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When Charitable Donations Fall – Who’s to Blame?

I was listening to a program on BBC Radio 4 yesterday morning (You and Yours) about the difficulties that charities are facing in these straitened times:

“Christmas is the season for giving and is often the big year-end push for many charities. But according to a report compiled by the Charities Aid Foundation and the National Council for Voluntary Organisations charitable donations have fallen by 20% in real terms in the past year, with £1.7bn less being given.”

There was a lot of interesting feedback from the expert contributors:

  • Sarah Miller head of public affairs at the Charities Commission commented that “The top complaint made to the FRSB (the Fundraising Standards Board) … is to do with the use of data, where people are perhaps being sent mailings that they don’t wish to receive or perhaps incorrect information is being used on mailings or they want to know where the data has come from or perhaps a mailing is going to a deceased family member and they’ve asked for it to stop and perhaps the charity still hasn’t made that change – so that’s the top complaint by far”.
  • John Low, Chief Executive of the Charities Aid Foundation stressed that “You must be as efficient in the way you run a charity as any business, and maybe more efficient, because it’s precious public money that you have and you have very serious responsibilities to your beneficiaries.

It is certainly true that as a charity or any form of non-profit organization, you have far less margin for error when mailing your donors than a commercial organization. If I get duplicate mail from a retailer that I shop at, or incorrectly addressed mail that obviously hasn’t been able to obtain postal discounts even if it was actually delivered, it might make me wonder whether their prices have to be inflated to allow for such inefficiencies – but I’ll still do the price comparison when next shopping. When I get duplicate or incorrectly addressed mail from a charity that I give to, I get upset that they’re wasting my donation. Even more so given that I know there are money-saving solutions (ranging from desktop software, to services and hybrid solutions) for ensuring that mail is not duplicated and correctly addressed. Moreover, many mailers upset next of kin by mailing to the deceased or simply waste large amounts of money by mailing to people who have moved.

Based on the feedback received by the FRSB, some charities have a pressing need to implement effective solutions for eliminating wastage in their direct mail:

  • Gone Away suppression will more than pay for itself by reducing print and post costs.
  • NCOA (National Change of Address) and other services will allow charities to mail donors at their new address.
  • Deceased and duplicate suppression will avoid the damage to the donor relationship that otherwise will inevitably occur.

Sarah Miller also told listeners:

“If there are ways that charities are interacting with you that you don’t like, do tell them. Tell them how you want to interact with them.”

I remember about 15 years ago, one of our customers working for Botton Village (a centre for adults with learning disabilities and other special needs in North Yorkshire in the UK) won a direct marketing award simply because they asked their donors how often and when they would like to be contacted and at what time(s) of year. This led to a significant increase in donations. These days of course, it is far less expensive to contact people by email, but some donors may prefer at least some communication by mail, or not want email contact. Consolidating and matching donor information when they may donate via the web or by post is obviously important – for example, so you can make sure that you claim Gift Aid for relevant donors, or avoid sending a scheduled communication if they’ve just donated.

Chris Mould, Executive Chairman of the Trussell Trust, the charity behind the UK Foodbank Network talked about how a front line food bank in the Network can get a web site at minimal cost with online data collection: “It doesn’t have to reinvent the wheel”. This chimed with John Low’s recommendation that charities can become more efficient by cooperating on their resource requirements.

One last and very important point: all the experts on the program agreed that fundraising campaigns really work  – regular communication with your donors is important to show where the money is going, but efficiency is even more important.

 

If you are a charity, struggling to get hold of your data quality challenges OR if you’ve noticed a major drop in donations and want to know if data quality is the cause, email us for a Free Data Quality Audit and we’ll highlight the issues that could be putting your initiatives at risk.

Data Quality and Gender Bending

We have all heard the story about the man who was sent a mailing for an expectant mother. Obviously this exposed the organization sending it to a good deal of ridicule, but there are plenty of more subtle examples of incorrect targeting based on getting the gender wrong. Today I was amused to get another in a series of emails from gocompare.com addressed to [email protected] The subject was “Eileen, will the ECJ gender ruling affect your insurance premiums?” 🙂 The email went on to explain that from December, insurers in the EU will no longer be able to use a person’s gender to calculate a car insurance quote, “which may be good news for men, but what about women…” They obviously think that my first name is Eileen and therefore I must be female.
Now, I know that my mother had plans to call me Stephanie, but I think that was only because she already had two sons and figured it was going to be third time lucky. Since I actually emerged noisily into the world, I have gotten completely used to Stephen or Steve and never had anyone get it wrong – unlike my last name, Tootill, which has (amongst other variations) been miskeyed as:

• Toothill                    • Tootil
• Tootle                      • Tootal
• Tutil                         • Tooil
• Foothill                    • Toohill
• Toosti                       • Stoolchill

“Stephen” and “Steve” are obviously equivalent, but to suddenly become Eileen is a novel and entertaining experience. In fact, it’s happened more than once so it’s clear that the data here has never been scrubbed to remedy the situation.
Wouldn’t it be useful then if there was some software to scan email addresses to pick out the first and/or last names, or initial letters, so it would be clear that the salutation for [email protected] is not Eileen?

Yes, helpIT systems does offer email validation software, but the real reason for highlighting this is that we just hate it when innovative marketing is compromised by bad data.  That’s why we’re starting a campaign to highlight data quality blunders, with a Twitter hash tag of #DATAQUALITYBLUNDER. Let’s raise the profile of Data Quality and raise a smile at the same time! If you have any examples that you’d like us to share, please comment on this post or send them to [email protected].

Note: As I explained in a previous blog (Phonetic Matching Matters!), the first four variations above are phonetic matches for the correct spelling, whereas the next four are fuzzy phonetic matches. “Toosti” and “Stoolchill” were one-offs and so off-the-wall that it would be a mistake to design a fuzzy matching algorithm to pick them up.

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.

Assessing Your Data Quality Needs

So you have data quality issues. Who doesn’t? Should you embark on a data quality project? Maybe but what are your objectives? Are there service issues related to poor data quality? Marketing issues? Other major integrations or warehousing projects going on? And once you clean up your data – then what? What will you do with the data? What benefit will a clean database pose for your organization? And without clear objectives, how can you even justify another major technology initiative?

Before any data quality project, it is critical to go beyond the immediate issues of duplicate records or bad addresses and understand the fundamental business needs of the organization and how cleaner day will enable you to make better business decisions. This will help you to establish accurate project parameters, keep your project on track and justify the investment to C level executives. So where do you begin? At the beginning.

Look beyond the pain.
In most cases, a specific concern will be driving the urgency of the initiative but it will be well worth the effort to explore beyond the immediate pain points to other areas where data is essential. Plan to involve a cross-section of the departments including IT, marketing, finance, customer service and operations to understand the global impact that poor data quality could be having on your organization.

Look back, down and forward.
Consider the data quality challenges you’ve had in the past, the ones you face today and the ones that have yet to come. Is a merger on the horizon? Is the company migrating to a new platform? Do you anticipate signficant staffing changes? Looking ahead in this way will ensure that the investment you make will have a reasonable shelf-life.

Look at the data you don’t have.
As you review the quality of the data you have, also consider what’s missing and what information would be valuable to customer service reps or the marketing department. It may exist in another data silo somewhere that just needs to be made accessible or it could require new data be collected or appended.

Be the customer.
Call the Customer Service Department and put them through the paces. Sign up for marketing materials online. Place an order on the website. Use different addresses, emails and nicknames. Replicate perfectly reasonable scenarios that happen every day in your industry and see how your infrastructure responds. Take good notes on the places where poor data impacts your experience and then look at the data workflow through fresh eyes.

Draw out the workflow.
Even in small organizations, there is tremendous value in mapping out the path your data takes through your business. Where it is entered, used, changed, stored and lost. Doing this will uncover business rules (or lack of) that are likely impacting the data, departments with complementary needs and or places in the workflow where improvements can be made (and problems avoided).

Think big and small.
Management and C-Level executives tend to think big. Data analysts and technical staff tend to think granularly and departmental users usually fall somewhere in the middle. Ultimately, the best solution can only be identified if you consider the global, technical and strategic business needs.

The challenges with identifying, evaluating and implementing an effective data quality solution are fairly predictable but problems almost always begin with incorrect assumptions and understanding of the overall needs of the organization. In some cases, the right data quality vendor can help you move through this process but ultimately, failure to broaden the scope in this way can result in the purchase of a solution that does not meet all the requirements of the business.

Click here to download a comprehensive Business Checklist that will help you to identify the data quality business needs within your organization. Then stay tuned for our next post in our Data Quality Project series.

Cleaner Data. Better Decisions.

We thought the best place to start the new Clean Data Blog would be with a quick look at our new tagline – Cleaner Data. Better Decisions.

It’s catchy – we know – but it’s also a very important principle to us. When a company is going down the path of planning a data quality solution there are many facets that are being considered. With data integration, warehousing, budget and implementation questions to answer – it’s easy to forget that the ultimate goal behind the initiative is to make better decisions. Period.

When we first speak to a company, this is the primary focus. To understand the business challenges that they face so that we can build a solution to  allow you to  TRUST your data. The more you trust your data, the more successful you and your organization can become. When you are confident that your customer data is accurate, you can market to them without ticking anyone off and without throwing money out the window. When you know that your transaction histories are linked to your customer contact data, you can rest easy that your customer service team can provide your customers with the best possible support and service. When you know that your metrics are accurate, you can feel confident planning merchandising and expansion strategies.

Makes sense right? We thought so.

Here’s the most important thing: When you know your data is good – you can move quickly, strategically and efficiently to outsmart your competition and drive the business forward. Hesitating because you’re not sure if what you are looking at is a real reflection of the current state of the business – can mean certain doom. It’s not a practical way to run a business in today’s market. And with strong data quality tools (like ours) it’s also unnecessary.

How clean is your data?

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