What is the definition of predictive marketing?
Predictive marketing is the use of algorithms and statistical models to predict customer behavior. By analyzing customer data such as :
- Their previous purchases ;
- Their browsing history ;
- Their engagement on social networks;
Predictive marketing can anticipate their future needs and provide them with personalized offers. The aim of predictive marketing is toincrease customer satisfaction and improve companies' return on investment.
The rise of predictive marketing
Predictive marketing enables companies to obtain more information about their existing customers in order to identify predictable patterns and future trends. In reality, this marketing practice has been around for a long time, but it required weeks or months of usingextract, transform and load (ETL)tools to collect Data from a range of sources.
In recent years, theuse of predictive marketing in business has grown rapidly thanks to these three factors:
- The massive amounts of data that marketers can access from all available sources ;
- Real-time data access;
- The introduction of cloud computing, which provides Big Data technologies.
Now that MarTech tools are more precise and sophisticated, marketers have access to better results and can measure advertising and audience sources to create campaigns with predictable responses.
Do you understand why it's so important for you to use predictive marketing for your ecommerce?
4 major advantages of predictive marketing
Now that you know more about predictive marketing, let's take a look at why you should use it for your ecommerce business.
1. Improve customer knowledge
One of the advantages of predictive marketing is that it enables your company to better understand your customers.
In fact, by analyzing sales data, purchase information, social network interactions and website navigation details, you get a more complete overview of your customers.
With this in-depth knowledge, you can better target your marketing efforts, personalize your content and deliver a more satisfying customer experience.
2. Customize offers
Now that you have a better understanding of your customers, you can personalize your marketing offers.
By using predictive algorithms, your company can recommend products or services that are likely to be of interest to a specific customer based on their purchase history or website navigation.
As you already know, this can help you increase your sales and build customer loyalty by offering personalized offers that meet their specific needs.
3. Reduce marketing costs
Traditional marketing can be expensive, especially for small businesses. If you want to optimize your marketing investment, you need to opt for predictive marketing.
And for good reason: predictive marketing can help you cut costs by providing precise information on your customers' needs and preferences. This analysis enables you to focus your efforts on those customers most likely to invest in your offers.
4. Increase sales
And yes, by using predictive algorithms to personalize your offers, you'll see your sales take off. Clearly, if you offer products or services that respond to customer needs and preferences, you'll increase your ecommerce sales overall.
6 essential steps to integrate predictive marketing into your ecommerce business
Now that we've told you about the benefits of predictive marketing, let's get down to business. How can you use predictive marketing for your ecommerce today?
1. Understanding predictive marketing
As we told you earlier in this article, predictive marketing uses algorithms to predict customer buying behavior. It analyzes customer data, such as :
- Purchase history ;
- Online behavior;
With this information, you can obviously personalize your offers and promotions for each customer. This is the first step you need to take to launch your predictive marketing campaign!
2. Collect data
To collect data for predictive marketing, you first need to determine what information is relevant to your business. In general, relevant Datas include customer contact information,purchase history, browsing behavior and demographic data.
Once you have identified them, you can use several methods to collect them, including :
- Online surveys: you can send e-mail surveys to your customers to gather information about their preferences, interests and purchasing behavior.
- Analysis of browsing data: using tools such as Google Analytics, you can collect information on your customers' browsing behavior, such as the pages visited and the time spent on each page.
- Transaction data: using tools such as Shopify or Adobe Commerce (Magento), you can extract data on your customers' transactions, including the items purchased and the amount spent.
- Social media data: if your company has a presence on social networks, you can extract data on customer interactions with your business, such as "likes", comments and shares.
To ensure that the data collected is accurate and reliable, you can use validation tools such as Data Ladder, Talend and Google Refine.
Once you've collected relevant data, you can use it to feed your predictive marketing strategy and personalize offers and promotions for each customer.
3. Analyze data
Data analysis is a key element of predictive marketing. Kiliba advises you to follow these steps:
- Exploratory data analysis: analyzes your collected data to understand trends and relationships between different variables. Use graphs, pivot tables and descriptive statistics to explore them.
- Feature modeling: uses modeling techniques to create predictive models. Common modeling techniques include regression, decision trees, clustering, neural networks, etc.
- Model evaluation: assesses the performance of predictive models using measures such as precision, recall, F-measure, ROC curve, etc.
- Model implementation: use predictive models to make business decisions, such as customer segmentation, offer personalization, price optimization, etc.
- Model re-evaluation: regularly re-evaluate predictive models to ensure they remain accurate and relevant to your business.
By using these steps, you can be sure of effectively analyzing your data to make informed business decisions based on accurate forecasts.
4. Use machine learning
Machine learning is increasingly used in predictive marketing, as it offers several advantages over traditional data analysis methods.
Indeed, machine learning is capable of processing large quantities of data at very high speeds, enabling real-time data analysis and faster decision-making.
What's more, machine learning makes it possible to identify patterns and complex relationships between variables that would be difficult to detect using traditional data analysis methods.
As a marketer, you need to use machine learning to help you understand your consumers' behaviors in more detail.
5. Personalize offers and promotions
The aim of the whole previous process is to be able to create offers and promotions that are perfectly tailored to your customers. For example, if a customer often buys women's clothing, you can offer them promotions on similar items. In this way, your e-commerce business benefits from a number of advantages, such as:
- Growth in customer satisfaction;
- Improving customer retention;
- Sales growth;
- Optimization of resources used.
Are you beginning to understand the benefits of setting up your predictive marketing campaign? Before concluding, let's take a look at the final step that will guarantee the success of your campaign.
6. Using market segmentation
As a reminder, market segmentation consists of dividing customers into groups based on their characteristics, such as demographics or purchasing behavior. For your predictive marketing campaign to succeed, it's vital that you divide your customers into groups according to their :
- Gender ;
- Geographical location;
- Purchase history ;
- Online behavior.
Obviously, by using market segmentation to personalize your ecommerce offers and promotions, you improve the relevance of your marketing campaign and increase the chances that your customers will buy your products or services.
Predictive marketing in a nutshell
In conclusion, predictive marketing is a powerful tool for online businesses looking to improve their marketing strategies and maximize their return on investment.
By using techniques such as data collection and analysis,machine learning, personalization of offers and promotions, and market segmentation, companies can better understand their customers and provide them with targeted, personalized offers.
At Kiliba, we offer predictive marketing emailing services to help you fully exploit the benefits of this innovative approach. We offer in-depth expertise in data collection and analysis, as well as proven experience in applying machine learning techniques to predict your customers' buying behavior.
Click here to boost your ecommerce results with email automation!