How to effectively manage your data for marketing automation success

As a data-driven initiative, marketing automation is heavily dependent upon the accuracy, completeness and validity of the data that the technology will run on.

Inaccurate and out-of-date data, gaps in information and poorly integrated data will all serve to compromise the core objectives of marketing automation – hindering the ability to create relevant, timely and engaging communications, and ultimately undermining efforts to increase loyalty and sales.

Worse still, marketing processes driven by bad data can actually damage customer relationships, as demonstrated by numerous studies.

“According to one survey, 55% of respondents had been sent information about an irrelevant product by a business in the previous 12 months,” says Nigel Turner, VP of information management strategy at Trillium Software. “A large minority (47%) said they are ‘annoyed’ when a business gets their personal information wrong, and 35% said such errors reduce their faith in the organisation to do a good job. So if you’re going to invest in marketing automation, then your first question has to be: ‘can we trust our data management techniques to support it?’ The truth might come as a shock!”

Indeed, poor data management is commonplace. In a recent survey by Demand Gen Report, it was revealed that more than 62% of organisations rely on marketing/prospect data that is 20 to 40% incomplete or inaccurate. Additionally, almost 85% of businesses said they are operating CRM and/or sales force automation databases with between 10 to 40% bad records.

Far too often, nobody takes responsibility for ensuring that key data is fit for business purpose, adds Turner.

“Marketing managers sometimes think of data as a technical problem and dump it on the IT department to resolve,” he explains. “But this is a mistake as data is a business asset, generated by business operations. Those in IT often feel their own competencies are to capture, store and secure it and make it accessible. They cannot bear full responsibility for its quality.”

Clearly, marketers must play a more active role in data management. So what do they need to do?

Data quality

A marketing automation platform will help users manage their lead process and build better qualified marketing leads, but the reality is that lead quality relies on the quality of your data and the context of your data. And this latter point is a big challenge.

“The information kept in marketing automation tools about a lead – e.g. their name, phone number, email, title, industry and company - are just pieces of the full picture. That information gets you just enough to contact the person for marketing offers. But what if you could add more to that?” asks Wynn White, VP of marketing at Birst.

He points to things such as:

• Behavioural data – social and web interactions of the prospect, their likes and dislikes.
• Historical data - past purchases, support issues and known requests.
• 360 view of customer – buyer interactions across all your channels – web, store, direct sales, etc.

White continues: “A holistic view of information is going to give you the boost that you to be different from competition, to offer your customers the products they sure would like, to personalise your emails and communication, and to surprise them with how much you know about them. Unless you have a way to integrate and analyse data from different channels and systems, you are not going to achieve that.”

But before attempting database integration to support marketing automation, the marketing organisation must standardise the data from across the organisation.

“In order to create appropriate and timely personalised communications, marketing automation systems need to draw upon customer data and interactions from across multiple communications channels, but it is impossible for any system to derive intelligence from cross-channel data unless each customer’s data in each channel source system can be matched to form a ‘single view’,” says Turner.

“Variations in formats and content need to be avoided, ideally resolved at source before proliferating across systems. Hence standardising the way data is formatted and how names, addresses, products and other content is expressed is essential. Cross-channel data standardisation is absolutely necessary to creating reliable customer intelligence. Only with reliable customer intelligence can marketing automation systems construct relevant and well-timed customer dialogues.”

Turner advises that the marketing organisation therefore work with IT to create a data quality compliance process for source data. This means ensuring that the data entering corporate and marketing systems and processes meets required standards for accuracy, completeness and validity. There are three main data compliance steps:

1. Assessing the quality of existing data and its degree of reliability and consistency. “There is no point in embarking on an expensive marketing systems implementation only to find that your data isn't of good quality, doesn't reconcile, and doesn't provide a reliable customer view,” says Turner. “Data profiling enables you to fully understand the issues in your data and determine what steps need to be taken to remedy them. Data quality assurance tools can also automate this process, enabling you to incorporate your own rules, so the data is not only validated for quality, but also for relevance to your specific marketing needs.”

2. Converting these rules into processes that transform and correct the data into a common format. “A standardised and corrected customer record ensures it will match associated data coming through other channels and legacy systems of data collection. This ensures that associated customer, financial, product, and historical data is linked to the correct person, and that any external data can be appended.”

3. Finally, the same process created for step two can also be embedded into your marketing systems to automate the validation and correction of data at the point of capture and to continually audit the data to check quality levels continue to meet defined requirements. “Marketing systems, supporting teams, processes and users will all have a high level of data consistency, quality and reliability serving their specific business requirements.”

Integration

When it comes to the integration process itself, White recommends that the first step should be to identify all the sources of information that matter.

“Understand where your customers consume most of your information - if your website is your mega phone to the market, then information about click-through paths is critical to your success. Don’t boil the ocean. Pick two to three sources to start. You can always add more,” he explains.

 Once you have identified the most vital sources of data about your customers, the second step is to integrate and bring all of them together. “This is where you need standardisation,” continues White. “For example, the click-through and web log data is normally kept in Big Data sources like Hadoop. Your social media channels provide APIs for reading information. While ‘access’ to information may be easy, the challenge is about standardising and ‘harmonising’ all different formats and shapes of data into one. This is where you would use a business intelligence platform that can transform your disparate data formats into logical groupings and subject areas.”

The last step is “rinse and repeat”. White adds: “Your customer’s taste and preferences can change. Your company’s products and service change. You may also discover new sources of information. For example, you may want to add your call centre data to discover sentiments and known issues. So it is important to iterate and refine your data standardisation and analytics to achieve your targets.”

James Rogers, chief marketing officer of OneSource, believes how a business approaches database integration to support marketing automation depends on a number of factors. The maturity of the organisation, the sophistication of the marketing automation solution, and whether the solution has been leveraged in conjunction with a CRM solution are all important factors to bear in mind.

“For a less sophisticated use of a marketing automation solution, the business can simply use the built in filtering capabilities in a data management solution to produce targeted lists that can be imported into the marketing automation solution for email campaigns,” he explains.

“For a more mature marketing automation solution that is integrated with a CRM, there are data management solutions that will integrate ‘out of the box’ to apply match and append, deduping and automatic enrichment of the database. API-level integrations offer many interesting opportunities for advanced companies combining out of the box solutions and integrations with professional services.”

A never ending process

But the process of data management doesn’t end once integration is complete. Indeed, once you start the process, it never stops. This means ensuring that standards of data entering the marketing systems are maintained, and that there is appropriate controls in place. This is something that Gerry Brown, senior digital marketing analyst at Ovum, raises concerns about.

“Quite a lot of marketing automation platforms focus on features on functions but have perhaps paid less attention to the kind of security and controls that you’d expect in an ERP or finance system, for instance, where you will be told you are unable to perform a particular action if you don’t have the privileges,” he explains. “In many MA systems, it is quite easy for you to push a button you shouldn’t and corrupt all of your data.”

In light of this, Brown recommends creating a “power user” in the marketing team to control access to the system to ensure that wrong messages aren’t sent out and data isn’t in need of reconfiguration.

“Marketing Automation isn’t like a CRM system where everybody can access it – you have to make sure it is not something that is ubiquitous within the organisation,” he adds. “The best examples of marketing automation are when you have a command centre with a power user who is a strong personality who is in control of all aspects of MA and oversees all the campaigns. Making sure you have a gatekeeper, telling people what they can and can’t do, is very important to the success of marketing automation.”

Indeed, ongoing effective data management in general is absolutely essential to the success of marketing automation.

Simon Bowker, country manager, UK&I, at Teradata eCircle , notes: “The amount of data businesses hold about their customers’ dramatically increases across multiple silos as they grow. Managing this data, establishing data sets and using it to give your customers the feeling that they are getting the information they want, when they want it and how they want it, is crucial. Marketers need to be able to sift through this data and make decisions quickly.”

Rogers further elaborates on the important role that effective data management plays to marketing.

“The ability of marketing automation to successfully perform its key capabilities of identifying and targeting the ‘ideal prospect’ is dependent on the breadth, depth and accuracy of the business information available. Successful deliverability, open rates and click through rates all hinge on the quality of the data that fuels the marketing processes,” he says.

“As well as ensuring the accuracy of data, effective data management can enrich a marketing automation solution with information that helps with targeting and prospecting, and can also be leveraged in the lead scoring/qualification and lead assignment processes. When a lead is submitted via lead form widget, call or chat, the data management solution can identify whether the company is ‘real’, the industry it's in, the size and revenues, number of employees and geographic location.  

“Data management can also provide the intelligence that the marketing automation solution uses for nurturing. The filter and logic on prospect activity levels, coupled with profiling of the prospect and their company, can lend to rules that can influence or determine engagement content.”

Fortunately, despite poor levels of data management having proliferated in recent times, there are indications that businesses are waking up to its importance. And CMOs in particular are starting to realise their department’s role in data management and quality, according to Turner – which will ultimately provide a considerable boost to their marketing automation efforts.

“They are recognising that it’s marketing managers who need to ‘own’ marketing data and marketing managers who must bring passion and determination to improving and managing its quality,” he says. “CMOs need to work with CIOs. Together, marketing managers can talk about what data really matters to them and where the real challenges are, while IT can advise on the data quality systems and solutions to help support improvements.”

Turner concludes: “Build a good data management foundation, and you’ll significantly improve your chances of marketing automation success.”