Machine Learning attribution modeling is a system that gathers historical data from your engagement or marketing efforts to create rules. 

These rules assign an accurate weight or conversion credit to each of your program’s touchpoints. It further allows brands to analyze the data quickly, accurately, and efficiently. 

The developed software has full integration capabilities with nearly any third-party solution and can match any client’s needs. It has 100% customizable features that can be configured within a short interval to build and launch programs quickly. 

NextBee is the marketing engagement and promotional platform for businesses with trending features. The effort is driven by an ever-growing core product development group that pushes for ever more advanced solutions for any brand’s need. 

Tailored Solutions for Multiple Industries 

• Member Associations Health & Wellness Facilities 
• Corporate Alumni Relations B2B
• Retail & eCommerce Stores 
• Schools & Universities Channel & Inbound Sales TeamsSolution Clients 
• New & Traditional Media HR & Talent Development Teams 

Attribution Modeling Defined 

Indeed, an engagement program is about creating relationships where users feel passionate, energetic, and committed toward a brand, but you won’t know which activities are accomplishing this without correct attribution. 

Single-Touch Attribution Modeling – is when a single activity directly equates to a conversion. Two parts of the equation can measure it with first contact or last contact.   

Attribution Modeling is when a set of rules determines how conversion credit is assigned to different marketing touchpoints, either singular or multi-point. 

Multi-Touch Attribution Modeling – is when every activity along the way to conversion is credited to have been part of the transformation. In other words, Activity A led to Activity B, which led to Activity C, which then equated to the conversion. For this type of attribution, the value credited are usually three types of multi-touch attribution: 

Time Decay – Asserts that as users get closer to conversion, the touchpoints are more critical/weighted. 

Position Based – It also denotes a U-shaped model. The first and last touchpoints are most heavier in weightage, with the middle ones assigned an even amount of value within this model.   

Even-Weighted – Assume all touchpoints are equally effective and carry the same weight. 

Machine Learning Attribution Modeling – The most effective type of attribution modeling is Machine Learning Attribution Modeling.  

It uses historical data from your engagement or marketing efforts to create rules for assigning proper credit to each of your touchpoints. 

In other words, each touchpoint correlates with value on its actual “performance” throughout the lifetime of the engagement program. This model brings a brand closer to the real-time value of each touchpoint to forecast more accurate planning. 

NEXTBEE & Data Science 

NextBee takes an in-house approach by working hand-in-hand with clients. Our robust system focused on Machine Learning Attribution logs all the historical data that a brand will need. 

We capture everything from social shares to sales and referrals. We do all of this to analyze rich data for a complete picture of the user’s journey. And, it is the first step in building an attribution approach. 

Once all the data is gathered, our team of experienced data science and analytics experts work with you to tag what information you’re using, configure it as feature sets, and then create models for experimentation and validation. 

Our data science process includes at least three stages: 

Stage 1 – The Discovery Stage: 

We’re conducting event correlation analysis during this stage, where we look for patterns and deduce “what” is happening. 

We look to see “Event A” happens, then how likely following that “Event B” happens? Or, if “Event A” is not happening, then how likely is it that “Event B” did not or will not happen? 

Stage 2 – The Agent of Change Stage: 

Once we have the raw data for correlations, we go beyond what’s happening and move into experimentation and observation. 

For example, you want to offer $2 for Facebook sharing with the hypothesis of, “this activity will lead to more referral clients.” It may, but it may not. 

By experimenting in smaller segments and proactively making these activities happen, we can see the outcomes. Then we dissect the data again to uncover micro-patterns of an engagement and can alter the variables. 

Stage 3 – Sentimental Analysis Stage: 

Sentimental Analysis for Attribution or Small Data Qualitative Analysis for Attribution is the kind of tool a company seeking for engagement.  

Sentimental Analysis gauges the customer’s opinion on a brand’s offering while giving companies the ability to figure out dramatic solutions to improve ROI. 

The way Sentimental Analysis works is that a company gathers data from its conversation interfaces. For instance, customer feedback surveys, customer and employee comment surveys, etc.  

Data analysis allows a brand to understand the overall “sentiment” for its products, image, and engagement. 

Combining this information with data analysis and experimentation from the previous steps, it becomes possible to adjust to meet client needs and gain an in-depth understanding. 

Conclusion

Attribution is all about how different parts of your engagement efforts work together as it provides additional value over time as more and more data becomes easily accessible. 

Your marketing, your products, and your customers are all changing. That is why we help brands to build an attribution modeling process that is robust and in-depth to transform businesses. 

This quick guide has covered an overview of just one of the features, ideas, and tools which NextBee offers to its clients. NextBee’s entire team assists with the most viable solutions providing the best-in-class customer service to ensure your company’s success.  

If you want to incorporate more effective engagement activities and garnering a more substantial ROI, consult our experts today.