Data-driven marketing has quickly become the hallmark of success for high-growth companies across the globe. The process of collecting, and interpreting large quantities of information, offers organisations an in-depth insight into the performance of sales and advertising activities. Moreover, this approach to tracking and measuring success can generate an average of 5-8x return on investment for each campaign spend.  

While the effectiveness of data-driven marketing is widely undisputed, many companies still struggle to implement it properly. Faced with numbers, graphs, and charts, CX teams often find it challenging to decide which insights are useful.

Luckily, navigating the big datasets doesn’t need to be a sticking point. In this article, I’ll show you a step-by-step process of metrics selection, tracking, and measuring progress over time.

How to track and measure data effectively?

an image showing a woman analysing big datasets.

Effective CX measurement ultimately boils down to having the end goal in mind. As a marketer, you must understand your business needs and then tailor your data-driven marketing strategy. Without a clear sense of direction, an overload can easily occur, stunting effective decision making and preventing the far-reaching benefits the analytics can offer.

Before diving deep into the metrics, companies should take a step back and consider their business objectives.

Let’s imagine that your goal is to increase the sales of a particular software product from 100 to 150 sales per month. Your current number of visitors to the web page is 500 per month. The target could be to increase this to 750 and enhance the time visitors spend on the page, so you can check the correlation between sales.

Additionally, you must work backwards to ensure that every piece of data collected is chosen for a reason. Here are a few questions that can help you start the measuring process on the right foot:

  • What do you seek to find from this data?
  • What decisions are you trying to influence?
  • What does a customer success story look like for you?
  • How long do you need to measure the progress for getting the relevant data?

Say you are considering increased investment into your social media platforms. To make a data-led decision on this, you could track metrics such as weekly page views, daily engagement, number of new followers, mentions, etc. With the end goal in mind, you can effectively use the data available to make a better decision for the business overall.

A great example of CX measurement done right is Deloitte. Through a combination of industry reporting, online surveys, and analysis, this company has achieved an unmatched reputation for analytics, which is now cited widely across industry reports and company business plans.

The benefits of data-driven CX measurement

While every company has its process of metrics selection, some key performance indicators are considered standard practice across industries. We can recognize these under a marketing funnel: unique website visits, social media engagement, or new leads generated against the monthly target.

These key performance indicators give marketers a real-time insight into the effectiveness of their marketing campaigns. Understanding how customers engage with their brand helps the marketing team perform only activities that meet customers’ needs.

It’s crucial to understand that CX measurement is not a one-size-fits-all solution. Metrics will change depending on the nature of the business and the industry. For software companies, demo requests can be an indicator of new sales, while for a fundraising project, website visits and social media engagement will likely predict donations. Ultimately, the CX measurement is all about having a clear picture of your outcome and choosing the data that can help you get there.

Making big data accessible and inclusive

an image showing a team engaged in data driven marketing strategy.

Let’s say you selected your metrics intentionally and started collecting the data that will further inform your business decisions. What could go wrong?

The biggest mistake companies can make is building a dataset only to let it sit and collect dust. To see the results, your analytics team has to track the metrics continuously and actively. You can start by monthly bringing together key people from each department to check progress against targets and monitor trends within a certain business aspect.

There are also many tools available to break down data into consumable chunks. Make sure you make it accessible to all employees across the business, and not just those with a mathematics degree. Enhancing the accessibility of the information helps companies to break down organisational silos, which allows all employees to play a role in key decision making.

While big data can seem like an analytics minefield, there are many resources available to help companies get it right. The ones that manage to master big data can be sure to revel in the rewards of increased sales, company growth, and improved customer relationships in the long run. To keep you on track, I’ve outlined our three top tips:

  1. Keep in mind your main strategy. Always have in mind the decisions you’re trying to influence.
  2. Take advantage of online resources but ensure they always align with your wider company goals and are industry specifics.
  3. Get the whole team on board by making your data accessible, tangible, and actionable.
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