6 Steps to Building a Powerful Customer Analytics System

February 14, 2019 | 4 minute read
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The success and sustainability of any business is dependent on having customers, and in order to acquire, grow, and retain customers, it is essential to know their needs, what they like, and what they dislike. Customer analytics is the systematic examination of a company’s customer information and customer behavior to identify, attract, and retain the most profitable customers, and is the driving force in deriving these insights. The more the business understands their customers’ buying behavior and lifestyle, the more accurate the business can classify their customers and make predictions that are targeted and meaningful for the customer. Targeting customers with the right product at the right time and with the right price improves the overall customer experience and journey.

Below are 6 steps to building a powerful customer analytics system for your organization.

1. Know Your Objective

Begin with the end in mind when asking questions about your customer. Focus on what you need to know to achieve your objective and gain clarity. Once you have established your objective, ask as many questions as possible around your objective.

For example, if your objective is to grow your customer sales revenue using a cross-sell strategy, you should ask the following questions:

  • Who are the customers we should cross-sell to?

  • What are the products they buy from us?

  • When do they buy from us? In the evening or at lunch time, on weekdays or on weekends?

  • How do they buy our products? Online or offline?

  • If offline, which store do they buy our products from?

  • What channel do they use to communicate to us?

  • What is their propensity to buy or respond to promotions?

  • Why do they buy the products? How do they use the products?

The answers to these questions can help justify a project as well as define its boundaries or limitations. 

2. Track the Metrics 

Once you've captured your data, you not only need to store it but use it to track key customer analytics metrics. The customer analytics metrics help the business understand how they are performing and whether the business is taking the right path towards their strategic goals.

Today, there are real-time dashboards that can effectively track key performance indicators. These dashboards help the business make key decisions in a timely manner, thereby increasing customer sales and revenue.

3. Analyze the Data

The first step in analyzing your data is to visualize it. Data visualizations make it easy to understand the results of your marketing and business strategy. Data visualization also helps the analyst identify outliers and patterns in the customer data that can point to the appropriate data analytics techniques for analysis and modeling. Then, the analyst can move on to explore, clean, and prepare the customer data for analysis and segment comparisons. From there, depending on the business objective, the analyst may choose to perform predictive or classification modeling. Usually, the analyst will explore several models and then select the best performing model based on the model results.

4. Evaluate the Model

At this stage, after the predictive or classification model has been built, the analyst will check that the model has been optimized and that all variables in the model are statistically significant with a p-value less than 0.05, if using a 5% level of significance. Next, the analyst will check with a domain expert to determine whether the variables in the final model make sense and whether the signs of the variable coefficients are correct.

To evaluate how well the model will perform on unseen data, metrics such as the overall accuracy, recall, specificity, and the area under the curve are computed from the confusion matrix. This is an important step because if the model checks are not performed, it is highly likely that the model coefficients are not all acceptable (e.g. because of incorrect signs or not being statistically significant). Taking action using the incorrect model coefficients will then result in grouping customers in the incorrect customer profile groups and customers may be targeted with the wrong products or with the incorrect communication channel. Misclassification of customers or products is a costly affair and should be avoided as much as possible.

5. Take Action

Predictive models represent a snapshot of what is currently happening in the business and what is likely to happen in the future. Predictive models are never static but are continuously changing and may even decay over a long period of time, moving from a highly accurate predictive model to a highly inaccurate predictive model. Due to the dynamic nature of the predictive/classification models, it is critical that the business monitors the changes so that it can adapt the marketing and sales strategies accordingly.

Over time, the customer analytics system should include new customer profiles, segments, response rates and classifications/predictions. It is important that the analyst can determine when the predictive/classification tool is no longer accurate or when the marketing campaign is no longer effective so that the proper steps are taken to modify the customer prediction/classification support system.

6. Automate

Once models are performing well and revenue is growing, it’s time to automate your system by integrating the customer data, business process, analytical techniques, and models with the business strategy. The benefits of automating your customer analytics system is that it allows triggers to be set up that alert the analyst when unacceptable changes in the model accuracy occurs. Changes in the model accuracy must be communicated to business stakeholders since promotional spending and business strategy as a whole will need to be adjusted with the reduced model accuracy. 

Further, a data-driven automated customer analytics system should be built with thresholds that, when met, can automatically update a change in a customer’s status and trigger when a promotional offer has a high likelihood of response. This alert will enable the business to be proactive and present the customer with the right promotion at the right time. Automation will also speed up the customer analysis, modeling and decision-making time, which ultimately makes for a more efficient and profitable business.

A dynamic customer analytics system saves an organization time, reduces customer analysis costs, and improves profitability. Even more so, it directly improves the customer experience with targeted and timely offers, which is the most important element of all. 

Carol Hargreaves

Professor Carol Hargreaves is the director of the data analytics consulting centre at the National University of Singapore. With over 30 years of work experience in the pharmaceutical, healthcare, consumer goods, and education industry, Carol is passionate about using analytics and machine learning techniques to build data-driven solutions for businesses.

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