Before, we’ve read here how the Martech and Adtech are revolutionizing the marketing industry after the Boom in web-technologies. However all these; will go under another bigger umbrella: Data-Driven Marketing. We all agree that any executive, marketer, and startup founder has realized the benefits of data-driven marketing strategy in their SMEs, Startups, and Corporations. Forbes recent studies “Data-driven and customer-centric: marketers turning insights into impact.” indicates that:
64% of survey respondents “strongly agree” that data-driven marketing strategy is essential to success in a hyper-competitive global economy.
25% of respondents invested between $25 million and $50 million in data analytics over the past two years. Nevertheless, nearly the same number – 23% – expect to invest between $50 million and $100 million over the next two years.
Customer behavior has clearly changed in recent years. Today’s consumers are more informed and connected than ever before. They face Data each second, and they generate Data each second. According to a report from Teradata, a majority, 87% of marketers consider data their organization’s most useful asset. That’s a considerable gain from 2013 when just 46 percent thought that way.
Marketers No longer can rely on Broadcasting their messages. The shotgun strategy doesn’t work anymore; they need to be snipers. Successful brands touch with consumers at the precise moment they’re about to make a decision, and this is what Data can do. Data can provide the marketers the weapon to, Target the exact customers, Find the right channels to reach them, improve the user experience, Understand customer demands and pain points and make all the marketing procedures much more comfortable than before. Analyzing Data properly can clarify all the chaos appeared by technology and provide a vivid insight to the marketers, advertisers, and sales teams.
Data-Driven Marketing Strategy Examples: Customer Segmentation by Data-Driven approaches
Customer segmentation is the process of separating customers into groups based on common characteristics so companies can market to each group more effectively. The goal of segmenting customers is to decide how to relate to customers in each segment to maximize the value of each customer to the business. Customer segmentation has the potential to allow companies to address each customer most effectively. Segmentation helps a company to create and communicate a targeted marketing strategy that will resonate with specific groups of customers, which improve customer service and establish better customer relationships.
We must find a way to measure the value of the different groups of the customers and categorize them in the right direction. In this way, we can provide each group with their specific needs and personalize the marketing strategies for each group. In the near past, clustering customers followed a similar method:
Assign index to each customer made of 3 digits.
The 1st digit is the quintile (1 to 5) of the customer recency
Recency = days since the last purchase in the last period
1 = top 20% wrt recency and 5 = bottom 20% wrt recency
The 2nd digit is the quintile (1 to 5) of the customer frequency
Frequency = number of purchases in the last period.
The 3rd digit is the quintile (1 to 5) of the customer monetary
Monetary = money spent in the last period
Customers with the same Indexes are on the same segment:
111 = best customers (recent purchase, high frequency, high spending)
555 = worst customers (old purchase, low frequency, low spending)
This naive approached have been used for many years for customer segmentation before Data Mining methods development and Big Data analysis availability. The approach is called naive because it can’t consider many different dimensions in analyzing the customer’s segmentation and usually made mistakes in correctly segmenting the customers. Nowadays, the approaches are getting far more diverse. One of the methods can be cluster analysis.
Cluster analysis is the use of a mathematical model to identify customer segments based on finding the smallest variations among customers within each group. A typical cluster analysis method is a mathematical algorithm known as k-means cluster analysis, sometimes referred to as scientific segmentation. As an example, imagine that you’ve got the following data from your customers:
- Annual Income
- Spending Score (1-100)
The clustering of the customers using K-means can be shown with such Diagram. We can add also many other dimensions to our dataset and implement the same algorithm (K-means) on them. There are tens of thousands of examples in which the marketers are using Data-Driven approaches to behave with their past, current and possible future customers.
Data-driven Marketing will empower you to:
1- Create automated marketing procedures
2- Don’t miss the different segments and diversity of the audience
3- Focus on loyal customers
4-Find the best marketing channels
5- Create personalized marketing campaigns
6- Enhance customer experience constantly
7- Create more engaged customers with the business
8- Learn from competitor’s mistakes
9- Act effectively on customer feedbacks
10-Test effectively your products and services
What are the main challenges in implementing a Data-Driven Marketing Strategy?
Any new advancement can bring up other new challenges. But there are plenty of challenges brands must overcome to communicate with their customers effectively using Data-Driven methods. “The Nielsen CMO report 2018” report, indicates that 79% of CMOs expect to invest more in marketing analytics and attribution over the next year this is while 74% of CMOs have little to no confidence they have the right technology in place to achieve their marketing goals; These prove that the industry is not still vivid. There are many misunderstandings, and of course, there are many technologic barriers. Here we list a few most important one of these challenges:
1- Blending data from multiple platforms and bringing everything together
Probably you’ve got Gigabytes of data in your organization — website analysis data in Google Analytics, a customized CRM, Sales data, etc. The challenge is to bring them all together and make the possible use out of them. Platforms like Google data studio claims to be able to gather everything together, but this is not an easy task, and still, many businesses face problems in doing so.
2- Cleaning Data and Accessing High-Quality Data
It’s a very common proverb in Data world which says: “Garbage in, Garbage out.” Cleaning Data and achieving high-quality datasets is usually the most time-consuming task in developing Data-Driven models.
3- Measuring the right KPIs
Every piece of data you track should serve a purpose. Tracking data without a clear goal is a waste of time. Lucky for marketers, finding valuable KPIs (key performance indicators) is relatively simple; all it takes is a little planning.
4- Using the right platforms and tools
Determining the right tools and platforms for the organization is essential. You should have a platform that takes in all your data activity across all your channels, so you can see what is happening throughout the customer journey and make wise decisions from that.