All modern businesses thrive on data. For a business operation to be successful, in fact, it needs to have at its disposal sufficient quantities of relevant data. But by itself, this isn’t enough. The data has to be managed and understood, otherwise its usefulness can be very limited.
PR and communications, in particular, need organized data in order to function: you have to know something about the message and about the message recipients for a piece of outgoing content to hit home. And here’s the crucial part: you have to have a system in place so that the information you need is readily accessible.
This is where data management strategies come in. There are a number of different ones to select from, with various different strengths. We’ll go through them, once we’ve clarified exactly what we mean by the term “marketing data management”.
Consider a library. It might have a truly outstanding collection of books within. However, if they’re all piled up haphazardly in a huge box, then a reader’s ability to pinpoint what they need will suffer. There has to be an organizational regime in place so that required material can be picked out with ease.
Marketing data works in a similar way. Businesses can now acquire data from a panoply of different sources, including on-site operations, social media activity, SaaS applications, and traditional marketing channels such as radio.
It’s important to get a good overall idea of these inputs and how they intersect, or the data gathered will not yield the insights needed. You can do this by applying a range of practices, including integration architecture (read this if you’re wondering: “What is integration architecture”?).
Ultimately, then, marketing data management is the understanding of this interaction of data sources with each other. The aim is to produce a consistent and uniform set of data that is as usable and meaningful as possible. Why is this important? Let’s find out.
The benefits of marketing data management
There are several advantages to using marketing data management – below we’ll look at seven of the most important ones.
1. Improving campaigns
Having data and marketing metrics organized in a meaningful way means that you can assess the relative success of your marketing campaigns, including those driven by email marketing, and institute improvements where warranted.
Additionally, incorporating advanced lead generation techniques into these campaigns can significantly boost engagement and conversion rates, leveraging both new and existing data insights.
2. Understanding marketing ROI
If you can access sales data and link them to marketing efforts through effective marketing data management, then you can get actionable insights into where outlay has given results in terms of better campaign performance. This can then inform your future business decisions on where to put money in terms of your marketing efforts.
3. Identifying target customers
Having organized data means that you can use sales and demographic information to help you get insights into your target audience. You can use segmentation to arrive at target customer desires and intent, and update models accordingly. To this end, it’s often a good idea to use an intent data provider as part of your data management strategy.
4. Making personalization possible
Personalization is a hugely important PR and marketing tool. Having effective data management gives you the customer information you need to apply a personalized approach to your individual customer communications. Utilizing banner generation techniques, which create customized banners based on the available customer data, can significantly enhance the effectiveness of personalized marketing efforts.
5. Propelling customers along
Having an effective data management strategy means you’re better equipped to know what stage of the customer journey your prospects, leads, and customers are on and how best to propel them to the next stage.
6. Removing data redundancy
If you have several different data-driven marketing systems with no overall organization, you can end up having duplicate sets of data, using up expensive storage for no purpose. Effective data management strategies enable you to avoid this.
7. Complying with regulations
If you’re storing and processing data, the compliance burden is becoming more pronounced, with fines increasing all the time. Having your data securely stored and managed is an imperative requirement so that you can stay on the right side of the statutory framework.
Marketing data management strategies and techniques
Let’s now turn to look at several key marketing data management strategies that you might employ.
1. Data integration
This is something we’ve already touched upon. It’s very easy to find yourself swamped with related but distinct forms of data from all manner of marketing sources.
You may have information relating to customer behavior at the checkout in response to a particular offer. On top of this, there can be click-through rate data from that offer’s influencer marketing exposure, as well as OOH information that you need to factor in. To understand the effectiveness of that offer, you need to find a way to get continuity across your data sources.
To efficiently manage and synchronize these multiple data sources, employing a robust project management software can be instrumental. This software not only helps in streamlining the workflow but also enhances collaboration among team members, ensuring that all marketing data is integrated cohesively.
To put it another way, to get the 360-degree picture that this data can deliver, you need to see to it that it all becomes integrated properly into one view.
To do this right, you need to understand the individual characters of each of your data types. You need to know the answers to such questions as: what do they relate to? What format is used? Over what period of time are they collected?
When you have a sound appreciation of the nature of the data available to you, and leverage data integration tools, you can then work towards having a consistency of shape across it all. Conversion routines can apply where necessary, and you can trim sampling windows so they match in scope.
Done right, data integration will deliver a vista of meaningful information that will be of the utmost use to your marketing message delivery.
2. Data enrichment
This is a technique aimed at filling in the blanks. Even the most detailed data collection enterprise can be lacking some important factors at times. Should you find that you don’t have the data you need for a given marketing push, you may be able to seek it out via other means, depending on how many sources of data you’re picking up and what they’re delivering.
For instance, let’s say you’re a SaaS business, selling structural solutions. You’ve recently been pushing your latest enterprise architecture EA package via social media, but you’ve found that you don’t have the click-through data due to an unfortunate oversight. However, what you do have is a ‘how did you hear about us?’ box, and this can give you a certain amount of what you require.
Data enrichment is a way of using what you have to fill in whatever gaps you may encounter. Good data management enables this because you’re more able to keep tabs on what data is available to you, from any number of sources. Data stitching is a very similar activity—taking data customer that is extant across a number of datasets and incorporating it into a single place and format.
3. Data mining
This is concerning what to do when you’re faced with massive amounts of raw data. Data mining is a means by which you can take this raw data and discern trends and overall correlations.
It can be a lengthy process but it can reveal some highly interesting and very usable insights into the success (or otherwise) of your marketing efforts, whether you’re using sophisticated marketing automation or just flyer drops around the block.
4. Data refining
This involves ridding the organization of duplicate data, or data that is plain wrong or just useless. You can use automated routines to look for where duplications appear across customer databases and other sets. You can do the same with data anomalies that arise from input error.
You can use data refining to look out for other slips that can skew data, such as dates entered in the wrong format, or misplaced decimal points.
The overall aim of data refining is to take raw data and impose a meaningful structure on it, at the same time as rendering it free of error and redundancy.
Quite often, these processes will be undertaken by external analysts, depending on the potential size of the data management operation. However, some businesses prefer for such activities to be the purview of in-house marketing teams. There are benefits to both approaches, and a lot can depend on the resources you have available.
Using marketing data management strategies
Whatever the marketing data you need, whether it be to do with the effectiveness of a personalized pop-up or a marketing email campaign, marketing data management is essential.
There are a number of different data marketing strategies, including concentrating on data quality and uniformity. But what they all have in common is the objective of producing data that is of most use to the business.
The other commonality is this: whatever marketing data management strategy you use, it’s imperative that all your staff, especially your marketing staff, are aware of what’s being done and what’s produced as a result. It would be a shame to see all that good data go to waste.
As ever with these things, it’s all about communication. It’s what marketing needs or it will fail to achieve all that it can. Communication from your customers, sure, but never forget about the communication that you need within the business. That way you’ll get the most out of any marketing data management strategy.
