Machine Learning – Wali AI Insights

Machine learning models for modern data-driven marketing

Data-driven marketing is now in charge. However, the effectiveness of marketing and advertising still needs to be improved. In this blog, we share some solid tips from 10+ years of research by machine learning scientists to all marketing professionals.

Chipotle sales increased by 81% through digital ordering in these difficult days, when many other businesses are experiencing a decrease in sales due to the pandemic.

“Investing in digital over the last several years has allowed us to quickly pivot our business with Q1 digital sales reaching our highest ever quarterly level of $372 million” — CEO of Chipotle, Brian Niccol, said in a statement.

Even in the current difficult situation, not every company is losing, but for sure the winners are those who have prepared for a more efficient and intelligent era.

Advanced technologies, such as machine learning, have been applied to more and more aspects of work. At Wali, we use all types of technology at work, and we think technology is a big part of our life. Our digital marketing service is built around identifying core KPIs, data-oriented technologies and reinforcement learning.

Here are some A.I. models and their possible applications in reality. These models could be easily adapted as a mindset and applied to any marketing campaign.

1: K-Means Clustering

K-Means is one of the most well-known clustering algorithms. In theory, K-Means clustering is a method of vector quantization that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.

Reference: K. Wagstaff, S. Rogers, S. Schroedl, “ Constrained K-means clustering with background knowledge “, Proc. 8th Int. Conf. Machine Learning, pp. 577–584, 2001.

Marketing professionals can utilize this algorithm to create segmentation for all their influencers. Every individual influencer has different characteristics. Grouping them into different segments makes the future match 65% faster. At the Wali tech team, we apply this algorithm to our huge set of influencers. It helps us match influencers more accurately to specific industries or one business.

2: Decision Tree

Decision trees are powerful machine learning models that are widely applied in real-world applications. They are defined by recursively partitioning the feature space, which is very easy to interpret. Enhancing the decision tree using reinforcement learning examines all features of a new data point to update model parameters.

Reference: A. M. Roth, N. Topin, P. Jamshidi, M. Veloso, “Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy”, arXiv preprint arXiv:1907.01180, 2019

One of the biggest pain points for influencer marketing is to keep matching the brand with a new set of influencers in a dynamic way. This algorithm can help this situation. By creating a decision tree, any marketing professionals can pick a new set of influencers for a brand in a short time. Combining the decision tree with reinforcement learning, the system continuously updates the corresponding decision trees automatically. For example, at Wali, we first created our own decision tree, then kept reinforcement learning on our decision tree. As we are receiving new data points, we can quickly identify the best time to match a specific influencer to a brand.

3: Logistic Regression

Logistic regression is a statistical model that uses a logistic function to model a binary dependent variable. There are many more complex extensions that are applied to various marketing applications.

One typical application is to use logistic regression to predict customer churn. The prediction result can help businesses maximize the usage of their marketing budget by spending money (such as email, push notification, or limited-quantity promotion) on loyal or potential loyal customers.

Credit: John Sullivan, “Churn Prediction: Logistic Regression and Random Forest”

Many times, we only use the numbers a third party platform provides for us to calculate the performance of marketing campaigns. Marketing professionals can also start calculating the loyalty of a customer set, and evaluate the marketing performance based on a combination of third party results and customer loyalty. The same thing can be applied to customer lifetime value.

At Wali, we track all the values above, then calculate the most valuable set of customers and influencers for the brand. This algorithm helps us to keep our promise of matching accuracy for our client.

The perfect marketing campaign is hard to produce. Data-driven strategies are helping us to find a better answer. We encourage as marketing professionals, our readers will adopt some methods from our blog and use them for their next marketing campaign.

Wali autopilot influencer platform is built with more complex ML models, so your brand can quickly match with the best audience. No recruiting, no negotiation.

Learn more about our no recruiting, no negotiation autopilot influencer platform at https://mywali.co/influencer.html

How to retain a strong brand identity while riding a trend #dollypartonchallange

Every modern business dreams of having their online store overflowing with traffic and a long line in front of their physical store. Take Amazon for example, during Prime day the company’s executives might be yelling at their tech team when the site does down due to the high volume of users searching for the best deals. but nothing can hide the smiles on their faces even though this is a serious issue.

This is one of the best problems to have and every business wants this. In the current era, social media channels are definitely one of the important ways that this can become a reality to get traffic. Now when we talk about social media, there is no way we can ignore “Trends”.

How to maximize the return from Marketing Budget

Ever heard of the saying “Don’t put all your eggs in one basket”? Well, there is a reason this saying has lasted through the ages. It’s practical and makes a lot of sense. In our digital world, we might not have baskets but we have channels. Your campaigns and marketing time and resources should not all go into one channel. But how do you know which to invest in and which has the highest yield in terms of conversions?

This is where customer infrastructure data analysis comes in. we are no longer just talking about waiting for your clients to come to your site or page and give you feedback that you feed into your CRM system. Businesses have to be ready to go the extra mile and get non-intrusive data from their client’s online behavior and use this data to their company’s advantage.

Find the source:

So you had visitors coming from a few places to say hi and shop around, but did you think to find out where these customers were coming from. Sure you can just ask them but not everyone is going to answer or some might think you are a bit too intrusive. But what if you could get this information and not have to jump through so many hoops.

By integrating tools that integrate into your CRM and existing campaigns, you are able to do more than just wait for your customer’s responses. Your business should be able to know where your clients are coming from and in what volume as opposed to your overall marketing schema. This not only saves you expenses but allows you to focus your budget on what areas yield the most. Plus adjust strategy for those that do not yield as much.