- \n
- They can recognize patterns that you may never have noticed.<\/li>\n
- They are never \u201cdone.\u201d\u00a0 They can constantly get smarter as more data is fed into them and can make subtle adjustments to correct for changes in the business.<\/li>\n
- They can utilize exponentially more data than a human could, and in a fraction of the time.<\/li>\n
- The output of the models can be used to automate decision making processes that would normally take weeks or months.\u00a0 For example, if your organization needs to decide\u00a0 \u201cis it actually worth it to send out coupons to this subset of our customers?\u201d\u00a0 Automating this decision could entail establishing cutoff points based on a customer’s propensity to engage and price sensitivity to maximize the value of the couponing program.<\/li>\n<\/ul>\n
Examples of Machine Learning Algorithms<\/strong><\/h4>\n
There are two commonly used algorithms that will solve this business question.\u00a0 One type of model that generates a lot of interest is a customer segmentation model.\u00a0 A segmentation model takes the data you have and separates customers into segments that have similar characteristics.\u00a0 Because there is no specific value we are trying to predict, the analyst and business users work together to identify what models are both statistically significant and actionable for the business.<\/p>\n
Customer Segmentation<\/h3>\n
The K-means clustering algorithm is an example of a commonly-used segmentation model.\u00a0 It starts by randomly defining groups and incrementally updating them so similar customers end up being grouped together. \u00a0The result is a handful of groupings that can be investigated further to identify what the segments\u2019 defining characteristics are.\u00a0 Here is an example where we are segmenting customers with machine learning\u00a0based on their average purchase size and income.<\/p>\n
![\"Kmeans](\"https:\/\/cdn2.hubspot.net\/hubfs\/5670923\/Kmeans%20Example.gif\")
<\/p>\nIn this case we used K=3. The Kmeans algorithm randomly defines \u201cK\u201d centers and assigns each customer to its nearest center.\u00a0 Then it iterates by moving the centers to the average of its assigned customers and then reassigning.<\/h6>\n
Customer Lifetime Value<\/h3>\n
Another commonly used model is a \u201cCustomer Lifetime Value\u201d model (CLTV).\u00a0 CLTV models predict the expected value that will come from each customer.\u00a0 At their simplest, they can use data speaking to purchasing behavior (last purchase date, number of purchases, and money spent) to predict what we expect each customer to spend over a specified time period.\u00a0 At their most advanced, they can include referral and cost to maintain, as well as how much customers will spend themselves. Once you know how much a customer\u2019s spending is worth to your business, then you know what you may be willing to spend in marketing and other costs.\u00a0 When paired with a clustering model, the projected value can be used to see which segments and characteristics are most or least valuable to your business.<\/p>\n
![](\"https:\/\/cdn2.hubspot.net\/hubfs\/5670923\/image-7.png\")
<\/a><\/p>\nImage by Nicky Cane<\/a> from ‘Why Should You Care About Customer Lifetime Value?<\/a>‘<\/h6>\n
How to Get Started<\/h3>\n
Machine learning can seem overwhelming, and if you haven\u2019t gotten started with its implementation, you are certainly not alone.\u00a0 Many companies haven\u2019t taken their first steps simply because they don\u2019t know where to begin. For others, it\u2019s seen as something you can tackle when you\u2019ve \u201cmade it\u201d with your data and analytics strategy. These concerns are understandable, but getting value from machine learning may be much easier than you think.<\/p>\n
You probably already have data on transactions: customer, product, location, time, and price. \u00a0As long as you have the basics, you have enough for a simple customer analysis, and that analysis can evolve over time as you collect more data.<\/p>\n","protected":false},"excerpt":{"rendered":"
Identifying and understanding your high spenders and frequent shoppers is vital to maintaining and improving…<\/p>\n","protected":false},"author":74,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[260],"tags":[319],"class_list":["post-11605","post","type-post","status-publish","format-standard","hentry","category-data-ai","tag-machine-learning-ai","topics-blog"],"acf":[],"_links":{"self":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts\/11605","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/users\/74"}],"replies":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/comments?post=11605"}],"version-history":[{"count":0,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts\/11605\/revisions"}],"wp:attachment":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/media?parent=11605"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/categories?post=11605"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/tags?post=11605"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}