When Direct Marketing Fails…

As a Direct Marketer in a tough economy, you are constantly being asked to do more with less.
Generate more leads. Improve campaign results. Increase brand awareness. Streamline operational efficiency. Unfortunately, poor quality customer and prospect data can sabotage your direct marketing efforts and your reputation in the process.

Want to get a handle on the ways that data could be tripping you up? Here’s a quick look at the top 5 Ways Bad Data is Hurting Your Direct Marketing Efforts AND how you can start to turn that trend around…

1. BAD DATA Wastes Money

In this most obvious example, companies with large, unkempt databases ‘pay’ for bad data, directly and indirectly. How much? Duplicate records mean duplicate print costs and double the postage but half the potential return while invalid or outdated addresses and garbage data is like throwing money out the window. Even in the digital world, bigger databases cost more money. Email vendors, marketing automation vendors and database marketing companies typically charge by volume of records – so larger, unclean databases will cost you more regardless of your marketing strategies.

2. BAD DATA Reduces Response Rate

While bad data has the potential to waste significant dollars per campaign, the flip side of that coin means your campaign response rates are compromised. In other words, when you send that direct mail campaign out, even if you get tons of responses – they are always divided by the ‘potential’ of your entire database (response/total sent). As a direct marketer (whether you use email, direct mail or telemarketing) the only way to demonstrate the maximum return on investment is to remove duplicate records, update old addresses and eliminate anyone who shouldn’t receive your marketing (which could include unsubscribers, gone aways or deceased records).  Now that return rate is an accurate reflection of your skill as a marketer and you wasted a lot less money in getting there.

3. BAD DATA Means Bad Targeting

How well do you know your customers and prospects? How many unique customers are in your CRM? Do you know how much they spend? What they like to buy? Where they shop? If you don’t – it means your direct marketing initiatives are being held back by a lack of information.  The remedy? Put all your customer data in one place, accurately entered and complete. This might mean merging contact data with household income. It might mean linking all their product purchases so you can determine buying habits. It might mean knowing ALL their addresses but choosing a master file so you know how and where to target them appropriately. For retailers this is known as a Single Customer View and it allows you to simply know more about your customer so you can use it to maximize your direct marketing efforts.

4. BAD DATA Hurts Your Reputation

Companies that market well have a better reputation. This needs no data to support it – simply check your kitchen counter for the catalogs and marketing pieces you’ve ‘saved’. What do they have in common? They were all mailed accurately (they made it to your mailbox) and they promote products that interest you (as compared to that motorcycle catalogue you may have tossed). And they showed up at the right time (like that home improvement coupon that arrived just after you purchased your new house). So to protect your marketing reputation you want to suppress and organize your data so that you don’t mail to the wrong people at the wrong time with the wrong message. Remove gone aways, deceased and anyone who does not want to get your mail. Your customers will thank you in the long run by continuing to purchase from your business (and not writing nasty things about you on Twitter). In addition, your ‘green’ credentials will soar by taking the initiative to reduce wasted mail.

5. BAD DATA Risks Opportunities

Almost everyone knows that Marketing and Sales go hand in hand and it’s all related to good data. When sales reps and account people have the correct information on the screen, they can go the distance in selling and up selling to prospects and customers. They can waste less time calling people that are not relevant and spend more time focusing on the ones who are. They can spend less time on each call searching for data or recollecting it and instead, wow the customer with how organized and efficient their interaction is. Accurate and complete data improves sales and wastes far less opportunities.

Mounting evidence suggests that cleaner, more accurate data has a direct impact on your direct marketing success and each year, more and more companies make data quality a key focus in both their marketing and technology departments.  Removing duplicates, updating addresses, gone away/deceased suppression and generating that all-important single customer view will allow you to

So what’s a Direct Marketer to do? Make every effort to regularly dedupe your database or CRM to keep the size reasonable and reduce waste across all your marketing initiatives. It’s also advisable to validate addresses and suppress gone aways and deceased records before a major mail campaign to reduce the waste associated with excess mailers and postage.  To take your Direct Marketing to the next level, work with your technology or database team to establish a comprehensive Single Customer View. This will allow you to use your existing customer data to drive better and more targeted Direct Marketing campaigns. You can also supplement existing data with a wide range of additional datasets. Lastly, keep good track of customers and prospects who unsubscribe or who don’t respond to specific types of mailings so you can market to only those receptive to your offer.

If you are interested in learning about how helpIT systems data quality toolset can address some of your Direct Marketing challenges, please feel free to contact us for a Free Consultation and Trial of our matchIT data cleansing software.

When Data Quality Goes Wrong…

When Data Quality Goes Wrong…

Whether you are a data steward or not, we’ve all experienced the unfortunate consequences of data quality gone terribly awry. Multiple catalogues to the same name and address. Purchasing a product through an online retailer only to find you have three different accounts with three different user names. Long, frustrating phone calls with customer service who can’t help you because they don’t have access to all the relevant info.

As the Director of Marketing for a data quality company, it brings me exceptional pain to see bad data quality in action. Such inefficiency is what gives marketing a bad reputation. It can ruin brands, destroy customer loyalty, waste opportunities and…let’s face it, it also kill trees.

Indeed, throughout our entire company, the water cooler occasionally buzzes with stories of bad data quality. So what’s a data quality company to do with all these DQ “blunders”? Call them out!

So this summer we’re going to dig through our box of examples and showcase a few #dataqualityblunders. We’ll try to be nice about it of course but the important part is that we’ll also highlight the ways that a good data quality strategy could have addressed these indiscretions. Because where there is bad data, there is also a clean data solution.

Have a #dataqualityblunder you’re just dying to spill?

We know that you’ve seen your fair share of data quality blunders. Send them in and win a $10 Starbucks gift card! Just email!


Where Is Your Bad Data Coming From?

As Kimball documents in The Data Warehouse Lifecycle Toolkit (available in all good book stores), there are five concepts that together, can be considered to define data quality:

Accuracy – The correctness of values contained in each field of each database record.

Completeness – Users must be aware of what data is the minimum required for a record to be considered complete and to contain enough information to be useful to the business.

Consistency – High Level or summarized information is in agreement with the lower-level detail.

Timeliness – Data must be up-to-date, and users should be made aware of any problems by use of a standard update schedule.

Uniqueness – One business or consumer must correspond to only one entity in your data. For example, Jim Smyth and James Smith at the same address should somehow be merged as these records represent the same consumer in reality.

So using Kimball’s list, we might know what kind of data we want in the database but unfortunately, despite our best intentions, there are forces conspiring against good data quality. While it doesn’t take a forensics degree, there are so many sources of poor data you may not even know where to look. For that, we’ve come up with our own list. Let’s take a look…

1. Data Entry Mistakes.

The most obvious of the bad data sources, these take the form of simple typing mistakes that employees can make when entering data into the system e.g. simple typos, entering data into the wrong fields, using variations on certain data elements.  Even under ideal circumstances, these are easy mistakes to make and therefore extremely common but unfortunately can be the source of high numbers of duplicate records.  But why is it so hard to get the data right? Consider these circumstances that can exacerbate your data entry process:

  • Poorly trained staff with no expectations for data entry
  • High employee turnover
  • Under-resourcing of call centres that leads to rushing customer exchanges
  • Forms that do not allow room for all the relevant info
  • Unenforced business rules because bad data is not tracked down to its source

2. Lazy Customers.

Let’s face it. Customers are a key source of bad data. Whether they are providing information over the phone to a representative or completing a transaction online, customers can deliberately and inadvertently provide inaccurate or incomplete data. But you know this already. Here are a few specific circumstances to look out for, especially in retail settings:

  • In store business rules that permit staff to enter store addresses or phone numbers in place of the real customer info
  • Multiple ‘rewards cards’ per household or family that are not linked together
  • Use of store rewards cards that link purchases to different accounts
  • Customers that subconsciously use multiple emails, nicknames or addresses without realizing it
  • Web forms that allow incorrectly formatted data elements such as phone numbers or zip codes
  • Customers pushed for time who then skip or cheat on certain data elements
  • Security concerns of web transactions that lead customers to leave out certain data or simply lie to protect their personal information

3. Bad Form

Web forms. CRMs. ERP systems. The way they are designed can impact data quality. How? Some CRM systems are inflexible and may not allow easy implementation of data rules, leading to required fields being left blank, or containing incomplete data. Indeed many web forms allow any kind of gibberish data to be entered into any fields which can immediately contaminate the database. Not enough space for relevant info or systems and forms that have not been updated to match the business process also pose a challenge. Many systems also simply do not perform an address check at entry – allowing invalid addresses to enter the system. When it comes to data quality, good form is everything.

4. Customization Simply Reroutes Bad Data

All businesses have processes and data items unique to that business or industry sector. Unfortunately, when systems do not provide genuine flexibility and extensibility, IT will customize the system as necessary. For example, a CRM system may be adjusted to allow a full range of user-defined data (eg to allow a software company to store multiple licence details for each customer). Where this happens, the hacks and workarounds can lead to a lack of data integrity in the system (e.g. you end up storing data in fields designed for other data types (dates in character fields).

5. Data Erosion is Beyond Your Control

Businesses and consumers move address. People get married and change their name. Business names change too plus contacts get promoted or replaced. Email addresses and phone numbers are constantly evolving. People die. No matter how sophisticated your systems are, some measure of data erosion is simply unavoidable. While good business rules will assist in updating data at relevant checkpoints, to maintain the best quality data, it’s important to update the data from reliable data sources on a regular basis.

6. New Data. Bad Data. Duplicate Data.

Many businesses regularly source new prospect lists that are subsequently loaded into the CRM. These can come from a variety of places including list vendors, trade shows, publications, outbound marketing campaigns and even internal customer communications and surveys. Although it’s exciting to consider procuring a new, large database of prospects, there are two ways this addition of data can go horribly wrong. First, the data itself is always suspect, falling prey to all the potential issues of data entry, data erosion and customer error. But even if you can corroborate or cleanse the data before entering, there is still a chance you will be entering duplicate records that won’t always be quickly identified.

7. Overconfidence

OK. So this may not be a true ‘source’ of bad data but it is the most important precipitating factor. You may think that by implementing business rules or by using a CRM’s built-in duplicate detection tools, that you are covered. In practice, business rules are important and valuable but are never foolproof and require constant enforcement, evaluation and updates. Moreover, built-in data quality features are typically fairly limited in scope and ability to simply detect exact matches. They simply not powerful enough to do the heavy lifting of a more sophisticated fuzzy and phonetic matching engine that will catch the subtle data quality errors that can lead to major data quality issues. This false sense of confidence means you can easily overlook sources of poor data and neglect to perform critical data quality checks.

So if you keep these seven bad data sources in mind – are you home free? Unfortunately not. These are simply the building blocks of bad data. When even just some of these conditions occur simultaneously, the risk of bad data multiplies  exponentially. The only true way to achieve the five-pronged data quality ideal outlined by Kimball (accuracy, completeness, consistency, timeliness and uniqueness) is through a comprehensive data quality firewall that addresses each of these components individually.

Stay tuned for more information on Best Practices in data quality that pinpoint specific business rules and software solutions to achieve true real-time data quality.