here.