5 Ways You’re Not Using Data in Your Marketing Strategy


Despite the fact that most experts in the technology sector have been continually advising on the need for widespread application of analytics dashboards and data-driven KPIs, most senior marketing teams are still utilizing data primarily for backward-looking analysis to measure performance rather than building analytics dashboards that drive future initiatives and planning.

Even with the advent of AI empowered predictive analytics, a recent study from McKinsey found that only 17% of businesses report they are actively using data to build future strategic planning and better CX models. What makes this an even more frustrating predicament is that the majority of brands are either underutilizing data that they already have at their fingertipsor are ignoring the absolute necessity to have a CDP that provides them with future-ready deployment strategy.

There’s often untapped value to be found in re-engineering data points or capturing new types of data to which your current CDP might not have, such as Zero Party Data (data supplied by the customer themselves to the brand.) What you can learn from a deeper dive into the data pool is a better understanding of your target audience and how to best optimize a CX campaign that speaks more effectively to your customers. A stronger commitment to data can make you more effective in converting new customersbut can also result in customers with higher brand engagement and higher lifetime value. 

Here are 5 areas where you can make that commitment count:

1. Know Your Target Audience

With broad audiences, it’s hard to deliver campaigns that are relevant to everyone. Therefore, one of the first steps to achieve better results in campaigns is to better know your target customer and segment the audience in a meaningful and actionable way, so you’re able to serve the direct needs of each small audience cluster, and potentially, each individual customer unit.

An effective way to perform audience segmentation and targeting is through a combination of marketing methods and data science. This framework allows you to have a comprehensive understanding of different consumer profiles with different behaviors and needs, so as to convey the right message to the right audience and ensure that products and services are clearly communicated to meet their needs and help them achieve satisfaction with your brand.

Data science enables the analysis of large volumes of data, with the use of sophisticated statistical techniques, which allow for finding patterns among consumers. Thus, demographic characteristics, geographic information, product use, and behavioral characteristics, for example, can be used to analyze and segment consumers. Design thinking processes, on the other hand, allow us to analyze consumers in depth and, thus, identify the most relevant factors to segment them according to their needs as well as to create personas. When both are combined, it is possible to identify the patterns of similarity and dissimilarity, considering the most important factors, in addition to understanding the relevant characteristics that differentiate and describe them, which supports the creation of campaigns that resonate better with them. 

This design-driven data science framework is normally based on in-depth qualitative interviews with consumers to understand customer profiles and needs more deeply: on the analysis of large volumes of customer data for generating insights about customers behaviors, preferences and profiles; on advanced analytics techniques and machine learning (ML) to cluster customers and perform statistical analysis; and on the use of frameworks such as jobs to be done to capture consumers’ needs. This process is iterative and provides a means for testing hypotheses generated on the qualitative interview. Also, as some of the consumer insights generated from data analysis are based on correlations, which does not imply causation, data insights can also suggest some points to be explored more deeply on the qualitative interviews.

2. Optimizing Acquisition Cost By Predicting Lifetime Customer Value

Marketers are always under a strict budget. It’s important to optimize spending to assess maximum ROI from their allocated budgets. Data analysis and machine learning can be great tools to improve customer acquisition processes and reduce its costs. Data can support the estimation of the customer acquisition cost (CAC) as well as the customer lifetime value (CLV),starting with a new customer’s first purchase or contract and ending with the moment of churn.

By calculating the CLV, companies can evaluate how much to invest in a customer and evaluate the different strategies and levels of investment that are needed in order to acquire new customers with higher value.

There are several different ways to calculate CLV, depending on whether the business operates contractually (e.g., Netflix, credit cards, SaaS business)or in a non-contractual setting (e.g., online retail, grocery stores) as well as if the transactions are discrete (e.g., monthly/yearly) or continuous.

A more complete CLV methodology uses predictive analytic models and requires advanced statistical knowledge to perform a more accurate estimation of the CLV of each customer, providing a more robust and dynamic metric. By segmenting customers with this metric, it’s possible to understand demographic and behavioral traits of the most valuable customers and even train a machine learning model to predict the CLV segment of new leads, which allows companies to optimize customer acquisition budget. Also, it’s possible to perform customer look-alike targeting to find new leads similar to the higher value ones.

Sounds pretty nifty, right? It is, and it’s the path that savvy marketers must take if they’re going to stay competitive. To do it right, however, you need to have the correct tools in place, like Group FiO’s Intelligent CDP, and you must also have the capability to develop comprehensive strategy behind it.

3. Building An Effective Predictive Analytic Model

Even though marketers are always reinforcing the importance of sending the right messages to the right people at the right time, they still use one-size-fits-all approaches to engage leads. Data analytics can address this challenge by driving greater personalization and by modeling consumer behavior through the use of predictive models. Proper use of these models helps to predict the likelihood that leads and consumers will perform certain actions, such as make a purchase or convert to the next step of the funnel. 

In order to be effective, a predictive analytic models should be dynamic and be able to adapt to changes with time. This can be done through the automation of data pipelines and processes to retrain the models on a regular basis. Also, the model should be scalable so as not be abandoned after the first use in a single campaign. When building a propensity model, it’s also very important to be aligned with the business about the variables (e.g., demographics, product use, buying history, interactivity, behavior) that should work as good predictors and to use experimentation to validate their accuracy.

With scalable and actionable models in hand, marketing teams can increase conversion rate, by defining better targets, and identifying audiences the right audiences at the right time for that right message.

4. Monitoring And Acting On Consumer Sentiments

In the digital world we live in, customers share a lot of information about their needs and the relationship with brands and products. Acquiring this information and analyzing it’s value is a key component for future ready businesses who understand the importance of the customized customer journey. By leveraging strategies such as zero party data and social listening, brands can understand customers’ emotions throughout their journey with products and services as well as their reactions to current CX initiatives. This helps to understand points of improvement to achieve higher frequency of use and consumption, increase brand advocacy, and build loyalty.

Data analytics enables the analysis of large volumes of text data based on opinions and complaints left by consumers on social networks. Machine learning techniques can perform sentiment classification (e.g., negative, neutral, or positive), using text as input data, and the results can be used to understand the opinion of customers: in relation to a particular product, service, or campaign; the feelings, pains, and desires of users when using a service in context from their point of view; and the relationship of different customer profiles with the brand. The information derived from this analysis can provide insights on how to improve the customer experience (CX) as a whole.

5. Increasing Lifetime Value With Personalized Actions

As mentioned earlier, customer lifetime value is an important indicator that should guide customer acquisition and retention. In addition to tracking this metric to support decisions on strategy, analytics can also help companies increase CLV through personalized offers and recommendations as well as predict the “next-best action” they can make to increase cross-selling and up-selling and capture more value from a client.

Research indicates that if a customer buys only once, there’s a 30% chance on average they will come back. However, it turns out that if the customer buys a second time, the chance of coming back a third time increases significantly. That’s why it’s important for marketers to act quickly to transform one-time buyers into two-time buyers.

One of the ways to re-engage customers after a first buy is through a welcome campaign, which can also have personalized offers based on customer profile (or to which segment they belongs to). Those offers can also be triggered by a next-best action model, which uses predictive machine learning models to estimate the likelihood of the customer buying specific products and considers the one that has the highest chance of converting in that moment to create the offer.

A next-best-action model can prescribe the content and messaging relevant to the customer’s segment, stage of sale, and suggest the right sales opportunities, specific offering, or action to minimize churn. A good recommendation or next-best-action model can increase conversion rate, CLV, and overall customer satisfaction with your brand.