The sheer volume of data generated by online consumers has proven to be both advantageous and a headache for marketers. It’s a lottery win in terms of it being a rich and powerful source of information for finding and targeting consumers, but it’s a curse in the sense that it must be handled properly to make use of it.
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. It’s understandable. The level of data coming in every day for companies continues to grow, and with that, there are bound to be mistakes.
But poor data management, quality and access are hindering marketers from realising significant returns on investments into marketing automation platforms. Businesses rely on efficiency to outperform their competitors, and automation plays a big part in this. With efficiency comes phenomenal gain, but effective automation cannot come about without accurate data.
The importance of accurate data
An organisation’s data is recognised as the most valuable asset of an enterprise, providing it is handled correctly. Failure to understand this can be very costly for any marketer.
Data is the main foundation of information, knowledge and ultimately the wisdom for correct decisions and actions. Mismanaged data, however, is worthless and sometimes even harmful.
If data is relevant, complete, accurate, timely, consistent, meaningful and usable, then it can propel an organisation into great success. Effective data helps in minimising potential errors and damages caused by them – particularly important for companies whose processes commonly use copy and paste, drag and drop and require linking, which come with greater risk of data errors.
When data is properly managed, updated and enhanced, it offers fast access to information for employees and greater work efficiency. The best way to achieve this is with a data management program (DMP), which aids in the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets.
A data program manager provides a centralised platform of customer data, which can then be segmented in order for brands to target select audiences via other martech or adtech engines, such as Demand Side Platform (DSP).
Thanks to a store of audience information, DMPs and DSPs allow marketers to find audiences similar to their current customer sell (lookalike modelling) to buy and target digital advertising and messaging to.
Deloitte Digital partner, Steve Hallam, positions DMP as a cornerstone, back-end technology for achieving consistency in cross-channel interactions with a customer or prospect. “There’s essentially a brain that sits in the middle of all your channels and has the message along with the particular segment or customer. Then you use the marketing technology proposition to deliver that message,” he says. “That’s becoming more important for cross-channel interactions and consistency. Someone who walks into a store, interacts in a mobile app, calls your contact centre or digital platform should have a consistent and cohesive experience.”
A DMP can offer just this by dealing with the complexities in aligning data from different channels. If there’s an interaction in one channel, the other channel knows about it.
How accurate data leads to efficiency
The world of marketing automation has the potential to make life so much easier for marketers all over the world. Using specialised software to automate repetitive tasks such as emails, social media and other website actions, marketers can simplify tasks and significantly reduce the time spent on them.
At its best, marketing automation is software and tactics that allow companies to buy and sell like Amazon – that is, to nurture prospects with highly personalised, useful content that helps convert prospects to customers and customers into repeat customers. This type of marketing automation typically generates significant new revenue for companies.
According to the 2016 MarTech Data Report, automation is seen as extremely important for today’s marketers and with this in mind, 36% of respondents said data management would be their top hire for 2016. Why? Because automation can’t happen without proper data management.
Marketing automation is heavily dependent upon the accuracy, completeness and validity of the data that the technology is 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, with many consumers admitting to feeling frustrated when companies get their personal details wrong.
So what does all this mean? It means that marketers must play a more active role in data management.
How to better manage your data
According to Nigel Turner, VP of information management strategy at Trillium Software, marketers need to 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.
Start small if you need to and concentrate on the main paths of information. Understand where your customers are most likely to source their information on you and follow these paths to gain the information you need. You can then start to harmonise and standardise all different formats and shapes of data into one. This can be broken into three steps.
1. Assess existing data
There is little point of embarking on an expensive marketing system implementation if your current data isn’t of high quality. Therefore, it’s crucial to assess the quality of existing data and its degree of reliability and consistency. Profiling your data allows you to understand the issues you might be facing and determine what measures need to be taken to remedy them. Data quality assurance tools can also automate this process, enabling you to incorporate your own rules so that it is of relevance to your specific marketing needs.
2. Converting rules 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 insures that associated customer, financial, product, and historical data is linked to the correct person, and that any external data can be appended.
3. Embed automatic validation and correction of data
Automating the validation and correction of data at the point of capture continually works to audit the data according to requirements. This way, marketing systems, supporting teams, processes and users will all have a high level of data quality and consistency.
Remember, however, that once integration is complete, the process of data management doesn’t stop. Data management is a never ending process that must be taken seriously if you want to reap the benefits.