data-driven marketing

Data-driven marketing is extremely crucial in the digital age. Customers today interact with brands at multiple touchpoints. So, unless marketers invest in bringing together data silos and devise campaigns based on insights, achieving higher ROI and driving customers down the funnel can be a difficult task.  

Let’s start by looking at a simple example of why data-driven marketing is effective.  

Let’s say you work in the marketing department in a large organisation. You’ve been tasked with developing a sales enablement pitch for prospective customers in the healthcare industry. One of the first things you’ll do is find out who your existing healthcare customers are, how they are using your service offering, and what their experience has been like with your service.  

Then, you’ll turn to your sales team to find out from their conversations with prospects, what they (the prospects) think about your service offering and how they think it will benefit their business.  

As a third rung, you’ll turn to external data sources to find out what healthcare companies typically look for and what they want from a service similar to yours. In other words, to ensure that your sales pitch is holistic, on-point and effective, you’ll collate ‘data’ from different sources and make sense of it to get the best possible outcome. This is exactly how data-driven marketing works.  

Like we’ve seen in our earlier blogs, marketers rely heavily on different sources of internal and external data to reach out to their customers or prospects. It could be data from direct email campaigns, social media interactions, cookies and more. These data points give marketers a range of insights from the location, gender and demographics of customers, to their browsing and purchasing patterns.  

Instead of leaving these data points in silos, if marketers are able to collate and make sense of them, they can develop more targeted marketing campaigns, which can lead to higher ROI. A statistic by Attomdata shows that 75% of marketers saw an increase in engagement when they used data-driven marketing, and marketers who used personalisation techniques often exceeded their revenue goals. 

The challenges of running data-driven marketing campaigns 

Data remains fragmented  

On one hand, internal data is spread across different teams within the organisation, and there isn’t a mechanism available to bring all this data together in one place, make it accessible to all, and draw analysis from it.  

On the other hand, data is scattered across external channels also because, especially in the last two years, customers have gone more digital. For example, they may have browsed your product or service on the desktop, and they may buy from their mobile device. In other words, the number of customer touchpoints have increased, and it has become all the more complicated for marketers to keep track of all this data. 

There’s lack of expertise in drawing insights from marketing data 

Marketers and companies at large do understand the effectiveness of data-driven marketing. In fact, 40% of brands plan to expand their data-driven marketing budgets, and 64% of marketers believe that a data-driven approach is essential in today’s scenario.  

But, the challenge they face is in lacking the talent and expertise to make sense of and use this data effectively. Marketers need to have a system in place to collect and centralise all customer data, so that they can start seeing patterns into what customers want, what their browsing patterns look like, and the journey they take towards a purchase. 

Customer journey is fragmented 

Unlike a few years ago when products or services were available only in stores, or on the website, today a customer’s journey is complicated. A customer may have interacted with your ad on Facebook, browsed your product or service on the desktop site, and purchased it in-store. 

There are multiple channels where customers interact with your brand. Tracking their journey and identifying patterns is difficult if there isn’t a unified tracking system in place, and a centralised place where all this data is stored for analysis. Marketers need to establish a tracking system in place to understand how and through which channels customers are interacting with their brand. 

Exploring data warehouses and data lakes to analyse marketing data  

Tackling these challenges requires time, strategy, investment and technology, which is also something companies are gradually starting to get a grasp of. Let’s look at what they are. 

Data warehouses for data virtualisation from different sources  

Marketing data warehouses (DW) are not an uncommon term. A data warehouse is one where all customer information from different sources (such as CRM, website, Google Analytics, campaigns) and different departments (such as finance, marketing, sales) is stored in one place.  

While companies earlier used to have in-house data warehouses, now, many are moving to cloud-based solutions like Google BigQuery, Microsoft Azure Data Warehouse and such.  

DWs are advantageous in that they allow marketers to gain a holistic view of how their brand has performed over a period of time. For example, a DW can show a marketer how many leads were generated in a month. If she wants to compare leads in a particular month with other months, she can view analysis churned out by the DW to see how other months performed, why they saw more leads coming in during other months, and which sources the leads came in from. DWs break silos, analyse data, reduce time spent on drawing insights from varied sources, and they can store large amounts of data, unlike a spreadsheet which can be limiting and expensive as a company grows in size.  

Data lakes for varied data types 

Aside from data warehousing, there’s another system called data lakes. The only advantages it offers over data warehouses is that it can store large amounts of structured and unstructured data (DWs store only structured data), it’s inexpensive, and it can provide files in CSV (DWs share data in SQL). 

As a Merit expert adds, “The key is to leverage the latest data analytics technologies to unify marketing data from multiple sources and analyse it in a central data warehouse. By using technologies like a data lake, we’re able to take in both structured and unstructured data and use all of this to make marketing recommendations. Even better if we’re able to leverage AI/ML-based models to predict outcomes of marketing campaigns and statistically recommend campaign ideas based on what has worked in the past.”  

Projections for data warehouses  

A report by Research & Markets pegged the data warehouse market to grow by $10.42 billion between 2022 and 2026, at a CAGR of 22.56%. And the data lakes market is set to reach US $30.2 billion by 2027 (IMARC Group). 

While data warehouses and data lakes show a promising future for marketers, companies still need to keep a few things in mind when taking the data-driven approach to reaching customers; 

  • They need to segment high-quality and poor-quality data from varied sources.  
  • They need to chart out what customer data and how much customer data they need to drive effective campaigns.  
  • They need to be transparent about the data they are collecting from a customer and not become intrusive by taking more than what is necessary for optimal customer experience. 
  • They need to invest in data scientists and data engineers to make sense of data and derive meaningful insights from it. 

Merit Group’s expertise in Marketing Data   

At Merit Group, we partner with some of the world’s leading B2B companies. Our data teams work closely with our clients to build comprehensive B2B marketing contact lists that provide a direct line to their target audience.  

If you’d like to learn more about our service offerings or speak to a marketing data consultant, please contact us here:

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