How to Leverage Data Analytics in Your Marketing Strategy – A Practical Guide
June 21, 2018 | Martech
The marketing department at any given organization typically has following common goals:
- Identifying potential customers and their requirements
- Identifying effective marketing channels to reach out to customers
- Strategizing effective ways to target and engage customers
- Measuring and improving customer satisfaction, and more
One way or another, to achieve these goals, the marketing team depends on data, which is collected via a multitude of sources. With the explosion of customer data in recent years, sifting through the information manually is becoming more and more difficult. That is why Data Analytics algorithms, which process this data with the help of machine learning and advanced analytics techniques, are quickly becoming an industry norm.
However, using advanced data analytics in itself is not a solution to all these marketing challenges. Much can go wrong if a well-thought-out approach is not practiced during the process.
Strategic Approach to Use Data Analytics for Marketing
Every business problem can be solved in multiple ways. Coming up with the most simplified and understandable solution is the challenge. By extracting insights from the valuable data obtained from various resources with data analytics, businesses can arrive at that solution much more efficiently.
For any given marketing objective, the approach boils down to the following 3 steps:
- Identifying the areas to be focused upon to achieve an objective
- Finding best quality data that can be used for providing best solution
- Choosing the best algorithm to solve scope of problem
For example, if improving a marketing campaign’s effectiveness is the objective, we first need to identify most relevant customer segment and marketing channels, and then using data to plan an effective campaign.
Let’s shed more light on that with some examples:
Objective 1: To find out whether a particular customer segment is likely to respond to our offer?
Usually in such cases, we have historical data covering details of all customers’ responses to past offers. Here, classification is done based on simple factors as follow:
- Customers who have used a past offer to make a purchase are tagged as ‘prospective customers’
- Customers who have visited the store using offer but did not make a purchase are tagged as ‘can be prospective customers’
- Remaining are tagged as not responded
Based on this historical data, one can train the model to classify customers. Generally, logistic regression algorithm is used for classification purpose. Support vector machine is another popular method for classification for such type of problems.
Objective 2: To know top buyers and identify customers having similar behaviour as top buyers.
Identifying customer groups who can be converted into top buyers and targeting them with specific campaign are critical steps for companies focusing on the incremental growth of their business.
Finding out unknown segments from data through clustering is the best way to address these challenges.
Clustering models assign every entity (here customers) into “clusters” based on their similar attributes. Every customer cluster or segment depicts a particular kind of customer behaviour. Cluster analysis can help in finding out these specific customer behaviour traits. Insights from these traits can be used to find out new potential customer segments as well as in developing marketing campaigns to target these potential customers.
Objective 3: To do Churn Analysis and formulate strategy to bring down the Churn Rate
In present times, traditional ways to determine prospect churn customers and implementing anti-churn marketing strategies to retain customers are becoming less and less effective. A modern and more effective method is comprised of data mining and analytics techniques, and involves following two key tasks:
- Predict whether a particular customer will churn and when it will happen;
- Understand why particular customers churn.
Let’s break down these two tasks into steps to understand exactly how things work:
- Select high value customers from the historical data
- Draw a random sample to create a training set of data for model
- The decision tree operator is applied to fit the data in the training set
- Create a model that predicts the likelihood of a customer to become a churner in the coming 3 months, 6 months, 12 months
- Save output of the modelling stage in a view table for performance evaluation of the final model and can be helpful for model improvement
Using this analytic based churn analysis, organizations can effectively predict when the customers tend to leave; therefore, can develop an effective strategy to retain them. Further, through automation, churn customers can be identified in real time as well.
Besides the above mentioned examples, there are numerous other areas were data analytics can be used in marketing to make procedures faster, more accurate, and achieve better results.