Nowadays, the old marketing strategies do not suffice. As the digital space harbors various businesses that are continuously embroiled in a race to attract their customers, it is necessary to understand a customer and their behaviors in order to win this race. For such purposes, a strategy called predictive marketing has been adopted by many businesses.
Simply put, predictive marketing involves the usage of processes and activities through which consumer data can be monitored heavily. Analysis and results from predictive marketing can help a business to understand and predict the future behaviors of their consumers. Predictive marketing borrows algorithms from Artificial Intelligence and Machine Learning. Predictive marketing can be used in the following ways.
Understanding the Consumer
As we discussed above, predictive marketing can be highly useful for understanding your customer. IT giants like eBay and Amazon have already incorporated it in their platforms. However, this does not mean that your business has to be a large one for utilizing predictive marketing. It can be convenient for small-sized businesses too where it can create considerable impact for the consumers. Developing predictive models requires a great deal of effort while it also takes much time.
You can create models based on clusters. Each cluster is a group that can have a list of a single or multiple common attributes. This type of clustering can be based on the behavior of either the customer or the product that is being sold by your business. For example, if your business sells clothes, then you can create clusters for your category of pants. Pants can be further divided into cargo pants, sweatpants, etc.
If you are interested in the prediction of a customer’s behavior, then you can create a propensity model. Such models can help you understand different metrics like:
- Is a new customer going to use my services?
- For how much time the customer is going to keep using my services?
- Is the customer going to buy a specific product or use a service?
By addressing these questions, the propensity model assists businesses to improve their services for a specific customer while it also helps to reduce efforts for another one.
Have you ever seen movie recommendations on IMDB? Do you notice how the website suggests movies with similar genres or actors? In order to engage in a similar strategy, you can apply collaborative filtering in your applications. In this way, you can use the analytics of a consumer and show them the right products for advertisements and promotions.
For example, consider you have a website that mainly deals in the sale of laptops. Now, you have a customer that always buys Lenovo’s Thinkpad series in bulk. How can collaborative filtering be used here? Well you can use predictive marketing statistics to create personalized ads for such consumers that fits their budgets and incorporates other factors from their past.
Often, the core foundation of a business’ predictive marketing is the regression analysis. In this strategy, a customer’s interaction with a website is judged and all the common variables are marked out.
For example, you have a business that offers installation of home windows. Now, in case a customer regularly contacts the company for the installation of a specific window like triple pane windows, then regression analysis can mark the correlation between the product and customer. This can be used to deliver a much better and personalized user experience to the customer. Likewise, regression analysis also determines the duration of time between each purchase.
The era where companies used text and spreadsheets for their customers is gone. Today, data visualization tools are used where the company’s management and think-tank works together to make key decisions.
Suppose you have a business that sells furniture to home in the U.S. state of Virginia. Now, your business is focusing on expansion and is moving to California. Do you intend to replicate your business practices from Virginia in California, too? Well if you do, then it is an ineffective strategy, and your expansion may face hurdles. Here you can use data visualization for the Californian populace where you can check which types of furniture the locals are interested in as well as see the furniture types that maybe detrimental to your business due to local atmosphere and choices.
Marketing is all about convincing a prospective buyer to use your products and services. Thus, predictive marketing can be extremely valuable in the management and generation of leads. Each lead can be assigned a “score” that can help to ascertain their seriousness with the business.
For example, you have a business that sells gaming PCs. Now, you have two potential clients. One of them is a casual client A who is just looking into your gaming products out of curiosity. On the other hand, you have a client B who is a professional gamer and is looking to buy the latest graphic card for running highly intensive games. Certainly, you cannot deal with both the clients with the same level of seriousness. Luckily, with predictive marketing your client B can be assigned a better score. Thus, the company can be more successful into converting them.
You can also use a certain model called identification model. In this model, leads are determined by going through the data of established customers. Predictive analytics determines the standard attributes between different demographics. For instance, using our above example, predictive marketing may indicate that professional gamers living in Arizona are more inclined to buy Nvidia graphic cards. Similarly, it can also be found out that the customers residing in North Carolina prefer AMD. Hence, potential leads living in both the states can be targeted with content containing their preferences.
Similarly, with such filtering and grouping of customers, they can also be contacted with email marketing, social media marketing, etc. For social media, the trends and discussions of customers on platforms like Twitter and Facebook can be beneficial for a business.