Before spending any money on outsourcing, SMB marketers first look for ways to optimize in-house data assets. And this will put them on the path to better digital campaign planning and performance that yield business results.
In this data-saturated and inadequate Martech environment, marketers and media planners have to do more than what their traditional role dictates.
In today's world 🌎, to see the semblance of success in digital initiatives, Marketers must assume the role of data experts for the organization they represent.
And such a position comes with the responsibility of venturing out beyond the traditional marketer's domain. From understanding the available data sources, acquire the ability to curate, clean, and make those vital datasets access and usage available to campaign planning and execution.
The main objective behind such exercise here is to make the data more valuable and make it work for you 👌. And who knows better about the data needs than the in-house marketer. They are in a better position to help themselves, with their collective knowledge about the brand and product they are promoting and understanding of existing and potential customer base.
If you are convinced with my argument so far, then, the inevitable question comes next; Is there any blueprint available? And How should one go about doing that? Usually, if the company has enough dough to spend, then they may try to shortcircuit this by hiring fancy pant consultants or purchase equally elegant technology tools.
Instead, My recommendation is Marketers first go back to basics, start doing how will they do it in the absence of such outside help. However, it's worth pointing out that I am not advocating against investing capital towards an expert or technology tool assistant. But, any effort to assemble an expert team or buying technology tools should come later in the journey towards maturity.
By postponing such investment should save companies from making a risky early investment, handing technical-debt, or high-cost ongoing maintenances 💲. Imagine the time, effort, and investment required to stitch any in-house technical solution together, or financial commitment comes with outsourcing to the SaaS application. For SMB's marketers, the cost of getting this wrong, facing the possibility of financially lasting consequences.
And, more than anything else, in the early stages, both of those approaches not poised to yield the expected business outcomes, mainly because the person in charge of the marketing department may not have much skin in the game. Instead, the ideal approach would be to spend your resources and energy internally first, focusing on understanding various data assets thoroughly. This effort should involve focusing on two main aspects of priming the data assets.
First, identify the data sources, ready for a surprise when you see them available in unexpected places in your business. Second, formulate the plan to maximize their values and usage in marketing.
🍺 Identify Data Sources
Try to have a clear insight on each unique data source - the whole lifecycle from how the data get created and organized and their role in operation along with the business impact.
And collect information along the aspect of whether the data source is siloed or integrated with other data sources, dependency, data quality, and any issues with governance and bias to consider, etc.
Having this baseline understanding and how to surface them in an expected format opens up a whole new level of opportunity. And enable your marketing department to unearth existing values while helping to generate business outcomes.
During this phase, any gaps identified among the data source with the potential to limit the marketing efforts. Then make sure to highlight that among stakeholders to address it.
At the end of this exercise, you must be able to answer all of the following questions regarding each data source.
- Identify and classify structured and unstructured data sources.
- Ensure the accessibility when and where needed.
- When data involves personally identifiable information (PII), confirm that measures are in place to protect data integrity and security.
- Ensure data governance in place to protect against unintentional data leakage and intentional breaches from bad actors.
- The current setup demands you to spend less time on planning, the more time on achieving the marketing goals.
- Easy enough to transform data into insights that can be ideal inputs to campaign plannings.
- Help you focus on efforts that help the business move forward by unlocking value in data and increase competitiveness.
💰 Maximizing Data Values
Having made this far must have changed your outlook on the in-house data sources and their values.
From here, with this refreshed understanding, only enable you to make use of those insights in campaign efforts from creation, execution to optimizations, and help unlock the unknown values and accelerate time to value in the marketing department.
Unlike the first phase, this second one should last for a while, and this entire journey goes through a progressive maturity model over a period. The three stages of maturity model comprise from rudimentary "In-house" efforts and siloed approach, to a more matured and technology-supported integrated model.
At the peak of the process and practice maturity, what was once mundane and time-consuming acts should become repeatable and predictable outcome-based activities in digital marketing.
👶 🧒👦 Starting stage
Loaded with the solid foundation of data sources understanding, you are starting to create an in-house data management framework and decide the level of sophistication suitable to your business today.
By this, I mean, you are taking a comprehensive look at the array of data sources available from structured to unstructured sources. And establish a baseline "self-defined data model definition," which unique to your business. The data model that makes it interconnections and interplay visible by defining the relationship between different data sources - highlighting where and how data collected to dependency.
For instance, you have the product, customer, and historical interaction information stored in the silos system inside or with the service provider system in the cloud. Defining this data model definition should see the attempt to normalize these data sources to have a single view of customer insight.
- Personal details about customer
- Existing customer or prospect
- Preferred communication channel
- Last used channel of communication
- Last time interaction with business
- Last time interaction inquiry or transaction
- Last revenue realized
- Lead quality
- Last follow-up action taken
- Product interested
- Product owned
- Cross-selling opportunity
- Account Manager responsible
- Average profitability
- Marketing Opt-in status
This list of attributes is not comprehensive, nor all of them relevant to your particular case. So, you must do the due diligence to add or update this model and make it relevant. Nevertheless, this sample must give you the idea of collecting and normalizing such datasets for baseline reference and usage in digital operation.
Additionally, having this data model in place helps you introduce the discipline and practice of ongoing data collection in a structured and meaningful way. Going forward, each campaign you execute and incoming data will add records in volume, and enrich the data quality in values.
The big companies and enterprises give this process the fancy name called "data architecture and Ingestion." In your case, the only difference is that doing at the size and scale necessary, and using the tools available and familiar to you and workforces.
Over time, You must see this dataset grow in value and importance to the marketing organization, and used as a primary input mechanism to optimize and mature your digital advertising practice.
🚶♀️ 🚶♂️ 🚶♀️ Transition stage
Having this in-house practice of data collection and insight generation for some time, transitioning to this next level of maturity becomes more comfortable for you and team.
At this stage, your in-house data collection, clean, and normalize practice matured to the scale, it will require sophisticated analytics to make sense of data into insights. And, this effort looks a lot less like building infrastructure from the ground up than data migration one.
Thanks to the efforts invested in the previous stage, you are ready with what you want out of these analytical tools to ensure return on investment and faster turnaround of any system implementation, whether you choose to develop in-house or go with the SaaS approach.
🕺 🏃♀️ 🕺 🏃♂️ Matured stage
When you are here, after an In-house offline model and working with advanced analytical tools means that your marketing practice matured.
At this stage, ideally, you must have a good number of predefined campaign templates to automate, which should generate the set expected business outcome most of the time. And free yourself from focusing on high-priority and experiments for future growth and new business initiatives.
However, it's worth pointing out that fundamentally nothing has changed from the standpoint of underlying principles that drive digital marketing and advertising efforts. It's still very much about predicting user behavior and having the array of responses ready to personalize and optimize your campaign to achieve a measurable business outcome.
At this level of maturity, your digital operation has the template to automate and supported by multiple technology tools to guide your business forward, from planning and attribution to insight generation and post-analysis.