With the latest advancement in technology, marketers now have access to volumes of data that can help them make better decisions when creating their campaigns. And one of the ways they can utilize this data is through statistical modeling.<\/p>\n\n\n\n
In this guide, I briefly discuss the various statistical models available and how you can use them to track KPIs.<\/p>\n\n\n\n
Statistical models are algorithms or sets of math that can help predict what can happen, find similar customers, recommend products based on what users are already interested in, and accomplish many other tasks based on the data you\u2019ve gathered. Below are the main statistical models you can use;<\/p>\n\n\n\n
\u00b7 Market segmentation<\/p>\n\n\n\n
\u00b7 Time Series<\/p>\n\n\n\n
\u00b7 Recommendation modeling<\/p>\n\n\n\n
\u00b7 Attribution modeling<\/p>\n\n\n\n
\u00b7 Incremental Attribution<\/p>\n\n\n\n
\u00b7 Supervised Learning<\/p>\n\n\n\n
\u00b7 Optimization<\/p>\n\n\n\n
Now, there are several KPIs you can use in online marketing, but today I\u2019ll look at four and how and when you can use these statistical models. These four include;<\/p>\n\n\n\n
\u00b7 Cost per lead<\/p>\n\n\n\n
\u00b7 Conversion rates<\/p>\n\n\n\n
\u00b7 Click-Through rate<\/p>\n\n\n\n
\u00b7 Optimum pricing<\/p>\n\n\n\n
Ready? Let\u2019s get started.<\/p>\n\n\n\n
We can define the cost per lead as the amount of money you have to spend to spike a customer\u2019s interest in your product. In most cases, you can always calculate this amount based on your history or consult with other businesses if you\u2019re new to the industry.<\/p>\n\n\n\n
The goal is always to find a marketing channel with a low cost per lead and a high conversion rate. And one way you can minimize the cost per lead is through market segmentation and recommendation systems. Below is how it works.<\/p>\n\n\n\n
Segmentation involves grouping customers into clusters based on demographics, character traits, income-levels, priorities, and many other factors. After grouping them, you then come up with marketing strategies suited for each cluster.<\/p>\n\n\n\n
For instance, speaking of demographics, different regions have different perceptions of betting and sports betting marketing strategies. Your strategy has to account for that in every demographic. The more your online marketing strategy appeals to a particular cluster, the longer you\u2019re likely to retain the customers.<\/p>\n\n\n\n
If marketers have built a large cluster or group, they can look for more people who fit the same characteristics. This strategy is also known as lookalike modeling.<\/p>\n\n\n\n
This method can also help generate leads to new products from the customers you already have. It is a model that predicts what a user might like. There are two types of recommendation systems, content-based and collaborative filtering. Content-based is where you suggest a product to a customer based on what they\u2019ve purchased before.<\/p>\n\n\n\n
If a customer loves a particular form of sports news, you can recommend a similar form of news. The same case happens in Movie service providers such as Netflix.<\/p>\n\n\n\n
On the other hand, collaborative filtering predicts that if a user loves Product A and another loves product B, those who like product A may probably like product B. In a sports scenario, fans who love soccer may also be interested in basketball or volleyball, according to skylinesoccer.org.<\/p>\n\n\n\n
On the other hand, conversion rates are the number of leads that eventually turn into customers. For instance, if you got 10,000 leads but only 1000 of them turned into customers, your conversion is 1000\/10000 = 0.1 or 10%.<\/p>\n\n\n\n
If you\u2019re using different marketing channels, a lot goes into calculating the conversion rates. Time series, attribution modeling, and incremental attribution can help determine which channels are converting and which can make a better combination. Below is how they work.<\/p>\n\n\n\n
<\/strong>This model analyzes what could have happened if you didn\u2019t use a specific marketing strategy within a period. For instance, you can explore the results you got from TV ads against what would have happened if you didn\u2019t use them. As a result, you can identify the effectiveness of your marketing channels, whether they are making a difference, and which of the media have a higher conversion rate.<\/p>\n\n\n\n Attribution modeling is the analysis of which marketing channel has the highest ROI or is responsible for a conversion. One mistake one can make is focus on the last interaction or the last marketing channel that a user used to make a sale.<\/p>\n\n\n\n However, according to Peter Foy of AgencyAnalytics, attribution modeling allows you to analyze first-interaction, last-interaction, time decay, Linear, last non-direct click. You may realize that the buyer came to your blog a couple of times, saw a retargeting ad, and finally made a purchase. As a result, the ad isn\u2019t the only channel that converted, but also the blog traffic. Therefore, you\u2019ll know which marketing channels to combine for a higher ROI.<\/p>\n\n\n\n This model establishes the conversions that only resulted from marketing. Regardless of how effective your marketing campaign is, you could be getting conversions from other channels. Once you know the amount of business your marketing efforts brought, you can accurately calculate the ROI. When you combine with attribution modeling, you\u2019ll know which channels to invest in and how much to invest to meet your goals.<\/p>\n\n\n\n Dmitry Klymenko of internetrix also states that a business can predict a customer\u2019s lifetime value with incremental attribution. Customer Lifetime Value (CLV) is a crucial KPI for sports betting companies, so this form of modeling can be pretty valuable.<\/p>\n\n\n\n This is the rate at which prospects or customers are clicking through your emails, ads, web content, or any other form of media. Like with the conversion rate, the click-through rate is calculated by the number of impressions you get by the number of clicks. If it\u2019s an email marketing campaign, it would be dividing the email clicks you got by the total number of emails you sent. Supervised learning is a statistical model that can help improve click-through rates. Below is how it works.<\/p>\n\n\n\n Unlike market segmentation, where you\u2019re using unsupervised learning or untrained data, supervised learning involves training the data to predict an outcome. The outcome can either be a category when dealing with classification or a value when dealing with regression. You can predict click-through rates using supervised learning. For instance, you can predict that an ad or post with specific phrases, images, or displayed at a certain time of the day will have a higher click-through rate. You can measure the results on a scale of 1 to 10, or a percentage between 0 and 100%.<\/p>\n\n\n\n This is the price that the seller achieves the highest profit, and it\u2019s often not the highest price on the product. When the price is too high, few customers will buy from you, so you sell few products. When it\u2019s too low, you sell several units but make a meager profit. The method to find the optimum pricing isn\u2019t set in stone. It\u2019s trial and error since several factors are in play. These factors include;<\/p>\n\n\n\n \u00b7 What drives your customers \u2013 is it the cheapest price, expensive, offering fulfillment, etc.?<\/p>\n\n\n\n \u00b7 What\u2019s your position in the market? \u2013 Are you offering luxurious products, cheapest products, etc.?<\/p>\n\n\n\n \u00b7 Competitor\u2019s prices \u2013 Check what your competitors are charging, their positioning, and the type of customers they have.<\/p>\n\n\n\n To avoid guesswork, optimizing can help you come up with the optimum pricing. Below is how it works.<\/p>\n\n\n\n This is where you optimize your marketing campaigns to suit the target audience. For instance, you can optimize your impressions based on what users like, the time of day they are likely to click, or the type of images they like to increase the click-through rate.<\/p>\n\n\n\n Optimization helps us deal with a significant flaw of modeling; you can\u2019t predict everything in the real world. But with optimization, you can make some tweaks based on the challenges that you face.<\/p>\n\n\n\n For instance, you can adjust the optimum pricing to the point that\u2019s profitable to you and acceptable to the customers. Optimization can also help minimize the amount of money you have to pay on Google ads since you know the keywords you should be going for. This is also known as optimum bidding.<\/p>\n\n\n\n And there you have it. Statistical models are quite helpful in measuring the performance of your digital marketing strategies. The goal is to know which and when to use them, and I\u2019ve just given you an example. They can be applied in most KPIs too. So, go ahead and give them a shot.<\/p>\n\n\n\nAttribution Modeling<\/strong><\/h4>\n\n\n\n
Incremental Attribution<\/strong><\/h4>\n\n\n\n
Click-Through Rate<\/strong><\/h3>\n\n\n\n
Supervised Learning<\/strong><\/h4>\n\n\n\n
Optimum Pricing<\/strong><\/h3>\n\n\n\n
Optimization<\/strong><\/h4>\n\n\n\n
Wrapping Up<\/strong><\/h2>\n\n\n\n
Partner With Us<\/strong><\/h2>\n\n\n\n