Every business knows the value of customer data. However, customer data is not valuable until it is properly processed. Many companies take a DIY approach to data processing where it is a costly, inefficient system involving dozens, if not hundreds, of ad-hoc projects managed by different teams. Through automation via machine learning (ML), data preparation can become a continuous, streamlined process that is less costly, less time consuming, and allows companies to gain insights from customer data faster.
The First Step is Finding and Collecting the Right Customer Data
The very first step for any marketing project is to find raw customer data and then collect it. Customer data can come from many sources, such as company websites, social media sites, and customer relationship management (CRM) platforms. Each system formats and stores customer data in different ways. Customer data could be unstructured, structured, or semi-structured. The data may also contain different data types such as text, numeric, categorical, and visual. All of these different formats mean that customer data must be processed and then converted into a uniform format.
Much of the data preparation process can be expedited through automation. However, for many companies, data preparation is still largely a manual process done in-house.
Data Preparation is Often a Manual Process — But it Doesn’t Have to Be
Preparing customer data for use in analytics and ML models involves numerous, time-consuming processes. Customer data must be cleaned, integrated, deduped, and normalized- and many data scientists still do most of this work themselves. Data scientists spend about 80% of their time on data prep. But before raw customer data goes through the cleaning process, the condition of the data must first be evaluated (data profiling). Once the data is profiled, any problems with the data must be corrected, and then data features must be created (feature engineering).
Raw Customer Data Must Be Profiled
Data profiling must be done to determine if the dataset is accurate, complete, and valid enough. Data profiling is typically performed by a data scientist or analyst who will closely inspect the data looking for a variety of potential problems such as null values, duplicated data, outliers, and skewed information. Many customer datasets are massive in size and scope, so it is not practical or scalable to profile massive datasets manually. And profiling real-time streaming customer data requires automation; it cannot be done manually.
Fixing Problems with Customer Data is Unavoidable
Besides generating trustworthy metrics, ML algorithms and models will not perform well if the data fed to them is riddled with problems such as out of date customer data fields, invalid or missing values, and outliers. Some algorithms will give an error if the data has missing values. And if the input data contains outliers, it can skew the algorithm training process.
Companies can use ML to automate the process of finding and fixing problems with customer datasets, saving data scientists time and speeding up the process. Data scientists must also spend a significant amount of time on feature engineering, another process that can be automated to a large degree.
Fast and Accurate Feature Engineering is Crucial
Feature engineering is crucial to ML because it greatly improves the accuracy of the model and the predictive power of the algorithm. Traditionally, feature engineering is a manual process that involves a lot of trial and error. However, the increasing necessity for real-time insights is making manual feature engineering impractical, if not impossible.
If you create an ML model for churn, you feature engineer the data going into the model so that the model is accurate for predicting churn. If you want to add real-time insights into the churn model (which involves a constant stream of new, raw behavior data coming in), you need to automate as much of the data cleaning and feature engineering process as possible to keep up with the flow of raw data.
Automation not only speeds up the process of feature engineering but also reduces the cost of model generation. Because the time data scientists need to spend overseeing the entire featuring engineering process is significantly reduced. ML can be used to automate featuring engineering.
Leverage Dynamic Segmentation & Insights
Once data has gone through the processes of data preparation, it undergoes segmentation for propensity prediction and scores, lead scoring, decisions, and recommendations. For many companies, segmentation is mostly a manual process- data scientists update the models based on new customer information. However, marketing segments change quickly. Keeping up with customers as they fall into and out of segments requires speed, precision, and scalability. Automating the segmentation process using ML allows marketing segments to be updated as new information comes in. Companies could opt to do segmentation in-house, but an auto-ML or an automated customer data platform with dynamic segmentation is far less time consuming.
Companies Spend Too Much Time Processing Customer Data
Most companies today spend far too much time processing customer data and not enough time leveraging that data to benefit their businesses. And too many companies are not leveraging real-time customer data because they use a DIY manual approach that precludes it. Historical customer data can be useful, but it is the insights from real-time customer data that is key. And the only way to leverage real-time customer data is through automation- automated collection, preparation, segmentation, and analysis. So the business can spend time in being creative with taking timely actions.