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According to research conducted by Forbes, Huffington Post, and Marketing Signals, 90% of all ecommerce startups don’t make it past their first 120 days and have to shut down. One of the main reasons for their failure is their inability to identify their most loyal customers, who are crucial for sustaining their business, due to a lack of understanding in interpreting data and, consequently, an inability to measure their CLV (Customer Lifetime Value).

The absence of knowledge about their critical customer base also obstructs the development of effective marketing strategies, resulting in increased spending and a shortened lifespan for the business.

So, how can this problem be addressed? The answer lies in Data Science. Through data analysis, ecommerce businesses can not only identify their critical customer base but also optimize their marketing investment.

A study by McKinsey demonstrated that if companies utilize data to make diversified, strategic, and integrated marketing investments, they can save 15% to 20% of their current marketing expenditure.

This is where marketing analytics plays a crucial role. Marketing Analytics, a branch of Data Science, focuses on analyzing data to determine consumer behavior, thereby measuring and evaluating the effectiveness of marketing and advertising strategies. These analyses enable the calculation of important metrics such as CLV.

Customer Lifetime Value (CTV)

Customer Lifetime Value (CLV) refers to the total revenue or profit generated by a client over the entire course of their relationship with a company. It serves as a metric to measure the total amount of money a customer spends (or is expected to spend) on products and services throughout their lifetime as a shopper.

The purpose of this metric is to simplify and analyze the database of all customers, obtaining midpoints of spend values and retention times. This enables companies to implement necessary improvements to their marketing campaigns. In simpler terms, access to customer lifetime metrics allows businesses to identify consumers who are more engaged with the brand and, therefore, are the most valuable.

Understanding CLV can help businesses retain customers by offering products and services that meet their needs, thus preventing them from seeking alternatives elsewhere. Additionally, it enables brands to identify those who are most likely to respond positively to their advertising campaigns.

In the long run, access to CLV data makes an ecommerce business sustainable over time. This is because the metric allows companies to enhance their marketing strategies and develop more effective ideas focused on retaining existing customers, rather than spending large amounts of money trying to attract potential clients about whom they know very little.

What can CLV help me find out?

In more concrete terms, understanding CLV allows a business to answer a series of questions that can help improve its products and services. For example:

• How frequently do customers purchase products and services?
• What is the average amount customers spend on each purchase?
• Who are the most valuable clients, and what are they most interested in?
• What areas of client service require improvement?
• Which messages do clients respond to best and through which channels?

Ultimately, marketing analytics provides comprehensive data that enables brands to identify VIP clients, develop promotions, launch offers, deliver compelling messages, and implement various strategies to increase engagement, thereby ensuring brand success.

Marketing analytics is just one of the many possibilities Data Science offers to enhance a business, but the potential is vast. At Kimia Group, Analyticae provides the tools and expertise to help companies understand their data, interpret it, and utilize it to develop effective marketing and advertising strategies. For more information, click here

With the development of the Internet and digital environments, marketing has changed significantly due to the emergence of a key factor: data from interactions between customers and companies. With this, not only did digital marketing evolve, but new trends appeared, such as the one we bring you today: data driven marketing.

We can define data driven marketing as a strategy that consists of the practice of a series of techniques and tactics that employ an enormous amount of data obtained through interactions between customers and companies. The information provided by this data, after processing and interpretation, allows professionals to better understand the preferences, behaviors and purchasing motivations of consumers. Consequently, they are able to develop efficient marketing strategies. In other words, this trend uses data to improve marketing communications and the customer experience.

In the same line, thanks to the data collected from customers, marketeres are able to predict future goals and actions. This gives them a great advantage, as they can develop personalized marketing strategies, optimized on every level, based on accurate information.

In fact, a large number of companies are using interaction data to obtain user feedback on customer experience management. And as a result, such strategies bring very significant returns on investment (ROI) and support remarketing and retargeting techniques.

Data driven marketing is the basis of several communication strategies, both those that drive sales and those that build customer loyalty and enhance brand image. Let’s take a look at its most important contributions:

• Helps strategy in omnichannel marketing: today, customers tend to interact with a company or brand through multiple channels. Therefore, maintaining data sets for each communication platform does not make sense in an omnichannel marketing strategy. All channels should share with each other the data they obtain from customers in order to coordinate efforts across the board and reinforce tactics in the media that require it. In this sense, data-driven marketing drives data sharing across channels. If data is shared across all channels, interactions will be more seamless as prospects move from one medium to another.
• Use demographic data to design campaigns: Likewise, data driven marketing is key in the use of demographic data to plan campaigns. This category of data includes: age, gender, geographic location, education, income level, employment profile, etc. Such information can come from completed forms, social media accounts and their social media interactions, etc. Collecting much of this data on potential and active customers is important to obtain detailed customer profiles.
• Data driven marketing helps to personalize the customer journey: Undoubtedly, providing personalized experiences to consumers is an indispensable tactic for many brands. Implementing data driven marketing provides data that will allow you to drill down and offer a unique level of personalization, such as product recommendations, discounts and targeted messaging.
• It is indispensable for predictive target analysis: Predictive analytics employs Machine Learning and advanced statistical models for the purpose of analyzing customer data, identifying patterns and forecasting future behavior. This massive data collection allows you to create better profiles and monitor the behavior of potential customers with a higher probability of conversion.

As you can see, data driven marketing has become an almost indispensable tool for marketers. If you want to take advantage of all the benefits of data-driven marketing, Analyticae is the company for you. From Marketing Automation, to customer segmentation, to lifecycle analysis, among others, they’ll help you achieve an optimal performance of your campaigns. For more information, click here.