Wednesday, May 22, 2024

5 Ways Brands Can Better Use Data Analytics

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Data analysis is prevalent in every industry, but data is often used for backward-looking analysis to measure performance rather than building analytics dashboards that drive future initiatives. This means that the majority of organizations aren’t making use of data they’ve already gathered. There’s untapped value in re-engineering data points to help your organization better understand its target audience, which can make marketing programs more effective at converting new customers and lead to customers with higher brand engagement and higher lifetime value.

This article explores five ways enterprise brands underutilize existing data and looks at how they can make better use of data analytics.

5 Ways Brands Can Better Use Data Analytics

While organizations generally recognize the value of data, in many ways they’re missing out on opportunities to use it to learn more about customers and their behavior, predict their long-term relationships with the brand, and personalize messaging and promotions to them. Here are five ways brands can better use data analytics to tap into those insights.

1. To Get to Know Their Target Audience

Organizations with broad audiences can find it difficult to deliver campaigns relevant to everyone. Identifying target customers and segmenting them into small audience clusters makes it easier to meet their direct needs with more specific campaigns.

Design thinking methods and data science are effective ways to perform audience segmentation and targeting. Design thinking processes help analyze consumers in depth and identify the most relevant factors to segment them by need, while data science enables the analysis of large volumes of data, with the use of sophisticated statistical techniques that find patterns among consumers. For example, demographic characteristics, geographic information, product use, and behavioral characteristics can be used to analyze and segment consumers to better target messaging.

There are three components to a design-driven data science framework:

  • Qualitative consumer interviews to understand customer profiles and needs
  • Customer data analysis to generating insights about behaviors, preferences, and profiles
  • Advanced analytics and machine learning (ML) to perform statistical analysis and cluster customers

This iterative process provides a means for testing hypotheses generated from the qualitative interviews. Because some insights generated from data analysis are based on correlations, which does not imply causation, for example, data insights can also suggest some points to be explored more deeply on the qualitative interviews.

2. To Predict Lifetime Customer Value and Optimize Acquisition Cost

Data analysis and machine learning can be great tools to reduce the cost of customer acquisition. Data can support the estimation of the customer acquisition cost (CAC) as well as the customer lifetime value (CLV), which predicts estimated profits over each customer lifetime.

By calculating the CLV, companies can evaluate how much to invest in a customer based on the potential return. Segmenting customers according to lifetime values lets you optimize acquisition costs by investing more in campaigns targeting leads that will likely generate more revenue throughout the whole lifecycle. The desired result is an inverse relationship between CAC and CLV, with a higher CLV.

There are several different ways to calculate CLV depending on the type of business. A complete CLV methodology uses probability models and requires advanced statistical knowledge in order to perform a more accurate estimation of the CLV of each customer, providing a more thorough, more dynamic metric. But even a simple approach to segmenting customers with this metric lets you understand demographic and behavioral traits of your valuable customers.

This could be used to train a machine learning model to predict the CLV segment of new leads, facilitating an optimal customer acquisition budget, for example. It could also be used to perform customer look-alike targeting to find similar new leads.

Learn more about data modeling.

3. To Forecast Behavior Using Propensity Models 

Marketing teams often use one-size-fits-all approaches to engage leads. Data analytics can drive greater personalization and better results by modeling consumer behavior through the use of propensity 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.

Many companies struggle with getting good outcomes with the use of propensity models. One reason is that they use propensity scores generated by customer relationship management (CRM) tools or marketing automation platforms, which are not specifically designed for their business. A propensity model should be dynamic and adaptable. Automating data pipelines and processes can help retrain the model on a regular basis. The model should also be scalable so it can be used in future campaigns.

A propensity model should also be aligned with variables specific to the business—for example, demographics, product use, and buying history—that make good predictors. Actionable propensity scores can help increase conversion rates by defining the incremental impact of being targeted, letting you target customers with better incremental responses. You can also offer higher discounts for customers with a lower propensity score who need more incentives.

Learn more about data pipeline design.

4. To Listen and React to Consumer Sentiments

Customers share a lot of information about their needs and their relationship with brands and products. Acquiring and analyzing this information provides a way to measure user satisfaction and loyalty. Approaches like netnography and social listening let companies understand customers’ emotions and their reactions to campaigns, making it possible to boost use and consumption and build loyalty.

Data analytics using natural language processing (NLP) 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 (negative, neutral, or positive for example) using text as input data. The results can be used to understand customer opinion.

Sentiment analysis has become an essential tool for marketing campaigns because it enables scalable analysis in real-time, making it possible to act on consumer feedback and personalize messaging to attract the target audience.

5. To Increase Lifetime Value by Personalizing Actions

Customer lifetime value should guide acquisition and retention activities. Analytics can also help companies increase CLV through personalized offers and recommendations. Research indicates that if a customer buys once, there’s a 30 percent chance on average they will return—but if the customer buys a second time, the chance return increases significantly. Which means it’s important to act quickly to transform one-time buyers into two-time buyers.

One way to re-engage customers is through a welcome campaign that makes personalized offers based on customer profile or segment. Those offers can also be triggered by a next-best action model, which uses predictive machine learning 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 content and messages relevant to the customer’s segment, stage of sales, and propensity and suggest the right sales opportunities, specific offerings, sales actions, and even actions to minimize churn. A good recommendation or next-best action model can increase conversion rate, CLV as well as consumer satisfaction, if the actions recommended meet the user needs.

Bottom Line: Make Better Use of Existing Data

Enterprises understand the value of data. They gather it in increasingly massive volumes, invest in ways to store it, and report on it to measure performance, fuel dashboards, and track results. But rethinking how they use the data they’ve already gathered to add data analysis for forward looking predictions can create a higher return from those investments and provide a far deeper understanding of their audience, making it easier to engage with them through marketing and sales campaigns.

Read Top 7 Data Analytics Tools to see the best software enterprises can use to implement the strategies covered in this article.

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