How To Build A Powerful Customer Analytics System

By Author: Jency Durairaj
Total Articles: 5
Comment this article

Today the sustainability and success of any business depend on customers, as markets have shifted to customer-centricity. The clearer the businesses are about their customers’ lifestyle and buying behaviours, the more accurately they can categorize their customers and make better business decisions.
Following are the essential steps to build a powerful customer analytics system.
Identify your objective
Start with the end in mind while framing questions about customers. Focus on what needs to be figured out in order to gain clarity and achieve the desired business goals. Once the objective is determined, ask as many queries as possible around the objective.
For instance, if the objective is to use a cross-sell strategy to increase customer sales and revenue, then the following questions should be asked:

Who are the buyers we should cross-sell to?
What are the products they are buying from us?
When do they purchase from us? Weekdays, weekends, lunch hour, or evening?
How are they purchasing the products? Offline or Online?
If offline, which store are ...
... they purchasing the products from?
What channels are they using to communicate with us?

And so on.
Finding out answers to these questions helps in justifying the project as well as defining the limitations or boundaries of the project.
Track KPIs
After capturing the data, besides storing it, companies must track essential customer analytics parameters, as they will help in understanding how their business is performing. This will also help businesses to determine whether they are on the right track towards their strategic goals.
Nowadays, real-time dashboards are easily available, using which businesses can effectively monitor their key performance indicators. Also, these dashboards help in making timely decisions, thereby enabling businesses to increase their customer sales and revenue.
Analyze the data
The first step in data analysis is visualizing the data. Data visualization makes the results of business and marketing strategy easily understandable. It also enables analysts to identify patterns and outliers in the customer data that can aid them in selecting the right data analysis technique for modelling and analysis. Then, the analyst can continue with exploring, cleaning, and preparing the customer data for analysis and segmentation. After this, based on the business objective, the analyst can choose to carry out classification or predictive modelling.
Generally, the analyst will experiment with various models before selecting the optimal one for them.
Examine the model
In this stage, after the classification or predictive model has been constructed, the analyst should evaluate if the model is optimized or not and that all variables in the model are statistically significant with a p-value less than 0.03 if a 3% significance level is used. Next, the analyst should collaborate with a domain expert to check whether the variables used in the final model are logical and whether the signs of the variable coefficients are right.
In order to analyse the performance of the model on unseen data, parameters like recall, overall accuracy, specificity, and the area under the curve are calculated from the confusion matrix. This step is highly important because if the model checks are not carried out, there is an increased probability that the coefficients of the model are not at all acceptable.
Proceeding with incorrect model coefficients will lead to categorization of customers in incorrect customer profile groups. This in turn will lead to targeting customers with the wrong product/service or through an incorrect communication channel. Improper classification of customers or products can significantly cost businesses and therefore must be avoided to the greatest extent possible.
Take action
Besides demonstrating the current happenings of the business, predictive models should also provide a holistic view of what might happen in the future. This means predictive models are not static, rather constantly changing and may even go downhill over the long term – moving from a highly accurate model to a highly inaccurate model. This dynamic nature of the classification/predictive models makes it necessary for businesses to track the changes and optimize their sales and marketing strategies accordingly.
Automate
Once the models become efficient and revenue is increasing, it is time to automate the system with business process, customer data, analytical techniques, etc. A dynamic customer analytics system improves profitability by reducing customer analysis expenditure and saving organization time. Further, it also improves customer experience through timely and targeted offers, which is the most crucial aspect of all.