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Customer Analytics: The Benefits of Understanding New Customer Metrics

In today’s competitive business environment, understanding your customers is more important than ever. With the help of customer analytics, businesses can gain a deeper understanding of their customer’s needs, behaviours, and preferences and use this information to improve their products and services, increase customer satisfaction and ultimately, drive sales and growth.

One of the key benefits of customer analytics is that it allows businesses to segment their customer base and tailor their marketing efforts to each group.

For example, a clothing retailer might use customer analytics to segment their customers based on age, income, and purchasing behaviour. They could then create targeted marketing campaigns for each segment, offering promotions and discounts to specific groups of customers. This type of targeted marketing is much more effective than blanket marketing campaigns, as it speaks directly to the customer’s needs and interests. I wrote about how you segment your customers by putting them into several clusters here.

Another critical aspect of customer analytics is predicting future customer behaviour. Businesses can identify patterns and predict future behaviour by analysing historical customer data. For example, a retailer might use customer analytics to predict which customers will purchase next month. This information can then be used to create targeted marketing campaigns for these customers, increasing the chances of a sale.

Understanding new customer metrics is an essential aspect of customer analytics as it helps businesses gain insights into the performance of their customer acquisition efforts. By tracking and analyzing metrics such as the number of new customers, the cost of acquiring new customers, and the revenue from a customer, businesses can make informed decisions about their marketing and sales strategies and improve their customer acquisition efforts.

Some of the key benefits that can be derived from understanding new customer metrics include the following;

  1. Improved customer acquisition: By tracking the number of new customers, businesses can understand the effectiveness of their customer acquisition efforts and make improvements where necessary. For example, suppose a company is acquiring fewer new customers than it would like. In that case, it can analyze its customer acquisition metrics to identify areas for improvement, such as its marketing campaigns or customer onboarding process.

  2. Increased customer revenue: Understanding revenue generated from new customers can help businesses determine the long-term potential of their customer base and make informed decisions about how much to invest in customer acquisition and retention efforts.

  3. Better return on investment: By tracking the cost of acquiring new customers, businesses can understand their customer acquisition efforts’ return on investment (R.O.I.) and make adjustments to maximize the R.O.I. For example, suppose the cost of acquiring a new customer is higher than the revenue generated. In that case, the business may need to reevaluate its acquisition strategy to make it more cost-effective.

  4. Improved marketing effectiveness: By analyzing customer data, businesses can identify the marketing channels that are most effective for acquiring new customers and allocate their marketing budget accordingly. This can maximize the return on investment of their marketing efforts and improve the overall effectiveness of their customer acquisition strategy.

Sadly, there is a but

To achieve all the benefits stated, you need to be able to dynamically identify the new customers out of thousands of transactions being processed monthly. That’s where PowerBI comes in. PowerBI to the rescue.

Let’s get started

The model required for this is nothing complicated. All you need are your sales, product, customer and date table.

You can download the data here.

The DAX that calculates the number of new customers is seen below.

Using February 2023 as an example, the DAX measure does this. February 2023 provides context to the DAX measure. The Customers_this_month variable gets a list of customers that purchased a product in February 2023.

For the Previous_customers, the minimum date is 01/02/2023. The churn_period_start is the minimum date minus 90 days which gives us 03/11/2022

Still, with the February 2023 context, the Previous_customers variable creates a new table containing a list of customers who purchased any product between November 4, 2022, and January 31, 2023. This means that customers on this table have not churned and shouldn’t be regarded as new customers.

The RETURN function returns the number of customers on the left table(cutomers_this_month) that are not on the right table (previous_customers).

Likewise, you can get the total sales for these customers using the Dax below.

The percentage is way easier to picture than the actual number. Let’s calculate the % of new customers and their sales respectively.

  1. Total customers

2. Percentage of new customers to total customers

3. Percentage of new customers sales to total sales

Now that we have gotten that underway, let’s see the output.

It goes beyond the table if your model is built correctly.

You can play around with the report here.

In conclusion, understanding new customer metrics is a valuable tool for businesses looking to gain a deeper understanding of their customers and drive growth. By leveraging customer analytics, companies can gain insights into customer behaviour, preferences, and purchasing patterns, allowing them to make informed decisions and improve the customer experience. The benefits of customer analytics are vast and far-reaching, from personalized marketing campaigns to optimized pricing strategies. By embracing new customer metrics, businesses can gain a competitive edge and stay ahead of the curve in an ever-evolving market.


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