Let's chat! When’s a good time?
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Marketers of today are often bombarded with various kinds of audience data through various tools. That data gives details related to the interests, pain points, and desires of web visitors, social media followers, and even interested prospects.
All of the relevant metrics give marketers actionable insights and direction that help them run effective campaigns that generate qualified leads that eventually turn into valuable customers.
However, tracking multiple behavioral metrics across several dashboards can get hectic.
For instance, learning about a prospect’s industry and their need to personalize their journey from the lead stage to billing requires marketers to crawl through information-dense multiple dashboards, which could fatigue them.
This can increase the chances of errors and oversights where brands may focus on unimportant metrics or interpret them inaccurately while running their campaigns.
In this article, let’s look at five ways marketers can deal with data overload effectively while running campaigns and converting prospects into loyal customers.
Data management tools pull information from multiple sources to one destination enabling marketers to gain visibility of their marketing pipelines quickly. These tools often allow users to create custom dashboards and analytics processes to streamline data-driven decision-making.
Apart from saving time and effort, these tools play a pivotal role in eliminating silos between marketing and sales, fostering a more collaborative approach to brand promotion.
You can leverage solutions like Segment to build a single source of truth. Additionally, tools like Zapier and Automate.io can get data from multiple sources which can simplify your marketing reporting workflow.
To choose the right data management tool, make sure that it can collect the data from all the sources that are relevant to your business, scale up as your needs grow, fit easily into your existing tech stack, and be adopted by your team members without much training.
A data governance framework consists of certain rules and processes that ensure your organization responsibly uses the data. In other words, this framework ensures that the data accessed by the marketers in your team are relevant, accurate, and secure.
Consequently, this leads to better leads and faster sales cycles while remaining compliant with data guidelines and regulations.
The essential components of a data governance framework in a marketing team include the benchmark for data quality, the definition of who has access to it, ensuring compliance with various privacy regulations such as GDPR, CCPA, etc., and managing the flow of data throughout its lifecycle from creation to archiving or deletion.
By ensuring you get clean and standardized data, centralizing data management, providing role-based access, breaking silos, and maintaining compliance, a data governance framework can help brand marketers reduce data overload.
Consider these two metrics:
The first metric, with a larger face value, may make you feel good or even boost your ego while providing you with little to no strategic insight. On the other hand, the second one not only gives you insights into what your customers find valuable but also tells you how many leads you’ve scored.
In simpler terms, marketers consider the first kind of metrics as vanity metrics and the second one as actionable ones. To keep your dashboards clean and lean it is crucial to focus primarily on the actionable metrics.
You can find the right performance metrics for your business by defining each marketing goal with numbers.
For instance, the goals can be getting effective leads and increasing customer engagement on various touch points. Their corresponding metrics can be the time taken by a lead to convert and email click-through rates.
After finalizing which key performance indicators (KPIs) you should care about, all you need to do is collect the sources of those metrics with your centralized data management tool.
The public release of several open and closed-source LLMs has made it easier for businesses to bring generative AI into various workflows of their organizations, such as content creation, communication, and report writing.
These tools can also be used to analyze large datasets to uncover actionable insights, make predictions, and suggest decisions. Fortunately, modern business intelligence (BI) tools with built-in AI features can be used by marketing teams for this purpose.
For instance, tools like DataChat make analytics accessible to everyone, even to professionals without technical expertise, by allowing them to interact with their data in plain English.
Apart from performing a lot of tedious tasks, these tools can deep dive into anomalies and issues, making troubleshooting proactive and limiting revenue losses. Furthermore, teams can also gain additional insights about customers and groups that aren’t usually possible with traditional BI tools.
With time, your business’ marketing needs will evolve. For instance, you might decide to target a new niche or run campaigns on a different platform.
As you integrate these changes into your brand promotion campaign, it is essential to ensure your overall workflow remains efficient and effective. You should only monitor the right metrics through the right tools to gain relevant insights.
You can simplify this process by looking at the overall efficiency of your marketing campaigns towards your goals such as lead generation and conversion. For instance, if you have captured fewer leads as compared to the previous quarter, you need to examine your lead generation process.
Additionally, to streamline this process even further, you can set up a small team or create an actionable checklist.
The number of data points that marketers have to track regularly consistently increases leading to fatigue induced by data overload. This prevents teams from gaining the right insights while ignoring the essential KPIs.
To curb this, marketers can adopt centralized data management tools, establish a data governance framework, focus more on actionable metrics rather than vanity ones, leverage conversational AI tools to gain insights and audit their marketing workflow regularly.
Get the latest best practices in Marketing Analytics
delivered to your inbox. You don't want to miss this!!
