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In this blog, I will be delving into the world of Data Quality Management in small to medium enterprises. In my many years of data management experience, necessary processes are not always seen as a “must have” and this seems to be rife in smaller organisations. These are less likely to be governed by stringent guidelines and rules or simply don’t have the IT or marketing resources to formally manage this. They soldier on, collecting data, maybe using the business model and processes they have been following since they were established. It works so why change it? I have often heard this reaction, especially in fast paced transactional businesses...

Businesses realise that profits aren’t what they used to be and acquiring new customers isn’t as easy as it historically was. We all know that customers are now savvier with their money and aren’t necessarily providing the repeat business that they previously blindly provided as loyal customers. Times are changing, as are the strategies businesses need to employ to acquire and keep their customers.

The growth in the use of digital capabilities and the volumes of data that can be accessed has made this a more important consideration. Active data gathering and the utilisation of that data for communications and interactions with their customers is becoming more and more important. This needs to be coupled with effective and essential management of the data quality. The data needs to be fit for purpose.

Data collection
Organisations either capture data or catch it. The two are very different. Capture of data to create new customers or update existing ones is the recording of key information. You know what you need and why.

Catching data is the process of recording anything and everything, essential or non essential. It is a greedy process, rather like using a net whilst fishing on a large industrial scale – catch what you can. It’s unnecessary, time consuming and wasteful.

Time and time again I’ve seen the classic unstructured approach where a business catches data with a few scattered rules. This data is anything that the customer may be telling them. The essential information like name and postal address are rightly captured, but the non essentials like “number of children” and so on, may be useless if selling a low cost, fast moving good. These can all be obtained externally if needed.

Businesses hunt and gather this data and throw it all onto their database with hopes that it will bring them more opportunities and revenue. However, this is more detrimental to a business. The business introduces more complexities to their already wasteful processes, and creates a mess that continues to spiral out of control. More worrying they may not be compliant and don’t work by the rules of the 1998 Data Protection Act (DPA) and the 2003 Privacy and Electronic Communications (EC Directive) Regulations.

There are two areas to focus on – customer data (i.e. what you collect) and data processing. For the purposes of an initial data strategy and collection programme, you need to focus on the data being relevant and genuine for the purposes of running the business and only kept for as long as required.

So with the rules established, the next priority is data quality. Data quality begins at source. This is the cheapest form of data quality management which is easily controlled and monitored. Any changes are easily implemented and data is relevant, useable and rich.

5 main points to achieve higher levels of data quality
Data quality begins at source which is the key when establishing best practice around data quality verification.

1. Discuss, create and finalise a business strategy: What are you trying to achieve as a business? Work with all relevant teams to establish what the data can be used for and how. This step is essential and shapes the way the rest of the data quality management programme works.
2. Data fields - Capture: After the business has established what it wants to do and why, the data capture needs to be defined. The business needs to be sure that it is capturing information that will ultimately lead to better decision making. This will ensure that the business is working more efficiently.
3. Humans: Human error and deliberate human actions can lead a database into a dark infestation of inaccurately recorded data. The element of human error needs to be identified and reduced, and this is only achievable by undertaking a series of internal audits and observations. Create procedures for data capture and incorporate the training into your induction and development plans.
4. Machines: The software you use may be relatively dated but still does the job. However, some rules can easily be put into place to make this system more effective. Decide what data is essential.
5. Hygiene at point of capture: Despite the necessary controls being in place, bad data will still get through. Bad addresses never seem to go away but there are a few free and low cost solutions available in the marketplace to support your data quality endeavours

Duplication – free: Your database is more than likely to have the capability to identify duplicates. This can be done when the user tries to insert a new customer. Duplicates should be checked for from point of capture before new records are transferred into the database. Automation is the key to performing duplication checks

Verification – low cost: Verifying the correct postal address at point of capture is easily achievable and goes a long way in ensuring that the data you put into your database is not garbage. These services are available to bring in-house, but also accessible as an online service e.g. QAS Pro.

How to avoid data degradation
Data can sit in the database for months, years and even decades. It sometimes doesn’t see the light of day and has no associated value. The customer may have lapsed many years ago, but businesses still hold this data in a hope that it will someday be of some use. However, that day will probably never come, which begs the question – why keep it?

This is where the phenomenon of data hoarding comes in. Collecting masses of information may not cost much to the business by way of operational costs, but the real value of the real data can become lost within the overwhelming amounts of data the business has decided to keep.

Data continually gets pumped into the database and every so often the business may decide to send out a mailshot to its entire base, hoping that someone will respond. It’s quite obvious that the response rates and ROI will be very low. It is essential for businesses to recognise the value of the data. However good your point of capture processes are, data will degrade very fast.
Around 18,000 people move house every day. Thirty per cent of people change email address every year. Consumers specify Mailing and Telephone preferences, and the number of deceased contacts grows daily. All businesses must be proactive in the maintenance of their data to extend the data lifecycle, and to ensure that brand reputation is protected and money is saved.

The maintenance of data isn’t something that occurs only before you use it for marketing purposes. It encapsulates the processes before use of the data and after use of the data. Processes need to be in place where all changes and updates are fed back into the database to ensure accuracy and consistency.

7 simple steps to help extend data life and retain data worth

1. Data Sources: All data sources must follow stringent protocols of data capture, processing and transfer. The organisation needs to ensure that all data arrives in a fit state (following above capture rules) for storage in a single database.
2. Regular De-duplication: Processes need to be established and automated (to reduce human error) to identify duplicates and merge records. This can potentially be conducted as part of the database in-house. If this option is not available, software can be purchased to perform de-duplication tasks.
3. Batch Cleansing: Data should be cleansed in its entirety on a less frequent basis. Dependent on activity, six months is sufficient for cleansing all addresses and standardising names, addresses and postal codes. This service is available as in house software or as an outsourced Bureau service. There are also online cleansing services e.g. Experian Intact which allow easy cleansing from your desk.
4. Suppressions: Prior to any mailing going out to a contact, the business must run their mailing file suppression screening. Suppression screening is available as an online, in house or outsourced service and the screening returns flags which must be fed back into the database to keep your records up to date and accurate.
5. Archiving: How old is old? Every business needs to have business rules in place to identify old data. Don’t be afraid of archiving. These customers need not be part of the main database, which means they can be managed as a separate subset to be targeted with alternative communications.
6. Customer Preference and adhoc updates: Define and implement rules around the capture and recoding of customer preferences and management. Appropriate systems must be in place to record vital information which may be the make or break in a sale. Address updates are equally as important and a defined set of processes will help achieve good levels of accuracy.
7. Audits: Whether or not things seem to be going well, reporting remains an integral part of early identification of problems with data. Weekly reports and where possible, daily monitoring of data coming into the organisation will help to maintain the data quality across the board. Monitoring is the key to Maintaining.

Data Quality Management need not be seen as a mammoth all singing and all dancing project. For smaller (and larger enterprises), data quality can be achieved in small controlled steps. As long as the desire is there, to move to a more organised, lean and effective way of working, the business can succeed in its quest without spending large amounts of money.

Marie Myles is Director of Consulting for Experian's Marketing Services division. She can adapt and apply her extensive skills in any sector where customer data management and the application of analytics and research is key to added value.

Getting employees on the frontline in a position where they believe their needs and wants are being heard is a great achievement and one that enables you to pre-empt issues, address concerns and manage any push back when working on data projects. But continuing this two-way communication is the key to success.

A great way of doing this is by creating a feedback mechanism - a solution that is so simple but so often overlooked. All this needs to be is a dedicated email address that people can use to provide feedback on the project whenever they choose. This must be regularly monitored and emails should be responded to within an agreed timeframe. Always be receptive to communication from employees and consider and respond to feedback that they have provided.

Do take advantage of your organisation’s intranet too. It’s a great place to feature up to date timelines and progress and even informal daily comments from project leaders on the latest achievements and even hurdles. Also, do engage people in honest communications, with honest feedback - using a human approach will help them become more accepting of what the business is trying to achieve.

Another option is to give people the opportunity to volunteer or nominate one person per team to be members of a steering group. This again promotes involvement and engagement, though the business must manage the costs of this – you can run the risk of spending a lot of man hours in meetings.

However, there is nothing stopping the business from being imaginative. Weekly or fortnightly meetings in person, on conference calls or web conferencing, can help to have regular contact with key individuals who will act as spokespeople for their colleagues. Manage it case by case.

A word of warning though. People should be managed to avoid feedback opportunities turning in to a wish list type of exercise. Though steering groups will enable you to learn about what people want, the business still needs to be prescriptive in terms of what will help it achieve its goals. The aim here is to get them on board, keep them informed and to positively connect with them.

Marie Myles is Director of Consulting for Experian's Marketing Services division. She can adapt and apply her extensive skills in any sector where customer data management and the application of analytics and research is key to added value.

My posts so far have focused on how taking the right steps can get end-users on side and help you implement a successful data strategy.

The next stage is to make sure your workforce has the understanding and expertise to actually use the tools you are implementing. There is nothing worse than having a “ta daa!” approach: “here’s the new strategy, associated systems and processes, and this is how you do it.”

We’ll have all seen it. Occasions when software is introduced but staff have no idea how to use the hardware. User uptake is likely to be low, compliance minimal and the strategy is doomed to failure. Being prepared well in advance by understanding people’s capabilities will help to determine what type of training they need.

These sessions should be conducted with plenty of Q&A opportunities – again always a winner when helping people understand what you are doing and why. Express the mutual benefits of this exercise and demonstrate how using a new process may, for example, be quicker, more effective and allow people more time to focus on things that they see as essential to their roles.

Conducting generic training as opposed to a beginner/intermediate/advanced approach is important. Identifying weaknesses within people’s skill sets can have a negative effect and turn them off – promote each negative as a positive.

Following regular communication and tailored training, employees in the business will feel well informed and well equipped to perform tasks assigned to them. They will feel empowered and confident with the necessary tools in the form of information and updated skill sets to follow new processes. Giving people this power will ensure they use it positively within the business. This will help move the project on its way to success.

Marie Myles is Director of Consulting for Experian's Marketing Services division. She can adapt and apply her extensive skills in any sector where customer data management and the application of analytics and research is key to added value.

So you’ve got lots of data, but do you know what to analyse and which metrics to use?

As a digital consultant, I spend a lot of time talking to clients from a wide range of sectors about their headaches and of course looking for ways to take away the pain. And if there is one single theme that unites clients with an online presence today, it’s the challenge of making sense of the vast array of data they now have available to them.

There’s a real risk that databases become bloated with masses of unused data, gathered at great expense, leading to a rather bad case of indigestion for all concerned.

We’ve come a long way since the days when a junior exec or even the intern was put in charge of managing a few keywords and sending out a generic email to customers. The performance of digital marketing is a c-level concern now, with everything from attribution analysis and KPI dashboards on the radar of the CMO or FD.

But how can you know what to analyse and what metrics to focus on? Here are my tips for avoiding data indigestion:

1. Just because you can access a huge range of search, email and purchasing data, doesn’t mean you should. Don’t get blinded by the vast amount of data out there. Start by identifying the behaviours of your most valuable customers, and what you need to drive those behaviours in other customers.
   Using this rationale focuses the mind on the information needed and makes the evaluation of which multichannel data to use a much easier decision.
2. Develop a channel-agnostic data segmentation strategy.
   Design a segmentation strategy that underpins all your marketing activity regardless of channel. Best practice is to create a segmentation set that is common currency across the whole of the business. It informs every decision and is the source of information on how to target and understand customers. Clearly ‘consistent’ is not the same as ‘static’, so continual development and refinement of segmentation sets is essential as markets evolve.
3. Always seek out channel solutions that are aligned with your segmentation strategy.
   Whilst traditional channels such as direct mail, email, SMS and outbound calling have been used to deploy segmentation, digital channels are becoming just as targetable. For instance segmentation has proven successful for on-site content- serving and digital advertising whilst social media (Facebook) lends itself well to micro-targeting. 
4. Keep the customer at the heart of what you do.
   Customer-centricity, data and insight is our mantra; make it yours.  Learn from one of our clients: a major financial services brand’s  mantra is to only use data to the benefit of the customers, in other words to responsibly offer the customer products and services which they know they can afford and which could be of benefit to them.

Gemma Carver is a digital consultant with Experian’s Marketing Services Division, specialising in customer engagement strategies for retail and financial services.

Time travel back ten years and it’s likely the online marketing department of most brands would be preoccupied with the creative execution of banner campaigns and moving away from a brochure ware website to either ecommerce or at least more customer engagement and interaction. Web Analytics and search were in their infancy and the major KPI for Marketing Directors were volume based metrics such as total visits and dwell times.  e-CRM consisted of mass broadcast email campaigns and in many cases the only source of data from the website (then web logs) sat within the IT department.

Time travel back just two years and the same online marketing department will have been transformed. Whilst someone will still be reviewing creative for a banner campaign (now called Display Advertising), there will have been a major evolution in the sources of data available to the team. The Online Marketing Manager will now have  access to data from his search bid management tools,  email behaviours  and affiliate tracking. Website analytics will be tracking browsing behaviour and possibly powering decision engines for cross and up-sells. The Marketing Director will be tracking a whole suite of KPI’s from unique visits, %repeat visitors, dwell time, no downloads, sales revenue, conversion rates, delivery rates, CTRs, registrations, unsubscribes – the list goes on.

One of the challenges is that all of these new sources of data have pretty much stood alone and provided more than one version of the truth! They have also been reporting at and been optimised at a campaign level – ie taking a short term tactical approach.  The skills needed to interoperate the data have spawned new occupations like search professionals and web analytics professionals. Most things digital move and evolve at such a pace that brands are it difficult to keep up to date nad are finding it difficult to source talent with the right skills. However with the exception of the jargon and little bit about the technology, are these skills really that much different from those of the traditional direct marketer?

Fast forward to the present day and the same marketing department is starting to see the convergence of all the data sources and direct links being made into the customer database. The desire for a true multichannel experience and one view of the customer, along with the advancement in technology, is the driving force. In just ten years the online marketing department has gone full circle from limited data to data overload. The opportunities are massive but there are a number of challenges to be faced.  Perhaps the most daunting is what to do with all the data. Where do you start?

It’s highly likely that the database already has some sort of segmentation, perhaps put in place from offline activity or from some of the online data that did find its way into the customer database eg email data. A good starting point would be to identify which online channels are driving the high value segments and focus your data management in this area. Investment in data and analytics could then be diverted into these channels and away from channels driving less desired segments.  Once this is in place you can start to get more granular with both the level of data and analytics that you undertake. For example in paid search which keywords are driving your high value segments and in the affiliate channel which of these arrangements drives the higher value customer and not just pure sales volume? Once you know this you can optimise your keywords to your segments up-weighting and down weighting bids accordingly or in the affiliate channel putting in place commission by affiliate type or even rewarding individual affiliates that drive the desired segments.

It really is imperative that brands put in place segmentation strategies for their online data either using their in-house skills or working with partners. Doing so will not only deliver improved ROI and competitor advantage but will place them in a pole position for dealing with the next wave of data whether that be social data or attribution data.  One thing seems certain; as innovation continues so does the complexity of the data.

Fast forward five years and there are no online and offline marketing departments just one team that encapsulates all channels and platforms. As the technology and the data converge so does the marketing structure. The customer database is at the heart of the brand driving ever increasing customer personalisation and the winners will be those brands that gather, interrogate and act upon the data and insights derived from it.

Mark Hales is a digital consultant with Experian’s Marketing Services Division, specialising in digital acquisition, with a keen interest in new technology.

Culinary luminaries despise dishes with too many ingredients – but is it the ingredients that pose the problem or the execution of the dish? Great ingredients, a talented chef, but no recipe.

Let’s take a growing business, with growing customer touch points. Increased customer interaction leads to increased data capture across multiple, growing channels. Data storage systems seem to multiply overnight and all-of-a-sudden marketers are faced with data overload - disparate sources, a plethora of data types and ever increasing customer expectations; the perfect recipe for a data management nightmare.

So why do so many businesses struggle to achieve optimum customer insight? The simple answer is the absence of a Single Customer View (SCV). More often than not, the ingredients are all there – plentiful data, excellent internal resources and established relationships with profitable customers, but the organisation’s strategy is often product-led and their data assets aren’t customer aligned. Without a customer-driven data strategy, marketers can’t hope to be truly customer-centric.

In addition, data management strategies to deal with increasing data volumes and sources and to create meaningful insights are often weak. The problem is compounded by a lack of buy-in from key stakeholders, which leads to the SCV agenda not being a priority or worse still, not existing at all.

Instead, new data storage systems are introduced to plug the gap – which ironically increases gaps elsewhere. Inevitably, the distance between each customer database grows, which leads to further isolation of data around the business. And so it continues...

Businesses need to recognise the results that an effective customer centric strategy will help them achieve and they need to learn that a Single Customer View is the recipe that brings all these ingredients together.

Five steps to developing your SCV and data strategy:

1. Understand your data sources and the data’s journey thereafter.
2. Set-out the policies and procedures to ensure that data storage is managed effectively.
3. Review your organisation’s data strategy, data quality assurance and optimisation – are you maximising the value of your data assets?
4. Are the necessary resources, including IT and staff expertise, in place to facilitate the SCV agenda?
5. Everyone on board? Nothing is more detrimental to a SCV initiative than key stakeholders resisting the change. Ensure all key influencers are privy to communications as soon as the SCV agenda is established.

Kiran is a Data Strategy consultant with Experian's Marketing Services division, specialising in ensuring that data is used as an asset and is maximised for a variety of purposes.