What is machine learning and how does it impact marketing analytics?

Machine learning: A practical definition

  • Supervised learning. This is the type of machine learning described above. For example, you know who purchased your products and you have a lot of data about your customers, so you have the data and the answers. You can apply a machine learning algorithm to figure out the rules that predict who will buy in the future, and what they will buy. Marketers will recognize this task as propensity modeling, and machine learning typically does a much better job at it than traditional rules-based techniques. Sales and marketing applications of supervised learning include classification tasks, such as propensity modeling (who will buy, who will leave), and regression tasks, such as forecasting and driver analysis (what will my sold revenue be, and why).
  • Unsupervised learning. You may not always have the answers you need for supervised learning, and that’s where unsupervised learning comes in. When using unsupervised learning you are not providing any upfront answers for training purposes as with supervised learning, you are simply letting the machine learning algorithm identify patterns in the data without any initial guidance. For example, in market segmentation, you may be looking at your addressable market and trying to figure out how to segment your prospects. In this case you will apply a range of clustering techniques to identify natural groupings among prospects and customers. If you have a lot of unstructured data, or records with many attributes, other use cases include dimensionality reduction, which is useful to discover patterns and process larger datasets.
  • Reinforcement learning. This type of machine learning deals with closed-loop systems that constantly learn and automatically act on learnings. It is used mostly in robotics, video games, and other uses cases where a human is not needed in the loop. Other than for recommender systems, reinforcement learning is not used very often in sales and marketing applications. It is rarely used in B2B applications, which usually require a human in the loop to review and validate allocation of resources.

A better technology: The regression use case

Figure 1: Linear regression benchmark
Figure 2: Random forest regression benchmark

A better technology: The propensity use case

Figure 3: Logistic regression benchmark
Figure 4: XGBoost benchmark

Other marketing analytics use cases

  • Segmentation. Gone are the days of slow, manual segmentation studies that would be performed once a year, at best. Clustering techniques such as K-Means, PCA, DBSCAN, and others allow us to segment customers and prospects in near real-time and identify micro-patterns invisible to the human analyst. We can now identify profitable segments and trends as soon as they emerge, and take advantage before our competitors do.
  • Price optimization. Techniques such as LASSO, combined with any number of scoring algorithms, allow us to develop customer-level demand models at scale, which enable customer-level pricing. Think airlines or Amazon targeting individual customers with individualized pricing. If implementation considerations are an issue, this can support segmented pricing instead. Either way, a more granular view of pricing, when supported by the proper operational systems and processes, can lead to significant revenue and bottom-line growth.
  • Voice of the customer. Natural language processing (NLP) algorithms can mine your customer reviews, calls, emails, and texts using sentiment analysis, and score them in real time to give you an objective, quantified view of what your customers are saying about you, what topics they bring up, and how positive or negative they are.
  • Next-best-action recommendations. Next-best-action (NBA) or recommender systems are primary applications of machine learning to customer management. When calibrated and maintained properly, they are great at consistently targeting customers with higher close rate offers that keep customers happy and coming back.
  • Customer churn prevention. Just as machine learning can deliver better prospect propensity scoring, it can also deliver better churn propensity scoring. Following a number of themes we’ve already explored, it can do so faster and at a more granular level, which in turn enables better targeted retention actions.

Notes

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Analyzr makes machine learning analytics simple and secure for midmarket and enterprise customers that may not have a full-fledged data science team

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Analyzr: Insights, Achieved

Analyzr: Insights, Achieved

Analyzr makes machine learning analytics simple and secure for midmarket and enterprise customers that may not have a full-fledged data science team